Relationship between Macroeconomic Variables and Stock ...shodhganga.inflibnet.ac.in/bitstream/10603/84912/1/thesis_pooja.pdf · Relationship between Macroeconomic Variables and

Relationship between Macroeconomic Variables and Stock

Market Development: Evidences from the Indian Economy

THESIS

Submitted in partial fulfilment

Of the requirements of the degree of

DOCTOR OF PHILOSOPHY

By

Pooja Joshi

2011PHXF412P

Under the Supervision of

Prof. A. K. Giri

BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE, PILANI

2015

BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE, PILANI

CERTIFICATE

This is to certify that the thesis entitled “Relationship between Macroeconomic Variables and

Stock Market Development: Evidences from the Indian Economy” submitted by Pooja Joshi

ID No. 2011PHXF412P for award of Ph.D. degree of the Institute, embodies original work

done by her under my supervision.

Signature of the supervisor

Name in capital letters : Prof. A. K. GIRI

Designation : Associate Professor

Date:

To my parents and my loving daughter

i

ACKNOWLEDGEMENTS

I would like to express my deepest gratitude to my supervisor, Prof. A. K. Giri, for his

valuable support, encouragement and direction throughout the entire duration of the

preparation of this thesis. He was always available when I need his advice, and I am grateful

for his patience, wisdom and immense knowledge.

I wish to acknowledge Prof. N V M Rao, Professor and Convenor DRC for his valuable

suggestions and guidance. I am grateful to Dr. Geetilaxmi Mohapatra, Prof. A K Vaish and

Dr. M Krishna Assistant Professor in the Department of Economics and Finance at Birla

Institute of Science and Technology, Pilani for their valuable comments and suggestions.

I want to acknowledge my sincere gratitude to all the faculty members of Economics and

Finance Department for their help and interest in my work. I also thank the supporting staff

of the department of Economics and Finance, BITS-Pilani for their cooperation and help. I

would like to extend my gratitude to the BITS-Pilani library support for their excellent

service.

Many of my friends offered their assistance in different ways; I want to acknowledge all my

friends. I thank my family, my parents: Mr. Pawan and Mrs. Shyama for their love and

affection, inspiration and constant support throughout my academic life. Special thanks to my

husband Jyotirmoy who contributed in numerous ways to the success of this project. Without

his assistance and encouragement my success would have been hampered. I would like to

thank my daughter Adwitiya who provided me support, patience and understanding during

my study. Last, but not the least, I would also like to thank my father-in-law and mother-in

law: Mr. Ashok and Mrs. Premlata for their continuous support.

I again thank all the people who are directly and indirectly involved in helping me to write

this thesis.

Pooja Joshi

ii

ABSTRACT

Stock market performance is considered as the reflector of financial and economic conditions

of a country. The dynamic linkage between macroeconomic variables and stock prices has

fetched increasing amount of attention from economists, financial analysts, investors and

policy makers, since 1980s. There are number of domestic and international macroeconomic

factors that potentially can affect the stock returns of the companies (Fama, 1981, Chen et al.,

1986). According to Fama (1981), there is a comprehensive group of macroeconomic

variables that influences the stock prices in the share market of any country. It is believed

that, if a country’s economy is performing well and expected to grow at a vigorous pace, the

market is frequently anticipated to reflect the same.

The relationship between macroeconomic variables and stock prices has been the focus of an

immense body of theoretical and empirical research since the 19th century. The debate over

the decades has been whether the movement in stock prices leads to the change in economic

activity or it is one of the causes of change. However, the literature suggests some

contradictory findings regarding which precise events or economic factors are likely to

influence the stock prices and the degree of influencing power of the economic factors.

Having generated strong controversy, the debate concerning the relationship between stock

market development and macroeconomic variables is still difficult to solve and causality hard

to pin down. Arguments both theoretical and empirical have been diverse. Some group of

studies advocates that the stock prices do respond to the changes in macroeconomic

fundamentals, but the sign and causal relationship might not hold equal for all the studies,

given different set for similar macroeconomic variables and also the methodologies used for

the study in this area are different (Fama (1981, 1990), Geske and Roll (1983), and Chen,

Roll, and Ross (1986)). Further, existing Financial and Economic literature advocates the

relationship between the stock market and macroeconomic variables, but, they do not specify

the type or the number of macroeconomic factors that should be included. Besides this, the

main key conclusion drawn from literature review is, that, so far, no study has been done on

the relationship between sectoral stock indices and respective sectoral GDP, which provides

the investors a new insight to track the changes in a particular sector of the stock market by

analyzing the movement of sectoral GDP of that particular sector. Thus, this study is the

initiative taken in this area.

iii

By now, it is a well knowned fact that the stock market development plays an important role

in economic prosperity, fostering capital formation and sustaining the economic growth of

the economy. Stock prices can be considered as an indicator of a country’s economic status

and social mood and are seen as a leading indicator of the real economic activity. New

theoretical and empirical research works provides support to the growing assertion that the

financial development is treated as a precondition for economic growth of a country.

Neverthless a vast majority of these studies have concentrated only on developed economies.

However, research on the relationship between real economic activity and the stock market in

developing countries, such as Latin American, Eastern Europe, Middle Eastern, and South

Asian countries, is still ongoing. Further, in respect to the Indian economy, few studies have

been conducted on the dynamic relationships between the stock market and macroeconomic

variables. Therefore, the primary purpose of this study is to empirically examine the

relationship between macroeconomic variables and stock market in India, following which,

the research is extended to cover the effect of deficits on stock market development in India

and the investigation of empirical relationship between sectoral Contribution of GDP and its

impact on respective sectoral indices. In particular, the study tries to examine the long-run

and short-run dynamic relationship along with the direction of causality between stock

market in India with different sets of domestic and international macroeconomic variables.

Towards this effort different models has been formulated, using the secondary data for

different time span and frequency, according to the need of the study. The empirical analysis

of the study began with testing the stationarity properties of the variables by applying Ng-

Perron unit root test. To study the long-run and short-run cointegrating relationship among

the variables ARDL bounds testing approach is used. The error correction term ECMt-1

identifies the speed of adjustment towards the equilibrium. VECM based Granger causality

test has been applied in the study to determine the direction of causality between

macroeconomic variables and Indian stock market. Additionally, CUSUM and CUSUMQ

have been employed to test the stability of the variables. Finally, to predict the long run and

short run shocks Variance Decomposition and Impulse Response Function techniques are

used in the study.

The study first undergoes for the empirical estimation of macroeconomic determinants of the

stock market development in India, using data for different time periods. The study is divided

into three parts as per the frequency and availablity of data. The first part of the study deals

with the estimation and discussion on the relationship between BSE Sensex and economic

iv

growth, along with some controlled macroeconomic variables. The macroeconomic variables

used for the study include GDP, crude oil prices, inflation (CPI), real effective exchange rate,

FDI and real interest rate, for the period from the year 1979 to 2014. The estimation results of

ARDL test confirms significant and positive influence of economic growth, exchange rate

and inflation on stock price movements in India. The results are consistent for both long run

and short run. The error correction model of ARDL approach reveals that the adjustment

process from the short-run deviation is quite high. Moreover, the VECM based Granger

causality test showed that there exists a short run unidirectional causality running from GDP

to BSE in India. Further, the result indicates the presence of long run causality for the

equation with the stock price as the dependent variable. The results of the VDC analysis show

that a major percentage of stock price change is its own innovative shocks.

Next part of the study deals with quarterly data and to know the relationship between stock

market development and macroeconomic variables. The variables used are Market

capitalization, Real Gross Domestic Product (GDP), Foreign Direct Investment, Foreign

Institutional Investment and Trade openness. The data employed covering the period from

1996: Q1 to 2014: Q3. The test results suggest that economic growth, FIIs and Trade

openness in India influence market capitalization positively. Consistent results are found for

FII and trade openness in short run also. The error correction model of ARDL approach

reveals that the adjustment process from the short-run deviation is low. The results of VECM

based Granger causality indicates the presence of long run causality for the equation with

Stock Market Capitalization (LMCAP) as the dependent variable whereas, in short-run the

change in trade openness causes a change in Stock Market Capitalization, whereas a change

in stock market capitalization will cause a change in FII. The results of VDC analysis shows

that out of the all of exogenous variables used for the study, trade openness is having

maximum shock on stock market capitalization.

Moreover, the study also focuses on short-run frequency data, to observe the relationship

between macroeconomic variables and the stock prices, by incorporating data for monthly

frequency variables. Further, the monthly study has been divided into two sections, which

constitutes two models in relation with different set of macroeconomic variables and stock

prices. The first section of the study highlights the relationship between fundamental

macroeconomic variables and Sensitivity Index of Bombay Stock Exchange (Sensex), using

the monthly time series data from the April 2004 to July 2014. The variables used for the

study are Sensex, Index of Industrial Production, Consumer Price Index, Real Effective

v

Exchange Rate, Call Money Rates and Gold price. The long-run estimates of ARDL test

showed a significant and positive influence of economic growth, Exchange Rate and Inflation

on stock prices. Further, the study confirms negative and significant relationship between

gold prices and stock prices in India. The results for IIP, Inflation and Gold prices are

consistent in short-run also. The error correction model of ARDL approach reveals that the

adjustment process from the short-run deviation is slow. The results of VECM indicates the

presence of long run causality for the equation with the stock price as the dependent variable.

The results of VDC analysis shows that out of the all of exogenous variables used for the

study, Gold price is having maximum shock on stock prices.

The second section of the monthly study focuses on the relationship between fundamental

macroeconomic variables and Index of National stock exchange (CNX nifty), using the

monthly time series data from the April 2004 to July 2015. The variables used for the study

are CNX Nifty, Index of Industrial Production, Foreign Institutional Investment, Gold price,

Treasury bills rate, Wholesale Price Index, International Crude Oil price and Real Effective

Exchange Rate. The long-run estimates of ARDL test showed a negative and significant

effect of crude oil prices, Inflation on stock prices. The results of the influence of both the

variables on stock prices are consistent in the short run as well. Further, for short-run the

study confirms positive and significant relationship for Gold, T-bill rates and Real Effective

Exchange Rate. The error correction model of ARDL approach reveals that the adjustment

process from the short-run deviation is high. The VECM based Granger causality test found

short run causality running from Inflation and crude oil price to National Stock Exchange in

India. It is also observed that bidirectional causality is running between Inflation and CNX

nifty index. Further, the result indicates the presence of long run causality for the equation

with CNX nifty index as the dependent variable. The results of VDC analysis shows that the

inflation and crude oil are having maximum shock on stock prices. The results of IRF shows

that in its response to the shocks of IIP it is observed that there is a negative relationship in

the long run.

The study next endeavors to focuse on the relationship between the deficit situation and stock

market development in India, by incorporating data for annual frequency variables. The study

for the relationship between deficits and stock market development has been divided into

two sections, the first section of which encomposes the estimation results of the relationship

between BSE Sensex and fiscal deficit, along with some controlled macroeconomic variables.

The macroeconomic variables used for the study include fiscal deficit, money supply (M3),

vi

inflation (CPI) and real interest rate, for the period from the year 1988 to 2014. The ARDL

results suggest a long run negative and significant relationship exists between budget deficit

and stock prices. However, the relationship does not show any significant results in the short

run. The error correction term is negative and significant and full convergence process

between stock prices and macroeconomic variables takes about two years in India. The

results of VECM based Granger causality test suggests that there exists a short run causality

running from fiscal deficit to stock price. Further, the result indicates the presence of long run

causality for the equation with the stock price as the dependent variable. The results of VDC

analysis show that the fiscal deficit plays an important role in explaining the variation in

stock prices in India.

The second part of the study on the relationship between deficits and stock market

development in India, discusses the estimation results of the relationship between stock

market development (MCAP) and twin deficit, along with some controlled macroeconomic

variables. The macroeconomic variables used for the study include current account deficit

(CAD), fiscal deficit, GDP, crude oil prices, trade openness and real effective exchange rate,

for the period from the year 1979 to 2014. The long-run estimates of ARDL test showed that

negative and significant relationship exists between the current account deficit (CAD) and

stock market capitalization. The results are consistent in short run also. The error correction

model of ARDL approach reveals that the adjustment process from the short-run deviation is

high. The results of VECM based Granger causality found short run causality running from

CAD to market capitalization in India. Further, the result indicates the presence of long run

causality for the equation with a market capitalization as the dependent variable. The results

of VDC analysis shows that crude oil price and CAD plays an important role in explaining

the variation in stock market development in India. The results of IRF shows that in its

response to the shocks of current account deficit, it is observed that there is a positive

relationship in the long run and reverse is observed in the case for the shocks of fiscal

deficits, throughout the period.

Finally, the study contributes to a new aspect of the relationship between macroeconomic

variables and stock prices by undergoing estimating the effect of sectoral GDP and controlled

macroeconomic variables on respective sectoral indices in India by employing quarterly data

covering the period from 2003:Q4 to 2014:Q4. The main variables used for the study include

Manufacturing sector index, electricity, gas and water supply sector index, service sector

vii

index, contribution of GDP in manufacturing sector, contribution of GDP in electricity, gas

and water supply sector, contribution of GDP in service sector, and the controlled

macroeconomic variables used for the study are Crude Oil Price, Real Effective Exchange

Rate, T-bill rates, Trade openness and WPI, a proxy for inflation. Principle component

analysis is used in this study to construct the composite index of manufacturing index;

electricity, gas and water supply index; and service index. For the purpose of study, three

models has been framed, in which each of the sectoral stock price indices is placed as

dependent variable; and Crude Oil Price, REER, T-bill rates, Trade openness and WPI along

with respective sectoral GDP worked as independent variables. The long-run and short-run

estimates of ARDL test for the model I (Manufacturing sector index and share of

manufacturing sector in GDP) showed that positive and significant relationship exists

between the manufacturing sector share in GDP with the manufacturing index. For model II

(Electricity, Gas and Water supply sector index and share of Electricity, Gas and Water

supply sector in GDP), the results show that the electricity, gas and water supply sector share

in GDP and inflation has a positive effect on electricity, gas and water supply index, unlike

short-run. For model III (Service sector index and share of Service sector in GDP), results

show that the service sector share in GDP and T-bills rate has a positive effect on service

sector index in the long-run and in short-run as well along with crude oil price. The error

correction model of ARDL approach reveals that the adjustment process from the short-run

deviation is high. The results of VECM based Granger causality test suggests a unidirectional

short-run causality running from sectoral GDP to respective sectoral stock indices in India.

Further, the result indicates the presence of long-run causality for the equation with

manufacturing index and electricity, gas and water supply index as the dependent variable,

but, except for the service sector index. To predict the long-run and short-run shocks variance

decomposition is used for the study, the result of VDC analysis, for all three models, show

that a major percentage of sectoral indices are its own innovative shocks.

To sum up, it can be concluded that economic growth plays an important role in the

development of stock market therefore,it can be said that the stock market acts as a predictor

of GDP. Additionally, the the positive influence of exchange rate on stock price movements

is also observed and the influence of inflation also comes out to be positive which proves

Fisher (1911) hypothesis. Thus, the estimated results of the study indicate that the Indian

stock market is sensitive to changes in macroeconomic fundamentals in the long run.

However, in the short run also few of the macroeconomic variables affect stock prices.

viii

Further, the stock prices are relatively exogenous in relation to most of the macroeconomic

variables selected for the study, as major percentage of the variation in the forecast of the

Indian stock prices is attributable to its own shocks. The results of the study suggest a

positive impact of macroeconomic variables on the stock market development in India.

Therefore, in order to facilitate stock marketdevelopment and economic growth,

macroeconomic development is solely desirable in developing countries like India. Moreover,

it is also true that the informed and sensible investor in India can attain super normal profit,

by tracking the historical data of stock market and the change in macroeconomic variables.

This may help the investors to formulate a profitable strategy to for trading and making

profitable decisions. The implications of the present study are multifaceted and the findings

of the study implies that, the stock markets can be flourished with economic growth of the

nation, because it plays a significant positive role in the developments of capital markets of

India. In a country, when the real GDP will raise it will help stock prices to increase and

boost up the investor’s confidence, with the growing economy.

Key words: Stock price, Stock market development, Economic Growth, Twin deficit, crude oil

price, inflation, Real Effective Exchange Rate, India, Auto Rergressive Distributed Lag

approach (ARDL), Vector Error Correction Method (VECM), Variance Decomposition Test

(VDC), Impulse Response Function (IRF).

ix

TABLE OF CONTENTS

Chapter1: Introductory background, Need and Objectives of the study Page No.

1.1. Introduction ...... 1

1.2. Need for the Study and Research Questions ..... 3

1.3. Objectives of the study ...... 7

1.4. Significance of the study ...... 8

1.5. Organization of the study ...... 9

Chapter 2: An overview of the developments in Indian stock market

2.1. Introduction .... 12

2.2. Historical Development of the Indian Stock Market .... 13

2.3. India as an emerging market economy .... 16

2.4. Reforms in the financial sector .... 19

2.5. Trends of the Indian Stock Market .... 23

2.6. Summary .... 27

Chapter 3: Theoretical underpinnings of the relationship between macroeconomic variables

and Stock market development indicators

3.1.Introduction ..... 28

3.2.Theoretical Background ..... 28

3.2.1. Random Walk and The Theory of Efficient Market Hypothesis (EMH) ..... 28

3.2.2. Arbitrage Price Theory (APT) ..... 31

3.3. Stock Market Development Indicators ..... 32

3.3.1. Stock Market Size ..... 33

3.3.2. Stock Market Liquidity ..... 33

3.3.2.1.Total value of shares traded ratio ..... 34

3.3.2.2.Turnover ratio ..... 34

3.3.3. Volatility ..... 35

3.3.4. Stock market index ..... 35

3.4. Macroeconomic Variables ..... 35

3.4.1. Money Supply ..... 35

3.4.2. Economic Growth ..... 38

3.4.3. Trade Openness ..... 41

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3.4.4. International Financial Flows ..... 44

3.4.4.1.Foreign Direct Investment (FDI) ..... 45

3.4.4.2.Foreign Institutional Investment (FII) ..... 46

3.4.5. Interest Rate ..... 47

3.4.6. Inflation ..... 49

3.4.7. Crude Oil Prices ..... 51

3.4.8. Exchange Rate ..... 54

3.4.9. Gold Prices ..... 57

3.4.10. Budget Deficit ..... 58

3.4.11. Current Account Deficit ..... 59

Chapter 4: Methodology and Data Issues

4.1. Introduction ..... 61

4.2. Methodology ..... 62

4.2.1. Ng-perron unit root test ..... 62

4.2.2. ARDL co-integration ..... 63

4.2.3. VECM based Granger Causality …. 65

4.2.4. Stability tests …. 66

4.2.4.1. CUSUM Test ..... 66

4.2.4.2. CUSUM of Squares Test ..... 67

4.2.5. Impulse Response Functions ..... 67

4.2.6. Variance Decomposition Technique ..... 68

4.2.7. Principal Component Analysis ..... 70

4.3. Data Issues ….. 71

4.3.1. Stock market ..... 71

4.3.1.1. Stock prices ..... 71

4.3.1.2. Stock market development ..... 71

4.3.2. Economic Growth ..... 72

4.3.2.1. Real Gross Domestic Product ..... 72

4.3.2.2. Index of Industrial Production (IIP) ..... 72

4.3.3. Real Effective Exchange Rate (REER) ..... 73

4.3.4. International crude oil price ..... 73

4.3.5. Foreign Direct Investment ..... 73

4.3.6. Foreign Institutional Investment ..... 73

xi

4.3.7. Inflation ..... 74

4.3.7.1. Consumer Price Index (CPI) ..... 74

4.3.7.2. Wholesale Price Index (WPI) ..... 74

4.3.8. Real Interest Rate ..... 75

4.3.9. Short term interest rates ..... 75

4.3.9.1. Treasury bill rates ..... 75

4.3.9.2. Call Money Rate (CMR) ..... 75

4.3.10. Fiscal Deficit ..... 75

4.3.11. Current Account Deficit ..... 76

4.3.12. Money supply (M3) ..... 76

4.3.13. Trade openness ….. 76

4.3.14. Gold Prices ..... 77

Chapter 5: Macroeconomic Determinants of the Stock Market Development in India

5.1.Introduction ..... 78

5.2.Review of Literature ..... 79

5.2.1. Studies of overall economies other than India ..... 79

5.2.2. Studies related to Indian economy ... 110

5.2.3. Summary of Literature review ... 124

5.3.Estimation results of the study using annual frequency data ... 126

5.3.1. Model specification … 126

5.3.2. Stationarity test and Lag length selection before co-integration ... 126

5.3.3. ARDL Bounds Test ... 127

5.3.4. VECM based causality ... 130

5.3.5. Variance Decomposition (VDC) Analysis ... 131

5.4. Estimation results of the study using quarterly frequency data ... 133






5.5. Estimation results of the study using monthly frequency data ... 139

5.5.1. Relationship between macroeconomic variables and Indian stock price ... 139

5.5.1.1. Model specification … 139

xii

5.5.1.2. Stationarity test and Lag length selection before co-integration ... 139

5.5.1.3. ARDL Bounds Test ... 141

5.5.1.4. VECM based causality ... 144

5.5.1.5. Variance Decomposition (VDC) Analysis ... 145

5.4.2. Relationship between Fundamental Macroeconomic Variables and CNX nifty

... 147

5.4.2.1. Model specification ... 147

5.4.2.2. Stationarity test and Lag length selection before co-integration ... 147

5.4.2.3. ARDL Bounds Test ... 148

5.4.2.4. VECM based causality ... 151

5.4.2.5. Variance Decomposition (VDC) Analysis ... 152

5.4.2.6. Impulse Response Function (IRF) ... 154

6.5. Summary … 156

Chapter 6: Fiscal policy variables and Stock Market Development in India

6.1. Introduction ... 158

6.2. Review of Literature ... 159

6.2.1. Studies of overall economies other than India ... 159

6.2.2. Studies related to Indian economy ... 163

6.3. Relationship between Fiscal Deficits and Stock Prices in India ... 165






6.4. Relationship between Twin Deficit and Stock Market Development in India … 172






6.3.6. Impulse Response Function (IRF) ... 178

6.4. Summary … 180

xiii

Chapter 7: Macroeconomic Determinants of Sectoral Stock Market Development in India

7.1. Introduction … 182

7.2. Review of Literature ... 183

7.2.1. Studies of overall economies other than India … 183

7.2.2. Studies related to Indian economy … 187

7.3. Model specification and data validation … 188

7.4. Stationarity test and Lag length selection before co-integration ... 189

7.5. ARDL Bounds Test ... 190

7.6. VECM based causality ... 193

7.7. Variance Decomposition (VDC) Analysis ... 195

7.8. Summary ... 195

Chapter 8: Summary and Policy Implications of the study

8.1.Summary and Conclusion ... 197

8.2.Policy Implications of the study ... 206

8.3.Contribution of the study ... 209

8.4.Limitations of the study ... 212

8.5.Scope for further studies ... 212

References ... 214

Appendixes … 239

A. Lag length selection criteria … 240

A.1. Akaike’s information criterion … 240

A.2. Schwarz’s information criterion … 241

A.3. Hannan-Quinn’s information criterion … 241

A.4. Final Prediction Error … 242

Publications form the Thesis ... 244

Brief Biography of the Candidate ... 245

Brief Biography of the Supervisor ... 246

xiv

LIST OF TABLES

Table 2.1: Key indicators of Indian stock market activity ..... 24

Table 5.3.1: Unit root test: Ng-Perron Test ... 127

Table 5.3.2: Lag Order Selection Criterion ... 127

Table 5.3.3: ARDL Bounds test ... 128

Table 5.3.4: Estimated Long Run Coefficients using ARDL Approach (Dependent variable:

LBSE) ... 129

Table 5.3.5: Estimated Short Run Coefficients using ARDL Approach (Dependent variable:

LBSE) ... 130

Table 5.3.6: Results of Vector Error Correction Model ... 130

Table 5.3.7: Variance Decomposition (VDC) Analysis ... 132



Table 5.4.3: ARDL bounds test results ... 134

Table 5.4.4: Estimated Long-run Coefficients using ARDL Approach (Dependent variable:

LMCAP) ... 135

Table 5.4.5: Estimated Short-run Coefficients using ARDL Approach (Dependent variable:

LMCAP) ... 136



Table 5.5.1.1: Unit root test: Ng-Perron Test ... 140

Table 5.5.1.2: Lag Order Selection Criterion ... 141

Table 5.5.1.3: ARDL bounds test results ... 142

Table 5.5.1.4: Estimated Long Run Coefficients using ARDL Approach (Dependent variable:

LBSE) ... 143

Table 5.5.1.5: Estimated Short Run Coefficients using ARDL Approach (Dependent variable:

LBSE) ... 144

Table 5.5.1.6: Results of Vector Error Correction Model ... 144

Table 5.5.1.7: Variance Decomposition (VDC) Analysis ... 146

Table 5.5.2.1: Unit root test: Ng-Perron Test … 148

Table 5.5.2.2: Lag Order Selection Criterion … 148

xv

Table 5.5.2.3: ARDL bounds test results ... 149

Table 5.5.2.4: Estimated Long-run Coefficients using ARDL Approach (Dependent variable:

LNSE) ... 150

Table 5.5.2.5: Estimated Short-run Coefficients using ARDL Approach (Dependent variable:

LNSE) ... 151

Table 5.5.2.6: Results of Vector Error Correction Model ... 152

Table 5.5.2.7: Variance Decomposition (VDC) Analysis ... 153

Table 5.5.2.8: Impulse Response Function (IRF) ... 154



Table 6.3.3: ARDL Bounds test ... 166


LBSE) ... 168


LBSE) ... 168





Table 6.4.3: ARDL bounds test results ... 174


LMCAP) ... 175


LMCAP) ... 176



Table 6.4.8: Impulse Response Function (IRF) ... 179

Table 7.1: Unit root test: Ng-Perron Test ... 190

Table 7.2: Lag Order Selection Criterion ... 190

Table 7.3: ARDL Bounds test ... 191

Table 7.4: Estimated Long-run Coefficients using ARDL Approach (Dependent variable:

LMANI, LEGWI, LSERI) ... 192

xvi

Table 7.5: Estimated Short-run Coefficients using ARDL Approach (Dependent variable:

LMANI, LEGWI, LSERI) ... 193

Table 7.6: Results of Vector Error Correction Model ... 194

Table 7.7: Variance Decomposition (VDC) Analysis ... 195

xvii

LIST OF FIGURES

Figure 2.1: Indian Stock Market Trends ..... 25

Figure 5.3.1: Plots of Stability Test ... 131


Figure 5.5.1.1: Plots of Stability Test ... 145

Figure 5.5.2.1: Plots of Stability Test ... 152

Figure 5.5.2.2: VDC analysis combined graph ... 153

Figure: 5.5.2.3 Impulse Response Function combined graph ... 155



Figure 6.4.2.: VDC analysis combined graph ... 178

Figure: 6.4.3. Impulse Response Function combined graph ... 179

xviii

LIST OF ABBREVIATIONS

∆ : First difference of the variables

AIC : Akaike’s information criterion

APT : Arbiterage Pricing Theory

ARCH : Autoregressive conditional heteroskedasticity proposed by Engle (1982)

ARDL : Auto Regressive Distributed Lag

BSE : Sensitivity index of Bombay Stock Exchange (Sensex)

CAD : Current account deficit as a percentage of GDP

CMR : Call Money Rate

CO : International crude oil price

CPI : Consumer Price Index

CSO : Central Statistics Office

DIPP : Department Of Industrial Policy & Promotion

ECM : Error correction model

ECT : Error-correction term

EG : Engle and Granger cointegration

EGWI : Electricity, Gas and Water Index

EMH : Efficient market hypothesis

FD : Fiscal Deficit as a percentage of GDP

FDI : Foreign Direct Investment

FII : Foreign Institutional Investors

FPE : Final Prediction Error

GARCH : Generalized Autoregressive conditional heteroskedasticity model proposed by

Bollerslev (1986)

GEGW : Electricity, Gas and Water supply sector share in GDP

GDP : Real Gross Domestic Product

GDR : Global Depository Receipts

http://dipp.nic.in/

https://scholar.google.co.in/scholar?hl=en&as_sdt=0,5&q=Global+Depository+Receipts

xix

GOR : Gold Prices

GMAN : Manufacturing sector share in GDP

GRT : Granger’s representation theorem

GSER : Service sector share in GDP

HQ : Hannan-Quinn’s information criterion

IIP : Index of Industrial Production

IRF : Impulse Response Function

JJ : Johansen and Juselius cointegration

L : Natural logarithm of the variables

LR : log likelihood ratio

M3 : Money Supply (broad money)

MANI : Manufacturing Sector Index

MCAP : Market Capitalization as a percentage of GDP used as a proxy for stock

market development

MNC : Multi National Corporation

NSCC : National Securities Clearing Corporation

NSDL : National Securities Depository Limited

NSE : National Stock Exchange represented by CNX nifty index

OLS : Ordinary Least Square Techniques

OTC : Over the Counter Exchange of India

PCA : Principal Component Analysis

RBI : Reserve Bank of India

REER : Real Effective Exchange Rate

RIR : Real Interest rate

SEBI : Securities Exchange Board of India

SERI :Service Sector Index

SIC : Schwarz’s information criterion

xx

TBR : Treasury bill rates (T-bill rates)

TO : Trade Openness (export+import/GDP)

VAR : Vector Auto Regression

VDC : Variance Decomposition

VECM : Vector Error Correction Method

WPI : Wholesale price Index used as a proxy for inflation

1

CHAPTER 1

Introductory background, Need and Objectives of the study

1.1. Introduction

The history has shown that the price of shares and other financial assets are an

important aspect of the dynamics of economic activity, performing a crucial role in the

economy of any nation. Further, many researchers have proved that the stock market plays an

important role in economic prosperity, fostering capital formation and sustaining the

economic growth of the economy (Charles and Adjasi, 2008; Essaied, Hamrita et al., 2009;

Pilinkus, 2015; Quayes, 2010). The stock market is one of the most vital components of a

free-market economy, as it helps to arrange capital for the companies from shareholders in

exchange for shares in ownership to the investors. Stock exchange provides businesses with

the facility to raise capital by selling shares to the investor (Black and Gilson, 1998). Stock

prices can be considered as an indicator of a country’s economic status and social mood and

are seen as a leading indicator of the real economic activity. Share prices also affect the

wealth of households and their consumption; savings and investment decisions. Thus, it can

be said that, the stock market is an integral part of the financial system of any economy, as it

plays a significant role in channelizing funds, connecting savers and investors, which led to

economic growth of the economy. Further, it is believed that there exist many factors to

which the stock market reacts, factors like the economic, political and socio-cultural behavior

of any country. Hence, investors carefully watch the performance of the stock markets by

observing the composite market index, before investing funds. The market index acts as the

yardstick to compare the performance of individual portfolios and also provides investors for

forecasting future trends in the market. Especially the stock markets of emerging economies

are likely to be sensitive to fundamental changes in macroeconomic structure and policies,

which plays an important role in achieving financial stability. Being one of the most

important pillars of the country's economy, the stock market is carefully observed by

governmental bodies, companies and investors (Nazir et al., 2010). Therefore, economic

policy makers and researchers keep an eye on the behavior of the stock market, as it’s smooth

and risk free operation is essential for economic and financial stability.

The dynamic linkage between macroeconomic variables and stock prices has fetched

increasing amount of attention from economists, financial analysts, investors, practitioners

and policy makers (Kwon and Shin, 1999). The claim that macroeconomic variables affect

2

stock market is a well-established theory in the literature and has been an area of intense

interest among academics, investors and stock market regulators since 1980s. In the past

three decades, there has been growing efforts made by researchers to estimate this

relationship since the attempt made by Fama (1981). Following his study, a number of

empirical studies explored this topic to understand the fundamentals of this association in one

country or in a selected group of countries. (Chen et al. (1986), Poon and Taylor (1992),

Fama (1991), Pearce & Roley (1988)) modeled the relation between asset prices and real

economic activities in terms of production rates, productivity, and growth rate of gross

national product, unemployment, yield spread, interest rates, inflation, dividend yields, and so

forth. In the last two decades, because of the globalization trend, a number of researchers –

such as Fama (1990), Geske and Roll (1983), Chen, Roll, and Ross (1986), Canova and de

Nicolo (1995) and Nasseh and Strauss (2000) also investigated the international effects of

macroeconomic indicators on stock prices. Theoretical work shows the significant positive

effect of stock market development on economic growth of specific economies (Levine and

Zervos (1998); Modigliani (1971) and Kunt and Levine (1996)). At the same time, the

development of the stock market is the outcome of many macroeconomic variables like

foreign direct investment, foreign institutional investment, exchange rate and economic

reforms (Gay (2008)), whereas, economic growth also plays an important role in the stock

market development in developing or developed economies. Duca (2007) argues that

countries doing well in terms of economic growth have better stock market performance.

These studies are different in terms of their hypotheses and the methodologies used. Other

previous studies have examined the short and the long run relationship between stock prices

or returns and some macroeconomic and financial variables such as inflation, interest rate,

output, etc. Within this group of studies, some studies seek to examine local and international

economic factors that affect stock prices or returns, while others examine factors that

determine stock return volatility (Semmler, 2006). Some other explores the role of monetary

policy in responding to or altering the stock market (Sellin, 2001). More recently, an

increasing amount of empirical studies has been focusing attention to relate the stock prices

and macroeconomic factors for both developed and emerging economies (Mukherjee and

Naka (1995), Maysami et al. (2004), Ratanapakorn and Sharma (2007), Rahman et al.

(2009)). These studies concluded that stock prices do respond to the changes in

macroeconomic fundamentals, but the sign and causal relationship might not hold equal for

all the studies. Based on the existing literature, it has been concluded that extensive research

has been conducted for developed economies. However, research on the relationship between

3

real economic activity and the stock market in developing countries, such as Latin American,

Eastern Europe, Middle Eastern, and South Asian countries, is still ongoing. Further, in

respect to the Indian economy, few studies have been conducted on the dynamic relationships

between the stock market and macroeconomic variables.

1.2. Need for the study and Research Questions

The last two decades have witnessed a dramatic change in world financial markets,

particularly the stock markets, and the fundamental causes of these changes were probably

the end of fixed exchange rates in the early 1970s and the progressive removal of

international financial flows. These changes resulted in a significant increase in the volatility

of prices and trade volumes and also lead to noticeable contradictions between market

sentiments and economic growth, due to irrational behavior of investors. The practical

consequences of these changes sometimes have discouraging and humiliating challenges for

policy makers and forecasters, and the investors have to bear greater risk and uncertainty

regarding their investment decisions. Therefore, to predict the possible changes in the stock

market those fundamental factors should be studied, who works as the triggers of changes

and drives the market. According to Fama (1981), there is a comprehensive group of

macroeconomic variables that influences the stock prices in the share market of any country.

If a country’s economy is performing well and expected to grow at a vigorous pace, the

market is frequently anticipated to reflect the same.

Indian stock market has developed in terms of number of stock exchanges and other

intermediaries, the number of listed stocks, market capitalization, trading volumes, turnover

of the stock exchanges, investor population and the price indices. The process of reforms has

led to a pace of growth almost unparalleled in the history of any country. The shape and

structure of the market have undergone remarkable changes in the recent past. The stock

market of emerging economics like India carries huge expectations of the investors. The

Indian stock market has also undergone tremendous changes since 1991, when the

government has adopted liberalization and globalization policies. As a result, there is a

growing importance of the stock market from the aggregate economic point of view.

Nowadays, the stock market has become a key driver of the modern market based economy

and is one of the major sources of raising resources for Indian corporate, thereby enabling

financial development and economic growth. In fact, Indian stock market is one of the

emerging markets in the world. The smoothing development process in Indian stock markets

continues to be breathtaking. From 3,739.69 points on March 31st, 1999, within nine years;

4

Bombay Stock Exchange (BSE) Sensitivity Index (SENSEX) had reached to 21,000 level

points in January, 2008. But this impact doesn’t last long as it was affected by the recent

global financial crisis of 2008-09; emerging euro-crisis; and the recent slowdown of the

Chinese economy. Now SENSEX is hovering around 25,500 points after breaching 30,000

points in march 2015, its all time high (BSE India); and similarly after the establishment of

nifty in 1994, it goes to its all time high breaching 9,000 points (NSE India). In the context of

this effect in Indian Stock Market, the critical question is whether the decades old

development or recent degradation in the markets are in any way influenced by the domestic

and international macroeconomic fundamentals. There are several studies concluding

contradictory results, based on different methodologies, regarding the interaction of share

market returns and the macroeconomic variables, viz-a-viz, Agrawalla (2006) stated that

rising indices in the stock markets cannot be taken to be a leading indicator of the revival of

the economy in India and vice-versa. However, Shah and Thomas (1997) supported the idea

that stock prices are a minor which reflect the real economy. Similarly, Kanakaraj et al.

(2008) examined the trend of stock prices and various macroeconomic variables between the

time periods 1997-2007 and tried to explore upon if the rise in the stock market can be

explained in the terms of macroeconomic fundamentals and concluded by recommending a

strong relationship between the two. Despite the growth of Indian stock market, it is suffering

from various typical weaknesses of an emerging market. First, speculation practices cause

high market volatility, which makes the market highly unpredictable. Second, it is widely

known that one of the biggest problems facing by investors is the lack of transparency.

Reporting requirements for listed companies are not well defined, and significantly less

comprehensive than those in the developed stock markets. Third, information disclosed to the

public is not clear and transparent, thus, not reliable. Due to all these problems, investors

become irrational and may base their actions on the decisions of others who are well

informed about market developments, by following the market consensus. In other words, the

herding behavior may exist in the Indian stock market. Therefore, from the point of view of

policy makers, investors and research practitioners, it is important to study the effect of both

domestic and international macroeconomic variables on the performance of the stock market

because both investors and policy makers mostly concern if the current market prices reflect

all publicly available information, such as information on inflation, economic growth, money

supply, exchange rates, interest rates, foreign inflows, gold prices, etc. Hence, this study tries

to investigate whether or not it is possible for market participants to make consistently

superior returns just by analyzing the movement in fundamental macroeconomic factors of

5

the country. In other words, the focus of this study is to find out the relationship between

stock prices and macroeconomic variables in India.

More recently, the renewed interest on the relationship between fiscal deficits and stock

prices has been partly informed by the sudden occurrence of the global financial turmoil, its

severity and potentially long-lasting impact and, in particular, the apprehension that such

large budget deficits could lead to stock market crash (Roley and Schall, 1988). In contrast,

however, other analysts claim that budget deficits have little effect on stock prices. Friedman

(1987), for example, characterized much of the links between fiscal deficits and stock market

crashes (via a collapse in asset valuation) as reflecting reliance on economic fallacies, as

witnessed throughout the 1980s when stock prices surged despite mounting fiscal deficits.

Whereas, there are cases, however, where current account deficits have been associated with

a strong and thriving economy. In the context of foreign capital-led growth, capital inflows

help to lift savings and investment constraint on growth. In such cases, national savings are

not sufficient to all new profitable investment projects. It is sometimes necessary for a

country to run current account deficit and rely on foreign savings to finance the savings

investment gap. Over time, the goods produced by the new capital will lead to increased

export earnings that will eventually generate trade and current account surpluses necessary to

repay foreign debt and the interest on it. Hence, current account deficits and foreign debt

accumulation generated by an investment boom might actually increase the rate of a

country’s economic growth where domestic savings are not sufficient. Further, an

unsustainable budget deficit indicates either future inflation rate or future tax rate increases.

Sargent and Wallace (1981) said that the unsustainable budget deficit will eventually have to

be managed because large budget deficit will increase inflation. Greenspan and Allen (1995)

investigated that a decrease in the budget deficit will reduce inflationary expectations.

Inflationary expectations may have reverse effects on equity prices. Budget deficits also

affect stock prices through anticipated future taxes, particularly if tax rates are below their

revenue-maximizing levels. Hall and Taylor (1993) and Ball and Mankiw (1995) claimed that

increase in budget deficit forecast future tax increases, which may decrease current

consumption by households and harm stock prices and vice versa. In contrast, however,

Friedman (1987) claims that budget deficits have little effect on stock prices. Since, the

research on the concepts of the deficit situation of the nations and their relationship with

stock market development are very scant, this proved to be a motivation to investigate the

relationship in Indian context.

6

Further, since no research has been done considering the impact of sectoral contribution

of GDP on the sectoral indices, as it provides a better understanding to investors and policy

makers to judge the market sentiments or a better approach to track the changes of a

particular segment of the industry, which are reflected by the respective sectors of the market.

This phenomenon worked as the motivation to study the relationship between sectoral

contribution of GDP and sectoral stock indices.

The empirical investigations of the present study are carried out on an annual time

series data, quarterly time series data and monthly time series data, with different time

periods. The choice of sample period is suggested by the availability of data and the

requirement of a good number of observations which is essential for empirical investigation.

Monthly data are used to grasp the short run dynamics of the stock prices and

macroeconomic variables. The study also focuses on the relationship between deficits and the

stock market development in India. Further, the study is also conducted on the relationship

between sectoral indices and sectoral contribution of GDP, respectively, which seems more

relevant to track the changes in the stock market, while predicting the changes in any

particular sector index, due to the change in the GDP of that sector only. The variables used

in empirical experimentation, their definitions, sources of time series data and the method by

which some of the time series are constructed are discussed and presented in the subsequent

chapters of the study

There have been divergent views in the literature with respect to the

choice/identification of measures of these variables. Further, in the context of frequency of

the variables used and availability of data from the same base period, the selection of

determinants become more complex. As mentioned earlier, Indian economy has undergone

tremendous changes after adopting liberalization and globalization policies, therefore, besides

the domestic variables some international macroeconomic factors like foreign capital inflows

(FII, FDI), trade openness, international crude oil prices, and the exchange rate (Real

effective exchange rate) has also been included in the study to know their relationship with

the stock market. Stock prices (Sensex and CNX Nifty) are incorporated in the study to

empirically observe the relationship of Indian stock prices with fundamental macroeconomic

variables. Accordingly, a measure of stock market development, i.e., market capitalization, is

also included in one of the models in the study, to capture its relationship with

macroeconomic variables of quarterly frequency.

For the purpose of the empirical study, Autoregressive Distributed Lag (ARDL) bounds

test is employed to determine the cointegration among the variables. Before going for co-

7

integration test, Ng-Perron unit root test is employed to check the stationarity of the variables.

After employing bounds test, the long run and short run dynamic relationship are estimated.

Further, to know the direction of causality, Granger causality based on Vector error

Correction Model is used for the variables. The stability of the variables has been examined

by using CUSUM and CUSUM Square tests. Additionally, Variance Decomposition (VDC)

and Impulse Response Function (IRF) are used to used to predict long run exogenous shocks

of the variables.

Hence, the primary motive of the present work is to answer the following research

questions:

Q.1. Do the key macroeocnomic variables included in this study have long-run

cointegrating relationship with Indian stock market proxied by BSE Sensex, CNX Nifty, and

market capitalization?

Q.2. Do these key macroeconomic variables have causal relationships during the

sample period? If so, what is the direction of the causality between BSE, NSE, market

capitalization and each of these variables in long-run and short-run?

Q.3. How does the stock market development indicators respond to an external shock

from any of these variables?

Q.4. To what extent can innovation in each of the key macroeconomic variables explain

the movements in stock market variables?

Q.5. How does the sectoral stock market indices being influenced by the set of sectoral

real activity in the Indian economy?

1.3. Objectives of the study

The present work is designed to address the linkage between macroeconomic variables

and stock market development in the present context for Indian economy. Accordingly the

objectives of the present study are set as follows:

1. The first objective is to examine the role of macroeconomic variables on the stock

market development in the context of financial innovation, liberalization,

globalization and asset market changes in India.

2. The second objective is to examine the dynamic relationship between fiscal policy

variables and the stock market development in India.

8

3. The third objective is to explore the relationship between sectoral stock market

indices and sectoral macroeconomic parameters.

4. The fourth objective is to evaluate the implications of evidence for framing

appropriate economic policies for improving stock market efficiency.

1.4. Significance of the study

The present study is expected to add several primary contributions to the existing

literature. First, it will add to the present literature by examining the relationship of the stock

market with a set of macroeconomic variables in emerging markets like India, in the present

context. Second, the study will apply different modern econometric methods like Auto

regressive Distributed Lag (ARDL), Vector Error Correction Model (VECM), Variance

Decomposition (VDC) and Impulse Response function (IRF), which may provide insight for

the existing literature if the analysis is sensitive to the methods employed. To the best of my

knowledge, this will be the first study to estimate sectoral contribution of GDP and its impact

on respective sectoral indices of stock market using data on the Indian economy. The

importance of the sectoral analysis is that, if any sector performs extremely well than it will

help policy makers and investors especially, to predict the changes in the prices of the stocks

of that particular sector. This study is expected to offer some insights for Indian

policymakers, investors, researchers and portfolio managers. Investors may be able to make

informed decisions based on macroeconomic dynamics and it is possible for them to decide

the ideal time to buy and sell the stocks. The study will be advantageous to know the

relationship of prices and economic activity; the direction of the outcome of the relationship

may enhance the predictive ability of policy makers; thus, both contractions and expansion of

the Indian economy may be forecasted and predicted with some degree of certainty.

Policymakers are mainly interested in exploring the determinants of the stock market, and

how stock market reflects the changes in domestic and international macroeconomic

variables of the economy, thus, the study will provide them a background to determine the

variables, which are expected to influence the stock market. Moreover, economic theory

suggests that stock prices should reflect expectations about future corporate performance, and

the corporate profits generally reflect the level of economic activities. If stock prices

accurately reflect the underlying fundamentals, then the stock prices should be considered as

the leading indicators of future economic activities, and not the other way around. Therefore,

the study of the causal relations and dynamic interactions between macroeconomic variables

9

and stock prices will help in the formulation of the nation’s macroeconomic policy. Further,

the study will provide an insight to researchers for future research in the area.

1.5. Organization of the study

The rest of the study is organized in seven chapters; Chapter 2 focuses on the overview

of Indian stock market, which aims to present a historical review of the development stages

of the Indian stock market since its establishment in 1875. The chapter has been organized as

follows. Section 2.1 presents an introduction to the chapter; Section 2.2 provides highlights

the historical development of the Indian stock market; section 2.3 discusses the case for India

as an emerging market economy. In Section 2.4, some of the major changes in the financial

sector of the Indian economy are briefly described; the section 2.5 consists of the description

of the trends of the Indian Stock Market; and the last section 2.6 outlines the summary of the

chapter.

Chapter 3 discusses the Theoretical Underpinnings of the study, establishing the

theoretical relationship of the macroeconomic variables with stock market development

indicators. This chapter passes through four sections. The section 3.1 of the study presents the

introduction to the chapter. Section 3.2 provides the theoretical background of the study,

which is again sub-divided into two parts; the first part of the section 3.2 talks about random

walk theory and the Theory of Efficient Market Hypothesis (EMH), which is considered as

the reason for the genesis of the concept of efficient capital markets; and the second part,

explains the theory of asset pricing or the Arbitrage Pricing Theory (APT). Section 3.3

discusses about the stock market development indicators, which encompasses stock market

size represented by market capitalization ratio, stock price, stock market liquidity and market

volatility. Section 3.4 discusses about the theoretical relationship of various macroeconomic

variables and stock market.

Chapter 4 addresses the econometric methods that are employed for the study. The

chapter encompasses two sections, the methodology and the data definition. The first section

comprises seven sub-sections. The first sub-section will give a brief introduction of the

chapter. Sub-section 4.2.1 presents the empirical methods for the unit root test (Ng-Perron)

used for the study, to test the stationarity of the variables. Sub-section 4.2.2 consists of the

description of the Autoregression Distributed Lag (ARDL) approach and bounds testing

approach, used to analyze short run and long run results of the study. In sub-section 4.2.3,

description of Vector Error Correction Model (VECM) based granger causality is provided.

Sub-section 4.2.4 presents an explanation of stability tests, for which CUSUM and CUSUMQ

10

are employed. Sub-section 4.2.5 composed of describing about Impulse Response Function

(IRF) which helps to examine the current and future behavior of a variable that following a

shock to another variable within the system. Sub-section 4.2.6 discusses Variance

Decomposition (VDC) analysis, which is used to determine the relative importance of each

innovation to the variables in the system; and sub-section 4.2.7 presents the methodology for

Principal Component Analysis that computes new variables called principal components

(PCs) as linear combinations of the original variables. And in section 4.3, all the variables

which have been used for the study are defined along with the sources of data collection.

Chapter 5 concentrates on the discussion of empirical results to study the

macroeconomic determinants of the Stock market development in India, using different

econometric techniques. The chapter is segmented into six sections; The first section 5.1,

gives a brief introduction of the chapter and econometric techniques used. Section 5.2

documents the established empirical relationship between the stock market and

macroeconomic variables Section 5.3 consist of yearly studies, incorporating empirical

results using yearly frequency data; Section 5.4 is composed of the quarterly study for the

estimation of the relationship between macroeconomic variables and stock market

development, based on the empirical finding using quarterly frequency of data; in the section

5.5, the results of the studies having monthly frequency data are discussed, showing the

empirical relationship between macroeconomic variables and stock prices; and the section 5.6

is composed of the summary of the findings of all the empirical studies performed in the

present chapter.

Chapter 6 demonstrates and discusses the empirical results to study the relationship

between deficits and the Stock market development in India, using different econometric

techniques. The chapter is segmented into three sections; The first section 6.1, gives a brief

introduction of the chapter and econometric techniques used. Section 6.2 documents the

established empirical relationship between the stock market development and fiscal policy

variables. Section 6.3 consists of the study of the relationship between fiscal deficit and the

stock prices in India, using yearly frequency data; Section 6.4 is composed of the study of the

relationship between twin deficit and the stock market development in India, using quarterly

frequency of data; and the section 6.5 is composed of the summary of the findings of all the

empirical studies performed in the present chapter.

Chapter 7 discusses the empirical results of the study of macroeconomic determinants

and the sectoral stock market development in India. The chapter starts with the introduction

followed by model specification, and by going through the empirical estimation techniques,

11

used for the study, the chapter ends with the summary of the findings of all the empirical

studies performed in the present chapter.

The last Chapter (Chapter 8) of the thesis presents the summary of the study and a brief

discussion of the implications and the major findings of the study. The first section 8.1 of the

chapter discusses summary and conclusion of the study. Section 8.2 of the chapter composed

of the policy implications of the study. Section 8.3 of the study shows the specific

contributions of the study. Section 9.5 of the chapter consists of the limitations of the study;

and in the last section 8.5, scope for further studies are mentioned.

12

CHAPTER 2

An overview of the developments in Indian stock market

2.1. Introduction

During the last two decades, Indian stock market faced various ups and downs.

Moreover, it is forced to severe corrections which were initiated by the SEBI and the

government of India. The important events, news and views which were published in

National dailies and various Magazines were considered to provide an idea about the trends

in the Indian Capital Market. Well-developed securities markets are the backbone of any

financial system. Apart from providing the medium for channelizing funds for investment

purposes, securities markets aid in pricing of assets and serve as a barometer of the financial

health of the economy. The Indian securities markets have witnessed extensive reforms in the

post-liberalization era in terms of market design, technological developments, settlement

practices and the introduction of new instruments. The markets have achieved tremendous

stability and as a result, have attracted huge investments by foreign investors. There still is

tremendous scope for improvement in both the equity market and the government securities

market. Prior to the early 1990s, most of the financial markets in India faced controls of

pricing, entry barriers, transaction restrictions, high transaction costs and low liquidity. A

series of reforms were undertaken since the early 1990s, so as to develop the various

segments of financial markets by phasing out administered pricing system, removing barrier

restrictions, introducing new instruments, establishing an institutional framework, upgrading

technological infrastructure and evolving efficient, safer and more transparent market

practices, which ultimately leads to the economic development of the nation. Since the study

is concerned with studying predictability in the Indian stock market, it is necessary as well as

logical to present the origin, any relevant details about the Indian stock market and its

important stock indices. To this end, we first present the history and origin of the Indian stock

market. Followed by historical overview, we will state some recent facts on the performance

of the Indian economy to get the knowledge of India’s current status as one of the most

important emerging market economies with huge growth potential and the role other

variables in its sustainability. Since we are concerned with the behavior of the stock market,

we then cite a few major structural, operational and regulatory reforms which were carried

out in the Indian stock market during the last one and-a-half decades since its reform process

started in the early nineties of the last century. This chapter has thus been organized as

follows. The next section gives highlights the historical development of the Indian stock

13

market, and section 2.3 discusses the case for India as an emerging market economy. In

Section 2.4, some of the major changes in the financial sector of the Indian economy are

briefly described; and the last section contains the summary of the chapter.

2.2. Historical Development of the Indian Stock Market

The history of Indian stock market is about 200 years old. Prior to this the hundis and

bills of exchange were in use, especially in the medieval period, which can be considered as a

form of virtual stock trading but it was certainly not an organized stock trading. The recorded

stock trading can be traced only after the arrival of the East India Company. The first

organized stock market that was governed by the rules and regulations came into the

existence in the form of The Native Share and Stock Broker’s Association in 1875. After

passing through numerous changes this association is today better as Bombay Stock

Exchange, which remains the premier stock exchange since its inception. The formation of

the native share and the stock broker’s Association at Bombay in 1875 was an important

early event in the development of the stock market. This was followed by the formation of

association/exchanges in Ahmedabad (1894), Calcutta (1908), and Madras (1937). In

addition, a large number of short-lived exchanges emerged mainly in rising periods to go

back into darkness during depressing times subsequently. Indian stock market marks to be

one of the oldest stock market in Asia. It dates back to the close of the 18th century when the

East India Company used to transact loan securities. In the 1830s, trading on corporate stocks

and shares in the Bank and Cotton presses took place in Bombay. However, the items in

which the trading took place increased tremendously by the end of 1839. Thereafter, the

concept of broker business was started which show momentum in the mid-18th century.

Though the trading was broad but the brokers were hardly a half dozen during 1840 and

1850. An informal group of 22 stockbrokers began trading under a banyan tree opposite the

Town Hall of Bombay from the mid-1850s, each investing a (then) princely amount of Rupee

one. This banyan tree still stands in the Horniman Circle Park, Mumbai. In 1860, the

exchange flourished and the number of brokers who are dealing in the trading of items goes

up to 60. Around 1860-61, there is no supply of cotton from America as the civil war took

place in America. Due to this, there is a concept of “Share Mania” that took place in India.

Further, the number of brokers increased from 60 to 250 in around 1862-1863. The informal

group of stockbrokers organized themselves as The Native Share and the Stock Brokers

Association, which, in 1875, was formally organized as the Bombay Stock Exchange (BSE).

In 1930, BSE was shifted to an old building near the Town Hall in Bombay. On 31 August

14

1957, the BSE became the first stock exchange to be recognized by the Indian Government

under the Securities Contracts Regulation Act. And finally in 1986, it developed the BSE

SENSEX index, giving the BSE a means to measure overall performance of the exchange.

Early 1960s was starting of bearish phase in the stock exchange as the Indo China war

took place. After the ban in forward trading and badla in 1969, the bearish trend became

worse. Badla in share trading means something in return. It is a system to carry-forward.

Badla is the charge, which the investor pays to carry forward his position. Using the Badla

tool or system, an investor can take a position in the scrip without actually taking delivery of

the stock. He can carry-forward his position on the payment of small margin. Financial

institutes helped to boost the sentiment by injecting liquidity in the market. In 1964, the first

Indian mutual fund came into market, named the Unit Trust of India.

The badla trading was resumed again in 1970s, under another form of hand delivery

contracts. But in 1974, 6th of July capital market had to face a bad news. The government

introduced the Dividend Restriction Ordinance (DRO); this rule restricting the companies for

the payment of dividend up to 12 per cent of the face value or one-third of the profits of the

companies can be distributed (Whichever was lower). With the news, the stock market

crashed again. Stocks went down by 20% and the market was closed for nearly a fortnight.

The stock market remained in a bearish trend until the optimism came to market with the

MNCs who forced to dilute majority stocks in their company in favor of Indian public. It was

the first time Indian public had the opportunity to invest in some of the finest MNCs. In 1977,

Mr. Dhirubhai Ambani entered in Indian stock exchange.

The period of 1980s, proved to be the growth period for Indian stock exchange. Indian

public discovered profitable opportunities in the stock exchange. It was the time when people

became aware of the stock exchange and started to get attracted and invest in the same. It was

the time when convertible debentures and public sector bonds were popular in the market.

New stock market entries like Reliance and LNT re-defined Indian stock market scenario.

Such factors enlarged volume in the stock exchange. 1980s can be characterized by the huge

increase in the number of listed companies in the stock market and increase in market

capitalization.

The 1990s can be described as the most decisive decade in the history of Indian stock

market. Everyone was talking about liberalization and globalization. The Capital Issue Act of

1947 was replaced in 1992. SEBI was emerging as a new regulator of the market. FII is

coming to India and re-rating India as one of the most attractive market in the world. Number

of new stock exchanges were rising in the county. Private sector mutual funds were welcome

15

in the market. Some very big scams of Indian scam history took place in 1990s. A major

scandal with market manipulation by a BSE member named Harshad Mehta took place in

1992. The impact of such incidence was very deep. Indian investors drove their money out of

the market for some years. With this BSE responded to calls for reform with intransigence.

The slow actions by the BSE helped radicalize the position of the government and opened

Indian government eyes, which encouraged the creation of the National Stock Exchange

(NSE), which created an electronic marketplace. New technology new systems were

introduced in Indian stock exchange. The Bombay stock exchange had two new competitors

in the market, the OTC (Over the Counter Exchange of India) and NSE established in 1992.

The national securities clearing corporation (NSCC) and National securities depository

Limited (NSDL) were established in 1995 and 1996 respectively. Option trading service was

started in 1995—1996. Rolling settlement was introduced in India in early 1998. 1990s are

known as era of Indian IT companies too. Wipro, Infosys, Satyam were some of the preferred

stocks. Telecom and Media sector also rising during the same time.

After Ketan Parekh scam in early 2000, Badla system was banned in Indian market and

rolling settlement was introduced in all scripts. Future trading and Internet trading started in

2000, these events changed picture of the old stock market. In 2001, The Unit Trust of India

(UTI) suspended the sale and repurchase of its popular US-64 scheme for six months. It

created panic among investors. One big incidence of VSNL (Videsh Sanchar Nigam Ltd.)

disinvestment took place in 2002. In 2003, the government took the decision to privatize PSU

banks and it again proved to be a market buster. In 2000s, FII money started coming in Indian

market in large volumes. NSE turnover exceeded the BSE. BSE rapidly automated, but it

never caught up with NSE spot market turnover. Global recession hits Indian market in late

2007 and throughout 2008. Big Satyam Scam was exposed in 2008 again and it hit investor’s

sentiments badly. Since the second quarter of 2009, we have been watching upward moving

trend in the markets again.

It has been a long journey for the Indian capital market to become organized, mature,

fairly valued, nicely regulated, liberal and more global. The Indian market is one of the most

attractive and developing markets today, which gives a stable and a high rate of return

compared to other countries.

16

2.3. India as an emerging market economy

The Indian economy has shown a remarkable performance over the last two decades.

From the year 1985 onwards, the problem of balance of payments was getting started in

India. In the middle of 1991, exchange rate of India was subjected to a severe correction. The

corrections were started with the decline in the value of the rupee leading up to mid-1991.

The action against this event was taken by the authorities of the Reserve Bank of India, as

they expended international reserves in order to slow down the decline in value of the rupee.

The reserves were near to depletion and the exchange rate was devalued sharply in the first

week of July, against major foreign currencies. By the end of 1990, the country was in a

serious economic crisis. After the occurrence of this major economic crisis, the Government

of India had taken some major reforms, in terms of globalization and liberalization, and thus,

the economy started experiencing a rapid economic growth with the inflow of increasing

foreign investment. According to International Monetary Fund, the Indian Economy is the

seventh-largest economy in the world in terms of nominal GDP and the third-largest by

purchasing power parity (PPP). The Central Intelligence Agency (the Fact Book Indian

Economy) stated that, India is also classified as Newly Industrialized Country, one of the G-

20 major economies, a member of BRICS and a developing economy with approximately 7%

average growth rate for the last two decades. India's economy became the world's fastest

growing major economy from the last quarter of 2014, replacing China's and India’s gross

domestic product (GDP) grew at 7.5% during the January-March of 2014-15 period, faster

than China’s 7% in the same period, mainly on account of improvement in services and

manufacturing sectors. (DNA, Indian economy overtaken china growth rate). As per the

report of Economic Survey 2007-08, show, the Indian economy registered a growth rate of

10.2 percent during the year 2007, the highest ever and after that, the GDP comes down to

3.7 in 2008-09, due to the Global financial crisis in that year so-called sub-prime housing

mortgage crisis. Because there was fiscal and monetary space, timely stimulus allowed the

economy to recover fairly quickly to a growth of 8.4 percent in 2009-10 and 2010-11. Since

then, however, the fragile global economic recovery and a number of domestic factors have

led to a slowdown once again (as per the Ministry of Finance). Therefore, the GDP in 2011-

12 also moderated to 6.5 percent from 9.8 percent in the 2010-11, as per the former finance

minister Pranab Mukherjee “the negative growth in the mining sector along with a slowdown

in the construction sector has also contributed to the decline in GDP growth”. The GDP

growth in 2012-13 was worse than expected, according to the Ministry of Finance, the

slowing growth rate in 2012-13 can be explained in terms of both global factors and domestic

17

factors. The slowdown in growth in advanced economies and near recessionary conditions

prevailing in Europe resulted not only in lower growth of international trade but also lower

capital flows. The growth rate of India’s exports declined. At the same time, however, the

international price of crude oil remained high. Hence, India’s trade and current account

deficits widened. Turning to domestic factors, rainfall in the monsoon season of 2012-13 has

been below normal, particularly in the key months of June and July. This affected sowing and

resulted in a lower growth rate of agriculture and allied sectors. The Gross Domestic Product

(GDP) growth rate for 2013-14 has revised upwards to 6.9 percent following adoption of the

new series with base year 2011-12. The GDP growth rate in the year 2014-15 is 7.4 percent

as per the statistics of the Central Statistics Office (CSO). The overall economic situation in

the country is looking better and the basic parameters of the Indian economy are moving in

the right direction, according to Union Finance Minister Arun Jaitley. As per the Indian

Economic Survey 2014-15, the Indian economy in 2014-15 has emerged as one of the largest

economies with a promising economic outlook on the back of controlled inflation, rise in

domestic demand, increase in investments, decline in oil prices and reforms among others.

Apart from India’s success in terms of high growth rates, there exist other important

factors necessary for the sustainability of the growing economy, which came into the picture

after the 1991 reforms, i.e., after the adoption of globalization and liberalization policies, the

role of international financial inflows and the degree of openness to trade has been increased,

accelerating the growth of the overall economy. Capital formation is an important element of

any economy. International financial inflows play a complementary role in the overall capital

formation of an economy, by filling the gap between domestic savings and investment.

International Financial inflows can be broadly categorized in two components, the FDI

(Foreign Direct Investment) and the FII (Foreign Institutional Investment). Foreign Direct

investment plays an important role in the growth of an economy. It is a direct investment by

the company situated in a country, into the production or business running in another country,

either by buying a company in the target country or by expanding operations of an existing

business in that country. Inflows of FDI have increased substantially after adopting

globalization and liberalization policies relating to FDI in 1991 by the government of India,

increased FDI from US$ 2696 million in 1996 to US$ 4,029 million in 2000-01 and in 2005-

06 inflow of FDI becomes more than doubled to exceed US$ 8,961 million in 2005-06. But

after 2005-06, the reported statistics show a steep increase in inflows: from US$ 22,826

million in 2006 to nearly US$ 37,758 million in 2014-15, as reported by the DIPP

(Department of Industrial Policy and Promotion). Thus, the trend analysis of the FDI data

18

from 1995-96 to 2014-15 shows that there is always a positive average trend of FDI in India

but if we deeply analyze the data, FDI flow in India. Further, the FIIs has emerged as

noteworthy players in the Indian stock market and their growing contribution adds an

important feature to the development of stock markets in India. To facilitate foreign capital

flows, developing countries have been advised to strengthen their stock markets. FII inflows

into Indian equities have been steady ever since the markets were opened up for it in 1993. It

owns a dominant 16% of Indian equities (worth US$147bn) and account for 10-15% of the

equity volumes. It injected US$ 23 billion in the Indian equity markets during the third

quarter of 2012, which is very negligible when compared to second quarter in the same year

due to lack of confidence among investors and the prevailing economic downturn. But in the

year 2014-15 FIIs have invested a net of US$ 43.5 billion, the highest investment in any

fiscal year. This huge investment is because of expectations of the investors for an economic

recovery, falling interest rates and improving earnings outlook. Although there are some

debate over the inherent weaknesses with FII flows and their destabilizing effects on equity

and foreign exchange markets, it cannot be ignored that India is increasingly becoming an

attractive destination for the global investors.

In India, the average percentage growth rate of inflation was 12.56 in the year 1988–

1989 which rose up to 19.30 in the year 1991–1992 and in the 2000–2001 the average

percentage annual growth rate of inflation was negative (−0.33). It turned positive in the

consecutive year, and in the last few years, the percentage annual growth rate of inflation

increased rapidly. Significantly, in 2008–2009 it crossed the level of double digits. Money

supply in India has increased considerably over the past decades, from Rs. 44.77 billion in

1988–1989 to Rs. 53.92 billion in the year 2000–2001 and to Rs. 76.33 billion in the year

2012–2013, showing an upward trend and becomes Rs. 108.519 billion in 2015. Given

India’s long history of running huge fiscal deficits, the sharp increase in its fiscal deficit over

the last few years is a major concern for both academicians and policy makers in India (Rao,

2009; Rangarajan, 2009). The fiscal deficit of India stood at 7.08 % of GDP in 1988. There

was a clear improvement till 1990. After 1991 it started to fall with minor fluctuations till

2007–2008, when it was at its minimum of 2.54% of GDP in the year 2007–2008. After

falling to 2.54 % in 2007–2008, the fiscal deficit to GDP ratio started rising again and was

around 5.7% in 2012 and the fiscal deficit for 2014-15 is 4% of gross domestic product (ENS

Economic Bureau, May 2015). In the year 2011-12 and 2012-13, current account balance for

India reached deficit levels of 4.6 % (US$ 78 billion) and 4.8 % of GDP (US$ 87 billion).

India’s current account deficit (CAD) for the January-March period narrowed sharply to $1.2

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19

billion (0.2 per cent of GDP) from $18.1 billion (3.6 per cent of GDP) in the same period last

year, which was also lower than $4.2 billion (0.9 per cent of GDP) in the October-December

quarter of 2013-14. “The lower CAD was primarily on account of a decline in the trade

deficit as decline in imports was sharper than that in exports,” the Reserve Bank of India

(RBI). Further, the Current account deficit is estimated to come down to 1.3 per cent

of GDP in the fiscal ending March, due to moderation in petroleum and gold imports, the

Reserve Bank of India (RBI).

With a stockpile of 25000 tonnes and accounting for around a quarter of the world

demand, gold imports in India increased from US$ 33 billion (in 2009-10) to US$ 57 billion

(2011-12) and US$ 53 billion (2012-13). Share of gold in imports has also increased

considerably from 7.6% (2005-06) to 12.6% (2011-12). The main reason for this

phenomenon is said to have been the increase in global prices of gold in the post financial

crisis scenario, as the world’s savers looked for ‘safe havens’ to park their savings. Since

2005, gold price has doubled in terms of US$ and tripled in terms of Rupee. Surpassing

returns on other investment, between 2007 and 2012, gold gave annual average returns of

23.7% as compared to 7.3% by Nifty (National Stock Exchange in India) and 8.2% by

Savings Deposits (Sehgal et al. 2012, cited in Economic Survey 2012-13). In addition, it acts

as a good hedge against inflation, which reduces real return on investments (inflation in India

stood at 9.6% and 8.9% for the years 2010-11 and 2011-12 respectively). Hence, the recent

spurt in gold demand and import by India is less about its historical affinity for consumption

as jewelry and more about investment dynamics. (Center for Budget and Governance

Accountability (CBGA), India). This brief presentation of statistical data on India’s economic

performance thus establishes that India is now one of the fastest growing economies in the

world with huge growth potential.

2.4. Reforms in the financial sector

In 1990s, India was undergoing extremely fragile financial condition arising out of

exceptionally severe balance of payment crisis, sluggish growth, and political instability,

therefore, India’s economic reforms began in 1991 when the newly elected congress

government, wanted to get rid of the pertaining economic and financial problems of the

country, thus, major policy changes and reform programs were initiated in most of the sectors

of the economy including, of course, the financial sector, to achieve short term stabilization

combined with a longer term program of comprehensive structural reforms. The reforms

initiated in 1991 were formulated by keeping in view the need for a system change, involving

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liberalization of government controls, a larger role for the private sector and greater

integration with the world economy. Consequently, some fundamental changes have taken

place in the Indian economy as a whole, and in particular, in the financial sector. Parallel with

the reforms in the banking sector, an action has been taken for the reforms of the capital

market, as it was an important part of the agenda of financial sector reforms. In 1991 India’s

capital market did not have any statutory regulatory framework. The process of reform of the

capital market was initiated in 1992 as per the guidelines recommended by the Narasimham

Committee. It aimed at removing direct government control and replacing it by a regulatory

framework based on transparency and disclosure supervised by an independent regulator.

Therefore, in the context of Indian capital market, which is the focus of the study, the first

step was taken in 1992 when the Securities and Exchange Board of India (SEBI), which was

originally established as a non-statutory body in 1988, was formed to a full-fledged capital

market regulator with statutory powers in 1992. The requirement of prior government

permission for accessing capital markets and for prior approval of issue pricing was abolished

and companies were allowed to access markets and price issues freely, subject only to

disclosure norms laid down by SEBI. Consequently, the stock exchanges, which were earlier

dominated by brokers and lacked effective supervision, are now much better governed.

Another important policy initiative in 1993 was taken; the economy was opened to

portfolio investment in two ways. The opening of the capital market to foreign institutional

investors (FIIs) meeting certain minimum standards were allowed to invest in Indian equity

and later also in debt instruments through secondary market purchases in the stock market. At

present 528 FIIs are registered with the Securities and Exchange Board of India (SEBI) and

around 150 are active investors. A second window for portfolio investment was provided by

allowing Indian companies to issue fresh equity abroad through the mechanism of Global

Depository Receipts (GDRs). This enabled Indian companies to raise resources from passive

investors in world markets instead of seeking active investors as is the case with joint venture

partners. Portfolio investment has expanded rapidly in the post-reform period (Montek S.

Ahluwalia, 1999).

An important reform measure had been taken place in the form of modernization of

technology of trading with the formation of a new stock exchange, called the National Stock

Exchange (NSE). The NSE was set up in 1994 as an automated electronic exchange as a

competitor to the oldest stock exchange of India viz., the Bombay Stock Exchange (BSE). It

enabled brokers in 220 cities all over the country to link up with the NSE computers via

VSATs and trade in a unified exchange with automatic matching of buy and sell orders with

21

price time priority, thus ensuring maximum transparency for investors. The introduction of

electronic trading by the NSE generated competitive pressure which forced the BSE to also

introduce electronic trading in 1995. This was the first step towards paperless trading, which

allowed dematerialization of share certificates with settlement by electronic transfer of

ownership from one account to another within a depository and dematerialization of shares.

Another major development concerning the secondary segment of the Indian capital market

was the introduction of Futures trading in 1999. A well-functioning market in index futures

would help in risk management and provide greater liquidity to the market.

Another measure was taken by the government of India in July 1991, by devaluing the

rupee by 24% as part of the initial stabilization program, and a dual exchange rate was

introduced in March 1992. Shortly after March 1993, the dual exchange rate was unified and

the unified rate was allowed to float. The cumulative effect of these changes was that

between June 1991 and March 1993 the exchange rate depreciated from $1= Rs. 20 to $1=Rs.

31, a depreciation of 35% in the dollar value of the rupee and a real depreciation (adjusting

for price changes) of around 27% viz.-a-viz., India’s major trading partners. This adjustment

in the exchange rate clearly helped the Indian industry to meet the import competition

resulting from trade liberalization. Exchange rate management has avoided the danger of

excessive rigidity and also the opposite dangers of overshooting with associated loss of

confidence (Montek S. Ahluwalia, 1999). As part of the process of transiting to an open

economy, the rupee was made convertible on the current account in 1993. The Indian

currency is set to be made fully convertible in phases over the five years ending 2010-2011.In

June 2008, the rupee appreciated to a ten-year high of US$ 39.29. The stability of the Indian

economy attracted substantial foreign direct investment, while high interest rates in the

country led the companies to borrow funds from abroad. The global financial crisis of 2007-

08, exerted pressure on crude oil prices, which gradually plunged to below $50 a barrel. Due

to this, inflow of dollar declined, with oil companies and investors purchasing more and more

dollars. Persistent outflow of foreign funds increased the pressure on the rupee, causing it to

decline. On March 5, 2009, the Indian currency depreciated to a record low of US$ 52.06

(Economy Watch, 2010).

India has 23 stock exchanges across the country, 20 of them being regional ones with

allocated areas of operation. The major stock exchanges of India are the Bombay Stock

Exchange and the National Stock Exchange. The Securities and Exchange Board of India

(SEBI) works as a regulatory authority, which regulates all the stock exchanges of the

country. The Bombay Stock Exchange (BSE) which was established in 1875, is the primary

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stock exchange of India. More than 5500 companies are listed on BSE, making it world's No.

1 exchange in terms of listed members. The companies listed on BSE command a total

market capitalization of USD 1.68 Trillion as of March 2015. It is also one of the world's

leading exchanges (5th largest in March 2015) for Index options trading (World Federation of

Exchanges). BSE is the first exchange in India and second in the world to obtain an ISO

9001:2000 certification. It is also the first Exchange in the country and second in the world to

receive Information Security Management System Standard BS 7799-2-2002 certification for

its On-Line trading System (BOLT). The growth in both investment and number of listed

companies in various stock exchanges in India, and also changes in trading rules and in the

volume of capital raised from private investors has helped in achieving globalization and

international competitiveness of Indian capital market and sparking a boom in its stock

prices.

BSE's popular equity index, the S&P BSE SENSEX, is India's most widely tracked

stock market benchmark index. The S&P BSE SENSEX (S&P Bombay Stock Exchange

Sensitive Index), also-called the BSE 30 or simply the SENSEX, is a free-float market-

weighted stock market index of 30 well-established and financially sound companies listed

on Bombay Stock Exchange. The 30 component companies which are some of the largest and

most actively traded stocks, are representative of various industrial sectors of the Indian

economy. The S&P BSE SENSEX is regarded as the pulse of the domestic stock markets in

India. Many newly important sectors like finance, pharmaceutical, healthcare are also well

represented in this index. As the oldest index in the country, it provides the time series data

over a fairly long period of time. With base at 1978-79 = 100, this index has been serving the

purpose of quantifying the equity price movements and it also reflects the sensitivity of the

Indian capital market in an effective manner. The growth of the equity market in India has

been phenomenal from last few years. Right from the early nineties the stock market

witnessed heightened activities in terms of various bull and bear runs. The BSE SENSEX has

captured all these events in the most judicial manner. The BSE SENSEX was initially a full-

market-capitalization weighted index. But since September 1, 2003, it follows the free-float

market capitalization methodology of index construction which is now considered to be the

widely followed index construction methodology on which majority of global equity

benchmarks such as MSCI, FTSE, STOXX, S&P 500, and Dow Jones are based. As of 21

April 2011, the market capitalization of S&P BSE SENSEX was about US$ 464 billion

(47.68% of the market capitalization of BSE), while its free-float market capitalization was

US$ 245 billion. During 2008-12, Sensex 30 Index share of BSE market capitalization fell

23

from 49% to 25% (SEBI Report, 2015) due to the rise of the sectoral indices like BSE PSU,

Bankex, BSE-Teck, etc. On 19 February 2013, the SENSEX becomes S&P SENSEX as BSE

ties up with Standard and Poor's to use the S&P brand for Sensex and other indices. On 4

March 2015, The Sensex breaches 30000 mark following steps taken by the Reserve Bank Of

India in cutting the repo rates.

2.5.Trends of the Indian Stock Market

The Indian stock exchanges hold a place of prominence not only in Asia but also at the

global stage. As mentioned above, the Bombay Stock Exchange (BSE) is one of the oldest

exchanges across the world, while the National Stock Exchange (NSE) is among the best in

terms of sophistication and advancement of technology. The Indian stock market scene really

picked up after the opening up of the economy in the early nineties. The whole of nineties

were used to experiment and fine tune an efficient and effective system, and right from the

era of globalization, the stock market started to work efficiently and showed its new heights,

at different phases of its development. There are times when the Indian stock market achieves

new heights, breaching its previous records and there are time also when stock market

plunges upto its extreme. Theses ups and downs of the stock market were definitely due to

some unavoidable circumstances created in the economy, because the stock market index is

also an important part of the economic cycle. The present section of the chapter gives a brief

overview of the development of the Indian stock market.

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Table 2.1: Key indicators of Indian stock market activity

Year

BSE

Sensex % Changei

MCAP

ratioiv CNX Nifty % Changei

1990-91 1049.53 NA 09.06 NA NA

1991-92ii 1879.51 79.08 11.81 NA NA

1992-93 2895.67 54.06 17.35 NA NA

1993-94 2898.69 00.10 22.19 NA NA

1994-95iii 3974.91 37.12 34.48 1203.06 NA

1995-96 3288.68 -17.26 38.43 962.63 -19.98

1996-97 3469.24 05.49 34.69 1006.59 04.56

1997-98 3812.86 09.90 30.66 1087.50 08.03

1998-99 3294.78 -13.58 30.35 955.39 -12.14

1999-00 4658.63 41.39 24.53 1368.62 43.25

2000-01 4269.69 -08.34 39.54 1334.76 -02.47

2001-02 3331.95 -21.96 31.06 1077.03 -19.31

2002-03 3206.29 -03.77 22.34 1037.23 -03.69

2003-04 4492.19 40.10 25.00 1427.50 37.62

2004-05 5740.99 27.79 45.13 1805.26 26.46

2005-06 8278.55 44.20 53.74 2515.48 39.34

2006-07 12277.3 48.30 66.29 3572.44 42.01

2007-08 16568.9 34.95 86.27 4896.60 37.06

2008-09 12365.6 -25.36 146.85 3731.03 -23.80

2009-10 15585.2 26.03 52.73 4657.77 24.83

2010-11 18605.2 19.37 86.36 5583.54 19.87

2011-12 17422.9 -06.35 94.58 5242.74 -06.10

2012-13 18202.1 04.47 55.30 5520.34 05.29

2013-14 20120.1 10.53 68.96 6009.51 08.86

2014-15 26556.5 31.98 70.00 7969.74 32.61 i Annual percentage changes calculated by author ii The year of adoption of liberalization and globalization policies iii NSE was introduced in the capital markets iv MCAP has been taken as a percentage of GDP, which is popularly known as Buffet Valuation Indicator

The table 2.1 represents the growth statistics for the key indicators of Indian stock

market activity. The Sensex has increased by over twenty five times from June 1990 to the

present. Using information from April 1979 onwards, the long-run rate of return on the S&P

BSE SENSEX works out to be 18.6% per annum (BSE india). In the mid of 1990, the

SENSEX touched the four-digit figure for the first time and closed at 1,001 due to good

monsoon in the country and excellent corporate results. The Sensex crossed the 2,000 points

with the start of the year 1992, i.e. january, because of the liberal economic policy initiatives

undertaken by the then finance minister and Former Prime Minister of India Dr Manmohan

Singh. And right in the next month of the year 1992, with the announcement of the market

friendly budget, the index surged to 3000 points. Further, in the consequtive month of 1992,

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the sensex crossed the 4,000 points due to the expectations of a liberal export-import policy.

The The development process of stock market wass give a jerk in October 1992 when the

Sensex registered a fall of 570 points (12.77 per cent) to close at 3,870, following the

Harshad Mehta securities scam. In the year 1999, the Sensex crossed the 5,000 mark,

because of the political factors, as the Bharatiya Janata Party-led association won the majority

in the 13th Lok Sabha election. In February 2000, the information technology boom helped

the Sensex to cross the 6,000 points. This record would stand for nearly four years, until May

2004,when the Sensex faced another fall of 565 points, due to the political unstability in the

country.

The gradual development process of Indian stock market continued in the subsequent

years, due to the affect of various economic and political, domestic and international

circumstances of the nation. On 22 May 2006, the Sensex plunged by 1,100 points during

intra-day trading, leading to the suspension of trading for the first time since 17 May 2004.

The volatility of the SENSEX had caused investors to lose Rs 6 trillion (US$131 billion)

within seven trading sessions. When trading resumed after the reassurances of the Reserve

Bank of India and the Securities and Exchange Board of India (SEBI), the Sensex managed to

move up 700 points, but still finished the session 457 points in the red. The Sensex eventually

recovered from the volatility, and on 16 October 2006, the Sensex closed at an all-time high

of 12,928.18. This was a result of increased confidence in the economy and also because

India's manufacturing sector grew by 11.1% in August 2006. On 29 October 2007, the Sensex

crossed the 20,000 mark for the first time with a massive 734.5-point gain. The journey of the

0.00

20000.00

40000.00

60000.00

80000.00

100000.00

120000.00

140000.00

199

0

199

1

199

2

199

3

199

4

199

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200

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200

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201

4

Figure 2.1: Indian Stock Market Trends

BSE Sensex

(Base : 1978-79 =100)

MARKET CAPITALISATION - BSE

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26

last 10,000 points was covered in just 483 sessions, compared to 7,297 sessions taken to

touch the 10,000 mark from its base value of 100 points. After a long spell of growth, the

Indian economy were experiencing a downturn, because the Industrial growth has been

faltering, inflation remains at double-digit levels, the current account deficit is widening,

foreign exchange reserves are depleting and the rupee is depreciating. Furthermore, the

breathtaking development process continued till the effects of the subprime crisis in the U.S.,

started to spread on Indian economy, and the investors panicked following weak global cues

accompanied by the fears of a recession in the US and consequently, in the third week of

January 2008, the Sensex experienced huge falls along with other markets around the world.

On 21 January 2008, the Sensex saw its highest ever loss of 1,408 points at the end of the

session. The most immediate effect of that crisis on India has been an outflow of foreign

institutional investment from the equity market. Foreign institutional investors, who need to

retrench assets in order to cover losses in their home countries and were seeking havens of

safety in an uncertain environment, have become major sellers in Indian markets. In 2007-08,

net FII inflows into India amounted to $20.3 billion. As compared with this, they pulled out

$11.1 billion during the first nine-and-a-half months of calendar year 2008, of which $8.3

billion occurred over the first six-and-a-half months of financial year 2008-09 (April 1 to

October 16). In addition, this withdrawal by the FIIs led to a sharp depreciation of the rupee.

Between January 1 and October 16, 2008, the RBI reference rate for the rupee fell by nearly

25 per cent, even relative to a weak currency like the dollar, from Rs 39.20 to the dollar to Rs

48.86 (Chart 2). This was despite the sale of dollars by the RBI, which was reflected in a

decline of $25.8 billion in its foreign currency assets between the end of March 2008 and

October 3, 2008. In March 2008, the Sensex dropped by 951.03 points on the global credit

crisis and distress, and the volatility in market continued till the mid of 2009. The Sensex

plunged by 869.65 points in July 2009, the day of Union Budget presentation in Parliament

on concerns over high fiscal deficit. Further, the Sensex closed at more than 21,000 points for

the first time, in November 2010. The Sensex crossed the historical mark of 30,000 after repo

rate cut announcement by RBI. But the volatility in the market still continues, due to the euro

crisis, Greece debt crisis and the emerging crisis of slow down of Chinese economy.

Thus, it can be concluded that the stock market movements are the effect of changes in

the various economic and political conditions of an economy. Furthermore, it is also observed

that various domestic and international macroeconomic factors works as the driving forces of

the Indian stock market.

27

2.6. Summary

The present chapter of the study tried to give a historical overview of the development

of stock markets in India and its major policy reforms. India is considered as the developing

country with many investment opportunities and higher growth potential. The Indian

Economy is the seventh-largest economy in the world in terms of nominal GDP and the third-

largest by purchasing power parity (PPP). The Central Intelligence Agency (the Fact Book

Indian Economy) stated that, India is a developing economy with approximately 7% average

growth rate for the last two decades. India's economy became the world's fastest growing

major economy from the last quarter of 2014, replacing China's and India’s gross domestic

product (GDP) grew at 7.5% during the January-March of 2014-15 period, faster than

China’s 7% in the same period, mainly on account of improvement in services and

manufacturing sectors. From the year 1991 onwards the economy of India has undergone

tremendous changes, after adopting globalization and liberalization policies, which enabled

the economy of India to prosper and grow with a great pace. The decade of 1990s proved to

be a decisive decade for Indian economy, as it introduced many major reforms in the year

like the formulation of Securities and Exchange Board of India (SEBI) in the year 1992 as

capital market regulator with statutory powers. The other important policy initiative was

taken in 1993 with the opening of the capital market to foreign institutional investors (FIIs)

and by allowing Indian companies to issue fresh equity abroad through the mechanism of

Global Depository Receipts (GDRs). Another important reform measure had been taken place

in the form of modernization of technology of trading with the formation of a new stock

exchange, called the National Stock Exchange (NSE). One more important measure was

taken by the government of India in July 1991, by devaluing the rupee by 24% as part of the

initial stabilization program. This adjustment in the exchange rate clearly helped the Indian

industry to meet the import competition resulting from trade liberalization. Thus, these

reforms introduced by the government of India, proved to be the milestones of Indian

economy, which changed the economic scenario of the country.

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CHAPTER 3

Theoretical underpinnings of the relationship between macroeconomic variables and

Stock market development indicators

3.1. Introduction

The present chapter of the study contains a review of the theory and evidence on, the

relationship between macroeconomic variables and stock market development.

Macroeconomic variables (such as inflation, interest rates, gold prices, trade openness,

deficits, international financial inflows, crude oil prices, economic activity, the money supply

and exchange rates) are closely examined to understand their effects on stock market

development. The change in macroeconomic variables may affect future dividends and cash

flows by affecting profitability which ultimately reflects changes in stock prices. It is

believed that the effects of macroeconomic variables on the profitability of individual

industries or sectors vary depending on their sensitivity to these variables. For example,

capital-intensive industries (such as banking sector industries or other non-banking financial

firms) are likely to be more sensitive to interest rate changes. Similarly, the earnings of

sectors such as retail and tourism are more likely to be affected by a slowdown in economic

activity. However, the slowdown is less likely to affect sectors, such as consumer staples or

health industries that produce goods and services that are essential to consumers. Moreover, it

seems that, if we move our focus of study from the determination of individual stock prices to

study the determination of the impact on the aggregate stock market using market indices, a

macro model of stock prices is more appropriate (than a micro model based on financial

ratios). Thus, the information contained regarding the changes in macroeconomic data that

affects investors’ expectations for the state of the economy is useful to predict the movement

in stock prices. Hence, the present chapter deals with the review of the theory behind and the

evidence for the macroeconomic factors that influence stock prices.

3.2. Theoretical Background

3.2.1. Random Walk and The Theory of Efficient Market Hypothesis (EMH)

Before discussing the efficient market hypothesis, we have to talk about random walk

which is considered as the reason for the genesis of the concept of efficient capital markets

(Brealey & Myers, 2000). Bachelier (1900), a French mathematician, concluded that the

stock price movement was unpredictable and followed a Brownain motion by empirical

study. Afterwards Kendall (1953), a British statistician, proved the behavior of stock and

29

commodity prices appeared amble. Instead of obtaining regular price cycles, he found the

movement of stocks and commodity prices followed a random walk. That specified that the

stock price changes are independent from the past prices (Brealey & Myers, 2000). Brealey &

Myers (2000) stated that the investors barely can receive any clue about tomorrow’s expected

change according to today’s price change. To be precise, the prices of the stock cannot be

predicted and it is impossible for investors to earn excess profits for a long period by only

depending on past series of returns. Therefore, the random walk theory worked as the base

for academicians, investors and regulatory authorities. For academicians, the random walk

behavior of prices is the base and tool to study and understand the movement of stock prices.

For investors, when they design their trading strategies, they have to consider the stock prices

followed a random walk or have to trace a particular pattern followed by stock prices.

In Fama’s (1965) article, the concept of efficient market was introduced in the

securities markets. The definition of efficient market was “a market where there are large

numbers of rational, profit-maximizers actively competing, with each trying to predict future

market values of individual securities, and where important current information is almost

freely available to all participants”. Formally, the EMH can be explained using the following

equation:

𝛺𝑡∗=𝛺𝑡 (3.1)

The left side represents a set of relevant information available to the investors, at time

“t”. The right side is the set of information used to price assets, at time “t”. The equivalence

of these two sides implies that the EMH is true, and the market is efficient.

Although this definition provides us with a first idea of what an efficient market is, it

does not really explain what is meant by available information. This is why Fama (1970)

included some elaboration on this definition, making a distinction between three types of

efficient markets1, based upon the level of information used by the market.

Weak form efficient market (level of information = historical price information);

Semi-strong form efficient market (level of information = all publicly available

information);

Strong-form efficient market (level of information = all information, both public and

private).

Originally the idea of different forms of market efficiency comes from Roberts (1967),

but Fama (1970) was most successful introducing the concept to the general public. With the

1 Fama (1991) revised these three terms to be predictability, event studies, and inside information for the weak form, semi-

strong form, and strong form, respectively.

30

additional knowledge on what is meant by available information, it becomes perfectly clear

what efficient markets are.

The weak form of the EMH confines itself to just one subset of public information,

namely historical information about the share price itself, i.e., it employs that asset prices

today incorporate all relevant past information, i.e., past asset prices, security dividends, and

trading volume. Knowing the past behavior of stock prices provides no indication of future

stock prices. In other words, the EMH theory hypothesizes that asset prices evolve according

to a random walk. Thus, asset prices cannot be predicted, and investors cannot beat the

market.

The semi-strong form EMH also incorporates the weak-form hypothesis. It states that

current asset prices fully reflect all available public information. Public information includes

not only information about an asset’s past price, but includes all information related to the

company's performance, future prospects of the company, expectations regarding driving

forces of the economy, and many other factors like the policies of the central banks,

government policies and the economic trends.

The strong-form EMH implies that the market is efficient: it reflects all information,

both public and private, incorporating the weak-form EMH and the semi-strong form EMH.

Therefore, in addition to relevant past information and public information, the strong form of

the EMH requires that asset prices fully incorporate more than past and public information. In

particular, the strong form of the EMH declares that stock prices reflect all information

(public as well as private) no investor would be able to profit above the average investor,

even if he was given new information.

There are wider implications of EMH. From an investor’s perspective, whatever level

of information, participants in the stock market may possess, they should not be able to

generate an abnormal profit. As mentioned before, in the world of a perfect capital market,

investors cannot consistently beat the market. This is consistent with the financial idea that

the maximum price that investors are willing to pay is the current value of future cash flows.

The current value of a future cash flow is usually evaluated by a discount rate, which

represents the degree of uncertainty associated with the investment, considering all relevant

available information.

From an economic standpoint, an efficient stock market will assist with the efficient

allocation of available economic resources. For instance, if the shares of a financially poor

company are not priced correctly, i.e., the shares are overvalued or undervalued, new savings

will not be invested within that industry. In the world of the EMH, the level of asset price

31

fluctuations, or volatility, fairly reflects underlying economic fundamentals. Along these

lines, Levich (2001) argues that policymakers interventions may disrupt the market, and

cause it to be inefficient. In the literature, the three forms of the EMH are usually used as

guidelines rather than strict facts (Fama, 1991). Also, most empirical studies have examined

the EMH in its weak or semi-strong forms, partly because the strong form is difficult to

measure, and there is a high cost associated with acquiring private information (Timmermann

and Granger, 2004).

3.2.2. Arbitrage Price Theory (APT)

The theory of asset pricing is a pricing model that seeks to calculate the appropriate

price of an asset while taking into account systemic risks common across a class of assets.

The Arbitrage Price Theory (APT) suggested by Ross (1976) has been an influential form of

asset price theory. APT is often viewed as an alternative of Sharpe’s (1964) capital asset

pricing model (CAPM)2 since the APT has the potential to overcome CAPM weaknesses: it

requires less and more realistic assumptions to be generated by a simple arbitrage argument

and its explanatory power is potentially better since it is a multifactor model. Whereas the

CAPM formula requires the market's expected return, APT uses the risky asset's expected

return and the risk premium of a number of macro-economic factors. Mathematically APT

can be expressed as:

𝑹𝒊𝒕 = 𝒓𝒊𝒇+ 𝜷𝒊𝑿𝒕 + 𝜺𝒕 (3.2)

Where 𝑅𝑖𝑡 is the return of the stock 𝑖 at time 𝑡, 𝑟𝑖𝑓 is the risk free interest rate or the

expected return at time 𝑡. 𝑋𝑡 is a vector of the predetermined economic factors or the

systematic risks while 𝛽𝑖 measures the sensitivity of the stock to each economic factor

included in 𝑋𝑡. 휀𝑡 , the error term, represents unsystematic risk3 or the premium for risk

associated with assets that cannot be diversified where (휀𝑡|𝑋𝑡)=0, 𝐸(𝑋𝑡) =0, and

𝐸(휀𝑡휀𝑡′|𝑋𝑡)=𝛴.

Ross (1976) shows that there is an approximate relationship between the expected

returns and the estimated 𝛽 𝑖𝑘 in the first step provided that the no arbitrage condition is

2We restrict our analysis to the APT theory since empirical studies on the CAPM fail to support the assumptions theory

(Semmler, 2006). However, CAPM is a single linear equation that links the expected return of an asset or a portfolio to its

expected risk. Thus, the CAPM is a single factor model: expected return is determined by a single factor systematic risk or

beta; whereas the APT is a muli-factor model: expected return is determined by more than one single factor (Lumby, 1980). 3 Unsystematic risk, also known as “nonsystematic risk,” "specific risk," "diversifiable risk" or "residual risk," can be

reduced through diversification. It is the type of uncertainty that comes with the company or industry you invest in. For

example, news that is specific to a small number of stocks, such as a sudden strike by the employees of a company you have

shares in, is considered to be unsystematic risk.

32

satisfied, i.e., the expected return E(Ri) increases as investors accept more risk, assuming all

assets in the market are priced competitively. This relationship can be represented as a cross-

sectional equation where the estimated 𝛽 𝑖𝑘 are used as explanatory variables:

𝐸(��𝑖) = 𝜆0 + 𝜆1��𝑖 + 𝜆2��2𝑖 + ⋯+ 𝜆𝑛��𝑛𝑘 + 𝜇𝑖 (3.3)

Where ��𝑖 is the mean excess return for asset 𝑖 and the 𝛽′𝑠 represent the sensitivity of a

security’s return 𝑛 to the risk factor 𝑘. The 𝜆𝑛’s represents the reward for bearing the risk

associated with the economic factor fluctuations. Equation (3.3) simply says that the expected

return of an asset is a function of many factors and the sensitivity of the stock to these factors.

Interestingly, APT failed to specify the type or the number of macroeconomic factors

for researchers to include in their study. For example, although Ross, et al. (1986) examined

the effect of four factors, including inflation, gross national product (GNP), investor

confidence, and the shifts in the yield curve, they suggested that the APT should not be

limited to these factors. Therefore, there is a large body of empirical studies that have

included a large number of different macroeconomic factors, depending on the stock market

they studied. Even though analysts can predetermine some economic factors, their selection

must be based upon reasonable theory (Chen et al., 1986). Before going for the empirical

relationships, it is mandatory to explore the theoretical underpinnings part of the relationship

of different macroeconomic variables with the stock market.

3.3. Stock Market Development Indicators

It is a well-known fact that, well-functioning stock markets can play an important role

in economic development processes and a sound economy can boost up the performance of

the stock market. A stock market can mobilize capital, enhance liquidity, diversify risk and

can effect saving decisions, on the other hand the economic prosperity of the country leads to

boost up investor confidence and encourage them for further investments. It is difficult,

however, to construct accurate measures of these functions. Therefore, the study has used

indicators that suit the purpose of the concept of stock market development, by including

proxies for stock market development that are most commonly used by academics and

practitioners (Pagano, 1993; Demirguc-Kunt and Levine, 1996a; Levine and Zervos, 1998a,

b; and Beck et al., 1999a). These indicators are associated with the size, liquidity and

volatility of the stock market. Brief and schematic descriptions of such indicators are as

follows:

33

3.3.1. Stock Market Size

Market Capitalization Ratio (MCR) is an indicator to measure the stock market size.

The market capitalization ratio equals the value of listed shares divided by GDP and the ratio

has frequently been used as a measure of stock market size by the analysts. The assumption

behind this measure is that overall market size is positively correlated with the ability to

mobilize capital and diversify risk on an economy-wide basis. The market capitalization

refers to the total value of listed shares on the stock exchange. Capitalization of a company is

calculated by multiplying the number of shares outstanding of that company by its share

price. The size of the stock market is a measure of the availability of finance (Rajan and

Zingales, 1996; Demirguc-Kunt and Maksimovic, 1998; and Subrahamanyam and Titman,

1999) and the ability to mobilize capital, diversify the risk and resources allocation processes.

Bekaert and Harvey (1995b, 1997) also argue that the ratio of equity capitalization to GDP is

a useful tool in characterizing the time-series of market integration. A large market size

(market capitalization as a percentage of GDP) suggests that the country is more likely to be

integrated into world capital markets. Furthermore, in an important empirical study,

Demirguc-Kunt and Levine (1996a) find that large stock markets measured by equity

capitalization to GDP are more liquid, less volatile, more internationally integrated, stronger

with regard to information disclosure laws and international accounting standards, and have

unrestricted capital flows than smaller markets.

3.3.2. Stock Market Liquidity

Liquidity is the term used to describe how easy it is to convert assets to cash. Market

liquidity is a market's ability to facilitate the purchase or sale of an asset without causing

much change in the asset's price. The stock market is said to be liquid if the shares can be

rapidly sold and the act of selling has little impact on the stock's price. Liquidity is an

important indicator of stock market development because theoretically more liquid stock

markets improve the allocation of capital to their optimal use, influence long term investment

decisions and facilitate technological innovation, thereby enhancing long term growth.

Greater liquidity also has a direct impact on the stock market performance. First, with the

increase in market activity, the information content for the share prices also increases and

more investors show their attention towards the stock. Second, to control the corporate

activities, the effective use of stock market requires that the market should be liquid. The

must condition for takeovers is that it requires a liquid capital market where bidders access a

huge amount of capital at short notice. Thus, it can be said that the stock market liquidity also

34

works as a function of corporate control. Therefore, stock market liquidity may be a good

proxy for information acquisition as well as the control function of capital markets. It is

believed that liquidity positively impacts the stock market. When stock prices rise, it is said

to be due to a confluence of extraordinarily high levels of liquidity on household and business

balance sheets, combined with a simultaneous normalization of liquidity preferences. On the

margin, this drives a demand for equity investments (Kostohryz, J., 2013)). Increased stock

market liquidity can also reduce the cost of equity capital through a reduction in the expected

return that investors require when investing in equity to compensate them for the risks, i.e.,

risk premium (Ahimud and Mendelson, 1986; Ahimud et al., 1997; Henry, 2000a, b). The

measure of liquidity would quantify all the costs associated with trading, including the time-

costs and uncertainty of finding a counterpart and settling the trade. There are two methods to

measure the stock market liquidity as described below:

Measure of Liquidity

3.3.2.1.Total value of shares traded ratio

The total value of the shares traded ratio equals total value of shares traded on the stock

market exchange divided by GDP. This ratio measures the trading of domestic equities on

domestic exchanges relative to the size of the market. It is the organized trading of firm

equity as a share of national output and therefore should positively reflect liquidity on an

economy-wide basis. A higher value traded ratio reflects greater liquidity in the market and

greater attractiveness for investors. If trading in the market represents the buying and selling

actions of investors in order to attain their desired position, then the speed at which new

information is incorporated into prices is measured by the trading activity. This ratio also

complements the market capitalization ratio; although market capitalization may be large,

but, there may be little trading.

3.3.2.2.Turnover ratio

The second measure of market liquidity is the turnover ratio. This ratio is equal to the

value traded divided by market capitalization. It measures the size of equity transaction

relative to the size of the stock market. The high turnover ratio is often used as an indicator of

low transaction costs. A higher turnover ratio may represent greater liquidity and market

efficiency. Brennan and Subrahmanyam (1996) find that the number of analysts following a

stock is strongly positively related to the liquidity of the stocks and that low turnover stocks

are followed by fewer analysts and thus are slower to react to information than high turnover

stocks. However, an excessively high turnover ratio may represent inefficiency or excessive

35

speculative trading. Bencivenga et al., (1996) gave a model in which excessive liquidity and

turnover lower the economic growth rates. Further, turnover also complements the total value

traded/GDP. While the total value traded /GDP captures trading compared with the size of

the economy, turnover measures trading relative to the size of the stock market.

3.3.3. Volatility

Volatility refers to the amount of uncertainty or risk about the size of changes in a

security's value. A higher volatility means that a security's value can potentially be spread out

over a larger range of values. This means that the price of the security can change

dramatically over a short time period in either direction. A lower volatility means that a

security's value does not fluctuate dramatically, but changes in value at a steady pace over a

period of time.

3.3.4. Stock market index

A stock index or stock market index is a measurement of the value of a section of the

stock market. It is computed from the prices of selected stocks (typically a weighted average).

It is a tool used by investors and financial managers to describe the market, and to compare

the return on specific investments. Or one can define it as an aggregate value produced by

combining several stocks or other investment vehicles together and expressing their total

values against a base value from a specific date. Market indexes are intended to represent an

entire stock market and thus track the changes in the market over time.

3.4. Macroeconomic Variables

3.4.1. Money Supply

The amount of money in an economy is referred to as the money supply or it is the total

amount of monetary assets available in an economy at a specific time. There are several ways

to define "money," but standard measures usually include currency in circulation and demand

deposits. Money supply is one of the components of monetary policy that any central bank

uses to cause a desired level of change in real activities. These frequent changes in the

monetary policy component are believed to have a significant effect on the stock market.

Therefore, it is important to analyze the relationship between money supply and an important

determinant of the economy, the stock market. The price of a stock is determined by the

present value of the future cash flows. The present value of the future cash flows is calculated

by discounting the future cash flows at a discount rate. The money supply has a significant

relationship with the discount rate and, hence, with the present value of cash flows. There are

36

many competing theories on how money supply affects stock market prices and has been

widely discussed in the economic literature. Although a strong relationship between the

money supply and the stock market prices has been found, but the relationship is still the

issue of debate, that the changes in money supply can be either predicted or unexpected by

the people. In the early 1970’s a number of papers were published that propounded that past

money supply data could be used to predict future returns (Sprinkel (1964), Homa and Jaffee

(1971), Hamburger and Kochin (1972), Hashemzadeh and Taylor (1988)). But the studies are

contradictory to the theory of efficient markets developed by Fama (1970), which states that

all available information should be reflected in current stock prices.

Friedman and Schwartz (1963) introduced the modern quantity theory that suggests that

an exogenous shock that increases the money supply changes the equilibrium position of

money with respect to other assets included in the portfolio. As a result, asset holders adjust

the proportion of their portfolios taking the form of money balances. This adjustment alters

the demand for other assets that compete with money balances such as equity shares. An

increase in the money supply is expected to generate an excess supply of money balances

which leads to an excess demand for shares. In this case, share prices are expected to rise.

Sprinkel (1964) pioneered the exclusive study on the relationship between money supply and

the stock market, using the quantity theory of money and concluded that there is a strong

relationship between the stock market and money supply in the United States.

Homa and Jaffe (1971) estimated the relationship between the money supply and stock

price index, in search of a forecasting tool in the implementation of investment strategies.

Their findings indicated that the price of any common stock is determined by three variables:

the level and growth rate of dividends, the risk-free rate of interest, and the risk premium.

The risk-free rate of interest being a function of money supply, they concluded that the

average level of stock prices is positively related to the money supply. Hamburger and

Kochin (1972) started with the standard valuation model and added current price level and

the corporate bond rate to capture the direct and indirect impacts of money supply on the

stock market. They concluded that changes in monetary growth could have a number of

different effects on the stock market. Pesando (1974) found empirical and theoretical

problems in the models used by Hamburger-Kochin and Homa-Jaffe. He concluded that the

inability of these models to generate accurate forecasts of stock prices was evidence against a

structural and stable relationship between money supply and common stock prices.

Rozeff (1974) examined stock market efficiency with respect to money supply data by

testing, regression models of stock returns on monetary variables and trading rules based on

37

money supply data. The evidence indicates no meaningful lag in the effect of monetary

policy on the stock market and that no profitable security trading rules using past values of

the money supply exist. Cooper (1974) tried to provide a plausible framework for estimating

the relationship between the money supply and the stock market returns, and, therefore to

offer another test of the efficient markets hypothesis. Cooper stated that although the quantity

theory of money and the theory of efficient markets hypothesis appear to be contradictory,

but, the major finding of the study is that the two theories are in fact complementary. The

results of the study offer additional support for the concept of market efficiency, since stock

returns lead rather that lag money changers.

Rogalski and Vinso (1977) further improved Rozeff’s (1974) analysis by synchronizing

the data so that the money supply data were generated at intervals that were same as those for

the stock return data and also by taking proper account of the autocorrelation in the time

series. The Rozeff’s results proved to be robust to these technical improvements. The study

concluded that “causality does not appear to go from money supply to stock prices, but rather

from stock prices to the money supply. Gautam Kaul (1987) hypothesized that the relation

between stock returns and inflation is caused by the equilibrium process in the monetary

sector. More importantly, these relations vary over time in a systematic manner depending on

the influence of money demand and supply factors. Post-war evidence from the United

States, Canada, the United Kingdom and Germany indicates that the negative stock return-

inflation relations are caused by money demand and counter-cyclical money supply effects.

On the other hand, pro-cyclical movements in money, inflation, and stock prices during the

1930’s lead to relations which are either positive or insignificant.

Chan, Foresi and Lang (1996) developed and tested a Money based Capital Asset

Pricing Model (M-CAPM). Inside money was used as a proxy for consumption. This was

justified on the grounds that in money (as opposed to outside money) can be viewed as

endogenous and varies with the transaction requirements of the economy. The use of money

as a proxy for consumption results in a stochastic discount factor which captures the risk

associated with monetary factors in addition to those associated with real factors. The use of a

broad monetary aggregate instead of consumption data should result in a more volatile

discount factor and a lower estimated coefficient of relative risk aversion compared to

consumption based models. Thorbecke (1997) used ten sizes-ranked portfolio in addition of

using a stock market index. He concluded that monetary tightening has the strongest negative

effect on the equity prices of small firms. This evidence is consistent with the hypothesis that

38

an important channel of monetary policy is that it affects small firms’ ability to borrow

(Gertler and Gilchrist, 1993).

3.4.2. Economic Growth

The growing importance of stock markets in developing economies around the world

over the last few decades has shifted the focus of many researchers, academicians, policy

makers and economists, to explore the relationship between the stock market and economic

growth. The idea that financial markets may be related to economic activities is not new, but

the interpretation of this relationship has changed over time, with changing international and

domestic economic environment and growing econometric techniques. Explaining such a

relationship involves assessing the direction of causality and the type of influence (positive

and negative). Does the stock market affect GDP, or is the causality in the opposite direction,

such that GDP triggers fluctuations in the stock market? Academic literature on the

relationship between financial development and economic growth dates back to as early as

the early twentieth century (Schumpeter, 1911). He argued that technological innovation is

the force underlying long-run economic growth, and that the cause of innovation is the

financial sector’s ability to extend credit to the “entrepreneur” and focused on the services

provided by financial intermediaries and argues that these are essential for technological

innovation and economic development. A well-developed financial system promotes

investment by identifying and investing in profitable business opportunities, mobilizing

savings, allocating resources, diversifying risks and facilitating trade activities. Joan

Robinson (1952), on the other hand, maintained that economic growth creates a demand for

particular types of financial arrangements, and the financial system responds automatically to

these demands, so that “where enterprise leads finance follows”. He concluded that banks

respond passively to economic growth. Until 1970’s, due to scarce literature, there was lack

of evidences on the relationship between financial markets and real output. Further, studies

by Goldsmith (1969), Shaw (1973) and McKinnon (1973) found that the development of

financial markets was significantly correlated with the level of per capita income.

Gurley and Shaw (1955) were the first to study the relationship between financial

markets and real activity. They argued that one of the differences between developed and a

developing country is that the financial system is more developed in the developing countries.

The argument was that financial markets could extend a borrower’s financial capacity which

improves trade efficiency. With well-developed financial market investors can be provided

with the necessary funds for their projects. They concluded that financial markets contribute

39

to economic development through enhancing the physical capital accumulation. A distinctive

feature of the theory Gurley and Shaw offered was an emphasis on financial intermediation,

and particularly the role of intermediaries in the credit supply process as opposed to the

money supply process. Goldsmith (1969) established a link between financial structure and

economic development by using data of data showing a well-defined upward secular drift in

the ratio of financial institutions' assets to gross national product for both developed and less

developed countries for the year 1860 to 1963. The author stated that the financial

superstructure of an economy "accelerates economic growth and improves economic

performance to the extent that it facilitates the migration of funds to the best user, i.e., to the

place in the economic system where the funds will yield the highest social return". As he

notes, though, it is difficult to establish "with confidence the direction of the causal

mechanism, i.e., of deciding whether financial factors were responsible for the acceleration of

economic development or whether financial development reflected economic growth whose

mainsprings must be sought elsewhere". Tobin (1969) focused on the impact that share prices

have on the cost of capital, and is captured by a coefficient known as Tobin’s Q, which is the

ratio of the market value of the current capital to the cost of replacement capital. When share

prices are high, the value of the firm relative to the replacement cost of its stock of capital

(Tobin’s Q) is also high. Consequently, this leads to increased investment expenditure and

thus to higher aggregate economic output as firms find it easier to finance investment

expenditures. This occurs because the investment would be easier as it would require a lower

share offering in a situation of a high share price.

Greenwald and Stiglitz (1990) proposed a theoretical model to examine the impact of

financial market imperfections on the long-term productivity growth of firms. Their model

focused on the failures of firms in selling equity securities, which help firms by diversifying

the risk of real investment. They argued that failures in stock markets limit the abilities of

firms to diversify the risks of their operations and hence lead to a reduction in the level of

such operations as an alternative means of risk management. They show that since the

restriction of firms' operations will limit the extent of "on-the-job training" and other learning

effects, as well as direct investment in productivity improvements, the stock market

imperfection will adversely affect the rate of productivity growth. Greenwood and Jovanovic

(1990) developed a model with two assets: safe, low-yield technology, and a risky high-yield

one, where the return on the latter is affected by an aggregate and a project specific shock. In

their model they emphasized both the informational and risk sharing roles of financial

markets in improving capital mobilization to the optimal use and hence in increasing growth.

40

Financial markets are able to offer agents a higher return than they invested individually

because they collect information that enables them to decipher the aggregate productivity

shock and they can better diversify project-specific risk due to the large portfolios they hold.

Therefore, financial markets allocate capital more efficiently and the resulting higher

productivity of capital increases growth. It is worth noting that in this model higher growth

stimulates increased participation in financial markets, which leads to the expansion of

financial institutions. Thus, a two-way causality between financial development and growth

emerges in their model.

Levine (1991) constructed an endogenous growth model in which the stock market

emerges to allocate risk, and explores how the markets alter investment incentives in ways

that change steady-state growth rates. He demonstrated that stock markets accelerate growth

by facilitating the ability to trade ownership of firms without disrupting the production

process occurring within firms and by allowing agents to diversify portfolios. He further

explained the effect of tax policies on growth both directly by altering investment incentives

and indirectly by changing the incentives underlying financial contracts. Levine's model used

the Diamond and Dybvig (1983) structure of preference to create liquidity risk and also to

include productivity shocks that create production risk. Liquidity risk and the productivity

risk create incentives for the formation of stock markets. Productivity risk lowers welfare and

discourages agents from investing in firms. The stock market allows investors to invest in a

large number of firms and to diversify away from idiosyncratic productivity shocks. This

raises welfare, the fraction of resources invested in firms, and the economy's steady-state

growth rate. In Levine's model, the stock market raises the growth rate by increasing the

productivity of firms or by improving the allocation of resources. Thus, the emergence of

stock markets to manage productivity and liquidity risk, accelerates growth by attracting

resources to socially productive firms.

King and Levine (1994) proposed a model in which innovation activities serve as an

engine of growth. A higher rate of successful innovation results in a high growth rate of

productivity. Financial markets appear in two different forms in the model. The first is where

the intermediaries’ acts like venture capital firms. They evaluate, finance and monitor the

risky and costly innovations. The second form is like the stock market. The present value of

the innovation is revealed in the stock market and selling the equity shares on the market can

diversify the risk associated with innovation. Therefore, according to King and Levine, the

better development of the financial market can improve the possibility of successful

innovations. They point out that financial institutions play an active role in evaluating,

41

managing, and funding the entrepreneurial activity that leads to productivity growth. Singh

(1997) concentrates on the role of stock markets in the liberalization process in the

developing countries in the 1980's and 1990's. He argues that stock market development is

unlikely to help in achieving quicker industrialization and faster long-term economic growth

in most developing countries. He had cited three reasons for the same. First, the inherent

volatility and the arbitrariness of the stock market pricing process under developing country

conditions make a poor guide to efficient investment allocation. Second, the interactions

between the stock and currency markets in the wake of unfavorable economic shocks may

exacerbate macroeconomic instability and reduce long-term growth. Third, stock market

development is likely to undermine the existing group-banking systems in developing

countries, which, despite their many difficulties, have not been without merit in several

countries, not least in the highly successful East Asian economies.

The most commonly used proxies for economic growth is per capita GDP. GDP

represents economic growth and economic growth is the increase in the inflation-adjusted

market value of the goods and services produced by an economy over time. It is

conventionally measured as the percent rate of increase in real gross domestic product, or real

GDP. Gross domestic product (GDP) is regarded as one of the important determinants of

stock market performance and has often been used to measure the growth of real economic

activity. Growth is usually calculated in real terms, i.e., inflation-adjusted terms to eliminate

the distorting effect of inflation on the price of goods produced. Real per capita GDP is often

used as a way of communicating average income, though it can also be used as a measure of

the wealth of the population of a nation, particularly in comparison to other nations. Per

capita income is often used to measure a country's standard of living. It is usually expressed

in terms of a commonly used international currency such as the Euro or US dollar, and is

useful because it is widely known, easily calculated from readily-available GDP and

population estimates, and produces a useful statistic for comparison of wealth between

countries. This helps the country to know their development status.

3.4.3. Trade Openness

The trade-to-GDP-ratio is often called the 'trade openness ratio'. The trade-to-GDP-

ratio is the sum of exports and imports divided by GDP. This indicator measures a country’s

'openness' or 'integration' in the world economy. It represents the combined weight of total

trade in its economy, a measure of the degree of dependence of domestic producers on

foreign markets and their trade orientation (for exports) and the degree of reliance of

42

domestic demand on foreign supply of goods and services (for imports). The indicator

reflects the liberalization policies of the economy and provides an insight for the investment

opportunities in a particular economy. A low ratio for a country does not necessarily imply

high (tariff or non-tariff) obstacles to foreign trade, but may be due to the factors like size and

geographic remoteness from potential trading partners. For example, it is generally the case

that exports and imports play a smaller role in larger economies than they do in small

economies. Trade openness promotes the efficient allocation of resources through

specialization and comparative advantage; it stimulates competition in domestic and

international markets, and allows for easier transmission of knowledge and technology across

countries. Opening up of an economy for the cross border flows of goods and services creates

a high competitive environment, which will drive down the revenue of existing firms and

diminish their profits, requiring them to search for external sources of finance (Quy-Toan and

Levchenko, 2004). These sources of finance will be available to the present firms and their

rich, elite owners only if they support the compulsory institutional reforms to make the

financial system efficient and well-functioning and by solving the problem of asymmetric

information. These reforms will extend the size of the financial system and works as an

important ingredient that stimulates financial sector development. Trade openness promotes

financial development, not just because it expands opportunities, but because it increases

competition (Rajan and Zingales, 2003).

It is not possible that one country is specialized in the production of all types of goods

and can deliver every service because all the countries differ in the resource endowments.

Each country is specialized in the production of a particular type of commodities. Thus, to

procure needed goods and services a country has to opt for the policy of trade openness.

Hence, the concept of absolute and comparative cost advantage theories emphasized that like

domestic and inter-regional trade, the international trade is also beneficial for the trading

countries. Therefore, when a country enters into trade with another country, it can export

those commodities in which its production cost is less, and can import those commodities in

which its production cost is high. This results in greater output and consumer welfare in both

the trading countries, which in turn, will lead to higher employment and hence economic

growth. Flexible trade openness policies attract foreign investors to invest in the stock market

of the economy, which gives a boost to the stock market development. Thus, the classical

economists were in favor of free trade policy, as they assumed that free trade among different

nations maximizes the output and employment of all the participating countries (Salvatore,

2010). Edwards (1998) noted that countries that are more open to the rest of the world are

43

better placed in capturing the advanced technologies of leading nations. However, some

economists argued that free trade between developed and developing countries shifts the

gains from developing to developed nation because developing countries are largely

dependent on the production of primary goods, whereas the developed nations,mostly

depends upon manufacturing products (Prebish, 1959; Singer, 1950; Myrdal, 1957).

Newbery and Stiglitz (1984) argued that the trade openness affects financial

development because free trade will result in uncertainty and income inconsistency of agents,

which in turn raises the demand for insurance and other financial services and thereby

increases the size of the financial system. Furthermore, free trade among different nations

generally will increase the demand for external finance, as they produce more financial-

dependent goods. Thus, free trade increases the demand for external finance and thereby size

and quality of the financial system. Quy-Toan and Levchenko (2004) noted that if there is

free trade between rich and poor countries, in rich countries, more trade would be associated

with faster financial development as they are specialized in financial-dependent good.

Whereas more trade lead to deterioration in the size of the financial system in poor countries,

as they import financial-dependent goods rather than produce them domestically. While,

Rajan and Zingales (2003) postulated that trade openness is linked with financial market

development, especially when cross-border capital flows are free, and that changes in

openness are associated with changes in the size of financial markets.

Peter (2003) concluded that free trade helps to develop the domestic financial markets

and then the economic growth. Because free trade expands the size of the market for

domestic goods, which in turn encourages the domestic production and thus production of

more goods and services, more capital is required. Therefore, allowing foreign capital into

domestic financial markets by financial openness increases the availability of funds, which in

turn lowers the cost of borrowing and thereby increases the investment and economic growth.

Thus, trade openness and financial openness are not substitutes rather they are

complementary to each other as their coexistence will result in the domestic financial sector

development and hence higher economic growth. The results are similar to the hypothesis

given by Rajan and Zingales (2003).

The theoretical as well as empirical research has strongly argued the possible links

between financial development and trade openness, particularly in the case of developing

countries. These researches can be characterized in two groups: i) one investigating the role

of financial development on generating gains in terms of trade openness; ii) the other

discussing the possibility that trade openness can influence the development of financial

44

systems. Comparing the links between financial development and trade, and between

financial openness and trade, many recent empirical studies have begun to establish the

possible linkage between financial development, financial openness and trade openness

altogether (e.g. Rajan and Zingales (2003) and Baltagi et al. (2009)). Rajan and Zingales’s

(2003) analysis, suggests that the simultaneous opening of both trade and capital accounts

holds the key to successful financial development. Baltagi’s (2009) finding provides a partial

support to the Rajan and Zingales (2003) hypothesis, and suggests that trade and financial

openness is statistically significant determinants of financial sector development.

3.4.4. International Financial Flows

In recent years the global financial flows have been increasing in volume in both the

developed and the developing economies, creating new opportunities and challenges for

policymakers. It is a general myth that the phenomenon of financial flows is new to the

economy. Though, the global financial system, at that time, was, according to the rules of the

classical gold standard, there was a massive flow of private capital across borders before the

First World War (1914- 1918), in the form of bonds financing railways, roads and other

infrastructure projects. Thus, it can be said that the present era of globalization represents the

recurrence of finance capital on a global scale. Initially the process of economic growth was

initiated by the respective governments by planning, developing and implementing the

agricultural, manufacturing and the infrastructure facilities in the country. Gradually these

facilities became inadequate for the economy due to technological innovations as it didn't

boost the economic growth of the country with much pace, resulting in less saving for further

investment. Since these domestic savings were inadequate, countries had to depend on

external sources of finance like loans from different countries. This capital taken from other

countries helped the economies to grow; this phenomenon took the form of foreign financial

investments which came in the form of overseas loans. It fills the gap between domestic

savings and its required investment for growth. Foreign capital plays a significant role in the

development of any economy. For the developed countries, it is necessary to sustain the

process of development. For the developing countries, it is used to increase the rate of

investments and to boost up the entire development of the nation in productivity of the

labour, machinery etc. which leads to economic growth. For the transition countries, it is

useful to carry out the reforms and become open economies by liberalizing its trade policies

to create conditions for stable and continuous growth, as well as to integrate into the world

economy. International financial flows can be classified into two categories:

45

3.4.4.1.Foreign Direct Investment (FDI)

Foreign Direct Investment is the investment from one country into another (normally

by companies rather than governments) that involves establishing operations or acquiring

tangible assets, including stakes in other businesses (Financial Times). In the era of

globalization, FDI is a major source of capital in most of developing economies where it

bridges the gap of capital, technology, managerial skill, human capital formation and more

competitive business environment. To increase their share of FDI flows, most of the countries

ease restrictions on foreign direct investment, strengthened macro stability, privatization of

state-owned enterprises, domestic financial reforms, capital account liberalization, tax

incentives and subsidies have been instituted (World Bank, 1997a).

The long-term impact of FDI inflows on the development of the domestic capital

market and on the increase of investors’ participation in stock exchange was established

earlier by Errunza (1983), while Yartey (2008) stated that FDI promotes institutional and

regulatory reforms which encourage greater confidence in the domestic capital market, which

further increases the variety of investors and trading volume. Adam and Tweneboah (2008 a,

b) highlight an indirect, but strong relationship between stock markets and FDI inflows. FDI

inflows are a source of technological progress and increasing employment in most developing

countries, which increases the production of goods and services and, ultimately, increases

GDP. Economic growth, then has a positive effect on the development of stock markets and

the rise of share prices. Using the cointegration method, the authors found evidence of a long-

term positive relationship between FDI and stock market development in Ghana. Some

economist found that FDI contributes by fulfilling the gap of technology, capital formation,

human capital, managerial skill and provide a more competitive environment for domestic

producers (Helpman and Kruman (1985); Lucas (1988)). While some other researcher found

that more focus and dependence on FDI may discourage the domestic industry. Entry of the

foreign companies in the market may lead to a reduction in market share of domestic

producers. The possibilities of economies of scale also suffer to the domestic producers,

which affect the productivity negatively (Adams (2009)). Adam and Tweneboah (2009)

observed the triangular relationship between these FDI and stock market development: (1)

FDI stimulates economic growth (2) Economic growth exerts a positive impact on stock

market development and (3) implication is that FDI promotes stock market development.

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3.4.4.2.Foreign Institutional Investment (FII)

As defined by the European Union Foreign Institutional Investment is an investment

in a foreign stock market by the specialized financial intermediaries managing savings

collectively on behalf of investors, especially small investors, towards specific objectives in

term of risk, return and maturity of claims. And SEBI’s definition of FIIs presently includes

foreign pension funds, mutual funds, charitable/endowment/university funds, asset

management companies and other money managers operating on their behalf in a foreign

stock market. Foreign institutional investment is a liquid nature investment, i.e., short-term

investment, which is motivated by international portfolio diversification benefits for

individuals and institutional investors in industrial country.

Foreign investment refers to the investments made by the residents of a country in the

financial assets and production process of another country. FII is a short term investment by

foreign institutions, in the financial markets of other countries. These institutions are

generally mutual funds, investment companies, pension funds and insurance houses. The

FIIis playing an important role in bringing in funds needed by the equity market.

Additionally, if the funds from multilateral finance institutions and FDI are insufficient, they

contribute to the foreign exchange inflow. According to Lalitha, S. (1992), the main reason

for opening stock market for FIIs was to attract foreign investments and stop countries from

raising more debts.

The waves of liberalization results in the appreciation of the stock price which is

followed by inflows from foreign investors (Bekaert and Harvey, 1998) and (Henry, 1997).

As the economy liberalizes the stock market shows more reaction to foreign investments. A

concern with the entry of FIIs is that they are positive feedback traders—traders who buy

when the market increases and sell when the market falls. This acts as a destabilizing factor,

because the sales by FIIs lead the stock market to fall further and their buys increase the stock

market as concluded by Radelet and Sachs (1998). Not only this, these trades push the stock-

prices away from the fundamentals as revealed by studies on the contemporaneous relation

between FIIs investments and equity returns based on monthly data (Bohn and Tesar, 1996,

Berko and Clark, 1997). The increasing role of institutional investors has brought both

qualitative and quantitative developments in the stock market viz., expansions of security

businesses, increased depth and breadth of the market, and above all, their dominant

investment philosophy of emphasizing the fundamentals has rendered efficient pricing of the

stocks (Khanna, 2002). Rangarajan (2000) suggested that foreign portfolio investments would

47

help the stock markets directly through the widening investor base and indirectly by

compelling local authorities to improve the trading system.

3.4.5. Interest Rate

The interest rate is one of the important ingredients of any economy, which is directly

related to economic growth. Generally, interest rate is considered as the cost of capital, means

the price paid for the use of money for a period of time. According to Kevin (2000), in an

organized financial sector of the economy the interest rates are guided through monetary

policy. However, for the unorganized financial sector, the rates are not controlled and may

fluctuate widely depending upon the demand and supply of funds in the market. An investor

has to evaluate the impact of the level and growth of interest rates, on the performance and

profitability of companies of different sectors of the economy. Further, from the point of view

of a borrower, the interest rate is the cost of borrowing money (borrowing rate). From a

lender’s point of view, the interest rate is the fee charged for lending money (lending rate). If

the rate of interest paid by banks to depositors increases, people will start to deposit their

capital from share market to the banks, resulting in the decreasing demand of shares, which

leads to decrease in share prices and vice versa. On the other way, when rate of interest paid

by banks to depositors increases, the lending interest rate also increases lead to decrease the

investments in the economy. Maysami et al. (2004) explains, when a substantial amount of

stocks is purchased with borrowed money, an increase in interest rate would make a stock

transaction more costly. Investors will expect a higher rate of return before investing, which

results the demand to fall and hence leads to price depreciation. The interest rate varies with

time, default risk, inflation rate, and productivity of capital, among others (Chandra 2004).

Aydemir and Demirhan (2009) stated that the relationship between stock market

capitalization rate and interest rate have preoccupied the minds of economists since they both

play important roles in influencing a country’s economic development. Theoretically, interest

rates have a negative impact on stock market performance. The logic behind the negative

relationship between interest rates and stock prices suggest that an upward trend in interest

rate enhances the opportunity cost of holding money and thus substitution between stocks and

interest bearing securities resulting declining stock prices. Thus, a change in nominal interest

rates should move asset prices in the opposite direction. According to French et al.(1987) an

increase in interest rates would avoid investors making high risk stock market investments

compared to low risk interest bearing security investments such as fixed deposits, savings

certificates, treasury bills etc. On the other hand, The Central Banks of the country use

48

interest rate is as a tool to control inflation. The change in interest rates by the Central Bank

would indirectly affect the stock market performance, and will lead to have a spillover effect

on overall economic development of the country. Thus, determination of ideal interest rate is

a very important policy decision that a country has to consider regularly (Pallegedara, 2012).

The role of interest rates in stock pricing models was not well established in the asset

pricing literature until Stone (1974) suggested a two-variable model formalizing a relation

between stocks, the market portfolio and yields in the debt market. Following Sharpe (1964),

Lintner (1965) and Mossin (1966), Stone showed that there were limitations in models such

as the CAPM due to the non-inclusion of interest rates as a unique factor. Stone verified that

interest rate changes are negatively related to stock returns and suggested that the interest rate

risk varied in a cross-section of stocks in homogenous groupings. This is confirmed by

Martin and Keown (1977) who verified this cross sectional variation in interest rate

sensitivity amongst certain classes of stocks. Lynge and Zumwalt (1980) enhanced Stone's

model with additional variables for short and long term interest rate impacts and

demonstrated significant differences in sensitivities in stock returns when the term structure

was accounted for. Following Stone’s (1974) work, Chance and Lane (1980) demonstrated

the higher sensitivities of bank and utility stocks to interest rate changes. Jensen et al. (1997)

found significant differences in interest rate sensitivity across industries to interest rate

changes. Utility firms exhibited very strong reactions to discount rate changes, as did

financial firms. They also found evidence of substantial positive effects following interest

rate decreases. Smirlock and Yawitz (1985) found that interest rate changes produce a very

rapid response in stock prices, but vary in their effects, depending on whether or not the

changes constitute new information.

According to Fama (1981) the expected inflation is often proxied by the short-term

interest rate and is negatively correlated with anticipated real activity, which in turn is

positively related to returns on the stock market. Therefore, stock market returns should be

negatively correlated with expected inflation. On the other hand, the influence of the long-

term interest rate on stock prices stems directly from the present value model through the

influence of the long-term interest rate on the discount rate. Rather than using either short-

term or long-term interest rates, Campbell (1987) analyzed the relationship between the yield

spread and the stock market returns. He argued that the same variables that have been used to

predict excess returns in the term structure also predict excess stock returns, deducing that a

simultaneous analysis of the returns on bills, bonds and stock should be beneficial. His results

support the effectiveness of the term structure of interest rates in predicting excess returns on

49

the US stock market. Kaul (1990) studied the relationship between expected inflation and the

stock market, which, according to the proxy hypothesis of Fama (1981) should be negatively

related since expected inflation is negatively correlated with anticipated real activity, which

in turn is positively related to returns on the stock market. Instead of using the short-term

interest rate as a proxy for expected inflation, Kaul (1990) explicitly models the relationship

between expected inflation and stock market returns. Smith (1990) found that stock prices

jump immediately after (and sometimes before) the Federal Reserve announces a cut in the

interest rate or discount rate, or Chase Manhattan announces a drop in its prime loan rate.

Zhou (1996) also studied the relationship between interest rates and stock prices using

regression analysis. He found that interest rates have an important impact on stock returns,

especially in long-term investment horizons, but the hypothesis that expected stock returns

move one-for-one with ex ante interest rates is rejected. In addition, his results showed that

long-term interest rate explains a major part of the variation in price dividend ratios. Besides,

he suggests that the high volatility of the stock market relates to the high volatility of long-

term bond yields and may be accounted for by changing forecasts of discount rates. Kunt

(1996) found that countries with lesser interest rates have a strong stock market as compared

to countries which have higher interest rate. The author also mentioned that developed

countries are usually having low interest rates due which their stock market’s performance is

extra-ordinary. Lee (1997) used a three-year rolling regressions to analyze the relationship

between the stock market and the short-term interest rate. He tried to forecast excess returns

(i.e. The differential between stock market returns and the risk-free short-run interest rate) on

the Standard and Poor 500 index with the short-term interest rate, but found that the

relationship is not stable over time. It gradually changes from a significantly negative to no

relationship, or even a positive although insignificant relationship. Jefferis and Okeahalam

(2000) worked on South Africa, Botswana and Zimbabwe stock market, where higher interest

rates are hypothesized to depress stock prices through the substitution effect (interest-bearing

assets become more attractive relative to shares), an increase in the discount rate (and hence a

reduced present value of future expected returns), or a depressing effect on investment and

hence on expected future profits.

3.4.6. Inflation

The effects of inflation on an economy are varied and can have either positive or

negative effects and is a subject of intense debate due to reported inconsistencies in the

effects and the complexity of the mechanisms. This debate is motivated partially by the

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theory that the stock market provides an effective hedge against inflation, (Bodie, 1976).

Theoretically, stock prices or returns should have a positive relation to inflation; this may be

due to the reasons that as firms are able to pass the extra costs to customers in the long-run

even if there are varying degrees pertaining to demand effects. However, in a high inflation

environment this pass-through policy may not be achieved effectively in the short run as it

also depends on the level of competition or market regulation that limits the individual firms

to raise prices to maintain or to increase their profitability. This shows that if inflation rises,

negative income effects, due to higher input costs, imply that stocks may decline in value in

the short-run. In the long-run, assuming firms are able to pass on rising costs and achieve the

desired profitability; stocks prove to be a good hedge against inflation. The argument that the

stock market serves as a hedge against inflation is based on the fundamental idea of Irving

Fisher (1930), and is known as the Fisher Effect. Fisher (1930) hypothesized that stock

market returns are independent of inflation expectations, but the two variables, namely

inflation and stock market returns are positively related. Fisher's conclusions and hypothesis

gave credence to the assertion that if inflation and stock market returns are positively related,

then, equities serve as a hedge against inflation. The Fischer hypothesis is of prime

significance in the field of global finance, because it sheds light on the expected nominal

stock market returns, which equates the sum of expected inflation and real rate of return. The

“Fisher Effect” postulates that expected nominal asset returns have a unitary effect on

expected inflation. Thus, the hypothesis predicts a direct positive relationship between

inflation and stock market return. Bodie (1976), Jaffe and Mandelker (1976), Nelson (1976),

Fama and Schwert (1977), Firth (1979) and Boudhouch and Richardson (1993) extended the

original concept of a Fisher Effect to examine the specific interrelationships between rates of

return on common stocks and the expected and unexpected rate of inflation. Bodie (1976)

also concluded that equities are a hedge against inflation as shares are a claim on real

underlying assets. If the underlying assets rise in value due to inflation, so should the price of

the share by a similar amount and therefore the real change should be unaffected.

However, Fama (1981), Gultekin (1983) and Kaul (1987) found that the Fisher

hypothesis do not hold even when income growth is controlled for (Balduzzi, 1995; Cochran

and Defina, 1993; Caporale and Jung, 1997). Further, Fama and Schwert (1977) reported an

“anomalous result” in that they found a negative relation between stock returns and inflation.

According to Fama (1981), the real activity is positively associated with the stock return, but

negatively associated with inflation through the money demand theory; therefore, stock return

will negatively influence by inflation. The negative relationship between inflation and stock

51

return can also be explained through the dividend discount model. Since, stock price can be

viewed as the discounted value of expected dividend, an increase in inflation may enhance

the nominal risk free rate and thus the discount rate leading to declining stock price. The

negative relationship between stock price and inflation support the proxy effect of Fama

(1981) which explains that higher inflation raise the production cost which adversely affects

the profitability and the level of real economic activity; since the real activity is positively

associated with stock return, an increase in inflation reduces the stock price. The study was

further done by Mandelker and Tandon (1985), and they found similar results. Chen et al.

(1986) explored a set of macroeconomic variables as a systematic influence on stock market

returns by modeling equity return as a function of macro variables and non-equity assets

returns for US. They empirically found that the macroeconomic variables such as industrial

production anticipated and unanticipated inflation, yield spread between the long and short

term government bond were significantly explained the stock returns. The authors showed

that the economic state variables systematically affect the stock return via their effect on

future dividends and discount rates.

Ram and Spencer (1983) found consistent evidence for a positive relation between real

activity and inflation and a negative relation between real activity and real stock returns.

Kaul's (1987) main hypothesis is that the equilibrium process in the monetary sector causes

the observed stock returns-inflation relation. Basically, money demand and counter-cyclical

money supply leads to negative relations between stock returns and expected-changes in

inflation, unexpected-changes in inflation, and changes in expected inflation since positive

shocks to output precipitate monetary tightening.

3.4.7. Crude Oil Prices

The relationship between oil prices and economic growth is believed to be an existing

fact. Chen et al., (1985) and Chen and Jordan, (1993) provide evidence that oil prices affect

stock returns. However, this relationship varies from country to country, according to the

dependency and consumption of each country on oil, and whether the country is an oil

importer or exporter. Increases in oil prices will be beneficial for those countries whose

export products are derived from crude oil or refined oil products. Thus, in theory as well as

empirical evidence suggests that, there should be a positive relationship between the oil price

and stock prices in those oil-exporting countries. But there should be a negative relationship

for oil importer countries the reason behind this is that the increases in oil price would lead to

increase the cost of production and, consequently, the expected cash flow would decrease or

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one can say that with the exception of oil producing firms the relationship between changes

in oil prices and share returns is negative because an increase in oil prices should result in

higher costs and, hence, lower equity values. Several studies have explored the oil price-

macroeconomics causal relationship and among them are Hamilton (1983), Burbridge and

Harrison (1984), Gisser and Goodwin (1986), Mork (1989), Loungani (1986), Hooker (1996),

and Hamilton (2000). Since oil is an essential input cost for final products in today’s

economy, it is reasonable to anticipate that oil prices can affect stock prices both directly and

directly. Oil prices can directly affect stock prices through its effects on the expected cash

flows and indirectly through its effects on discount rates. That is, changes in the price of oil

may directly affect future cash flows via its effects on the cost of final products in the

economy, which would cause opposite changes in stock prices. Whereas, regarding the

discount rate, changes in the price of oil may affect stock prices via its effects on the expected

inflation rate and the expected real interest rate. For instance, a higher price of oil places

upward pressure on expected domestic inflation. In this case, a higher expected inflation rate

is positively related to the discount rate and is negatively related to stock prices. Also, a

higher price of oil could cause the real interest rate to rise. As a result, the rate of return

required by investors would increase, which would cause a decrease in stock prices (Huang et

al., 1996).

Hamilton (1983) made a major contribution in this context and argued that the most

recessions after World War II was preceded by increasing oil prices. Various explanations are

mentioned as the reason of the relationship between oil prices and economic activity.

Between these explanations, temporizing GDP growth and inflation due to higher oil prices

appears to be most preferred. According to the author, the data (real GNP, unemployment,

implicit price deflator for non farm, hourly compensation per worker, import prices, and M1)

indicated that economic recession preceded an oil price increase over 3-4 quarters, with

recovery starting after 6-7 quarters.

Huang, Masulis and Stoll (1996) describe the theoretical linkage between crude oil and

stock returns using economic linkages at a general level. The stock valuation of a company is

based on the discounted values of expected future cash flows. Movements in oil prices can

influence these parameters for many reasons. Oil is a basic ingredient, real resource and an

essential material which is used for the production of many goods, and can be considered as

an important variable like other variables viz., labor and capital. Higher oil prices cause

movements in expected costs and would depress stock market performance. Oil price

movements also influence stock market performance through the mechanism of discount rate.

53

The discount rate is used to evaluate the company’s intrinsic value from the expected

inflation rate and interest rate, which may depend on expected oil prices. For instance, for oil

importing country a rise in oil prices may influence the balance of payments negatively.

Therefore, a higher inflation rate is positively linked to the discount rate and consequently

negatively linked to the stock performance. Going one step further, since oil is a commodity,

expected oil prices can be used as a proxy for the expected inflation rate. The interest rate is

also closely related to the oil price. As mentioned before, oil is a major resource and therefore

higher oil prices compared to the general inflation level could drive the interest rate upwards.

A higher interest rate will make bonds more attractive and motivates investors to change their

portfolios by buying bonds and selling stock, and lead to falling stock prices. Mussa (2000)

presented a variety of channels through which higher oil prices can affect the global

economy. First, there will be some decrease in demand and therefore a swift of income from

energy consumers to energy producers. Second, there will be an increase in the cost of

production and a pressure on yield margins. Third, a higher oil price will influence the price

levels and the level of inflation. This will vary with the degree of monetary tightening. The

expected duration of the rise in price levels will create incentives for oil suppliers to expand

the production and investments. Furthermore, this all will have both direct and indirect

influences on the financial markets.

Kilian (2007) stated that higher oil prices may be transmitted to changes in stock prices

through increases in the cost of production and will cause a sudden change in the expected

future cash flows. This will depend on the level of the costs of oil. He also added another

view and argued that the oil prices affect the performance of firms through the change in

consumer expenditures and firm expenditures. In this view, there will be both a reduction in

demand from the consumers and firms as well. There will be a reduced demand for the

company’s output, because consumer spending will increase in response to increasing oil

prices, since this is an important energy resource for households. The negative effect of

higher oil prices on consumption, investments and stock prices is also documented by Lardic

and Mignon (2008). The authors argued in the same context, consumption is affected through

its relationship with the disposable income and the investments are influenced due to higher

costs of the company. Higher costs will cause a reduction in the profits and the discounted

sum of expected future dividends, which are key drivers of stock prices (Lescaroux and

Mignon, 2008). Filis (2010) mentioned that oil prices affect the overall stock market

performance in both direct and indirect ways. The direct negative influence can be justified

by the fact that oil price increases create uncertainty in financial markets, which in turns

54

decreases stock prices. The indirect negative effect can be explained due to the

aforementioned reasons, namely the increase in production level and the increase in inflation

rates, as a result of increasing oil prices.

However, the relationship between oil prices and the stock market is a complicated

theory and cannot be explained only by the concepts of higher costs or higher revenues and

the demand and supply curves. It is a well-documented fact that stock markets are very

sensitive to the changes in oil prices. As stated above, the changes in oil prices happen due to

various reasons and does not show the same type of effect every time. Considering a rise in

the demand for oil due to developing economies, there could be a positive linkage between

oil prices and stock returns. Another reason is speculation; the oil can be kept in hidden

reserves form, to generate the scarcity and because of the belief that the cost of production in

the future will become higher. Furthermore, the change in oil price can be due to natural

disasters and internal conflicts.

3.4.8. Exchange Rate

The Exchange rate is the value of a nation’s currency in terms of the value of another

country’s currency. An exchange rate, thus has two components, the domestic currency and a

foreign currency, and can be quoted either directly or indirectly. In a direct quotation, the

price of a unit of foreign currency is expressed in terms of the domestic currency. In indirect

quotation, the price of a unit of domestic currency is expressed in terms of the foreign

currency. An exchange rate that does not have the domestic currency as one of the two

currency components is known as a cross currency, or cross state. Exchange rates can

fluctuate for many reasons, including macroeconomic factors that affect the behavior of

market participants. Firms face a significant source of risk from exchange rate fluctuations,

because these fluctuations increase the volatility of their realized cash flows. There are three

types of exchange rate viz., nominal exchange rate, real exchange rate and real effective

exchange rate while Olisadebe (1991) identified two additional exchange rates namely

nominal effective exchange rate and equilibrium exchange rate. The exchange rate can be

floating or fixed. Floating exchange rates are those in which currency rates are determined by

market forces and are the norm for most major nations, some nations prefer to fix or peg their

domestic currencies to a widely accepted currency like the US dollar.

The nominal exchange rate is defined as the number of units of the domestic currency

that can purchase a unit of a given foreign currency. A decrease in this variable is termed

nominal appreciation of the currency and an increase in this variable is termed nominal

55

depreciation of the currency (Under the fixed exchange rate regime). The real exchange rate

is defined as the ratio of the price level abroad and the domestic price level, where the foreign

price level is converted into domestic currency units via the current nominal exchange rate.

An increase in real exchange rate is termed as an appreciation of the real exchange rate, and a

decrease is termed as depreciation. The real rate tells us how many times more goods and

services can be purchased abroad (after conversion into a foreign currency) than in the

domestic market for a given amount. Real effective exchange rate is the weighted average of

a country's currency relative to an index or basket of other major currencies adjusted for the

effects of inflation. The weights are determined by comparing the relative trade balances, in

terms of one country's currency, with each other country within the index. Nominal effective

exchange rate is the unadjusted weighted average value of a country's currency relative to all

major currencies being traded within an index or pool of currencies. The weights are

determined by the importance a home country places on all other currencies traded within the

pool, as measured by the balance of trade. Equilibrium exchange rate is the exchange rate at

which the supply for a currency meets the demand of the same currency. As foreign exchange

rates are affected by a number of factors, the equilibrium exchange rate in turn, are also

influenced by its supply and demand. Hence equilibrium is achieved when a currency's

demand is equal to its supply.

Stock prices are negatively affected by unfavorable changes. Exchange rate fluctuations

increase the business risk of domestic firms which are involved in imports and exports. If the

value of the domestic currency appreciates it makes domestic products and services more

expensive in foreign markets (the opposite is true for importers). Fluctuations in exchange

rates affect firms due to changes in their costs, revenues and incomes. Investors can

experience fluctuations in the value of their investments if these are held in foreign-

denominated assets, so that appreciation of the domestic currency results in lower returns,

and vice versa. Changes in the exchange rate can occur with changes in inflation and interest

rates. Depreciation of the domestic currency can lead to a rise in inflation and therefore affect

interest rates, thus impacting on stock prices. Franck and Young (1972) investigated the

impacts of exchange rates on the stock prices of multinationals across six currencies during

the period of the Bretton Woods agreement. Their study, using non-parametric tests, was

unable to report consistent effects.

The theoretical underpinning of the relationship between exchange rate and stock prices

could be traced to two main theories that relate these segments of financial markets. Firstly,

the traditional approach, which assumes that exchange rates leads stock prices. This theory

56

hypothesized that stock prices and exchange rates can interact was incorporated in “flow

oriented” models given by Dornbusch and Fisher (1980), which postulate that exchange rate

movements cause stock price movements. In the language of Granger Sims causality, this is

termed as “unidirectional” causality running from exchange rates to stock prices, or exchange

rates “Granger-cause” stock prices. This model is built on the macro view that as stock prices

represent the discounted present value of a firm’s expected future cash flows. Gavin (1989),

also postulated same and stated that the transmission channel is from exchange rate

fluctuations which affect firms‘ values via changes in competitiveness and changes in the

value of firms‘ assets and liabilities, dominated in foreign currency, thereby affecting firms‘

profits and therefore the value of equity. The second is “stock-oriented” economic theory

captured in the portfolio balance model which postulates a negative relationship between

stock prices and exchange rates (Branson et al. 1977). The crux of the theory is that a rise in

domestic stock prices would attract capital flows, which increase the demand for domestic

currency and cause the exchange rate to appreciate. In contrast to “flow oriented” models,

“stock-oriented” or “portfolio balance theory” postulate that movement in stock prices

Granger-cause movements in the exchange rate via capital account transactions. The degree

to which stock oriented models explain currency movements are a function of stock market

liquidity. Jorion (1991) concluded that appreciation of local currency reduces the profit for an

exporting firm and thereby affect its value of stock price negatively.Consequently, all firms

may react sooner or later to changes in the exchange rates. Even if a firm does not directly

involve in the export import business, Adler and Dumas (1984) show domestic firms that

have minimal international activities can still be affected by the exchange rate movements if

their input prices, output prices, or product demand depends on the fluctuation of exchange

rate. Depending on the moment in time when exchange rates change, a company might face:

(1) transaction exposure, that arises whenever the firm commits or is contractually bounded

to make or receive a payment at a future date denominated in a foreign currency; (2)

translation exposure, arising from the need to globally consolidate the financial reports of a

multinational company from affiliates‘ reports denominated in various currencies; and (3)

economic exposure, seen as the change in the firm‘s present value as a result of changes in

the value of the firm‘s expected future cash flows and cost of capital, induced by unexpected

exchange rate changes.

The importance of stock markets in an economy cannot be overlooked. This is because

the stock markets act as a means of channeling and diversification of domestic savings and

foreign capital for enhanced investments and capital formation.

57

3.4.9. Gold Prices

Gold has been used in the market since 1971 as a commodity. The importance of gold

has been increased in the present world due to the financial crisis in the present economic

world. The investors are investing in the Gold. In the recent decade the gold prices and oil

prices rise day by day. Gold is treated as an alternative investment avenue. It is often stated

that gold is the best preserving purchasing power in the long run. Gold also provides high

liquidity; it can be exchanged for money anytime the holders want. Gold investment can also

be used as a hedge against inflation and currency depreciation. From an economic and

financial point of view, movements in the price of gold are both interesting and important. It

is often argued that investment in gold is historically associated with fears about rising

inflation and/ or political risk. However, financial markets do not currently show the classic

symptoms associated with such fears. Gold is a financial instrument that owns the

characteristics of both a commodity and currency. In the past it was used as money and as a

medium of exchange. Nowadays, it acts as a store of wealth and it is a known instrument for

investment uses. It has been highly demanded for many reasons such as scarcity, highly

mobile, liquidity and uniformity. The price of gold depends on the supply and demand for the

commodity and government auction policy. Throughout history, gold is also considered to

reduce risks and portfolio diversification (Ciner, 2001). Gold is also stored in central banks

for various reasons, such as diversification, economic security, physical security, confidence,

income and insurance (Tully and Lucey, 2007). Throughout the recent decade the demand for

gold has been expanding rapidly. The economic recession, high inflation rates and reduction

in world gold production may be reasons for that (Do, Mcaleer and Sriboonchitta, 2009).

Since gold is also used to hedge the risks, investors tend to replace their shares with gold,

which results in a lower demand for shares and volatility on stock markets. Therefore, getting

a better understanding of this linkage will help investors and firms to diversify their portfolios

and reduce their risks. Due to unstable world markets, there is an increasing interest in gold.

Some financial theories argue that gold could be considered as a safe investment when the

economic environment is uncertain. When other investments are decreasing, gold usually

increases. Gold is mostly considered as independent from other factors, and therefore it is

believed that it is low correlated with stock (Baur and Lucey, 2010). However, the theoretical

linkage between gold and stocks is unclear, and there is a lack of theoretical research. An

increase in gold prices attracts investors towards the commodity market, might decrease

investor preference towards the equity market. This indicates that a negative relationship is

expected between gold and silver, and stock market returns.

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3.4.10. Budget Deficit

The current and the future economic growth of the economy depend on country’s stock

market performance up to some extent and the stock market performance depends on the

country’s deficits. This is partly due to the notion that large budget deficit could affect current

and future economic growth of the nation through its effects on the stock markets. The budget

deficit is the amount by which a government, company, or individual's spending exceeds its

income over a particular period of time and the opposite phenomenon is called a budget

surplus. There can be many causes of the budget deficit, which includes a most elementary

phenomenon that when a government spends more than it earns revenue. Further, reducing

tax rates may also cause a deficit if spending isn't reduced to account for the decrease in

revenue. However, the world is more complex and a bit more than the basic analysis is

required. Essentially, large deficits entail additional risks to the economy which include a loss

in investor’s confidence (domestic and foreign) and adverse effects on the volume of

transactions. Specifically, a loss in investor’s confidence would cause a shift of portfolio

away from home currency assets into foreign currency assets which would limit the ability of

the country to finance its liabilities and increase the country’s exposure to exchange rate

fluctuations. This situation could weaken capital spending and ignite a drop in asset prices

which would further restrain real economic activity.

Theoretically, it is true that when the budget of the country is in deficit, it will depress

the stock prices and undermine the investor’s confidence. Hence, the firm’s ability to get

capital on favorable terms will be diminished to a large extent. Large budget deficits could

lead to stock market crash (Roley and Schall, 1988). According to Geske and Roll (1983), the

expected directional impact of budget deficits on stock return should be negative. This is

because a government budget deficit exerts upward pressure on the nominal interest rate

which, in turn, lowers expected returns on stocks. They argued further that, increases in risk

premia, due to fiscal deficits, expose investors to an uncertainty surrounding the reaction of

the Central Bank and thus further confound the equity market. Friedman (1987) discussed the

link between budget deficit and stock prices crush by reflecting it as “reliance on economic

fallacies”, moreover stock prices surged 1980s, despite of mounting deficits and perhaps

investor did not consider budget deficits a major problem. The economic news has impacted

on stock prices and cause variations in stock returns (Cutler, David et al. 1987). Hall and

Taylor (1993) claimed that increase in fiscal deficit forecast future tax increase, which may

cause a reduction in current consumption expenditures by households and harm stock prices.

This explanation supports the notion of Ricardian Equivalence hypothesis. Budget deficits

59

impose costs on the economy and have many effects, (Ball & Mankiw, 1995) investigated

that the effect of fiscal deficit followed by single initial effect: a deficit tends to reduce the

national savings, reduced investment, reduced exports and create the flow of assets overseas.

Greenspan and Allen (1995) investigated that a decrease in the budget deficit will reduce

inflationary expectations. Inflationary expectations may have reverse effects on equity prices.

For example, an increase in inflationary expectations may give benefit to equity instruments

by decreasing the real value of corporate debt, thus increasing the firm’s value. On the other

hand, a decrease in the future inflation rate may decrease equity values because the real value

of debt increases, reducing the firm’s value. Furthermore, a decrease in inflationary

expectations decreases nominal interest rates, which may cause stock prices to go up because

lower rates mean a higher present value of the future stream of corporate earnings. But lower

inflationary expectations may also lower the expected future stream of earnings, which could

lower stock prices. So the inflationary expectation effect on stock prices may be neutral or

indeterminate

3.4.11. Current Account Deficit

Due to the existence of large and continuous global current account imbalances in the

last two decades, economists, policymakers and researchers have paid attention to the issue of

current account deficits. The current account deficit is a measurement of a country’s trade in

which the imports exceeds the value of goods and services it exports. Determinants of current

account balances are of considerable interest in open economies. The behavior of the current

account balance contains important information about the economic performance of any

country, and also provides valuable macroeconomic policy recommendations. Less

theoretical literature has been done on the relationship between the current account deficit

and stock market. Sachs (1982) proposed the model for current account, named fundamental

equation of the current account. This model features a risk free bond as a unique fundamental

instrument. Therefore, this model is inappropriate to study the impact on the dynamics of

current account.

The current account deficit is the broadest measure of the flow of goods, services and

investment into and out of a country. Economists carefully watch the current-account deficit

because of its implications for the currency and domestic economic growth. Increasing the

deficit means that the economy must borrow abroad to finance its imports. When the current

account deficit is increasing, foreigners will lose faith that they will get their money back and

will worry about buying the country’s stocks, bonds and other assets. The implication could

60

be that the currency will depreciate, interest rates will rise and the economy will stall.

Thorbecke (1994), using the APT, in the US, demonstrated that the trade deficit was a source

of systematic risk and unexpected increases in the trade deficit reduced equity returns.

According to Thorbecke (1994) there are several reasons why the trade deficits might be a

source of systematic risk affecting asset returns. First, an increase in the trade deficit might

have implied a drop in demand for Australian goods and thus in the cash flow of Australian

companies. Second, a larger trade deficit might cause investors to expect protectionism, such

as restriction on imports or a high interest rate. Third, the trade deficit might ultimately raise

the price of foreign goods and cause inflation. Many have demonstrated that inflation affects

stock prices. Fourth, to finance these massive deficits foreigners had to hold more and more

Australian stocks and bonds. According to the principle of portfolio diversification they

would have become increasingly reluctant to allocate additional wealth into dollar assets.

Thus, because the trade deficit forced foreigners to hold more dollars, it might have raised the

risk premium on Australian assets. Fifth, news of higher trade deficits depreciates the dollar,

raises interest rates, and increases fear among Australian investors that foreign investors

would sell dollar-denominated stocks. For all these reasons news of large trade deficits could

have increased the perception of the systematic risk in holding Australian equities.

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CHAPTER 4

Methodology and Data Issues

4.1. Introduction

Many macroeconomic time series data theoretically have long-run relationship. It is

also widely claimed that these time series data evolve over time such that their mean and

variance are not constant. Relying such non-stationary time series data may lead

macroeconomists to wrongly conclude that two variables are related when in reality they are

not. This phenomenon is well knowned in the literature as spurious regression (Stock and

Watson, 2006).

The typical metod to analyze a non-stationary process is either to detrend or difference

the data depending on the type of trend. While these methods may provide stationary

variables for the regression, they can cause a loss of significant long-run information and

omitted variables bias.

Granger’s representation theorem (GRT) introduced an effective method to analyze the

non-stationary process without losing valuable long-run information as with differencing or

detrending techniques. This method is well known in literature by the contegration technique.

There are few methods available in literature to examine the long-run relationship

among variables based on the idea of GRT. One is the Engle and Granger (EG) cointegration

and the other is Johansen and Juselius (JJ) cointegration tests. However, the latest

cointegration technique proposed by Pesaran et al. (2001) as Auto Regressive Distributed Lag

(ARDL) provides some econometric ans estimation advantages over both EG and JJ

cointegration techniques. The following sections will provide the detailed discussion of these

techniques used in the study.

Further, the econometric literature confirms that when the macroeconomic series are

cointegrated, there will be causality in atleast one direction among the variables. According

to literature, VECM based granger causality is best suitable for multivariate data set.

The dynamic relationship among macroeconomic time series data are tested through the

VAR models. In the model, some lagged variables may have co-efficients which change

signs across the lags and this together with the interconnectivity of the equations, could

render it difficult to see what effect a given change in variable would have upon the future

values of the variable in the system. In order to alleviate this weakness, statistician normally

uses impulse response function (IRF) and variance decomposition (VDC) techniques. The

impulse response function traces out the responsiveness of the dependent variables in the

62

Vector Auto Regression (VAR) framework to shocks to each of the variables. Whereas, the

variance decomposition allows the proportion of the movements in the dependent variables

not only due to their own shocks, but also to the shocks in the other variables in the system.

The sixth section of this chapter describes the technique in detail.

Section 4.3 of the chapter addresses the data issues related to data definition,

transformation, nature of the data and the sources of data collection.

4.2. Methodology

4.2.1. Ng-perron unit root test

The ADF and PP unit root tests are known (from MC simulations) to suffer potentially

severe finite sample power and size problems. Firstly, the ADF and PP tests are known to

have low power against the alternative hypothesis that the series is stationary (or TS) with a

large autoregressive root (DeJong, et al, 1992). Secondly, the ADF and PP tests are known to

have severe size distortion (in the direction of over-rejecting the null) when the series has a

large negative moving average root.

Ng and Perron (Econometrica, 2001), building on some of their own work (Perron and

Ng, 1996) and work by Elliott, Rothenberg, and Stock (Econometrica, 1996), new tests to

deal with both of these problems. Their tests, in contrast to many of the other “new” unit root

tests that have been developed over the years, seem to have caught on as a preferred

alternative to the traditional ADF and PP tests. The family of Ng-Perron tests (which includes

among others, modified DF and PP test statistics) share the following features. First, the time

series is de-meaned or detrended by applying a GLS estimator. This step turns out to improve

the power of the tests when there is a large AR root and reduces size distortions when there is

a large negative MA root in the differenced series. The second feature of the Ng-Perron tests

is a modified lag selection (or truncation selection) criteria. It turns out that the standard lag

selection procedures used in specifying the ADF regression (or for calculating the long run

variance for the PP statistic) tends to underfit, i.e., choose too small a lag length, when there

is a large negative MA root. This creates additional size distortion in unit root tests. The Ng-

Perron modified lag selection criteria accounts for this tendency.

Ng and Perron (2001) construct four test statistics that are based upon the GLS

detrended data. These test statistics are modified forms of Phillips and Perron and statistics,

the Bhargava (1986) statistic, and the ERS Point Optimal statistic. First, define the term:

𝐾 = ∑ 𝑦𝑡−12 /𝑇2𝑇

𝑡=2 (4.2.1.1)

63

The modified statistics may then be written as,

Using the GLS detrended ỹ𝑡 data, the efficient modified PP tests are defined as

𝑀𝑍𝑡 =(𝑇−1ỹ𝑡

2−𝑓0)

2𝐾 (4.2.1.2)

𝑀𝑆𝐵 = (𝐾

𝑓𝑜)1/2

(4.2.1.3)

𝑀𝑍𝑡 = 𝑀𝑍𝛼 × 𝑀𝑆𝐵 (4.2.1.4)

𝑀𝑃𝑇 = {

(𝑐2𝐾−𝑐𝑇−1ỹ𝑡2)

𝑓0 , 𝑖𝑓 𝑥𝑡 = {1}

(��2𝐾 +1−𝑐)𝑇−1ỹ𝑡

2

𝑓0 , 𝑖𝑓 𝑥𝑡 = {1, 𝑡}

(4.2.1.5)

Where, 𝑓0 is an estimate of the residual spectral density at the zero frequency.

The statistics 𝑀𝑍𝛼 and 𝑀𝑍𝑡 are efficient versions of the PP Zα and Zt tests that have

much smaller size distortion in the presence of negative moving average errors. Again the

choice of the autoregressive truncation lag, p, is critical for correct calculation of f0. Here p is

chosen using the Modified Information Criteria (MIC(p)) of Ng and Perron (2001) as p =

pMIC = arg minpMIC(p) where:

𝜏𝜏(𝑝) = (��𝑃2)−1��2 ∑ ỹt−1

2𝑇𝑡=𝑝𝑚𝑎𝑥+1 (4.2.1.6)

��𝑝2 = (𝑇 − 𝑝𝑚𝑎𝑥)

−1 ∑ ut−12𝑇

𝑡=𝑝𝑚𝑎𝑥+1 (4.2.1.7)

4.2.2. ARDL co-integration

The study adopts an Auto-Regressive Distributed Lag (ARDL) bounds testing approach

developed by Pesaran et al (2001) to model the long run determinants. This approach has

some econometric advantages over the Engle-Granger (1987) and maximum likelihood-based

approach proposed by Johansen and Juselius (1990), and Johansen (1991) cointegration

techniques. First, the bounds test does not require pre-testing of the series to determine their

order of integration since the test can be conducted regardless of whether they are purely I(1),

purely I(0), or fractionally integrated. Second, endogeneity problems and inability to test

hypotheses on the estimated coefficients in the long-run associated with the Engle-Granger

(1987) method are avoided. According to Pesaran and Shin (1999), modeling the ARDL with

the appropriate lags will correct for both serial correlation and endogeneity problems. Jalil et

64

al (2008) argues that endogeneity is less of a problem if the estimated ARDL model is free of

serial correlation. In this approach, all the variables are assumed to be endogenous and the

long run and short run parameters of the model are estimated simultaneously (Khan et al,

2005). Third, as argued in Narayan (2004), the small sample properties of the bounds testing

approach are far superior to that of multivariate cointegration (Halicioglu, 2007). The

approach, therefore, modifies the Auto-Regressive Distributed Lag (ARDL) framework while

overcoming the inadequacies associated with the presence of a mixture of I(0) and I(1)

regressors in a Johansen-type framework. Fourth, the long and short-run parameters of the

model in question are estimated simultaneously. Lastly, The ARDL has superior small

sample properties compared to the Johansen and Juselius (1990) cointegration test (Pesaran

and Shin, 1999). The procedure will, however crash in the presence of I(2) series.

Following Pesaran et al. (2001) as summarized in Choong et al. (2005), we apply the

bounds test procedure by modelling the long-run equation as a general vector autoregressive

(VAR) model of order p, in t zt :

𝑍𝑡 = 𝑐0 + 𝛽𝑡 + ∑ ∅𝑖𝑍𝑡−𝑖 + 휀𝑡, 𝑡 = 1,2,3… , 𝑇𝑝𝑖=1 (4.2.2.1)

With 𝑐0 representing a (k+1)-vector of intercepts (drift), and β denoting a (k+1)-vector

of trend coefficients. Pesaran et al. (2001) further derived the following vector equilibrium

correction model (VECM) corresponding to (4.2.2.1):

∆𝑍𝑡 = 𝑐0 + 𝛽𝑡 + ∏𝑍𝑡−1∑ Γ𝑖∆𝑍𝑡−𝑖

𝑝𝑖=1 + 휀𝑡, 𝑡 = 1,2,3… , 𝑇 (4.2.2.2)

Where the (k+1)x(k+1)-matrices ∏ = 𝐼𝑘+1 + ∑ 𝛹𝑖𝑝𝑖=1 and 𝛤𝑖 = −∑ 𝛹𝑗 , 𝑖 =𝑝

𝑗=𝑖+1

1,2,3. . , 𝑝 − 1 contain the long-run multipliers and short-run dynamic coefficients of the

VECM. Zt is the vector of variables yt and xt respectively. yt is an I(1) dependent variable

defined as lnYt and xt=[yit, i=1,2,3..., T] is a vector matrix of ‘forcing’ I(0) and I(1) regressors

as already defined with a multivariate identically and independently distributed (i.i.d) zero

mean error vector 휀𝑡 = (휀1𝑡, 휀′2𝑡)

′, and a homoskedastic process. Further, assuming that a

unique long-run relationship exists among the variables, the conditional VECM (4.2.2.2) now

becomes

∆𝑌𝑡 = 𝑐𝑦0 + 𝛽𝑡 + 𝛿𝑦𝑦𝑦𝑡−1 + 𝛿𝑥𝑥𝑥𝑡−1 + ∑ 𝜆𝑖∆𝑦𝑡−𝑖 + ∑ 𝜉𝑖∆𝑥𝑡−1 + 휀𝑦𝑡, 𝑡 = 1,2,3, … , 𝑇𝑝−1𝑖=0

𝑝−1𝑖=1

(4.2.2.3)

Where 𝛿𝑖 are the long run multipliers, 𝑐0 is the drift, and 휀𝑡 are white noise errors.

65

Bounds Testing Procedure

The implementation of the ARDL approach involves two stages. First, the existence of

the long-run nexus (cointegration) between the variables under investigation is tested by

computing the F-statistics for analyzing the joint significance of the coefficients of the lagged

levels of the variables. Pesaran and shin, 1999 and Narayan, 2004 have provided two sets of

appropriate critical values for different numbers of regressors (variables). This model

contains an intercept or trend or both. One set assumes that all the variables in the ARDL

model are I(0), and another assumes that all the variables are I(1). If the F-statistic lies above

the upper-bound critical value for a given significance level, the conclusion is that there is a

non-spurious long-run level relationship with the dependent variable. If the F-statistic lies

below the lower bound critical value, the conclusion is that there is no long-run level

relationship with the dependent variable. If it lies between the lower and the upper limits, the

result is inconclusive. The approximate critical values for the F-test were obtained from

Pesaran and Pesaran (1997). The general form of the null and alternative hypotheses for the

F-statistic test is as follows:

𝐻0: 𝛿1 = 𝛿2 = 𝛿3 = 𝛿4 = 𝛿5 = 0; Against the alternative 𝐻1: 𝛿1 ≠ 𝛿2 ≠ 𝛿3 ≠ 𝛿4 ≠ 𝛿5 ≠ 0

Secondly, if the cointegration between variables is identified, then one can undertake

further analysis of long-run and short-run (error correction) relationship between the

variables.

4.2.3. VECM based Granger Causality

The Granger representation theorem suggests that there will be Granger causality in at

least one direction if there exists a cointegration relationship among the variables, providing

that they are integrated order of one. The direction of causality is investigated by applying

Vector Error Correction Model (VECM) granger causality approach only after confirming the

presence of co-integrating relationship among the variables in the study. Granger (1969)

argued that VECM is more appropriate to examine the causality between the series at I(1).

VECM is restricted form of unrestricted VAR and restriction is levied on the presence of the

long run relationship between the series. The system of error correction model (ECM) uses

all the series endogenously. This system allows the predicted values to explain itself both by

its own lags and lags of forcing variables as well as the lags of the error correction term and

by residual term. Engle and Granger (1987) caution that the Granger causality test, which is

conducted in the first differences variables by means of a vector autoregression (VAR), will

66

be misleading in the presence of cointegration. Therefore, an inclusion of an additional

variable to the VAR system, such as the error correction term would help us to capture the

long run relationship. To this end, an augmented form of the Granger causality test involving

the error correction term is formulated in a multivariate pth order vector error correction

model. The VECM equation is as follows:

(

∆𝑥1𝑡

∆𝑦1𝑡

∆𝑦2𝑡

∆𝑦3𝑡..∆𝑦𝑛𝑡)

=

(

𝐶1𝐶2𝐶3𝐶4..𝐶𝑛

)

+ ∑

[ 𝛽11𝑖 𝛽12𝑖 𝛽13𝑖 𝛽14𝑖 . . 𝛽1𝑛𝑖

𝛽21𝑖 𝛽22𝑖 𝛽23𝑖 𝛽24𝑖 . . 𝛽2𝑛𝑖

𝛽31𝑖 𝛽32𝑖 𝛽33𝑖 𝛽34𝑖 . . 𝛽3𝑛𝑖

𝛽41𝑖 𝛽42𝑖 𝛽43𝑖 𝛽44𝑖 . . 𝛽4𝑛𝑖

. . . . . . .

. . . . . . .𝛽𝑛1𝑖 𝛽𝑛2𝑖 𝛽𝑛3𝑖 𝛽𝑛4𝑖 . . 𝛽𝑛𝑛𝑖]

𝑝𝑖=1

(

∆𝑥1𝑡−𝑖

∆𝑦1𝑡−𝑖

∆𝑦2𝑡−𝑖∆𝑦3𝑡−𝑖..∆𝑦𝑛𝑡−𝑖)

+

(

𝛾1

𝛾2

𝛾3𝛾4

.

.𝛾𝑛)

𝐸𝐶𝑀𝑡−1 +

(

휀1𝑡

휀2𝑡

휀3𝑡

휀4𝑡

.

.휀𝑛𝑡)

(4.2.3.1)

The C’s, β’s and ’s are the parameters to be estimated. ECMt-1 represents the one

period lagged error-term derived from the co-integration vector and the ε’s are serially

independent with mean zero and finite covariance matrix. From the Equation (5.5.1) given

the use of a VAR structure, all variables are treated as endogenous variables. The F test is

applied here to examine the direction of any causal relationship between the variables. The

coefficients on the ECM represent how fast deviations from the long-run equilibrium are

eliminated. Another channel of causality can be studied by testing the significance of ECM’s.

This test is referred to as the long run causality test.

4.2.4. Stability tests

4.2.4.1. CUSUM Test

The CUSUM test (Brown, Durbin, and Evans, 1975) is based on the cumulative sum of

the recursive residuals. This option plots the cumulative sum together with the 5% critical

lines. The test finds parameter instability if the cumulative sum goes outside the area between

the two critical lines. The CUSUM test is based on the statistic:

𝑊𝑡 = ∑ 𝑤𝑟/𝑠𝑡𝑟=𝑘+1 , 𝑡 = 𝑘 + 1… . , 𝑇 (4.2.4.1)

Where w is the recursive residual defined above, and s is the standard error of the

regression fitted to all T sample points. If the b vector remains constant from period to period,

E[𝑊𝑡] = 0, but if 𝛽 changes, 𝑊𝑡 will tend to diverge from the zero mean value line. The

significance of any departure from the zero line is assessed by reference to a pair of 5%

significance lines, the distance between which increases with t. The 5% significance lines are

found by connecting the points.

67

[𝑘, ±0.948(𝑇 − 𝑘)1/2] and [𝑇, ±3 × 0.948(𝑇 − 𝑘)1/2]

Movement of 𝑊𝑡 outside the critical lines is suggestive of coefficient instability.

4.2.4.2. CUSUM of Squares Test

The CUSUM of squares test (Brown, Durbin, and Evans, 1975) is based on the test

statistic

𝑆𝑡 = ∑ 𝑤𝑟2/∑ 𝑤𝑟

2𝑇𝑟=𝑘+1

𝑡𝑟=𝑘+1 (4.2.4.2)

The expected value of S under the hypothesis of parameter constancy is

𝐸[𝑆𝑡] = (𝑡 − 𝑘)/(𝑇 − 𝑘) (4.2.4.3)

Which goes from zero at t=k to unity at t=T. The significance of the departure of S

from its expected value is assessed by reference to a pair of parallel straight lines around the

expected value. See Brown, Durbin, and Evans (1975) for a table of significance lines for the

CUSUM of squares test.

The CUSUM of squares test provides a plot of 𝑆𝑡 against and the pair of 5 percent

critical lines. As with the CUSUM test, movement outside the critical lines is suggestive of

parameter or variance instability.

4.2.5. Impulse Response Functions

The impulse response function (IRF) is one of the essential tools for interpreting VAR

model results. The IRF allows researchers to examine the current and future behavior of a

variable that following a shock to another variable within the system. The IRF is a useful tool

for determining the magnitude, direction, and the length of time that the variables in the

system are affected by a shock to another variable. To estimate IRFs, some practical issues

need to be considered. The VAR model needs to be transformed into the vector moving

average (VMA) representation. Enders (2010) advocate that this transformation is an

essential feature of Sims’s (1980) methodology since it allows for tracing out the effects of

various shocks on variables contained in the VAR system. In the case of a VAR model with

two variables included, the form of the IRFs can be written as shown in Enders (2004):

[𝑌𝑡

𝑍𝑡] = [��

��] + ∑

𝐴𝑖

1−𝑏12𝑏21[

1 −𝑏12

−𝑏21 1] [

휀𝑌𝑡−𝑖

휀𝑍𝑡−𝑖]∞

𝑖=0 (4.2.5.1)

68

[𝑌𝑡

𝑍𝑡] = [��

��] + ∑ [

𝜃11𝑖 𝜃12

𝑖

𝜃21𝑖 𝜃22

𝑖 ] [휀𝑌𝑡−𝑖

휀𝑍𝑡−𝑖]∞

𝑖=0 (4.2.5.2)

And;

𝑋𝑡 = 𝜇 + ∑ 𝜃𝑖휀𝑡−𝑖∞𝑖=0 (4.2.5.3)

Where 𝜃𝑖 is the IRFs of disturbances. Therefore, the IRF is found by reading off the

coefficients in the moving average representation of the process. If the innovations 휀𝑡−𝑖 are

contemporaneously uncorrelated, the interpretation of the impulse response is

straightforward. For example, the ith innovation of 휀𝑡 is simply a shock to the ith endogenous

variable in the system Enders (2004).

However, the residuals generated by the VAR models are usually contemporaneously

correlated. This is because in a VAR model only lagged endogenous variables are admitted

on the right-hand side of each equation (in addition to a constant term), and hence all the

contemporaneous shocks which impact on Xt are forced to feed through the residuals, uit

(Kuszczak and Murray, 1986). While this may not cause a problem in the estimation of the

VAR model, the impulse responses and variance decompositions derived from the initial

estimates of the VAR model could be affected such that any adjustment to the order in which

the variables are entered in the system could produce different results (Kuszczak and Murray,

1986). Thus, there is a need to impose some restrictions when estimating the VAR model to

identify the IRFs. In this regard, a common approach is the Cholesky decomposition, which

was originally applied by Sims (1980). The Cholesky decomposition overcomes the problem

of contemporaneous relationships among the innovations error terms within the estimated

VAR model by identifying the structural shocks such that the covariance matrix of the

estimated residuals is lower triangular. In fact, the Cholesky decomposition suggests that

there is no contemporaneous pass-through from 𝑌𝑡 to the other variable, 𝑧𝑡. More formally, in

the VAR, the matrix error structure becomes left triangular, [𝑒1𝑡

𝑒2𝑡] = [

1 −𝑏12

0 1] [

휀𝑌𝑡

휀𝑍𝑡]. In

practice, this means that the Cholesky decomposition attributes all the effect to the variable

that comes first to the target variable in the VAR system.

4.2.6. Variance Decomposition Technique

For any variable, short run variations are due to its own shocks, but over time other

shocks contribute to these changes as well. Forecast error variance decomposition (FEVD) is

a method available to examine this interesting phenomenon. In fact, while the IRFs analyze

69

the dynamic behavior of the target variables due to unanticipated shocks within a VAR

model, variance decompositions determine the relative importance of each innovation to the

variables in the system. That is, variance decompositions can be considered similar to 𝑅2

values associated with the dependent variables in different horizons of shocks. Enders (2010)

show how to write FEVD to conditionally calculate n-period forecast error 𝑋𝑡+𝑛 considering

the VMA representation of VAR presented in equation (4.2.6.1) as:

𝑋𝑡+𝑛 − 𝐸𝑡𝑋𝑡+𝑛 = 𝜇 + ∑ 𝜃𝑖휀𝑡+𝑛−1𝑛−1𝑖=0 (4.2.6.1)

Considering Yt, the first element of the Xt+n matrix in equation (4.2.6.2), the variance of

the n-step-ahead forecast error can be calculated as :

𝑌𝑡+𝑛 − 𝐸𝑡𝑌𝑡+𝑛 = 𝜃11(0)휀𝑌𝑡+𝑛 + 𝜃11(1)휀𝑌𝑡+𝑛−1 + ⋯+ 𝜃11(𝑛 − 1)휀𝑌𝑡+1 + 𝜃12(0)휀𝑍𝑡+𝑛 +

𝜃12(1)휀𝑍𝑡+𝑛−1 + ⋯+ 𝜃12(𝑛 − 1)휀𝑍𝑡+1 (4.2.6.2)

Or

𝜎𝑦(𝑛)2 = 𝜎𝑦2[𝜃11(0)2 + 𝜃11(1)2 + ⋯+ 𝜃11(𝑛 − 1)2] + 𝜎𝑍

2[𝜃12(0)2 + 𝜃12(1)2 + ⋯+

𝜃12(𝑛 − 1)2] (4.2.6.3)

Where 𝜎𝑦(𝑛)2 and 𝜎𝑍(𝑛)2 denote the n-step-ahead forecast error variance of 𝑌𝑡+𝑛 and

Z𝑡+𝑛, respectively. The first part of the equation (4.2.6.3) shows the proportion of variance

due to the variables own shock, 𝑌𝑡, while the second part of eqthe equation (42.6.3) shows the

proportion of variance due to the other variables shock, 𝑧𝑡. Theoretically, the first part

decreases over time while the second part of the variance increases. However, it is typical for

a variable to explain almost all of its forecast error variance at a short horizon and smaller

proportions at longer horizons (Enders, 2010). From this standpoint VDC is useful to assess

the Granger causal relationships among variables when the variance decomposition results

imply that one variable explains a high portion of the forecast error variance of another

variable. That is, when a shock 휀𝑧 explains none of the forecast error variance of the sequence

𝑌𝑡 at all forecast horizons, i.e., 𝜕𝜎𝑦2/𝜎𝑧

2 ≈ 0, we may say that 𝑌𝑡 evolves indecently of the 𝑍𝑡

shocks, 휀𝑧. Also, when a shock to the 𝑍𝑡 sequence, 휀𝑧, explains the entire forecast error

variance of the sequence the 𝑌𝑡 at all forecast horizons, i.e., 𝜕𝜎𝑦

2

𝜎𝑧2 ≈ 100%, may say that 𝑌𝑡

sequence is totally endogenous (Enders, 2010).

70

4.2.7. Principal Component Analysis

Principal component analysis is a multivariate technique that analyzes a data table in

which observations are described by several inter-correlated quantitative dependent variables.

The goals of PCA are to find and extract the most important information from the data and

compress the size while at the same time keeping the important information and simplify the

description of data, and then the structure of the observations and variables can be analyzed.

(Abdi and Williams 2010)

The PCA computes new variables called principal components (PCs) as linear

combinations of the original variables. The first principal component is required to have the

largest possible variance (in other words, inertia and therefore explains the largest part of the

inertia of the data table). The second has to be orthogonal to the first and have the second

largest possible inertia. The rest of the components are computed likewise. The values of

these new variables for the observations are called factor scores, which can be interpreted

geometrically as the projections of the observations onto the principal components (Abdi and

Williams 2010). To be able to find the principal components there is a need for both vectors

and matrixes.

The different principal components are acquired from the singular value decomposition

of the data table. Specifically with 𝑋 = 𝑃∆𝑄𝑇, the matrix of factor scores, denoted F is

obtained as, Equation (4.2.7.1):

𝐹 = 𝑃∆ (4.2.7.1)

Where P is the I×L matrix of left singular vectors, Q is the matrix of the right singular

vectors and ∆ is the diagonal matrix of singular vectors. The squared diagonal matrix (∆2) is

equal to ᴧ which is the diagonal matrix of the (non-zero) eigenvalues of XTXand XXT.

The inertia of a column is defined as the sum of the squared elements of this column

and is computed as, Equation (4.2.7.2):

𝛾𝑗2 = ∑ 𝑥𝑡,𝑗

2𝐼𝑖 (4.2.7.2)

The sum of all the 𝛾𝑗2 is denoted I and it is called the inertia of the data table or the total

inertia. Note that the total inertia is also equal to the sum of the squared singular values of the

data table.

The matrix Q gives the coefficients of the linear combinations used to compute the

factor scores. This matrix can also be interpreted as a projection matrix because multiplying

71

X by Q gives the values of the projections of the observations on the principal components,

Equation (4.2.7.3):

𝐹 = 𝑃∆= 𝑃∆𝑄𝑄𝑇 = 𝑋𝑄 (4.2.7.3)

The strengths of the principal component analysis are that a large amount of variables

can be used without adding much to the complexity of the model.

4.3. Data Issues

The present section of the study deals with data definition and validation. The different

set of variables and their proxies has been used for the purpose of estimating empirical results

of the study. Those variables and their proxies are defined in the present section along with

the sources of their procurements. All these variables are used in different combinations

according to the requirement and general model specification of the study.

4.3.1. Stock market

Stock market development is usually measured by stock market size, liquidity,

volatility, concentration and integration with world capital markets. The present study

incorporates two measures of stock market growth, which are as follows:

4.3.1.1. Stock prices

The stock market index is Sensex (or BSE 30), an index of 30 well established and

financially sound companies listed on the BSE. The Sensex is intended to represent an entire

stock market and thus track the market changes over time. Therefore, in this study, we have

taken the sensitivity index of BSE (Sensex) to track the changes in the market over time (with

respect to other macroeconomic variables) represented by LBSE. The data for BSE Sensex is

available both in annual and monthly frequency and the data has been taken from the official

website of Bombay Stock Exchange.

4.3.1.2. Stock market development

One of the measures of stock market development is market capitalization as a

proportion of GDP. This measure equals the value of listed shares divided by GDP. Market

Capitalization to GDP is a long-term valuation indicator that has become popular in recent

years, given by Warren Buffett (2001), hence, also known as the Buffett Valuation Indicator4.

According to him “it is probably the best single measure of where valuations stand at any

4 In 2001, Warren Buffet remarked in a Fortune Magazine interview that "it is probably the best single measure

of where valuations stand at any given moment.

http://archive.fortune.com/magazines/fortune/fortune_archive/2001/12/10/314691/index.htm

72

given moment”. Hence, it is an important tool to gauge the overall attractiveness of the stock

market in any country. The assumption behind this measure is that overall market size is

positively correlated with the ability to mobilize capital and diversify risk on an economy-

wide basis. Therefore, in this study, we have taken Market Capitalization as a percentage of

GDP 5as a proxy for stock market development. Quarterly frequency data for MCAP as a

percentage of GDP is used for the study and the data has been taken from Handbook of

Statistics on Indian economy, RBI.

4.3.2. Economic Growth

Economic growth is one of the important macroeconomic variables, and its relation to

the stock market is an important aspect of the study. For the purpose of yearly and quarterly

estimation of the studies the data for GDP is available, but, for monthly study, the proxy for

GDP, i.e., IIP (Index of Industrial Production) is used. The present study incorporates two

measures of economic growth, which are as follows:

4.3.2.1. Real Gross Domestic Product

Real GDP represents economic growth and economic growth is the increase in the

inflation-adjusted market value of the goods and services produced by an economy over time.

It is conventionally measured as the percent rate of increase in real gross domestic product, or

real GDP6. Gross domestic product (GDP) is regarded as one of the important determinants

of stock market performance and has often been used to measure the growth of real economic

activity. Growth is usually calculated in real terms, i.e., inflation-adjusted terms to eliminate

the distorting effect of inflation on the price of goods produced. This helps the country to

know their actual development status. Therefore, real GDP has been included for the purpose

of study. The data for real GDP is available in both annual and quarterly frequency and the

data has been collected from Handbook of Statistics on Indian economy, RBI.

4.3.2.2. Index of Industrial Production (IIP)

The present study has taken Index of Industrial Production (LIIP) as the proxy for

economic growth. The IIP as a monthly indicator is widely used for assessing both the current

state and the short-term outlook for GDP (NBER`s Business Cycle Dating Committee,

Sédillot and Pain, 2003) one of the main reasons why the IIP was considered to be a good

5 Other indicators of stock market development that has been used in the literature include the number of listed

companies, changes in the stock market index etc. We focus on market capitalization as a percentage of GDP

because it is less arbitrary than the other measures. In addition, Demiguc-Kunt and Levine (1996) have shown

that different measures of stock market development are highly correlated. 6 Statistics on the Growth of the Global Gross Domestic Product (GDP) from 2003 to 2013, IMF, October 2012

73

proxy for GDP was that the value added by industrial production represented a substantial

share of GDP. The data for IIP has been obtained from the official website of the Ministry of

Statistics and Program Implementation, Government of India.

4.3.3. Real Effective Exchange Rate (REER)

The Real Effective Exchange Rate (REER) is the weighted average of a country’s

currency relative to an index or basket of other major currencies adjusted for the effects of

inflation. Or, conceptually, the REER, defined as a weighted average of nominal exchange

rates adjusted for relative price differential between the domestic and foreign countries,

relates to the purchasing power parity (PPP) hypothesis (RBI Bulletin). The Indian rupee’s

strength is calculated based on a basket of six major currencies and also against 36

currencies, both based on weights assigned as per bilateral trade. The currency basket is more

relevant as this represents a wider set of trading countries. Here, for the purpose of the study

REER based on 36 currency indices has been taken to know that over a trend how the change

in the exchange rate has an impact on stock prices. The data for the REER has been collected

from Handbook of Statistics on Indian economy, RBI.

4.3.4. International crude oil price

Changes in the international crude oil prices are often considered an important factor

for understanding fluctuations in stock prices. For the purpose of study, international crude

oil prices per 1000 barrels have been used. The data for international crude oil prices is

available in both yearly and quarterly frequency and the data has been collected from World

Bank database.

4.3.5. Foreign Direct Investment

FDI is increasingly being recognized as a major source of economic development. The

general belief is that FDI facilitates the transfer of technology, organizational and managerial

practices, skills and access to international market. Therefore, to access the impact of foreign

capital inflows, we have taken Foreign Direct Investment (FDI). The data for FDI has been

collected from Handbook of Statistics on Indian economy, RBI.

4.3.6. Foreign Institutional Investment

Foreign Institutional Investment refers to the investments made by the residents of a

country in the financial assets and production process of another country. FII is a short term

investment by foreign institutions, in the financial markets of other countries. The FII is

playing an important role in bringing in funds needed by the equity market. Additionally, if

74

the funds from multilateral finance institutions and FDI are insufficient, they contribute to the

foreign exchange inflow. According to Lalitha, S. (1992), the main reason for opening stock

market for FIIs was to attract foreign investments and stop countries from raising more debts.

The FIIs has emerged as noteworthy players in the Indian stock market and their growing

contribution adds as an important feature of the development of stock markets in India. The

data from FII has been taken from Handbook of Statistics on Indian economy, RBI.

4.3.7. Inflation

Inflation is a rise in prices of several items over a period of time. It is measured through

various indices and each provides specific information about the prices of items that it

represents. The index could be the Wholesale Price Index (WPI) or the Consumer Price Index

(CPI) for specified categories of people like agricultural workers or urban non-manual

employees. Each of the indices is created in a specific manner with a certain year as the base

year and they consider the price change over a year. Inflation represents one of the major

threats to stock investors. However, high inflation is not always bad and low inflation need

not always be good for the equity markets, as the impact will differ from companies and

sectors across different time horizons. Therefore, the present study incorporates two measures

of economic growth, which are as follows:

4.3.7.1. Consumer Price Index (CPI)

CPI and has been used as the proxy for inflation to identify its relationship with Indian

stock prices. Consumer price index (CPI) measures changes in the price level of a market

basket of consumer goods and services purchased by households. The CPI is a statistical

estimate constructed using the prices of a sample of representative items whose prices are

collected periodically. The data for CPI has been collected from Economic Survey,

Government of India.

4.3.7.2. Wholesale Price Index (WPI)

Wholesale Price Index (WPI) is taken as the proxy of inflation. This index is the most

widely used inflation indicators in India. WPI captures price movements in a most

comprehensive way. It is widely used by Government, banks, industry and business circles.

Important monetary and fiscal policy changes are linked to WPI movements. The data for

WPI is monitored and updated on a monthly basis, taking into account all the 679 items that

form the index. The data for WPI is taken from the Official website of Economic Adviser,

Ministry of Commerce and Industry.

75

4.3.8. Real Interest Rate

Several studies have established the fact that the interest rate and stock prices are

closely related. The interest rate is one of the important macroeconomic variables, which is

directly related to economic growth. Generally, interest rate is considered as the cost of

capital, means the price paid for the use of money for a period of time. From the point of

view of a borrower, the interest rate is the cost of borrowing money (borrowing rate). From a

lender’s point of view, the interest rate is the fee charged for lending money (lending rate).

The impacts of interest rate on stock exchange provide important implications for monetary

policy, risk management practices, financial securities valuation and government policy

towards financial markets. A real interest rate is the interest rate that has been adjusted to

remove the effects of inflation to reflect the real cost of funds to the borrower, and the real

yield to the lender. Therefore, real interest rates have been taken for the study. The data for

real interest rate has been taken from World Bank database.

4.3.9. Short term interest rates

Short term interest rates are the interest rates on loan contracts or debt instruments such as

Treasury bills, bank certificates of deposit or commercial paper-having maturities of less than

one year. The present study includes two proxies of short term interest rate, which are as

follows:

4.3.9.1. Treasury bill rates

Short Term Treasury Bills Rate has been taken as the proxy of interest rate in the study

(TBR): Interest rate varies with default risk, time, and marginal productivity of capital

(Chandra 2004). Increasing or decreasing of interest, encourages substation between

speculative, market instrument, and stock market. The data for the T-bill rates is collected

from Handbook of Statistics on Indian economy, RBI.

4.3.9.2. Call Money Rate (CMR)

Call money rate is considered as a proxy of short term interest rates. To know the

impact of short term interest rates on stock prices, call money rates has been used for the

study. The data for call money rate has been retrieved from Handbook of Statistics on Indian

economy, RBI.

4.3.10. Fiscal Deficit

FD is the Fiscal Deficit as a percentage of GDP. Fiscal deficit/surplus is the difference

between the government’s expenditures and its revenues (excluding the borrowed money).

http://www.nasdaq.com/investing/glossary/l/loan

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The study includes the fiscal deficit as a percentage of total gross domestic products (GDP)

because it is an important metric as it shows how large the fiscal deficit is in relation to

overall output in the economy. Additionally, fiscal deficit is usually communicated as a

percentage of its gross domestic product (GDP). The data for fiscal deficit has been collected

from World Bank database.

4.3.11. Current Account Deficit

Essentially, large deficits entail additional risks to the economy which include a loss in

investor’s confidence and adverse effects on the volume of transactions. The study includes

the current account deficit as a percentage of total gross domestic product (GDP) because it is

an important metric as it shows how large the current account number is in relation to overall

output in the economy. The data for CAD as a percentage of GDP has been taken from

Handbook of Statistics on Indian economy, RBI.

4.3.12. Money supply (M3)

The amount of money in an economy is referred to as the money supply. In this study,

money supply has been measured through M3 (Broad money) and has increasingly been

recognized as a major source of financial development. Money supply is one of the most

basic parameters in an economy and measures the abundance or scarcity of money. Plenty of

money circulating in the economy, both makes more money available to invest in stocks and

also makes alternative investment instruments, such as bonds less attractive. Therefore, to

know the relationship between money supply and stock prices M3 has been used for the study

and the data for money supply has been taken from Handbook of Statistics on Indian

economy, RBI.

4.3.13. Trade openness

The trade-to-GDP-ratio is the sum of exports and imports divided by GDP. This

indicator measures a country’s 'openness' or 'integration' in the world economy. It represents

the combined weight of total trade in its economy, a measure of the degree of dependence of

domestic producers on foreign markets and their trade orientation (for exports) and the degree

of reliance of domestic demand on foreign supply of goods and services (for imports). The

indicator reflects the liberalization policies of the economy and provides an insight for the

investment opportunities in a particular economy. It is believed that openness of the economy

helps to attract foreign investment. This in turn increases the activities on the stock market as

firms would attempt to raise investment funds (capital) from the stock market and therefore

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we have taken TRADE is total of export and an import divided by the GDP of the country

and is a measure of openness. The data for trade openness has been taken from Handbook of

Statistics on Indian economy, RBI.

4.3.14. Gold Prices

Gold is a substitute investment avenue for Indian investors. The importance of gold has

been increased in the present world due to the financial crisis in the present economic world.

The investors are investing in the Gold. Gold is treated as an alternative investment avenue. It

is often stated that gold is the best preserving purchasing power in the long run. Gold

investment can also be used as a hedge against inflation and currency depreciation. From an

economic and financial point of view, movements in the price of gold are both interesting and

important. The measurement of gold taken for the study is USD/Oz. The data for gold has

been obtained taken from the official website of gold price (http://goldprice.org/).

All the variables are taken in their natural logarithm, during empirical estimation .

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CHAPTER 5

Macroeconomic Determinants of the Stock Market Development in India

5.1. Introduction

In the last three decades, numerous studies have examined the dynamic relationships

between macroeconomic variables and stock market, particularly for developed economies

such as the U.S., United Kingdom (UK), Germany, and Japan. The pioneering studies of the

field are carried out by Fama (1981, 1990), Geske and Roll (1983), and Chen, Roll, and Ross

(1986). However, the hypothesis and methodologies used for the studies in this area are

different. A large pool of studies investigated the predictability of stock returns for real

economic activity. Examples of such studies are Estrella and Hardouvelis (1991), Estrella and

Mishkin (1996), and Domain and Louton (1997). An extensive amount of research focuses on

the integration of stock markets across the economies. Examples of these studies are Jeon and

Chiang (1991), Kasa (1992), Arshanapalli and Doukas (1993), Becker, Finnerty and

Friedman (1995), and Longin and Solnik (1995). Another dimension in previous literature

examined the short and the long-run relationship between stock prices and domestic and

international macroeconomic variables such as inflation, exchange rate, FIIs, FDIs, money

supply, interest rate, output and many more. Within this group of studies, some studies

examined macroeconomic factors that affect stock prices, while others examined factors that

determine stock return volatility (Semmler, 2006).

This chapter of the study deals with the discussion of empirical results derived using

different econometric techniques, to know the relationship between different macroeconomic

variables and the Indian stock prices. The econometric methodologies used for estimating the

empirical results of the studies are, Ng-Perron unit root test is utilized to check the order of

integration of the variables. Lag-length selection criteria are used to determine the

appropriate lag length for the model. The long run relationship is examined by implementing

the ARDL bounds testing approach to co-integration. VECM method is used to test the short

and long run causality and variance Decomposition and Impulse Response Function are used

to predict long run exogenous shocks of the variables.

The chapter has been segmented into five sections; the first section presents a broad

literature review based on the relationship between macroeconomic variables and stock

prices; the second section encomposes the yearly studies, incorporating empirical results

using yearly frequency data; the third section is composed of the quarterly study for the

estimation of the relationship between macroeconomic variables and stock market

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development, based on the empirical finding using quarterly frequency of data; in the fourth

section, the results of the studies having monthly frequency data are discussed, showing the

empirical relationship between macroeconomic variables and stock prices; and the fifth

section is composed of summary of the findings.

5.2. Review of Literature

For this study, it is not viable to survey all the literature in every dimension. However,

the present study focuses on the causal relationship between macroeconomic factors and

stock prices. Therefore, in this section, we will discuss the studies showing the relationship

between macroeconomic variables and stock prices. The first section will discuss the relevant

studies from overall economies, the studies related to Indian economy will be provided in the

second section.

5.2.1. Studies conducted in Rest of the World

Asprem (1989) investigated the relationship between stock indices, asset portfolios and

macroeconomic variables in ten European countries. The study uses quarterly data from 1968

to 1984. Correlation and regression techniques were adopted for estimation. Variables used

for the study were changes in industrial production, real gross national product, gross capital

formation, employment, exports, exchange rate, consumption, interest rate, inflation and

money supply. Results showed that employment, imports, inflation and interest rates, are

negatively correlated with stock prices. Changes in imports may be viewed as an indicator for

changes in consumption. Thus, the relation between imports and stock prices is evidence in

support of the consumption capital asset pricing model.

Campbell and Hamao (1992) studied the predictability of monthly excess returns on

equity portfolios over the domestic short-term interest rate in the U.S. and Japan during the

period January 1971 to March 1989. A highly restricted model was estimated and tested for

the study, in which expected excess returns in Japan and the U.S. are driven by a common

unobserved variable, so that they are perfectly correlated. The paper found that similar

variables, including the dividend-price ratio and interest rate variables help to forecast excess

returns in each country. In addition, in the 1980's U.S. variables help to forecast excess

Japanese stock returns.

Pesaran and Timmermann (1995) examined the robustness of the evidence on the

predictability of U.S. stock returns, using recursive modeling approach. Monthly time series

data from January 1954 to December 1992. Variables used for the study include S&P 500

index, one month T-bill rate, producer price index (inflation), twelve month discount bond

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rate, rate of change in industrial production index. It is found from the study that the

predictive power of various economic factors over the stock returns changes through time and

tends to vary with the volatility of returns.

Canova and Nicolo (1995) analyzed the relationship between stock returns and real

activity from the point of view of a general equilibrium, multi country model of the business

cycle, using correlation and regression techniques. The data set consists of quarterly data on

real stock returns, dividend yields and real GNP, consumption and investment for the US, the

UK, France, Germany and Italy for the period 1970-1991. We found from the study that

when government expenditure shocks drive the international cycle the association between

real GNP growth and stock returns is primarily due to the strong positive effect these

disturbances have on dividend payments. And, when technology shocks drive the cycle, the

association is weaker because dividend yields are less correlated with GNP.

Mookerjee and Yu (1997) explored the nexus between Singapore stock returns and

macroeconomic variables, using cointegration and causality techniques. Monthly time series

data from October 1984 through April 1993 was used. Macroeconomic variables used for the

study were money supply, nominal exchange rates and aggregate foreign currency reserves

and all-share price index for the Singapore stock market. The results indicated that three of

the four macro variables are cointegrated with stock prices, suggesting potential inefficiencies

in the long run. The causality tests and forecasting equations provide conflicting evidence on

the informational efficiency of the stock market in the short run.

Cheung and Ng (1998) studied the empirical evidence of long run co-movements

between five national stock market indexes and measures of aggregate real activity, including

the real oil price, real consumption, real money supply and real output (GNP), using

cointegration and error correction mechanism (ECM). The quarterly stock index and

macroeconomic data from 1957:Q1 to 1992:Q2 for Canada, Germany, Italy, Japan, and the

U.S. was considered for the study. The findings showed that the real stock market indexes are

typically cointegrated with measures of the countries’ aggregate real activity such as the real

oil price, real consumption, real money stock, and real output. Based on the ECM, it was also

found that the real returns on stock indexes are generally related to deviations from the

empirical long run relationship and to changes in macro variables.

Garcia and Liu (1999) examined the macroeconomic determinants of stock market

development, particularly market capitalization for fifteen industrial and developing countries

from 1980 to 1995, using correlation and regression techniques. The study focused on the

determinants of stock market capitalization as a proxy for stock market development.

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Macroeconomic variables considered were real income and income growth rate, the savings

and investment and the financial intermediary development. The paper found that the

variables like real income, saving rates, financial intermediary development, and stock

market liquidity are important determinants of stock market capitalization; and stock market

development and financial intermediary development are complements instead of substitutes.

Kwon and Shin (1999) investigated that whether current economic activities in Korea

can explain stock market returns by using a cointegration test and a Granger causality test

from a vector error correction model by using monthly data from January 1980 to December

1992. The macroeconomic variables used for the study include the production index,

exchange rate, trade balance, money supply, Korea Composite Stock Price Index (KOSPI)

and Small-size Stock Price Index (SMLS). The results showed that stock market index and

macroeconomic variables are cointegrated. The study also found that the stock price indices

are not a leading indicator for economic variables.

Gjerde and Saettem (1999) investigated to what extent important results on relations

among stock returns and macroeconomic factors from major markets are valid in a small,

open economy by utilizing the multivariate vector autoregressive (VAR) approach on

Norwegian data. Monthly time series data from 1974 to 1994 was used for the study.

Variables used include stock returns, interest rates, inflation, industrial production,

consumption, OECD industrial production index, foreign exchange rate NOK/USD, and oil

prices. The results suggested that real interest rate changes affect both stock returns and

inflation, and the stock market responds accurately to oil price changes. On the other hand,

the stock market shows a delayed response to changes in domestic real activity.

Nasseh and Strauss (2000) explored the existence of a significant, long-run relationship

between stock prices and domestic and international economic activity in six European

economies, namely, France, Germany, Italy, Netherlands, Switzerland and the U.K, using a

vector error correction model (VECM). The data set consists of quarterly data from 1962:Q1

to 1995:Q4 for real industrial production indices and business surveys of manufacturing

orders (real domestic macroeconomic activity), money market or call interest rates (short-

term interest rates) and long-term government bond rates (long-term interest rates); and the

industrial (INSEE) share price index represents stock prices (SP) for France, the all share

price index is used for Germany, Netherlands and Switzerland, the MSE share price index for

Italy, and the FT 500 share price index for the U.K. The results showed that the stock price

levels are significantly related to industrial production, business surveys of manufacturing

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orders, short- and long-term interest rates as well as foreign stock prices, short-term interest

rates and production.

Canova and Nicolo (2000) analyzed the empirical interdependencies among asset

returns, real activity, and inflation from multicountry and international points of view, using

the VAR model. Monthly data from 1973:1 to 1995:12 was used for the study. Variables used

were - measure of nominal stock returns (SR), slope of the nominal term structure (TERM),

real activity growth (IP), and inflation (INF). The findings of the study suggested that

innovations in nominal stock returns are not significantly related to inflation or real activity,

that the U.S. term structure of interest rates predicts both domestic and foreign inflation rates

and domestic future real activity.

Maysami and Koh (2000) examined the long-term equilibrium relationships between

the Singapore stock index and selected macroeconomic variables, as well as among stock

indices of Singapore, Japan, and the United States by using month-end data for the period

from January 1988 to January 1995. Variables used for the study were weighted average

closing prices for all shares listed on the Stock Exchange of Singapore, exchange rate of the

Singapore SDRs (Special Drawing Rights), Money Supply (M2), Consumer Price Index,

Industrial Production Index, 3-month Interbank Offer Rate, yield on 5-year government

securities, stock-price index of the United States, stock price index of Japan, Total Domestic

Export from Singapore. The methodology adopted was Vector Error-Correction Models

(VECM) to examine the dynamic relations. The study concluded that changes in Singapore’s

stock market levels do form a co-integrating relationship with changes in price levels, money

supply, short- and long-term interest rates, and exchange rates. While changes in interest and

exchange rates contribute significantly to the co-integrating relationship, those in price levels

and money supply do not. This suggests that the Singapore stock market is interest and

exchanges rate sensitive. Additionally, the article also concluded that the Singapore stock

market is significantly and positively co-integrated with stock markets of Japan and the

United States.

Ibrahim and Yusoff (2001) analyzed dynamic interactions among macroeconomic

variables such as real output, price level, and money supply, exchange rate, and equity prices

for the Malaysia (Kuala Lumpur Composite Index (KLCI)), using cointegration and vector

auto regression techniques. Monthly time series data from January 1977 to August 1998 was

considered for the study. The findings showed that the money supply exerts a positive effect

on the stock prices in the short run. However, money supply and stock prices are negatively

associated in the long run.

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David E. Rapach (2001) examined the effects of money supply, aggregate spending,

and aggregate supply shocks on real US stock prices in a structural vector auto regression

framework. Macroeconomic variables used for the study include S&P 500 index deflated by

the implicit GDP deflator, 3 month T-bills rate and GDP. Quarterly time series data from the

period 1959: Q3–1999: Q1 was used for the study The empirical results indicated that each

macro shock has important effects on real stock prices.. The real stock price impulse

responses to the various macro shocks follow to the standard present-value equity valuation

model, and they shed considerable light on the well-known negative correlation between real

stock returns and inflation.

Wongbangpo and Sharma (2002) investigated the role of selected macroeconomic

variables, i.e., GNP, consumer price index, money supply, interest rate and the exchange rate

on the stock prices in five ASEAN countries, namely, Indonesia, Malaysia, Philippines,

Singapore and Thailand, using cointegration and Granger causality. The data set consists of

monthly data from 1985 to 1996 for Jakarta composite stock price index (JCSPI) for

Indonesia, Kuala Lumpur Stock Exchange Composite Index (KLSE) for Malaysia,

Philippine Stock Exchange Composite Index (PSE) for Philippine, Stock Exchange of

Singapore Index (SES) for Singapore and the Stock Exchange of Thailand Index (SET) for

Thailand. The study observed long and short term relationships between stock prices and the

macroeconomic variables; and the macroeconomic variables in these countries cause and are

caused by stock prices in the granger sense.

Ewing (2002) studied the response of the NASDAQ Financial 100 index to

macroeconomic news, by using generalized impulse response analysis. Monthly time series

data from January 1988 to September 2000 was used for the study. Macroeconomic variables

used for the study were the coincident index (real output), changes in the fed funds rate

(stance of monetary policy), spread between Baa and Aaa corporate bond rates (interest rate

spread), consumer price index. The results indicated that a monetary policy shock reduces

financial sector returns, having a significant initial impact effect that continues to affect

returns for around 2 months. Unexpected changes in economic growth have a positive initial

impact effect, but exhibit no persistence. An inflation shock is associated with a negative and

statistically significant initial impact effect which lasts for up to 1 month after the time of

shock. The financial sector responds immediately to an unanticipated rise in risk, but the

effect does not persist into the future.

Carlstrom T.C., Fuerst S.T., Ioannidou P.V. (2002) studied the relationship between

stock prices (S&P 500) and the GDP of US for quarterly data from 1961:Q1 to 2001:Q1. The

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simple correlation technique was used for the study and concluded that future GDP growth

affects current stock prices, and this change in stock prices affects future GDP growth. It was

also found that an upcoming decline in productivity will lower GDP tomorrow and cause the

stock market to drop today.

Flannery and Protopapadakis (2002) studied that whether future GDP growth affects

current stock prices, and this change in stock prices affects future GDP growth. GARCH

model was used to estimate the study. Seventeen macro-series announcements over the

period from 1980 to 1996 were taken into consideration. From the study it was found that six

of the seventeen macro variables are strong risk factor candidates. Of these, two inflation

measures (the CPI and the PPI) affect only the level of the market portfolio’s returns. Three

real factor candidates (balance of trade, employment/unemployment, and housing starts)

affect only the return’s conditional volatility. A monetary aggregate (generally M1) affects

both returns and conditional volatility.

Ibrahim and Aziz (2003) analyzed dynamic linkages between stock prices (month end

values of the Kuala Lumpur Composite Index (KLCI)) and four macroeconomic variables

viz-a-viz real output (industrial production index), price level (CPI), money supply (M2) and

exchange rate (bilateral Ringgit exchange rate) for the case of Malaysia using cointegration

and vector auto regression techniques. The data was of monthly frequency for the period

from January 1977 to August 1998. Empirical results suggested the presence of a long-run

relationship between these variables and the stock prices.

AL-Sharkas, Adel (2004) paper analyzed long-term equilibrium relationships between a

group of macroeconomic variables and the Amman Stock Exchange index (Jordan), by using

macroeconomic variables, namely, industrial production index, the consumer price index,

money supply (M2) and the Treasury bill rate. Quarterly data from 1980:Q1 to 2003:Q3 was

used along with the methodology of vector error correction model (VECM) was used for the

study. The results of the study showed that these macroeconomic variables are cointegrated

i.e., there exists a cointegrating relation among the variables.

Nishat, Shaheen and Hijazi (2004) analyzed long-term equilibrium relationships

between a group of macroeconomic variables and the index of Karachi Stock Exchange by

using Granger-causality and VAR techniques. Macroeconomic variables used for the study

were industrial production index, the consumer price index, Ml, and the value of an

investment earning the money market rate. Data of quarterly frequency from 1973:Q1 to

2002:Q4 was used for the study. The results showed that all the variables are cointegrated and

it was also indicated that the industrial production is the largest positive determinant of

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Pakistani stock prices, while inflation is the largest negative determinant. The results also

confirmed that macroeconomic variables Granger-caused stock price movements and the

reverse causality were observed in the case of industrial production and stock prices.

Naceur, Ghazouani and Omran (2005) tried to identify the main macroeconomic

determinants of stock market development and examined the impact of financial intermediary

development on stock market capitalization. The study was conducted using an unbalanced

panel data from twelve MENA region countries, using yearly data from 1990 to 1999.

Macroeconomic variables used were Income, savings rate, investment rate, credit to the

private sector, M3, stock market liquidity and macroeconomic stability. The study found that

saving rate, financial intermediary (specially credit to private sector), stock market liquidity

(specially the ration of value traded to GDP) and the stabilization variable (inflation change)

are the important determinants of stock market development; and it was also found that

financial intermediaries and stock markets are complements rather than substitutes in the

growth process.

Menike (2006) investigated the effects of macroeconomic variables on stock prices in

emerging Sri Lankan stock market (Colombo Stock Exchange), using multivariate regression.

Monthly time series data from September 1991 to December 2002 was considered for the

study. The variables used for the study were money supply, exchange rate, inflation rate and

interest rate. Findings suggest that inflation rate and exchange rate react negatively to the

stock prices of the Colombo Stock Exchange (CSE) and the negative effect of Treasury bill

rate implies that whenever the interest rate on Treasury securities rise, investors tend to

switch out of stocks causing stock prices to fall.

Yusof and Majid (2007) explored both short- and long-run dynamics between the

macroeconomic variables and stock market behavior in Malaysia (Kuala Lumpur Composite

Index (KLCI)) during the post 1997 financial crisis, using Autoregressive Distributed Lag

(ARDL) model. Monthly frequency data from May 1999 to February 2006 was used for the

study. Macroeconomic variables used were industrial production index (IPI), federal funds

rate (FFR), real effective exchange rate and interest rate (T-bill rate). The study concluded

that changes in the FFR, seems to have a significant direct impact on the Malaysian stock

market behavior during the period of analysis. This implies that any changes in the US

monetary policy may affect the Malaysian stock market.

Humpe and Macmillan (2007) examined whether a number of macroeconomic

variables influence stock prices in the US and Japan, using cointegration analysis. Monthly

data from January 1965 to June 2005 was used for the study. Variables used for the study

86

include industrial production, the consumer price index, money supply, long term interest

rates and stock prices in the US and Japan. The results suggested that for the US, stock prices

are positively related to industrial production and negatively related to both the consumer

price index and a long term interest rate. However, for Japan, stock prices are influenced

positively by industrial production and negatively by the money supply.

Thomas Nitschka (2007) studied the international evidence for return predictability and

the implications for long-run covariation of the G7 stock markets, using VECM techniques.

Quarterly data from the period 1969:Q4 to 2005:Q1 was considered for the study. The

findings suggested that there exists a common temporary component in international stock

markets that is reflected in the predictive power of short-run variations in the U.S.

consumption-wealth ratio, cay, for excess returns on foreign stock markets at the business

cycle frequency. This common component is responsible for 15 to 60 percent of the

covariation between 3-year excess returns on the G7 stock markets.

Coleman and Tettey (2008) investigated the effects of macroeconomic indicators on the

performance of Ghana Stock Exchange (GSE), using Cointegration and the error correction

model techniques. Quarterly time series data from the period 1991:Q1 to 2005:Q4 were used.

Variables used for the study include GSE all-share-index (GSI), inflation, real exchange rate,

interest rates (91 day T bill rates) and Ashanti Goldfields Company (AGC) dummy. The

findings of the study revealed that lending rates from deposit money banks have an adverse

effect on stock market performance and particularly serve as a major hindrance to business

growth in Ghana. And it was also found that inflation rate has a negative effect on stock

market performance.

Hasan and Nasir (2008) examined the relationship between macroeconomic variables,

namely inflation, industrial production, oil prices, short term interest rate, exchange rates,

foreign portfolio investment, money supply and the stock prices of Pakistan (Karachi Stock

Index), by using monthly data from June 1998 to June 2008 by employing ARDL approach.

Results of ARDL long run coefficients reveal that industrial production, oil prices and

inflation are statistically insignificant in determining equity prices in the long run while

interest rates, exchange rates and money supply have a significant long run effect on equity

prices. The error correction model based upon ARDL approach captures the short term

dynamics of prices and it also confirms that changes in industrial production, oil prices and

inflation are not statistically significant in the short run while changes in interest rates,

exchange rates, and money supply have significant short term effect. However, foreign

87

portfolio investment has significant short term effect in the short term and no long term effect

in the long term.

Abdul Rashid (2008) investigated the dynamic interactions between four

macroeconomic variables and stock prices in Pakistan, using cointegration and Granger

causality tests. Variables used for the study were the general Share Price Index, consumer

price index, manufacturing output index (industrial production), nominal exchange rate and

the market rate of interest. Monthly frequency data from June 1994 to March 2005 were used

for the study. The results revealed that there is long run bidirectional causation between the

stock prices and all the said macroeconomic variables with the exception of consumer prices

that only lead to stock prices.

Abugri (2008) investigated whether dynamics in key macroeconomic indicators like

exchange rates, interest rates, industrial production and money supply in four Latin American

countries significantly explain market returns. The Morgan Stanley Capital International

(MSCI) world index and the U.S. 3-month T-bill yield was also included to proxy the effects

of global variables. Vector autoregressive (VAR) model was adopted for empirical

estimation, using monthly data from January 1986 to August 2001. The study found that the

global factors are consistently significant in explaining returns in all the markets. The country

variables are found to impact the markets at varying significance and magnitudes.

Sohail and Hussain (2009) examined the long-run and short-run relationships between

Lahore Stock Exchange and macroeconomic variables such as index of industrial production,

money supply (M1), interest rate and CPI in Pakistan. Quarterly data starting from 1973:1 to

2004:4 were used for the study. Cointegration test and VECM approach were used to

estimate the results of the study. The results revealed that there is a negative impact on

consumer price index on stock returns, while the industrial production index, real effective

exchange rate, money supply had a significant positive effect on the stock returns in the long-

run.

Adam and Tweneboah (2009) examined the impact of macroeconomic variables on

stock prices in Ghana, by using quarterly data from 1991-Q1 to 2007-Q4. The variables used

for the study were the Databank stock index, inward foreign direct investments, the Treasury

bill rate, the consumer price index, average crude oil prices, and the exchange rate as

macroeconomic variables. For the study co-integration test and vector error correction models

(VECM) were adopted to examine both long-run and short-run dynamic relationships. The

paper established that there exist long-run cointegration between macroeconomic variable

88

and Stock prices. The VECM analysis shows that the lagged values of interest rate and

inflation has a significant influence on the stock market.

Hussainey and Ngoc (2009) investigated the effects of macroeconomic indicators (the

interest rate and the industrial production) on Vietnamese stock prices. For the study monthly

time series data from January 2001 to April 2008 was used. The methodology introduced by

Nasseh and Strauss and Canova-de-Nicolo to investigate the linkage between stock prices and

macroeconomic indicators was used. It was found that there are statistically significant

associations among the domestic production sector, money markets, and stock prices in Viet

Nam. Another novel finding was that the US macroeconomic fundamentals significantly

affect Vietnamese stock prices. Finally, the results showed that the influence of the US real

sector is stronger than that of the money market.

Charles K. D. Adjasi (2009) analyzed the impact of macroeconomic uncertainty on

stock‐price volatility in Ghana. The method of analysis is in two stages. The first stage

estimates uni-variate volatility models for each macroeconomic variable; namely consumer

price index (proxy for inflation), exchange rate, money supply, interest rates, oil price, gold

price, and cocoa price using the exponential generalized autoregressive conditional

heteroskedasticity (EGARCH) model. In the second stage volatility effect of macroeconomic

variables on stock prices is estimated using the most recent squared residuals from the

mean‐conditional variance of macroeconomic variables as exogenous variables in the

conditional variance equation of the stock price. The results showed that higher volatility in

cocoa prices and interest rate increases volatility of the stock prices, whilst higher volatility in

gold prices, oil prices, and money supply reduces volatility of stock prices.

Keray Raymond (2009) focused on the interrelationships between stock prices and

monetary indicators by examining the dynamics between these variables for Jamaica using

monthly data from January 1990 to March 2009. Variables used for the study were Jamaica

stock exchange index, money supply, interest rate, inflation rate and the exchange rate. The

Johansen cointegration test was used to determine the long term relationship between stock

prices and monetary variables. It was found that the variables were co-integrated with

significant relationships in line with a priori expectations. Coefficients from the co-

integrating vector, normalized on the stock price, suggest that the JSE Main Index is

positively influenced by the inflation rate and M3 and negatively by the exchange rate,

interest rate and M2.

Rahman, Sidek and Tafri (2009) studied the interactions between selected

macroeconomic variables and stock prices for the case of Malaysia, using VECM. Monthly

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time series data from January 1986 to March 2008 was considered for the study. Variables

used for the study include Kuala Lumpur Composite Index (KLCI),industrial production

index (IPI), real exchange rate (RER), money supply (M2), reserves (RES) and interest rates

(TB). The study concluded that all six variables contribute significantly to the co-integrating

relationship. This proves that the Malaysian stock market is sensitive to changes in the

macroeconomic variables. Furthermore, based on the variance decomposition analysis, the

paper highlights that the Malaysian stock market has stronger dynamic interaction with

reserves and industrial production index.

Shiu-Sheng Chen (2009) investigated whether macroeconomic variables can predict

recessions in the stock market, i.e., bear markets of the US, using parametric and

nonparametric approaches. Monthly returns of the S&P 500 price index from February 1957

to December 2007 and macroeconomic variables used were interest rate spreads, inflation

rates, money-stocks, aggregate output, unemployment rates, federal funds rates, federal

government debt, and nominal exchange rates. Results suggested that among the

macroeconomic variables, yield curve spreads and inflation rates are the most useful

predictors of recessions in the US stock market.

Alam and Uddin (2009) studied the relationship between stock index and interest rate

for fifteen developed and developing countries- Australia, Bangladesh, Canada, Chile,

Colombia, Germany, Italy, Jamaica, Japan, Malaysia, Mexico, Philippine, S. Africa, Spain,

and Venezuela, using the random walk model. Monthly data from January 1988 to March

2003 was considered for the study. It was found that for all of the countries, interest rate has a

significant negative relationship with share price and for six countries it is found that changes

of the interest rate has a significant negative relationship with changes of share price. So, if

the interest rate is considerably controlled for these countries, it will be the great benefit of

these countries’ stock exchange through demand pull way of more investors in share market,

and supply push way of more extensional investment of companies.

Rjoub, Tursoy and Gunsel (2009) studied the effects of macroeconomic factors on

stock returns of Istanbul Stock Market, using monthly frequency data from January 2001 to

September 2005. The methodology employed was OLS techniques, using macroeconomic

variables, namely, the term structure of interest rate, unanticipated inflation, risk premium,

exchange rate and money supply. The results of the study indicated that there exist a

significant pricing relationship between the stock return and the tested macroeconomic

variables; namely, unanticipated inflation, term structure of interest rate, risk premium and

money supply have a significant effect in explaining the stock market returns in various

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portfolios. But these results showed a weak explanatory power based on the findings. This

means that there are other macroeconomic factors affecting stock market returns in ISE other

than the tested ones.

Pilinkus and Boguslauskas (2009) studied the short-run relationship between

Lithuanian stock market prices and macroeconomic variables by employing the Impulse

response function by using monthly data from January 2000 to June 2009. The

macroeconomic variables used in this paper were as follows: seasonally adjusted gross

domestic product (GDP) at previous year prices; harmonized consumer price index (HCPI),

the narrow money supply (M1), unemployment rate (UR); three months Vilnius inter-bank

offered rate. The result revealed that the GDP and money supply have a positive effect on

stock market prices while most of the time unemployment rate, exchange rate, and short-term

interest rates negatively influence stock market prices.

Dickinson (2010) explored the relationship between selected European stock markets

(France, Germany, UK and US) and macroeconomic fundamentals, using a vector error

correction model (VECM). Monthly data from January 1988 to December 1995 was

considered for the study. Variables used include a real share index (own currency), real share

index ($US terms), industrial production, real interest rate and real exchange rate. The study

found a long run relationship between the stock market index and the selected

macroeconomic variables.

Lijuan and Ye (2010) studied the relationship between macroeconomic factors and

stock prices in China (Shanghai composite Index), using monthly frequency data from

January 2008 to December 2009. The methodology employed for the study was multi-linear

regression model. Macroeconomic variables used for the study include exchange rate,

corporate goods price index, interest rate (base rate for RMB deposit), macroeconomic

prosperity index (the consistency index reflects the basic trend of the current economy,

synthesized from industrial production, employment, social demand and social income),

consumer confidence index, money supply, national foreign exchange reserve. The results of

the study showed that the change in stock price is mainly affected by the exchange rate,

interest rate, macroeconomic prosperity index, consumer confidence index and corporate

goods price index.

Cherif and Gazdar (2010) studied the influence of the macroeconomic environment and

institutional quality on stock market development, using data from 14 MENA countries over

the period of 1990-2007. Both panel data and instrumental variable techniques were used for

the empirical estimation. Variables used were; income level, savings, investment rate,

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financial intermediary development, stock market liquidity, and macroeconomic stability.

The study found that income level, saving rate, stock market liquidity, and interest rate

influence stock market development with the expected theoretical signs. The results also

showed that the banking sector and the stock market sectors, are complementary instead of

being substituted. It was also found that the institutional environment as captured by a

composite policy risk index does not appear to be a driving force for the stock market

capitalization in the region.

George Filis (2010) examined the relationship among the consumer price index,

industrial production, stock market and oil prices in Greece. Cointegration, VECM and the

multivariate VAR model used to study the data. Monthly data from January 1996 to June

2008 was used. Findings suggested that oil prices and the stock market exercise a positive

effect on the Greek CPI, in the long run. The cyclical components analysis suggested that oil

prices exercise significant negative influence on the stock market and oil prices are

negatively influencing CPI, at a significant level.

Nicholas M Odhiambo (2010) examined the relationship between banks and stock

market development in the South Africa by adopting ARDL-Bounds testing approach.

Annual time series data from the year 1969 to 2008 has been used for the study. The variables

used were, bank development (ratio of the domestic credit to the private sector to GDP), the

stock market development (the stock market capitalization to GDP), per capita real GDP, a

savings ratio to GDP and inflation. The empirical results show that there is a distinct positive

relationship between banks and stock markets in South Africa. The results apply irrespective

of whether the model is estimated in the short run or in the long run. Other results show that

in the short run, the stock market development in South Africa is positively determined by the

level of savings, but negatively affected by the rate of inflation and the lagged values of the

stock market development. However, in the long run, the stock market is positively

determined by real income and the inflation rate.

Victor and Kuwornu (2011) examined the relationship between macroeconomic

variables and stock market returns, using three multivariate APT models with the dependent

variables as Ghana All Share Index. Monthly time series data from January 1992 to

December 2008 was considered for the study. Macroeconomic variables used were consumer

price index, crude oil price, exchange rate and 91 day Treasury bill. The empirical results

revealed that there is a significant relationship between stock market returns and three

macroeconomic variables; consumer price index, exchange rate and the Treasury bill rate.

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Where, consumer price index had a positive significant effect, while exchange rate and the

Treasury bill rate had negative significant influence on stock market returns.

Adaramola, Anthony Olugbenga (2011) investigate the impact of macroeconomic

indicators on stock prices in Nigeria (study based on the individual firm’s level), using both

time series and cross-sectional data. Quarterly data from 1985:Q1 and 2009:Q4 were used for

the analysis. The macroeconomic variables used for the study were money supply (BRDM),

interest rate (INTR), exchange rate (ECHR), inflation rate (INF), oil price (OIL) and gross

domestic product (GDP). The empirical findings of the study revealed that macroeconomic

variables have varying significant impact on stock prices of individual firms in Nigeria. Apart

from inflation rate and money supply, all the other macroeconomic variables have significant

impacts on stock prices in Nigeria.

Hussain (2011) investigated the return and the volatility response of major European

and US equity indices to monetary policy surprises by utilizing extensive intra-day data on 5-

min price quotes (from September 1, 2000 through September 30, 2008) along with a

comprehensive dataset on monetary policy decisions and macroeconomic news

announcements. Return-generating model and Volatility response model were adopted for the

estimation. The results of the study indicated that the monetary policy decisions generally

exert immediate and significant influence on stock index returns and volatilities in both

European and the US markets. The findings also showed that press conferences held by the

European Central Bank (ECB) that follow monetary policy decisions on the same day have a

clear impact on European index return volatilities.

Yu Hsing (2011) examined the macroeconomic determinants of the U.S. stock market

index, using the GARCH model. Quarterly time series data from 1978:Q1 to 2010:Q1 was

used for the study. Macroeconomic variables used for the study were the stock market index

in U.S., real output in U.S., stock earnings, the government debt, the money supply, the real

short-term interest rate in the U.S., the real long-term interest rate in the U.S., the nominal

effective exchange rate (NEER), the expected inflation rate, the foreign stock market index,

and the foreign interest rate. The findings suggested that a higher real GDP, a higher stock

earning, a lower government debt/GDP ratio, a lower M2/GDP ratio, a lower real Treasury

bill rate, a lower real corporate bond yield, a higher nominal effective exchange rate (NEER),

a lower expected inflation rate, a higher U.K. stock index, or a lower U.K. Treasury bill rate

would cause the U.S. stock market index to rise.

Oseni and Nwosa (2011) examined the volatility in the stock market and

macroeconomic variables in Nigeria, and used lag-augmented vector auto regression (LA-

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VAR) Granger Causality test for annual data from 1986 to 2010. Variables used for the study

were real GDP growth, inflation rate, interest rate and stock returns. The results of the

findings revealed that there exists a bi-directional causal relationship between stock market

volatility and real GDP; and there is no causal relationship between stock market volatility

and the volatility in interest rate and inflation rate.

Olweny and Kimani (2011) investigated the causal relationship between stock market

performance and economic growth in Kenya, using cointegration and granger causality test.

Quarterly time series data for the period 2001:Q1-2010:Q4 was considered for the study.

Variables used for the study include NSE 20-share index (Nairobi stock exchange), GDP and

CPI. The variables were found to be cointegrated with at least one co-integrating vector. And

the results of the Granger causality test indicated that the causality between economic growth

and stock market runs unilaterally or entirely in one direction from the NSE 20-share index to

the GDP.

Ade. O. Adenuga (2011) examined the relationship between stock market development

and economic growth in Nigeria, using vector error-correction model (VECM) technique.

Quarterly data from 1990:Q1 to 2009:Q4 was employed for the study. Variables incorporated

in the study were economic growth (rate of change of real GDP), macroeconomic stability

(CPI), Investment Ratio (gross fixed capital formation divided by nominal GDP), Market

Capitalization Ratio (market capitalization as a ratio of GDP), Capital Flows (foreign direct

investment as a percentage of GDP), Banking Sector Development (domestic credit provided

by the banking system to the private sector relative to GDP). The study showed that the

model validates the hypothesis that stock market development promoted economic growth in

Nigeria during the period of analysis.

Hsing (2011) examined the relationship between Hungary’s stock market index and

relevant macroeconomic variables by employing the GARCH model. Monthly frequency data

from 2000:Q1 to 2010:Q2 was used for the study. Macroeconomic variables used for the

study real output, government debt, money supply, real interest rate in Hungary, nominal

effective exchange rate (NEER), expected inflation rate, foreign stock market index and

foreign interest rate. The study found that Hungary’s stock market index has a positive

relationship with real GDP, the ratio of the government debt to GDP, the nominal effective

exchange rate and the German stock market index, a negative relationship with the real

interest rate, the expected inflation rate and the government bond yield in the euro area, and a

quadratic relationship with real M2 money supply.

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Zivengwa, Mashika, Bokosi and Makova (2011) explore the causal link between stock

market development and economic growth in Zimbabwe using annual time series data for the

period 1980 to 2008. The stock market development was measured using two variables,

namely stock market capitalization as a ratio of GDP and value of stocks traded as a ratio of

stock market capitalization and the macroeconomic variables used were per capital real GDP

and investment. Vector Autoregressive (VAR) and Granger Causality tests were applied for

the estimations. The results showed a unidirectional causal link that runs from stock market

development to economic growth and there is evidence of an indirect transmission

mechanism through the effect of stock market development on investment.

Abu-Libdeh, H., and Harasheh, M. (2011) investigated the correlation and causality

relationships between stock prices in Palestine and some macroeconomic variables, namely

GDP, inflation, exchange rate, Libor rate and balance of trade, by employing quarterly data

from March 2000 to June 2010. The methodology used includes regression analysis and

Granger Causality Test. The results of the regression analysis indicated a significant

relationship between the macroeconomic variables used and stock prices. Further, the

causality analysis neglected any kind of causal relationships between each particular

macroeconomic variable and stock price.

Ali, M. B. (2011) investigated the impact of changes in selected microeconomic and

macroeconomic variables on stock returns at Dhaka Stock Exchange, using monthly

frequency data from July 2002 to December 2009. A Multivariate Regression Model

computed with Standard OLS Formula has been used to estimate the relationship. The

variables used for the study include DSE all share price index as dependent variable and

inflation (CPI), industrial production index and foreign remittance as macroeconomic

predictor variable and market price/earnings commonly known as Market P/E and monthly

average growth in market capitalization measured in percent as macroeconomic predictor

variables. The study found that inflation and foreign remittance have a negative influence and

industrial production index; market P/Es and monthly percent average growth in market

capitalization have a positive influence on stock returns.

Hsing (2011) examined the relationship between the Czech stock market index and

selected macroeconomic variables using quarterly data from 2002:Q1 to 2010:Q2. The

variables used for the study include real output, government borrowing, money supply,

domestic real interest rate, CZK/USD exchange rate, expected inflation rate, foreign stock

market index, and foreign interest rate. Regression techniques were used for the estimation of

the study. The study found that the Czech stock market index is positively associated with

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real GDP and the German and US stock market indexes, is negatively influenced by the ratio

of government borrowing to GDP, the domestic real interest rate, the CZK/USD exchange

rate, the expected inflation rate and the euro area government bond yield, and exhibits a

quadratic relationship with the ratio of M2 to GDP.

Barbic and Jurkic (2011) tested the presence of informational inefficiencies in stock

markets of selected CEE countries (Croatia, Czech Republic, Hungary, Poland and Slovenia)

analyzing the relationship between stock market indices and macroeconomic variables,

namely, including inflation rate, broad money supply, money market interest rate and foreign

currency reserves. Johansen cointegration method and Granger causality test were employed

for empirical estimation. Cointegration results showed a long run relationship between stock

market indices and macroeconomic variables, especially in the case of Poland and Czech

Republic. And the results of granger causality test revealed that there is no causal linkage

between any macroeconomic variable and stock market index in Croatia, Hungary and

Poland; money supply and foreign exchange lead stock index in Czech Republic, while

inflation rate and money market interest rate lead Slovene stock index; and stock market

index leads money market interest rate in Hungary and Czech Republic, foreign exchange

reserves in Slovenia and money supply in Poland.

Athapathu and Jayasinghe (2012) examined the causal relationship between stock

market performance and economic growth in Sri Lanka for annual data from the year 1997 to

2008. Econometric methods such as co-integration analysis, error-correction mechanism and

Granger causality tests were employed to investigate the relationship between market

capitalization of all share price index; and real and nominal GDP. Results revealed that a

unidirectional causal relationship is observed between stock market performance indicators

and GDP growth.

Muhammed Monjurul Quadir (2012) investigated the effects of macroeconomic

variables like Treasury bill, interest rate and industrial production on stock returns on the

Dhaka Stock Exchange for the period from January 2000 to February 2007 by using monthly

time series data and Autoregressive Integrated Moving Average (ARIMA) model was

adopted to determine the relationship between stock return and macroeconomic variables.

Though the ARIMA model founds a positive relationship between Treasury bill, interest rate

and industrial production with market stock returns, but the coefficients have turned out to be

statistically insignificant.

Tsai (2012) estimated the relationship between the stock price index and exchange rate

for six Asian countries, using ordinary least squares method. Monthly data for the stock and

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foreign exchange markets in Singapore, Thailand, Malaysia, the Philippines, South Korea,

and Taiwan from January 1992 to December 2009 were used. The results of the study showed

that the data in all six Asian countries have a similar pattern in the various coefficients

obtained from different quantile functions. The coefficients are more significantly negative

when the exchange rates are extremely high or low. The negative coefficients support the

portfolio balance effect in these two markets, which states that the increase (decrease) of the

returns of stock price index will decrease (increase) the exchange rate, which means the

domestic currency appreciates (depreciates).

Muthike and Sakwa (2012) studied weather macroeconomic indicators can be used as

predictors of the stock exchange index trends. Annual time series data from 1976 to 2008 was

used for the study. Variables used were money supply, inflation rate, T bill rate, gross

domestic product and the foreign exchange rate, and the Nairobi Stock Exchange (NSE) 20

share index. The findings of the study showed that the 91-Day Treasury Bills and the

Inflation rate were the only clear leading macroeconomic indicators on the NSE 20-Share

Index. The money supply and real exchange rates were both leading and lagging

macroeconomic indicators on the NSE 20-Share index; and the gross domestic product

showed the weakest relationship with the NSE 20 Share index.

Sajjad, Shafi, Jan, Saddat and Rehman (2012) examined the relationship between

Karachi stock exchange and macroeconomic variables, i.e. inflation rate, exchange rate,

treasury bills and interest rate by using monthly time series data from January 2005 to

December 2010. The co-integration test and granger casualty was applied to drive the short

and long-term investigation. The results found bi-directional granger causality between KSE

and exchange rate and unidirectional granger causality exists from interest rates to KSE.

Mehmet Gencturk, Ismail Celik and Omer Binici (2012) studied the causal relationship

between Istanbul Stock Exchange (ISE) stock prices, dollar rate, consumer price index,

interest rates and industrial production index by using monthly data from January 2005 to

July 2011. Methodology adopted was Johnsen-Juselius co-integration test, Vector Error

Correction Model (VECM). Research showed that the existence of a long-run relationship is

only between ISE and industrial production index. VECM showed a unidirectional causality

from stock prices to industrial production index.

Ochieng, D. E., & Adhiambo, E. O. (2012) investigated that whether the changes in

macroeconomic variables can be used to predict the future NSE All share index (NASI) for

Kenya. The three key macroeconomic variables were examined which includes lending

interest rate, inflation rate and 91 day Treasury bill (T-bill) rate from March 2008 to March

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2012. The data were analyzed using regression methods. It was concluded that that the 91 –

day T bill rate has a negative relationship with the NASI while inflation has a weak positive

relationship.

Javed and Akhtar (2012) investigated the risk-return relationship of three major

macroeconomic indicators, money supply, term structure, and interest rate with stock returns

of 50 firms listed at the Karachi stock exchange of Pakistan, for monthly data from July 1998

to December 2008. The study employed GARCH model to demonstrate the behavior of the

variance of macroeconomic variables in relation with stock returns. The results found a

significant relationship between the macroeconomic indicators and stock return, and showed

that macroeconomic indicators as risk factors influence the movement of returns. The money

supply risk positively effects, stock returns and exchange rate shock negatively effects stock

return.

Martin Sirucek (2012) focused on the effect, implication, impact and relationship

between selected macroeconomic variables and wider US indices S&P 500 and industrial

Dow Jones Industrial Average (DJIA). Macroeconomic variables used for the study were

inflation, interest rates, money supply, producer price index, industrial production index, oil

price and unemployment for annual data from 1999 to 2012 for USA. Correlation and

regression techniques, adopting the OLS method were used for the study. The results showed

that the producer price index, industrial production index, oil price and Dow Jones index are

having a stronger relationship than between these factors and S&P 500.

Douglas (2012) attempted to use the Arbitrage Pricing Theory framework to explain the

variations on the returns on the Ghana Stock Exchange, by employing Ordinary Least

Squares Regression, cointegration analysis and Granger causality tests. Monthly data from

1991 to 2009 was considered for the study. Macroeconomic variables used for the study

include inflation rate, Cedi-USD exchange rate, 91-Day T-Bill rate, broad money supply

(M2), World Cocoa and Gold prices and World Crude Oil prices. The results of the Ordinary

Least Squares regression analysis showed that four out of the seven macroeconomic variables

possess statistically significant power for stock returns on the Ghana Stock Exchange:

inflation rate, the treasury bill rate, money supply and world crude oil prices. Further, the

results of Granger cointegration test signal the existence of an overall long-run relationship

between stock returns and the observed variables on the GSE, the same could not be said of

the long-run relationship between individual macroeconomic variables and stock returns. On

the contrary, the Johansen and Juselius cointegration test shows the existence of at least two

cointegrating relationships between stock returns and the macroeconomic variables. Further,

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the Engle and Granger causality test points to unidirectional causality between stock returns

and the foreign exchange rate and the money supply

Hussin, Fidlizan, Abu and Awang (2012) focused on the relationship between the

development of Islamic stock market and macroeconomic variables in Malaysia. Vector Auto

Regression (VAR) method was applied for the estimation of the study. The variables

involved in this research are Kuala Lumpur Syariah Index (KLSI), Industrial Production

Index (IPI), Consumer Production Index (CPI), Aggregate Money Supply (M3), Islamic Inter

Bank Rate (IIR) and Exchange Rate of Malaysian Ringgit-United States Dollar (MYR) for

monthly data from April 1999 to October 2007. The results of the study showed that Islamic

stock prices are co-integrated with the selected macroeconomic variables, and the stock price

is related positively and significantly with IPI and CPI variables but related negatively and

significantly with M3 and MYR variables.

Zohaib Khan, Sangeen Khan and Lala Rukh (2012) studied the impact of interest rate,

exchange rate and inflation on stock returns of KSE 100 index for Pakistan. Monthly data

from July 2001 to June 2010 was used for the study. Multiple regression models were applied

for the estimation. The results showed that the exchange rate has significant impact on stock

returns of KSE 100 index.

Osisanwa and Atanda (2012) examined the determinants of the stock market returns in

Nigeria by employing the OLS techniques using annual data for the period between 1984 and

2010. Their variables were consumer price index, exchange rate, broad money, interest rate

and real per capital income. The findings showed that exchange rate, interest rate, money

supply and previous stock return levels are the primary determinants of stock returns in

Nigeria. Critical analysis of this study shows that the method used for the analysis is not

popular and widely used. In time series analysis, the ordinary least squares regression results

might provide a spurious regression if the time series are non-stationary. Again, consumer

price index is not an accurate index for inflation; this is because the index takes the price of

fixed representative basket and does not consider the price of investment.

Oriwo, E. A. (2012) investigates the relationship between macroeconomic variables on

NSE All share index of Kenya (Nairobi Securities Exchange), by using monthly frequency

data from March 2008 to March 2012. The three key macroeconomic variables used for the

study were Interest rate, Inflation Rate and 91 day T bill. The methodology applied include

ARDL approach. The findings of the study indicated that 91 –day T-bill has a negative

relationship with the NSE All share Index while Inflation do have a weak positive

relationship with the NASI index.

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Taiwo, M., Taiwo, A. and Olowookere, D. (2012) analyzed the impact of crude oil

price, stock price and some selected macroeconomics variables on the growth of the Nigerian

economy for annual data from 1980 – 2010. Co-integration and Error correction model were

used for estimation purpose. Macroeconomic variables used for the study were Growth rate

of Gross Domestic Product, Growth rate of stock price indexed by GDP, Growth rate of oil

price indexed by GDP, Interest rate and Real exchange rate. From the study it was found that

crude oil price, stock price and exchange rate have significant influence on the growth of the

Nigerian economy.

Ibrahim and Shah (2012) examine the interrelations between bank lending,

macroeconomic conditions and financial uncertainty for an emerging economy, Malaysia.

Macroeconomic variables used for the study include real bank loans, real GDP, nominal

lending rate, real stock prices and a measure of stock market volatility. Cointegration,

causality and vector auto regressions (VARs) techniques were used for the estimation for

quarterly data from the period 1991:1–2011:2. The study found a long run positive relation

between real output and both real bank credits and real stock prices.

Joseph Tagne Talla (2013) investigated the impact of changes in selected

macroeconomic variables on stock prices of the Stockholm Stock Exchange by applying

monthly data from January 1993 to December 2012. Variables used for the study were

Consumer Price Index (CPI) as a proxy for inflation rate, Exchange Rate (ER), Money

Supply (MS), Interest Rate (IR) and on the Stockholm Stock Exchange indices (OMXS30).

To estimate the relationship, unit root test, Multivariate Regression Model computed on

Standard Ordinary Linear Square (OLS) method and Granger causality test was used. Based

on estimated regression coefficients and t-statistics, it was found that inflation and currency

depreciation have a significant negative influence on stock prices. No unidirectional Granger

Causality was found between stock prices and all the predictor variables under study except

one unidirectional causal relation from stock prices to inflation.

Kalyanaraman and Tuwajri (2013) analyzed the long run relationship between five

macroeconomic variables viz-a-viz., consumer price index, industrial output, money supply,

exchange rate, oil prices along with the global stock prices proxy S&P 500 index and Saudi

all share stock index, using cointegration and VECM (vector error correction model).

Monthly data from January 1994 to June 2013 was considered for the study. It was found

from the study that all macroeconomic variables are found to impact stock prices, but

Standard and Poor’s 500 index does not affect Saudi stock prices. The results also showed the

presence of long run causality from the explanatory variables to the stock prices. Short run

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causality test finds a two-way causality between stock prices and oil prices. Impulse response

function showed that industrial production shocks pushes up stock prices while consumer

price index shocks pulls it down. Variance decompositions showed that historical stock prices

are the major driver of Saudi stock prices.

Babayemi Asare, Onwuka, Singh and James (2013) examined the panel data of seven

major African stock markets with a view to investigate the long run relationship between

these markets and some vital macroeconomic variables, using the Panel residual based test on

Pedroni and error correction based test of the Wasteland. The macroeconomic variables were

Stock Market index, External Debt, Money Supply and Foreign Direct Investment. The

African Stock Markets of the following countries used in the study were Botswana, Egypt,

Ghana, Kenya, Morroco, Nigeria, and South Africa for annual periodic data from 1988-2011.

The result showed that in the long run, Foreign Direct Investment (FDI) and External Debt

exert a positive impact on the African stock markets while negative impact will be recorded

for Money supply. However, the extent is much greater in FDI, as for every 1% increment in

its value brings about 2.01% change in market value.

Bellalah and Habiba (2013) investigated the long run relationship between

macroeconomic indicators, namely, trade, oil prices, rate of interest, money supply (M3),

index of industrial production and stock exchange price indices for USA, Japan and China.

Monthly time series data from January 2005 to May 2010 was considered for the study.

ARDL co-integration approach was used for empirical estimation. The results showed that

rate of interest, industrial production index and Money supply (M3) are positively related to

the stock prices in the long run and short run, for USA and China; and the rate of interest is

positive and highly significant in the long run for Japan.

Zhou, Zhao, Belinga and Gahe (2013) examined the macroeconomic factors that affect

the stock market development in Cameroon, using the Calderon-Rossell model, by applying

monthly data from January 2006 to December 2011. Variables used for the study include

Stock Market Capitalization, Domestic Credit to the Private Sector, Stock Market Value

Added Ratio, Gross Domestic Product per Capita, Gross Domestic Investment, Gross

Domestic Saving, Current Inflation Rate, Real Interest Rate, Foreign Direct Investment and

Net Capital Flows. The results of the study found that the stock market liquidity and financial

openness represented by foreign direct investment and private capital flows are important

determinants of stock exchange development in Cameroon.

Gupta and Modise (2013) examined both in-sample and out-of-sample predictability of

South African stock return using macroeconomic variables, using monthly data covering the

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sample period between January 1990 and December 1996, and the out-of sample period

commencing from January 1997 to June 2010. Variables used for the study were All share

index (real stock returns), relative long-term bond yield, relatively 90 days T bill rate, term

spread, the employment growth rate, inflation rate, real effective exchange rate, broad money

supply growth rate, narrow money supply growth rate, industrial production growth rate,

relatively money market rate, world oil production growth rate, and crude oil price growth

rate. For the in-sample test, the t-statistic corresponding to the slope coefficient of the

predictive regression model was used, and for the out-of-sample tests the MSE-F and the

ENC-NEW test statistics were employed. For the in-sample tests, the results showed that

different interest rate variables, world oil production growth, as well as, money supply have

some predictive power at certain short-horizons. For the out-of-sample forecasts, only interest

rates and money supply show short-horizon predictability. Further, the inflation rate shows

very strong out-of-sample predictive power from 6-month-ahead horizons.

Wang and Ajit (2013) investigated the impact of stock market development on

economic growth in China. To this end, the quarterly data from 1996 to 2011 was used and

the cointegration framework was adopted for empirical investigation. Variables used for the

study were market capitalization, real GDP, real government spending, and real money

supply (M1). It was found from the study that the stock market development generally does

not contribute positively to economic growth in developing countries if the stock market is

mainly an administratively-driven market.

Ikramullah, Ahmed, Kamel and Yaqoob (2013) study investigates the link between the

macroeconomic variables and equity returns in Pakistan, using monthly frequency data from

November 1991 to March 2013 of KSE-100 index, consumer price index (LCPI), the

industrial production (LIP), the exchange rate (LER), the money supply (LM2), and the

interest rate (LIR). The methodology used was the ARDL approach.The findings of the study

suggested that, in the short run, all the used macroeconomic variables affect stock returns.

While in the long run industrial production and money supply have a positive and

incremental impact on consumer price index. On the other hand, the interest rate has a

negative impact on stock returns.

Issahaku, Ustarz and Domanban (2013) examined the existence of causality between

macroeconomic variables and stock returns in Ghana (Ghana Stock Exchange), using

monthly time series data from the period January 1995 to December 2010. The methodology

employed was Vector Error Correction (VECM) model and Granger Causality tests.

Macroeconomic variables used include exchange rate (Cedi/United State dollar rate), the

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Consumer Price Index (to represent inflation), treasury-bill rate, money supply and FDI. The

findings of the study revealed that a significant long run relationship exists between stock

returns and inflation, money supply and Foreign Direct Investment (FDI). In the short-run, a

significant relationship exists between stock returns and macroeconomic variables such as

interest rate, inflation and money supply. Further, a causal relationship running from stock

returns to money supply, interest rate and FDI has also been revealed.

Naseri and Masih (2013) examined the long-term equilibrium relationships between

FTSE Bursa Malaysia Emas Shariah Index as a proxy for Islamic stock market and three

selected macroeconomic variables, namely, money supply, consumer price index and

exchange rate, using a vector error correction model (VECM). Monthly data from November

2006 to September 2013 was used for the study. The findings suggested that there is a

cointegration between Islamic stock market and chosen macroeconomic variables and

macroeconomic variables have had an influence on the Islamic stock market in Malaysia.

Abdelbaki (2013) investigated the relationship between macroeconomic variables and

Bahraini stock market development by using the Autoregressive Distributed Lag model.

Monthly frequency time series data from January 1990 to December 2007 was used. The

market capitalization as a percentage of GDP was used as a proxy of stock market

development. Macroeconomic variables used for the study include GDP, investment rate,

saving rate, credit to the private sector, per capita income, M2, FDI and GDP Deflators. The

findings of the study suggested that income level, domestic investment, banking system

development; private capital flows and stock market liquidity are important determinants of

Bahraini stock market development

Haroon and Jabeen (2013) examined the impact of macroeconomic variables, i.e. 3-

Months, 6-Month and 12 Month Treasury Bill Rate (Proxy of Interest Rate), Consumer Price

Index, Wholesale Price Index and Sensitive Price Index (Proxy for Inflation) with Karachi

Stock Exchange - KSE 100 Share index of Pakistan, using monthly frequency data from July

2001 to June 2010. Coefficient of correlation and regression analysis have been used to test

the hypothesis. The study examined the impact of inflation indices, interest rate (treasury

bills), on KSE movement. Further, the results showed that there was a significant relationship

between macroeconomic variables and KSE-100 Share index. The study further revealed a

significant impact of treasury bills on KSE-100 index.

El-Nader and Alraimony (2013) examines the causes of stock market development in

Jordan, using monthly frequency data from January 1990 to December 2011.The

methodology employed for the study includes co-integration and VECM techniques. The

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study used market capitalization as a percentage of GDP as a proxy for measurement of stock

market development and the macroeconomic variables used were nominal GDP, nominal

money supply (M2), total value traded ratio, growth rate of stock market liquidity, gross

capital formation, net remittances to Jordan, consumer price index and credit to the private

sector. The estimated findings demonstrated that the variables, namely, Money Supply, Total

Value Traded relative, Gross Capital Formation, Consumer Price Index (CPI), and Credit to

private Sector all have positive and considerable influences on stock market development. On

the other hand, nominal GDP and net remittances have a negative impact. The Johansen and

Juselius multivariate cointegration and variance decomposition analysis also confirmed the

presence of both a long-term and the short-term dynamic relationship between the Stock

market capitalization as a percentage of GDP and macroeconomic variables.

Mirza Vejzagic and Hashem Zarafat (2013) examined the long-term equilibrium

relationships between selected macroeconomic variables and the FTSE Bursa Malaysia

Hijrah Shariah Index. The methodology used for the study was VECM, VDC and IRF by

using monthly data from 2006 September to 2012 September. Macroeconomic variables used

for the study were, exchange rate, money supply, CPI and interest rate. The result shows that

there exists a cointegrating relationship, along with identification of the exogeneity and

endogeneity of the variables. It is depicted that FTSE Bursa Malaysia Hijrah Shariah Index

leads major macroeconomic variables which are interest rate, money supply, consumer price

index, and exchange rate.

Asma Rafique, Amara, Naseem and Sultana (2013) studied the impact of four

macroeconomic variables, i.e. GDP per capita, gross domestic savings, inflation and discount

rate on KSE index of Pakistan for annual data from 1991 to 2010. Statistical Package for

Social Sciences (SPSS) was used to test the multiple regression models. Results indicated that

GDP per capital and gross domestic savings have a significant and positive impact on KSE

Index. On the other hand, discount rate and inflation (being measured through CPI) possess a

significant but negative impact on KSE Index.

Sarwar, Aftab, Khan, and Qureshi (2014) examined the role of macroeconomic factors

like merchandize import, CPI, industry index, trade balance, exchange rate index, crude oil

prices, merchandize export, broad money supply and dollar price on stock return Karachi

Stock Exchange (KSE), Pakistan, using monthly data for the period from January 1997 to

December 2013. Multiple regression and correlation techniques were employed for the

empirical findings of the study. The findings showed that the trade balance and exchange rate

negatively affect the KSE 100 stock index, contrary to the merchandize import, CPI, industry

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index, crude oil price, merchandize export, broad money supply, and dollar price that affects

it positively.

Anigbogu and Nduka (2014) examined the long-run and causal relationship between

stock market performance and economic growth in Nigeria employing quarterly data for the

period from 1987:Q1 to 2012:Q4. The study used the Johansen Maximum Likelihood

cointegration technique, Vector Error Correction Model framework, Granger Causality,

Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD). The

variables used for the study include real GDP, inflation, investment ratio, savings ratio,

turnover ratio, total value of shares traded ratio, market capitalization ratio, capital flows and

banking sector development. The results of the cointegration test confirmed that there exists a

long-run relationship between stock market performance and economic growth, while the

causality test results suggested that stock market performance causes economic growth with

feedback. Further, the Impulse Response Function (IRF) and Forecast Error Variance

Decomposition (FEVD) suggest that shocks from the stock market do not impede economic

growth.

Khodaparasti, R. B. (2014) studied the role and impact of macro variables on the

Iranian stock market, using monthly frequency data from 2007- 2011. Macroeconomic

variables used for the study include exchange rates, inflation, industrial index and money

supply (M1). The methodology employed was the Vector auto regression approach. The

results of the study showed that the exchange rate and industrial index have more effect on

the stock market than inflation and M1.

Ibrahim and Musah (2014) investigated the effects of macroeconomic variables on

stock market returns by employing the Johansen multivariate co-integration approach and

vector error correction model (VECM) by using monthly data from September, 2000 to

September, 2010. Variables used for the study were, inflation (INFL), exchange rate (EXR),

broad money supply (M2), interest rate (INTR), index of industrial production (IIP) and

Ghana Stock Exchange index (GSEI). Results of cointegration analysis showed the existence

of the long-run relationship between stock returns and macroeconomic fundamentals.

However, the Granger causality test could not establish causality from any direction between

macroeconomic variables and stock prices. Results from both the impulse response functions

and variance decomposition suggested that among the macroeconomic variables, shocks to

inflation, money supply and exchange rate do not only explain a significant proportion of the

variance error of stock returns but their effects persist over a long period.

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Pradhan, Arvin, Hall, & Bahmani (2014) examined the relationship between banking

sector development, stock market development, economic growth, and four other

macroeconomic variables in ASEAN countries, using principal component analysis for the

construction of the development indices and a panel vector auto-regressive model for testing

the Granger causalities. The data set consists of annual time series data from 1961 to 2012 for

banking sector development (BSD), stock market development (SMD), per capita economic

growth (GDP), and a set of four other macroeconomic variables (MED), namely foreign

direct investment (FDI), trade openness (OPE), inflation rate (INF), and government

consumption expenditure (GCE). The sample countries consists of the ten countries, among

the ARF-26 that are recognized as ARF-Member Countries (AMC), which includes Brunei,

Burma, Cambodia, Indonesia, Laos, Malaysia, Philippines, Singapore, Thailand, and

Vietnam. The second broad sample consists of the nine countries, among the ARF-26 that are

recognized as ARF-Dialogue Partner Countries (ADC) which includes Australia, Canada,

China, India, Japan, New Zealand, the Korean Republic, the Russian Federation, and the

United States. The third broad sample consists of the six countries, among the ARF-26 that

are recognized as ARF-Observer Countries (AOC), which includes Papua New Guinea,

Mongolia, Pakistan, East Timor, Bangladesh, and Sri Lanka. The fourth sample consists of all

26 countries (ATC) that were included in the AMC, ADC, and AOC. The empirical study

founds the presence of both unidirectional and bidirectional causality links between these

variables.

Inyiama and Ekwe (2014) determined the relationship between All Share Index (the

proxy for capital market performance) and real gross domestic product, monetary policy rate,

inflationary rate and foreign exchange rate (the proxy for macroeconomic variables of the

study), applying annual frequency data from 1985 to 2013. The methodology adopted for the

study includes Granger Causality procedure, multiple regression models in the form of

Ordinary Least Square (OLS) method, correlation technique and Johansen cointegration

procedure. The findings of the study suggested that there is a unidirectional causality running

from log of All Share Index to foreign exchange rate. Johansen cointegration tests revealed a

long run relationship among the variables.

Hussain, Rasool, Baig, Fayyaz, & Mumtaz (2014) analyzed the long run and the causal

relationship between stock prices and selected macroeconomic variables for Islamabad Stock

Exchange using monthly data from January 2001 to December 2010. Methodology employed

was co-integration and Granger causality tests. The macroeconomic variables used for the

study include Industrial Production Index (IPI), Interest Rate of three month treasury bills

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(IR), Wholesale Price Index as inflation (WPI), Real Exchange Rate (ER), Exports (X),

Foreign Exchange Reserve (FER), Imports (M) and Money Supply (MS). The findings of the

study showed no causal relationship except exports while the long run relationship was

existed between stock prices and macroeconomic variables for ISE.

Samontaray, Nugali & Sasidhar (2014) studied the impact of different macro-economic

variables on the returns of the Saudi stock market (Tadawul All Stock Index (TASI)), using

monthly frequency data from December 2003 to December 2013. The methodology

employed for the study includes correlation and regression techniques. Macroeconomic

variables used for the study were Oil West Texas Intermediate (WTI), Saudi Export and PE

Ratio. The study confirmed that TASI is positively correlated with the three economic

variables considered, viz., Oil WTI, Saudi Exports and Price Earnings ratio. Since the three

independent variables are significantly correlated with the dependent variable, the step-wise

regression confirmed the significant importance each of these three variables have in

predicting the TASI. Further, it is observed that these three variables explain about 93% of

variation in TASI.

Mutuku& Ng’eny (2014) investigated the dynamic relationship between stock prices

and four macroeconomic variables in Kenya, using VAR and VECM framework, by

employing quarterly frequency data from 1997:Q1 to 2010:Q2. The variables used for the

study include Nairobi share prices (NSE), nominal gross domestic product-GDP, consumer

price index-CPI, Treasury bond rate and Nominal exchange rate-EXR. The study revealed

that positive relationships were found between the Nairobi share prices (NSE); the growth

rate (GDP), exchange rate (EXR) and T-bill rate (TBR). However, the study found a negative

relationship between NSE performance and consumer price index (CPI). The short term

analysis reveals that the relationship between the variables, adjust to equilibrium at a speed of

3.8% per quarter. The study nullifies the argument that the stock market can hedge inflation.

Kibria, Mehmood, Kamran, Arshad, Perveen and Sajid (2014) studied the impact of

macroeconomic variables on stock market returns in Pakistan (KSE 100 index of Pakistan)

using annual time series data from the year 1991 to 2013. Macroeconomic variables used for

the study include Inflation, GDP Per Capita, GDP savings, Money supply and Exchange rate.

The methodology employed for the study was Correlation Analysis, Granger Causality test

and Regression Analysis. The results of the Granger Causality test showed that there exists

unidirectional causality from GDP, savings and Exchange rate to Money supply. On the other

side, GDP savings also unidirectional Granger Cause the KSE. The results of Regression

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Analysis showed that the Inflation, Exchange rate, Money supply, GDP per capita and GDP

savings has a positive significant impact on the KSE 100 index.

Ouma and Muriu (2014) investigated the impact of the macroeconomic variables on

stock returns in Kenya (Nairobi Securities Exchange), using the Arbitrage Pricing Theory

(APT), Capital Asset Pricing Model (CAPM) framework and Ordinary Least Square (OLS)

technique. Monthly data from the period January 2003 to December 2013 was used.

Macroeconomic variables used for the study were money supply (M2), exchange rate,

inflation (CPI) and interest rate (91-T bill rates). The findings of the study suggested that

money supply, exchange rates and inflation affect the stock market returns in Kenya. The

exchange rate is, however, found to have a negative impact on stock returns, while interest

rates is not important in determining long rung run returns in the NSE.

Abdullah, Saiti & Masih (2014) investigated the lead-lag relationship between stock

market index and macroeconomic variables, using wavelet analysis, cointegration and

VECM. Monthly data from January 1996 to September 2013 was considered for the study.

Variables used include Kuala Lumpur Composite Index, exchange rate, inflation, government

bond yield, short-term interest rate and export. Findings suggested that the cointegration

relationship does exist between KLCI and selected macroeconomic variables. The results of

the error correction model, the generalized variance decompositions as well as the wavelet

cross-correlation analysis suggested that the short-term interest rate, KLCI and government

bond yields are exogenous variables; especially, the short-term interest rate is the most

leading variable.

Yu Hsing (2014) examined the relationship between the Romanian stock market index

and relevant macroeconomic variables by using quarterly data from 2001-Q4 to 2010-Q2.The

variables used for the study were Romanian stock market index, Industrial production index,

Government borrowing to GDP, M2 to GDP, domestic real interest rate, the nominal

effective exchange rate, expected inflation rate, and stock market index of U.S. For this study

GARCH model was adopted for empirical work. The study found that the Romanian stock

market index is positively affected by industrial production and the U.S. stock market index

and negatively associated with the ratio of government borrowing to GDP, the domestic real

interest rate, the expected inflation rate and the euro area government bond yield.

Barnor, C. (2014) examined the relationships between selected macroeconomic

variables and their effect on the stock market returns on the Ghana stock market, by

employing monthly frequency time series data from January 2000 to December 2013. The

methodology applied for the study, include multiple regression, VAR and VECM techniques.

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Macroeconomic variables used were inflation rate, exchange rate, interest rate, and money

supply. The findings revealed that interest rates and money supply had a significant negative

effect on stock market returns; however, exchange rates had a significant positive effect on

stock market returns. Moreover, inflation rate did not significantly affect stock market returns

in Ghana.

Pimentel and Choudhary (2014) analyzed the relationship of high inflation and interest

rates with stock returns of Brazil, using a tri-variate vector autoregressive (VAR) model.

Monthly time series data from May 1986 to May 2011 was considered for the study. The

findings suggested a bi-directional causal relationship between stock returns and inflation.

Pilinkus (2015) analyzes relationships between a group of macroeconomic variables

and the Lithuanian stock market index, i.e. OMX Vilnius index, using granger causality test.

Monthly time series data from December 1999 to March 2008 was considered for the study.

Variables used for the study were gross external debt (GED), gross domestic product (GDP),

gross domestic product deflator (GDPd), index of energy products (IEP), export volumes

(Ex), producer price index of industrial production (PPI), index of capital goods (ICG),

harmonised consumer price index (HCPI), import volumes (Im), index of durable consumer

goods (IDCG), granted permits for new residential buildings (GP), money supply in a narrow

sense (M1), money supply in a broader sense (M2), balance of payments (BP), investment in

tangible fixed assets (ITFA), retail trade index (RTI), unemployment rate (UR), final

consumption expenditure (FCE), changes in prices of industrial production (CPIP), index of

own-account construction work carried out within the country (IOCW), construction price

index (CPI), index of non-durable consumer goods (INCG), foreign direct investment (FDI),

index of intermediate goods (IIG), employment rate (ER), manufacturing index (MI),

exchange rate of the Litas against the US dollar (ExR), average number of hours actually

worked per employee per month (AHW), government final consumption expenditure

(GFCE), overnight Vilnius interbank offered rate (VILIBOR1N), one month Vilnius

interbank offered rate (VILIBOR1M), three months Vilnius interbank offered rate

(VILIBOR3M), six months Vilnius interbank offered rate (VILIBOR6M), one year Vilnius

inter-bank offered rate (VILIBOR1Y), the difference between one year and overnight Vilnius

interbank offered rates (VILIBOR1Y_1N), general government financial balance (GGFB),

general government revenue (GGR), general government expenditure (GGE), general

government debt (GGD), net export (NEx). The research revealed that some macroeconomic

variables (e.g., GDP deflator, net export, foreign direct investment, etc.) lead Lithuanian

stock market returns, some macroeconomic variables (e.g., GDP, material investment,

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construction volume index, etc.) are led by the OMXV index and, finally, some

macroeconomic indices (e.g., money supply, payment balance, etc.) and the stock market

returns Granger-cause each other.

Ahmad, Abdullah, Sulong and Abdullahi (2015) investigated the causal relationship

between stock market returns and macroeconomic variables in Nigeria using Autoregressive

Distributive Lag (ARDL) and a Vector Autoregressive Model (VAR). Annual time series

data of six variables, namely; broad money supply, nominal effective exchange rate, short

term T-bills rate, foreign direct investment, gross domestic per capita income, and gross

domestic saving from 1984-2013 were employed in the study. The Bounds test revealed that

the stock market returns and the macroeconomic variables were cointegrated and, thus, a

long-run equilibrium relationship exists between them. The results of Granger causality tests

showed that some of the macroeconomic variables were having bidirectional causality with

the stock market returns; while others have unidirectional causality. Furthermore, the impulse

response function indicated that the impact of shocks in broad money supply, nominal

effective exchange rate, gross domestic per capita income and short-term treasury bill rate on

the stock market returns in this study was consistent with other stock market empirical

results. The variance decomposition test indicated that the stock market returns can be

explained by gross domestic saving and nominal effective exchange rate.

Ilahi, I., Ali, M., & Jamil, R. A. (2015) investigated the linkage between

macroeconomic variables, namely inflation rate, exchange rate and interest rate on stock

market returns in Pakistan (Karachi stock exchange), by employing monthly frequency time

series data from January 2007 to December 2012. The methodology applied was Multiple

Linear Regression for the purpose of data analysis. The study found that there is a weak

connection between macroeconomic variables and stock market returns.

Nkechukwu, G., Onyeagba, J., and Okoh, J. (2015) studied the effect of

macroeconomic variables on stock market prices using annual time series data for Nigeria for

the period 1980-2013. OLS regression technique, Johansen cointegration and VECM based

on arbitrage pricing theory (APT) was applied for data analysis. The macroeconomic

variables utilized were gross domestic product (GDP) and broad money supply (M2). The

results of the findings indicated that the GDP has significant long-run negative effect on

Nigerian stock market prices and M2 has significant long-run positive effect on stock prices.

Further, there exists a unidirectional causal effect between GDP and stock prices with

direction running from stock prices to GDP.

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5.2.2. Studies related to Indian economy

Shah and Thomas (1997) argue that because of the enabling government policies stock

market in India is more efficient than the Indian banking system, both in terms of quality of

information processing and imposition of transaction cost. Their research supports the idea

that stock prices are a mirror which reflect the real economy, and are relatively insensitive to

factors internal to the financial system such as market mechanisms. However the arguments

require more explanation.

Naka, Mukherjee and Tufte (1998) analyzed relationships among selected

macroeconomic variables and the Indian stock market, using a vector error correction model.

Quarterly time series data from 1960:Q1 to 1995:Q4 was used. Macroeconomic variables

used for the study were real output (IPI), inflation (CPI), money stock (M1) and interest rate

(money market rate in the Bombay inter-bank market) and Sensex. The results suggested that

three long-term equilibrium relationships exist among the variables. It was also found that

domestic inflation is the most severe deterrent to Indian stock market performance, and

domestic output growth is its predominant driving force.

Pethe and Karnik (2000), using Indian data for April 1992 to December 1997, attempts

to find the way in which stock price indices are affected by and affect other crucial

macroeconomic variables in India. But this study runs causality tests in an error correction

framework on non cointegrated variables, which is inappropriate and not econometrically

sound and correct. The study, of course avers that in the absence of cointegration it is not

legitimate to test for causality between a pair of variables and it does so in view of the

importance attached to the relation between the state of the economy and stock markets. The

study reports weak causality running from IIP to share price index (Sensex and Nifty) but not

the other way round. In other words, it holds the view that the state of the economy affects

stock prices.

Muradoglu, Taskin And Bigan (2000) investigated the relationship between stock

returns and macroeconomic variables in emerging markets like Argentina, Brazil, Columbia,

and Mexico from South America; Portugal and Greece from Europe; Korea from the Pacific

rim; Jordan, Pakistan, and India from Asia; and Nigeria and Zimbabwe from Africa., using

cointegration and granger causality test. Monthly time series data from 1976 through 1997.

Variables used were, stock returns, exchange rates, and interest rates were assumed to be

linear in a set of local and global information variables, whereas inflation and industrial

production were assumed to be linear in a set of local information variables only. The global

information variable was the return on the S&P 500 index, which represents the world market

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portfolio, and controls for the degree of market liberalization. Local informational variables

were, return on country indices, exchange rates, interest rates, inflation, and industrial

production index, which is a measure of general economic activity and proxies for GDP. The

results of the study explored that the two-way interaction between stock returns and

macroeconomic variables is mainly due to the size of the stock markets, and their integration

with the world markets, through various measures of financial liberalization.

Mohtadi and Agarwal (2001) examined the relationship between stock market

development and economic growth for 21 emerging markets, using a dynamic panel method.

Annual data from 1977 to 1997 was used for the study. Stock Market Variables used were,

market capitalization ratio, total value of shares traded ratio, and turnover ratio; and

macroeconomic variables include growth, foreign direct investment, investment (real

investment divided by GDP) and Secondary School Enrollment. The results suggested a

positive relationship between several indicators of the stock market performance and

economic growth both directly, as well as indirectly by boosting private investment behavior.

Biswal and Kamaiah (2001) addressed the behavior of stock market development

indicators, namely, market size, liquidity, and volatility and examined whether these

indicators have exhibited any trend changes after India liberalized its financial policies.

Variables considered for the study were three stock market indicators, viz.,size, liquidity and

volatility, and two time series trend break techniques of Perron were applied on monthly data

of Bombay Stock Exchange. Data for market capitalization and turnover ratio range from

1991:1 through 1998:12 while that for the value traded spans from 1989:1 through 1998:12.

Required price data for constructing volatility series has been collected as the average

monthly value of the BSE Sensitive Index for the period 1983:12 through 1998:12. The study

suggested that the stock market has become larger and more liquid, in the post liberalization

period. In respect of volatility, however, the market does not exhibit any significant change.

Bilson, Brailsford & Hooper (2001) addressed the question of whether local

macroeconomic variables have explanatory power over stock returns in emerging markets,

incorporating six Latin American countries, eight Asian countries, three European countries,

one Middle Eastern country and two African countries, using correlation and regression.

Monthly data from January 1985 to December 1997 was used for the study. Macroeconomic

variables used were money supply (M1), consumer price index, industrial production index

and exchange rate. The results show that while emerging stock markets are segmented to a

degree, there is significant commonality in return variation across markets. Furthermore, little

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evidence of common sensitivities to the extracted factors was found when the markets are

considered in aggregate, but common sensitivity is found at the regional level.

Bhattacharya and Mukherjee (2002) studied the nature of the causal relationship

between stock prices and macroeconomic aggregates in India, by applying the techniques of

unit–root tests, cointegration and the long–run Granger non–causality test proposed by Toda

and Yamamoto (1995), Variables used for the study were the BSE Sensitive Index and the

five macroeconomic variables, viz., money supply, index of industrial production, national

income, interest rate and rate of inflation using monthly data from 1992-93 to 2000-01. The

study found that there is no causal linkage between stock prices and money supply, stock

prices and national income and stock prices and interest rate; index of industrial production

leads the stock price; and there exists a two-way causation between stock price and rate of

inflation.

Pretorius (2002) estimates cross-section and time-series models to determine the

fundamental factors that influence the correlation and evolvement of the correlation between

emerging stock markets, using Ordinary Least Square (OLS) methodology. Quarterly data

from 1995:Q1 to 2000:Q2 was considered for the study. Ten emerging stock markets

(according to the Emerging Market Database definition) with the highest market

capitalization was used in the study. Variables used were, inflation, exchange rate, trade and

industrial production. The results showed that only the extent of bilateral trade and the

industrial production growth differential were significant in explaining the correlation

between the two countries on a cross-sectional basis. In addition, countries in the same region

are more correlated than countries in different regions.

Chancharoenchai, Diboog Lu & Mathur (2005) investigated the relationship between

domestic macroeconomic variables and stock excess returns to evaluate the effects of

macroeconomic variables on excess returns and assess market efficiency in the six Southeast

Asian economies prior to the 1997 Asian crisis. Monthly data from January 1987 to

December 1996 was considered for the study. GARCH model was used for the empirical

estimation. Variables used were interest rate (risk-free rate of return), inflation and excess

stock returns. The results showed that some macroeconomic variables evidently had a certain

predictive power for excess returns and their volatility.

Srivyal Vuyyuri (2005) investigated the causal relationship between the financial and

the real sectors of the Indian economy using multivariate cointegration and Granger causality

tests. Monthly time series data from July 1992 to December 2002 was used for the study.

Financial variables included were interest rates, inflation rate, exchange rate, stock return,

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and real sector is proxied by industrial productivity. The results showed that there exist a long

run equilibrium relationship between the financial sector and the real sector and

unidirectional Granger causality was also found between the financial sector and the real

sector of the economy.

Nikkinen, Omran, Sahlström & Äijö (2006) investigated how global stock markets are

integrated with respect to the U.S. macroeconomic news, announcements, using data from the

period July 1995 to March 2002. Methodology adopted was GARCH volatilities around ten

important scheduled U.S. macroeconomic news announcements on 35 local stock markets

that are divided into six regions. These regions were the G7 countries, the European countries

other than G7 countries, developed Asian countries, emerging Asian countries, Latin

American countries and countries from Transition economies. The results showed that theG7

countries, the European countries other thanG7 countries, developed Asian countries and

emerging Asian countries are closely integrated with respect to the U.S. macroeconomic

news, while Latin America and Transition economies are not affected by U.S. news.

Yartey (2008) examined the institutional and macroeconomic determinants of stock

market development using a panel data of 42 emerging economies for the period 1990 to

2004. Macroeconomic variables used for the study were income level, banking sector

development, savings and investment, stock market liquidity, macroeconomic stability,

private capital flows and institutional quality. The study found that macroeconomic factors

such as income level, gross domestic investment, banking sector development, private capital

flows, and stock market liquidity are important determinants of stock market development in

emerging market countries.

Agrawalla and Tuteja (2008) examined the interaction between stock prices and a few

important macroeconomic variables for India using cointegration analysis and granger

causality. Monthly time series data for the period November 1965 to October 2000 was

considered. Macroeconomic variables used were share price index, industrial production,

money supply, credit to the private sector, exchange rate, wholesale price index, and money

market rate. The study reported unidirectional causality running from economic growth

proxied by industrial production to share price index and not the other way round.

Lekshmi R. Nair (2008) examined the macroeconomic determinants of stock market

development in India for monthly data from 1993-94 to 2006-07.Cointegration and error

correction modeling were used for the analysis. Macroeconomic variables used for the study

were turnover ratio (As an indicator of stock market development), inflation rate, real income

and its growth rate, financial intermediary development, foreign institutional investment,

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exchange rate and SBI Prime lending rate. The results showed that there exists a long run

relationship between all the macroeconomic variables used and stock market development.

Variables like real income and its growth rate, interest rate and financial intermediary

development significantly affect stock market development in the short run.

Gay, Jr. (2008) investigated the time-series relationship between stock market index

prices and the macroeconomic variables used were, exchange rate and oil price for Brazil,

Russia, India, and China (BRIC) using the Box-Jenkins ARIMA model. Monthly data from

March 1993 to June 2006 was used for the study. The study found no significant relationship

between respective exchange rate and oil price on the stock market index prices of either

BRIC country, this may be due to the influence other domestic and international

macroeconomic factors on stock market returns, warranting further research.

Shahid Ahmed (2008) investigated the nature of the causal relationships between Indian

stock prices, and the key macroeconomic variables for the period March, 1995 to March,

2007 using quarterly data. Variables used were index of industrial production, exports,

foreign direct investment, money supply, exchange rate, interest rate, NSE Nifty and BSE

Sensex. Johansen`s approach of co-integration and Toda and Yamamoto Granger causality

test were applied to explore the long-run relationships while BVAR modeling for variance

decomposition and impulse response functions was applied to examine short run

relationships. The study revealed that stock prices in India lead economic activity except

movement in interest rate as interest rate seem to lead the stock prices. The study concluded

that the movement of stock prices is not only the outcome of behavior of key macroeconomic

variables, but it is also one of the causes of movement in other macro dimension in the

economy.

Seetanah, Sannassee & Lamport (2008) examined simultaneously banking sector

development, stock market development, and economic growth in a unified framework for 27

developing countries, using rigorous panel VAR procedures. Annual time series data from

1991 to 2007 was considered for the study. The variables used were real per capita gross

domestic product, investment ratio, openness and secondary enrolment ratio. Results showed

that stock market development is an important ingredient of growth, but with a relatively

lower magnitude as compared to the other determinants of growth, particularly with banking

development. Interestingly, stock market development and banking development are seen to

be complement to each other and moreover there are important indirect effects through

‘investment channel’ to grow.

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Habibullah, Baharom and Fong (2009) examined the impact of inflation and output

growth on stock market returns and volatility in selected Asian countries, namely India,

Japan, Korea, Malaysia and Philippines, using monthly data from 1991 to 2004. GARCH (1,

1) model was employed for the estimations. Variables used include the Consumer Price Index

(CPI), major stock index or share prices and Index of Industrial Production (IIP). It is found

from the study that macroeconomic volatility, which is measured by movement in inflation

and output growth, has a weak predictive power for stock market returns and volatility in

these countries. The movements of the inflation rate have significant impact to the stock

market returns, either positive or negative depending on the inflation rates and their

fluctuation in that country. While output growth movements have a significant effect to stock

market volatility, countries with relatively higher output volatility is associated with higher

conditional volatility of stock returns, which is positive effect but is negative for countries

which have relatively lower output volatility.

Srivastava, A. (2010) attempted to establish relationship between change in

macroeconomic factors and stock market returns, using monthly time series for the period

April 1996 to January 2009. The methodology applied for the study was Johansen

multivariate cointegration and vector error correction model (VECM). Macroeconomic

variables used for the study include IPI, WPI, interest rate, exchange rate of Indian rupee with

US dollar and MSCI world index. The findings of the study concluded that emerging

economies like India in the long term are more affected by domestic macroeconomic factors

than global factors. The main domestic macroeconomic factors affecting the stock market in

the long run are industrial production; wholesale price index and interest rate.

Sharma and Mahendru (2010) analyzed the long-term relationship between BSE and

macroeconomic variables of India, using simple correlation and regression techniques.

Monthly time series data from January 2008 to January 2009 was taken. Macroeconomic

variables used for the study include change in exchange rate, foreign exchange reserves,

inflation rate and gold prices. The results revealed that exchange rate and gold prices

significantly affect stock prices, whereas the influence of foreign exchange reserves and

inflation on stock prices is negligible.

Dharmendra Singh (2010) explored the causal relationship between stock market index,

i.e. BSE Sensex and three key macroeconomic variables of the Indian economy by using

correlation and Granger causality tests. Monthly time series data have been used from April,

1995 to March, 2009 for all the variables, like, BSE Sensex, wholesale price index (WPI),

index of industrial production (IIP) and exchange rate (Rs/$). The granger causality test

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indicated that IIP is the only variable having bilateral causal relationship with BSE Sensex.

WPI is having a strong correlation with Sensex but it is having unilateral causality with BSE

Sensex.

Hosseini, Ahmad & Lai (2011) investigated the relationships between stock market

indices and four macroeconomics variables, namely crude oil price (COP), money supply

(M2), industrial production (IP) and the inflation rate (IR) in China and India, using

cointegration and VECM. Monthly data from January 1999 to January 2009 was used for the

study. The results of the study indicated that there are both long and short run linkages

between macroeconomic variable and stock market index in each of these two countries.

Al-Jafari, Salameh and Habbash (2011) examined the links between the

macroeconomic variables (real economic activity, inflation, interest rate, money supply and

exchange rate) and stock prices for sixteen developed and sixteen emerging markets by using

quarterly data from the period of January 2002 to December 2008. The methodology used for

the study includes Granger causality test and Pedroni panel cointegration tests. The results of

the study showed a significant causal relationship between macroeconomic variables, with

the exception of interest rate and money supply, and stock prices for the developed and

emerging markets. The study also found a significant causal relationship between stock prices

and macroeconomic variables for developed and emerging markets with the exception of the

exchange rate and money supply for developed markets. The findings also showed a positive

long-run relationship between real economic activity level and stock prices for developed

markets. Furthermore, the results found that the relationship between macroeconomic

variables and stock return in emerging markets is significantly more established than in

developed markets.

Haque and Hossain (2011) estimated the impact of stock market development on

economic growth in the SAARC region, namely, Bangladesh, India, Pakistan and Srilanka,

using two dynamic panel models for the period from 1980 to 2008. The macroeconomic

variables used for the study include per capita growth rate of GDP, Market Capitalization

ratio, value traded ratio, turnover ratio, domestic investment to GDP ratio, foreign direct

investment to GDP ratio, Secondary school enrollment as a percentage of the school

population, openness ratio and domestic credits to GDP ratio. The first model tries to assess

the stock market effect directly after controlling for other variables, whereas the second one

does it by having its influence through investment. The study found that none of the dynamic

model is effective to identify the stock market linkage to per capita growth rate in SAARC

region.

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Pal and Mittal (2011) examine the long-run relationship between the Indian capital

markets (BSE Sensex and S&P CNX Nifty) and key macroeconomic variables such as

interest rates, inflation rate, exchange rates and gross domestic savings (GDS) of Indian

economy, by using Quarterly time series data from January 1995 to December 2008. Error

correction mechanism (ECM) has been applied to derive the long run and short-term

statistical dynamics of the study. The ECM shows that the rate of inflation has a significant

impact on both the BSE Sensex and the S&P CNX Nifty. Interest rates on the other hand,

have a significant impact on S&P CNX Nifty only. However, in case of foreign exchange

rate, significant impact is seen only on BSE Sensex. The changing GDS is observed as

insignificantly associated with both the BSE Sensex and the S&P CNX Nifty.

Naliniprava Tripathy (2011) investigated the market efficiency and the causal

relationship between selected Macroeconomic variables and the Indian stock market during

the period January 2005 to February 2011 by using Ljung-Box Q test, Breusch-Godfrey LM

test, Unit Root test, Granger Causality test. The macroeconomic variables used for the study

were interest rate (91-days T-bill rate), inflation rate (WPI), exchange rate, international

market (S&P 500 index) and BSE Sensex. Weekly frequency data were used for the study.

The study confirmed the presence of autocorrelation in the Indian stock market and

macroeconomic variables. And the Granger-causality test showed evidence of the

bidirectional relationship between interest rate and stock market, exchange rate and stock

market, international stock market and BSE volume, exchange rate and BSE volume. The

study also reported unidirectional causality running from the international stock market to the

domestic stock market, interest rate, exchange rate and inflation rate.

Lairellakpam and Dash (2012) focused on identifying the factors affecting the volatility

in Indian stock markets (S&P CNX Nifty), while considering certain macroeconomic

variables, including exchange rates, crude oil prices, interest rates and gold prices. Monthly

frequency time series data from January 2000 to June 2011. The methodology employed for

the study include vector autoregressive (VAR) techniques and Granger causality tests. The

results of the study indicated that none of the macroeconomic factors Granger-caused

changes in Nifty returns, while changes in Nifty returns unidirectionally Granger-caused

changes in INR/USD exchange rates.

Pramod Kumar Naik and Puja Padhi (2012) investigates the relationships between the

Indian stock market index (BSE Sensex) and five macroeconomic variables, namely,

industrial production index, wholesale price index, money supply, treasury bill rates and

exchange rates over the period 1994:04–2011:06. Johansen’s co-integration and vector error

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correction model was applied to explore the long-run equilibrium relationship between stock

market index and macroeconomic variables. The analysis revealed that there exist a long-run

equilibrium relationship between macroeconomic variables and the stock market index. It is

also observed that the stock prices positively relate to the money supply and industrial

production but negatively relate to inflation. It is also found that there exist bidirectional

causality between industrial production and stock prices, whereas unidirectional causality

from money supply to stock price, stock price to inflation; and interest rates to stock prices.

Makan, Ahuja and Chauhan (2012) studied that whether some of the identified

macroeconomic factors can influence the Indian stock market in India, using simple

correlation, regression techniques and granger causality test. The variables used were,

industrial production index, consumer price index, interest rate (call money rate), exchange

rate, gold price, oil price, foreign institutional investment and BSE Sensex. Monthly time

series data from April 2005 – March 2012 was considered. The study concluded that three

(exchange rate, foreign institutional investment and call money rate) out of seven variables

are relatively more significant and likely to influence the Indian stock market. The study also

indicated a positive relation between FII; call money rate and the Sensex, whereas exchange

rate and Sensex showed a negative relation.

Narayan and Narayan (2012) examined the impact of US macroeconomic conditions—

namely, exchange rate and short-term interest rate (3 month T-bill rate) - on the stock markets

of seven Asian countries (China, India, the Philippines, Malaysia, Singapore, Thailand, and

South Korea), using daily data for the period 5 January 2000–25 January 2010. OLS and

GARCH techniques were used for the estimation. Sample data is divided into a pre-crisis

period (pre-August 2007) and a crisis period (post-August 2007). It was found that, in the

short-run, the interest rate has a statistically insignificant effect on returns for all countries,

except for the Philippines in the crisis period. On the other hand, except for China, regardless

of the crisis, depreciation has a statistically significant and negative effect on returns.

Basher, Haug and Sadorsky (2012) estimated a structural vector auto regression model

to investigate the dynamic relationship between oil prices, exchange rates and emerging stock

markets including India. Monthly time series data from January 1988 to December 2008 was

considered for the study. Variables were collected on global oil production, oil prices, global

real economic activity, exchange rates, emerging market stock prices and interest rates. The

findings suggested that positive shocks to oil prices tend to depress emerging market stock

prices and US dollar exchange rates in the short run; and a positive oil production shock

lowers oil prices while a positive shock to real economic activity increases oil prices.

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Yahyazadehfar and Babaie (2012) investigated the impact of macroeconomic variables

such as interest rate, house price and gold price on the stock price in capital market of Iran.

Monthly time series data from March 2001 to April2011 was used. The study was based upon

a vector auto regression (VAR) model and Johansen-Juselius Cointegration test. The study

found a positive relationship between stock price and house price, but the relationship

between nominal interest rate and the gold price with stock price were found to be negative.

Further, the results of Impulse Response Functions showed that stock price reaction to the

shocks is very fast. The variance decomposition analysis indicated that although most of the

fluctuation in stock price can be attributed to itself, but among the selected variables, the

house price has main role on stock price fluctuation.

Aurangzeb (2012) identified the factors affecting performance of the stock market in

three South Asian countries, namely, Pakistan, India and Sri Lanka, using data from 1997 to

2010. Correlation and regression were used as the methodology. The variables used for the

study include stock performance, interest rate, inflation, exchange rate and foreign direct

investment. The results of the study indicated that foreign direct investment and exchange

rate have significant positive impact on the performance of the stock market in South Asian

countries, while; interest rate has a negative and significant impact on the performance of the

stock market in South Asia.

Sarbapriya Ray (2012) explored the impact of different macroeconomic variables on

the stock prices in India, using annual data from 1990-91 to 2010-11. The variables used for

the study were BSE Sensex, balance of trade, call/notice money rate, CPI, FDI, foreign

exchange reserve, GDP, gross fixed capital formation, gold price, index of industrial

production, broad money supply, demand deposits of banks, demand deposits with RBI,

crude oil prices, exchange rate and the wholesale index of price. Multiple regression model

and Granger causality test were used for the estimations. The study revealed that there is no

causal association between stock price and interest rate, stock price and index of industrial

production, but unidirectional causality exist between stock price and inflation, stock price

and foreign direct investment, stock price and gross domestic product, stock price and

exchange rate, stock price and gross fixed capital formation. However, bidirectional

causality exist between stock price and foreign exchange reserve, stock price and money

supply, stock price and crude oil price and stock price and whole price index

Singh, Tripathi & Lalwani (2012) examined the primary factors responsible for

affecting Bombay Stock Exchange (BSE) in India, using monthly frequency data from

January 2007 to December 2012. Macroeconomic variables used for the study include foreign

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exchange rate and inflation, along with the linear regression techniques. The results of the

regression analysis suggest that both exchange rate and inflation significantly affects the

performance of BSE Sensex.

Malarvizhi, Thenmozhi and Jaya (2012) focused on analyzing the long term dynamic

relationship between the GDP and Nifty index of India, using quarterly data from June 2000

to March 2010. Cointegration test and granger causality were used to estimate the results of

the study. The results suggested that there is a bidirectional causal relationship between GDP

and Nifty.

Sharma and Chaitanya (2013) explored the influential relationship between the Sensex

of Bombay Stock Exchange (BSE) and selected macroeconomic variables of India by using

Stepwise Regression model. Quarterly frequency data from 2005:Q1 to 2011:Q2 was

considered for the study. Macroeconomic variables used for the study include GDP, IIP,

WPI, foreign exchange rate, gold rate and crude oil rate. The findings of the study revealed

that there is an influential relationship on SENSEX by Industrial Production and Foreign

Exchange Rate

Dey (2013) investigated the relationship between foreign exchange rates, foreign

exchange reserve and BSE Sensex return (India) using monthly frequency data from March

1992 to June 2012. The methodology applied includes correlation analysis, regression

analysis, Johansen co-integration test and granger causality. The results of regression analysis

found that there is a significant impact of returns of exchange rate, foreign exchange reserves

on the returns of BSE-Sensex return. Also, the findings of Johansen co-integration test

proved that, variables are not co-integrated and hence, have not long term relationship.

Further, the Granger causality test concludes that, foreign exchange rate causes the BSE-

Sensex return.

Vashishtha, S. D., Singh and Kumar (2013) examined the relationships between

economic growth rates and Indian capital market sensitivity, using monthly frequency data

from April 2006 to March 2011. Simple regression and correlation techniques were employed

for the study using variables, namely, IIP and WPI and S&P BSE Sensex. The result showed

that there exist an inverse relationship between the S&P BSE Sensex and IIP; and BSE

Sensex and WPI.

Sireesha (2013) investigated the impact of selected macroeconomic factors upon the

movements of the Indian stock market index (S&P CNX Nifty), using monthly frequency

data from January 1993 to December 2012.The macroeconomic variables used for the study

include CPI, Gross Domestic Product (GDP) growth rate, the Index of Industrial Production

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(IIP), Money Supply (M3), exchange rate returns of USD-INR, Foreign Institutional

Investors (FIIs), Domestic Institutional Investors (DIIs). Gold returns and Silver returns.

Nifty along with gold and silver prices by using linear regression techniques. The study

concluded that stock returns are significantly influenced by inflation, GDP, and exchange

rates, thus, stock returns can be used to hedge against these variables.

Md. Al-Mamun (2013) studied the effect of macroeconomic & market specific

dynamics on stock market development in 11 global growth generator countries (3G),

namely, Bangladesh, China, Egypt, India, Indonesia, Iraq, Mongolia, Nigeria, Philippines, Sri

Lanka and Vietnam, using the panel ARDL model for eight out of eleven 3G countries over a

period of 1980-2011. To measure stock market development, growth in market capitalization

of listed companies in respective countries was used as a proxy variable. And the dependent

variables included in the study were domestic credit provided by the banking sector, gross

domestic savings, gross domestic product, total value of stock market trading, stock market

turnover ratio, real interest rate, and foreign direct investment. The study confirmed that

several macroeconomics i.e. foreign direct investment, real interest rate and stock market

operating characteristics have a significant long run contribution to the development of stock

market and thereupon a sustained economic growth.

Parmar, C. (2013) studied the impact of macroeconomic variables, namely reverse repo

rate, CRR, SLR, Repo rate, inflation rate, CPI, Index of industrial production, gold rate, oil

rate and exchange rate on Indian stock market, by applying monthly frequency data from

January 2004 to December 2012. The methodology employed include regression and

correlation techniques. The results of the study concluded that in the long term the Indian

stock market is more driven by domestic macroeconomic factors rather than global factors.

Pathan and Masih (2013) studied, the direction of causality between the stock market in

India (BSE-Sensex) and macroeconomic variables, namely, interest rate, exchange rate, FII,

WPI, and money supply (M3), by applying monthly data from April 2004 to February

2013.Methodology adopted was Vector Error Correction Method (VECM). The findings of

the study provided evidence of a stable, long run equilibrium relationship between the stock

market and economic growth in India. The study reconfirmed the traditional belief that the

real economic variables continue to affect the stock market in the post-reform era in India and

also highlights the insignificance of certain variables with respect to the stock market.

Kumar Rakesh (2013) studied the effect of macroeconomic factors on Indian stock

market performance, using monthly frequency data from January 2001 to May 2013. The

data reduction technique of factor analysis was used to derive the factors which determine the

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performance of the stock market in India. Macroeconomic variables used for the study

include money supply (M3), CPI, gold prices, crude oil prices, foreign exchange reserves,

FDI, FII, call money rate, balance of trade, foreign exchange rate, repo rate and industrial

growth rate. The findings of the study suggested that the industrial growth rate performance

plays a significant role in influencing the stock market.

Subburayan and Srinivasan (2014) explored the effects of macroeconomic variables on

stock return of the CNX Bank index of Indian stock market, using monthly data from January

2004 to December 2013. The macroeconomic variables, namely exchange rate, interest rate

and inflation rate were considered for the study. The methodology employed for the study

includes regression, co-integration test and Granger causality test. The findings of the study

suggested that bank stock returns are having fixed long run relationship with selected

macroeconomic variables and the exchange rate and interest rate affect positively on bank

stock returns. Further, bank stock returns have a unidirectional causal relationship with the

exchange rate.

Kumar and Singh (2014) analyzed the impact of Macroeconomic Variables on Sensex

of India. The three Macroeconomic Variables, namely, Wholesale Price Index, Index of

Industrial Production and Exchange Rate were considered for the purpose of the study, along

with monthly data from January 2008 to December 2012. The study employed regression

analysis and correlation analysis as a part of the methodology. The study found a high

correlation among the variables, namely, WPI, IIP, Exchange Rate and Sensex and it was also

found that there is exists a significant relationship between macroeconomic variables and

Sensex.

Venkatraja, B. (2014) investigated the relationship between the Indian stock market

performance (BSE Sensex) and five macroeconomic variables, namely, index of industrial

production, wholesale price index, gold price, foreign institutional investment and real

effective exchange rate over the period April 2010- June 2014 using monthly data. The

multiple regression technique was employed for the purpose of study. The study revealed that

the Wholesale price index, index of industrial production, foreign institutional investment and

real effective exchange rate have a high degree of positive influence on the Sensex. It was

also found that Sensex is inversely influenced by changes in the gold price. Further, of the

five variables, the coefficients of all the variables except index of industrial production are

statistically significant.

Dasgupta (2014) studied the relationships between BSE Sensex and seven key

macroeconomic variables, both in the long-run and short-run, by using descriptive statistics,

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correlation test results, ADF tests, Johansen and Juselius’s cointegration test and Granger

causality test. Monthly data have been used from April 2007 to March 2012 for all the

variables, i.e., BSE Sensex, index of industrial production, wholesale price index, crude oil

prices, gold prices, money supply, exchange rate and foreign exchange reserve. Johansen and

Juselius’s cointegration test of the study pointed out at least one cointegration vector and long

run relationships between BSE Sensex with index of industrial production, gold prices,

money supply and foreign exchange reserve. Further, the Granger causality test found some

short-run unilateral or bilateral causal relationships between BSE Sensex with the

macroeconomic variables.

Kantesha Sanningammanavara, Kiran K. V., and Rakesh H. M. (2014) examined the

relationship between various economic indicators and the Indian stock market by using

simple correlation and regression techniques. Yearly data from From April 1998 to March

2014 was used for the study, which includes the variables like BSE Sensex, GDP Growth

rate, Inflation rate (WPI), Exchange rate (Rs/USD), Gross Domestic Savings as % of GDP,

Gross Capital Formation as of GDP, Real Interest Rate, and the Unemployment Rate. The

researchers found that the Depreciation in the Rupee against the Dollar has led to decrease in

the share prices. It has a negative impact on the stock prices and Increase in the Inflation rate

has led to decrease in the share prices.

Tripathi and Seth (2014) examined the causal relationships between the stock market

performance and selected macroeconomic variables in India, using monthly data from July

1997 to June 2011. The methodology employed for the study was Regression, ARCH model,

Granger causality and Johansen Co-integration test by using variables, namely, exchange

rate, the Index of Industrial Production (IIP), interest rate, money supply, oil prices and WPI;

and stock market indicators, namely BSE India Sensex, BSE India market capitalization and

BSE India market turnover. The study found a significant correlation between stock market

indicators and macroeconomic factors.Further, the overall explanatory power of the

regression model is 23.8%, 23.3% and 16.9% respectively for Sensex, Market capitalization

and Market Turnover. The causality test found that there exists unidirectional causality from

the stock market to the real economy.

Ray, H., & Sarkar, J. (2014) investigated the dynamic relation between the stock

market and the select macroeconomic variables in India, by employing monthly data for the

period from January 1991 to April 2008. The variables used for the study include an Index of

Industrial Production (IIP), Whole Sale Price Index (WPI), Money Supply (M3), Yields on

91-day Treasury Bills (YTB), Yields on Long-term (10-year) Government Bonds (YLGB),

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Competitiveness of Domestic Currency measured by the price of one US $ expressed in terms

of the Rupee (EX) and the BSE SENSEX 30(Index) to represent Stock Market Prices. The

methodology used for the study co-integration analysis and Granger casualty tests. The

findings of the study showed that the long-run stock market behavior is positively related to

output and exchange rate, and negatively related to short- and long-term interests, money

supply and inflation. Further, the results of the causality and innovation analysis suggested

that the stock market influences the economic activities, more specifically the industrial

activities and the market are expected to be more sensitive to the shocks of itself over the

projected period of the study.

Mohanamani and Sivagnanasithi (2014) investigated the impact of macroeconomic

variables on the behavior of Indian Stock market, using monthly frequency data from April

2006 to July 2013, employing variables, namely, BSE Sensex, Call Money rate, Exchange

rate between Indian Rupees and US dollar, Foreign Institutional Investment, Industrial

productivity, money supply and wholesale price index. The Methodology used includes

Granger Causality tests. The empirical analysis of the study revealed that Indian stock market

is positively related to wholesale price index, money supply and industrial productivity.

Further, the results of In the Granger Causality showed that the wholesale price index and

industrial productivity influence the stock market to a great extent.

Billah, Shah, Bhanja & Samantaraya (2014) estimated the relationship between stock

prices and exchange rates of eight Asian countries, using correlation and regression

techniques. Monthly data from February 1996 to September 2013 was considered for the

study. In accordance with the portfolio balance effect, it was observed that stock prices and

exchange rates are negatively correlated at all frequencies. In particular, the negative

correlation grows with higher time scales (lower frequency intervals). The findings from

quantile regression also suggested that the coefficients are more inclined to be negative when

exchange rates are extremely high.

5.2.3. Summary of Literature review

The objective of detailed literature review was to point out the contradictory views

regarding the effect of macroeconomic variables on the stock prices with reference to the

empirical analysis approach of cross-sectional and time series data. From this comprehensive

literature review, several key conclusions can be drawn. One of them states that, while the

existing theories hypothesize a link between macroeconomic variables and stock markets,

they do not specify the type or the number of macroeconomic factors that should be included.

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Thus, the existing empirical studies, reviewed in this chapter, have shown the use of a vast

range of macroeconomic variables to examine their influence on stock prices. A brief

summary of the literature review indicate that the macroeconomic variables that were mainly

used by the researchers are Index of Industrial production, real gross national product, gross

capital formation, employment, exports, exchange rate (Real Effective Exchange Rate,

Nominal Effective Exchange Rate), consumption, interest rate (T-bill rate, call money rate),

inflation (Producer Price Index, Consumer Price Index and Wholesale Price Index), aggregate

foreign currency reserves, Crude oil price, real consumption, consumption expenditures,

investment expenditure, federal funds rate, Foreign Direct Investment, Foreign Institutional

Investment, foreign portfolio investment, GDP deflator, trade balance, school enrollment,

trade openness, money supply (M1, M2, M3), unemployment rate, gold prices, foreign

exchange reserves, macroeconomic prosperity index, consumer confidence index, corporate

goods price index and gross fixed capital formation. And to study the impact of these

macroeconomic variables the dependent variables used for the study are, stock market

capitalization, stock market index, market liquidity and stock market turnover ratio. All the

researches are conducted by applying different methodologies, namely, correlation analysis,

regression analysis under Ordinary Least Square (OLS) method, generalized autoregressive

conditional heteroskedasticity (GARCH) model, cointegration tests using Vector Auto

Regression (VAR) framework, causality tests by employing Vector Error Correction Model

(VECM), and Auto Regressive Distributed Lag (ARDL) approach. These researches are

conducted using different sets of data periods starting with the frequency of daily data,

weekly data, monthly data, quarterly and annual data, further, all the studies use time series

data and the studies with multi country data uses the cross-sectional approach.

The other key conclusion drawn by the study indicates that, while previous studies have

significantly improved our understanding of the relationships between macroeconomic

variables and stock prices, the findings from the literature are mixed given that they were

sensitive to the choice of countries, variable selection, and the time period studied. It is

difficult to generalize the results because each market is unique in terms of its own rules,

regulations, and type of investors. Additionally, the VAR framework, cointegration tests,

Granger causality tests, and GARCH models were commonly used to examine the

relationships between stock prices and macroeconomic variables. However, there is no

definitive guideline for choosing an appropriate model. Further, the review of literature

clearly indicates that there exists a large pool of studies for developed economies regarding

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the investigation of the relationship between macroeconomic variables and stock prices, but

there is a shortage of literature concerning emerging stock markets.

5.3. Estimation results of the study using annual frequency data

The present section of the study includes the estimation results for the relationship

between macroeconomic variables and the stock prices, by incorporating data for yearly

frequency variables. The study empirically estimated the effect of fundamental

macroeconomic indicators7 on stock prices with the help of econometric techniques in India.

The study uses annual data covering the period from 19798 to 2014.

5.3.1. Model specification

The following general specification has been used in this study to empirically examine

the effect of economic growth and other fundamental macroeconomic factors on the stock

market.

𝐿𝐵𝑆𝐸 = 𝛼0 + 𝛼1𝐿𝐺𝐷𝑃 + 𝛼2𝐿𝐶𝑂 + 𝛼3𝐿𝐶𝑃𝐼 + 𝛼4𝐿𝑅𝐸𝐸𝑅 + 𝛼5𝐿𝐹𝐷𝐼 + 𝛼6𝐿𝑅𝐼𝑅 + 휀𝑡

(5.1)

5.3.2. Stationarity test and Lag length selection before co-integration

Before we conduct tests for co-integration, we have to make sure that the variables

under consideration are not integrated at an order higher than one. Thus, to test the

integration properties of the series, we have used Ng-Perron unit root test. The results of the

stationarity tests are presented in Table 5.3.1. The results show that all the variables are non-

stationary at levels. The next step is to difference the variables once in order to perform

stationary tests on differenced variables. The results show that after differencing the variables

once, all the other variables were confirmed to be stationary. It is, therefore, worth

concluding that all the variables used in this study are integrated of order one i.e. difference

stationary I(1). Therefore the study uses autoregressive distributed lag (ARDL) approach to

co-integration. In addition, it is also important to ascertain that the optimal lag order of the

model is chosen appropriately so that the error terms of the equations are not serially

correlated. Consequently, the lag order should be high enough so that the conditional ECM is

not subject to over parameterization problems (Narayan, 2005; Pesaran, 2001). The results of

these tests are presented in Table 5.3.2. The results of Table 5.3.2 suggest that the optimal lag

length is one based on both LR, FPE, SIC and HQ.

7 The study excludes the variable Money Supply (M3) because of the high correlation of M3 with inflation,

exchange rate and FDI. 8 The study limits to the starting period as 1979-80 due to the non-availability of data on BSE Sensex prior to

this period.

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Table 5.3.1: Unit root test: Ng-Perron Test

Variables With constant and trend Stationarity

Status Mza MZt MSB MPT

LBSE 0.624 0.461 0.739 38.204 I (1)

ΔLBSE -16.386 -2.861 0.174 1.499

LGDP 2.210 2.215 1.002 86.222 I (1)

ΔLGDP -15.289 -2.717 0.177 1.780

LCO -2.858 -1.172 0.409 8.501 I (1)

ΔLCO -16.390 -2.820 0.172 1.651

LCPI -12.87 -2.492 0.193 2.073 I (1)

ΔLCPI -16.161 -2.841 0.173 1.518

LREER 0.142 0.093 0.652 28.471 I (1)

ΔLREER -14.298 -2.640 0.184 1.840

LFDI -0.365 -0.207 0.566 20.950 I (1)

ΔLFDI -16.359 -2.857 0.174 1.508

LRIR -7.083 -1.881 0.265 3.459 I (1)

ΔLRIR -14.593 -2.685 0.270 3.818 Source: Author’s own Calculation by using E-views 8.0

∆ denotes the first difference of the series. L implies that the variables have been transformed in natural logs.

Table 5.3.2: Lag Order Selection Criterion Lag LogL LR FPE AIC SIC HQ

0 -62.752 NA 1.01e-08 4.288 4.650 4.410

1 194.262 373.839* 9.36e-14* -7.409 -4.144* -6.311*

2 266.632 70.176 1.21e-13 -7.917* -1.749 -5.842 * indicates lag order selected by the criterion

LR: sequential modified LR test statistic (each test at 5% level)

FPE: Final prediction error

AIC: Akaike information criterion

SC: Schwarz information criterion

HQ: Hannan-Quinn information criterion

5.3.3. ARDL Bounds Test

After determining the order of integration of all the variables in table 1 and lag length

selection in table 5.3.3, the next step is to employ an ARDL approach to co-integration in

order to determine the long run relationship among the variables. By applying, the procedure

in OLS regression for the first difference part of the equation (5.1) and then test for the joint

significance of the parameters of the lagged level variables when added to the first regression.

The F-Statistics tests the joint Null hypothesis that the coefficients of lagged level

variables in the equation (5.1) are zero. Table 5.3.3, reports the result of the calculated F-

Statistics & diagnostic tests of the estimated model. The result shows the calculated F-

statistics are 5.5113. Thus the calculated F-statistics turns out to be higher than the upper-

bound critical value at the 5 percent level. This suggests that there is a co-integrating

relationship among the variables included in the model, i.e. Sensex (LBSE), Crude Oil Prices

(LCO), Inflation (LCPI), Exchange Rate (LREER), Foreign Direct Investment (LFDI) and

Real Interest Rate (LRIR).

128

Table 5.3.3: ARDL Bounds test

Panel I: Bounds testing to co-integration:

Estimated Equation : LBSE = F (LGDP LCO LCPI LREER LFDI LRIR)

Indicators

Optimal lag 01

F – Statistics 5.5113

Panel II: Diagnostic Tests:

Diagnostic Tests Indicators

Normality J-B value 0.8901

Serial Correlation LM Test 1.5214

Heteroscedasticity Test (ARCH) 1.0145

Ramsey Reset Test 0.0724

The second step is to estimate the long- and short-run estimates of ARDL test. The long

run results are illustrated in Table 5.3.4. The results show that a rise in GDP has positive

effect on stock prices. The coefficient of GDP, Inflation (LCPI), and Exchange Rate

(LREER) are statistically significant at 1%. It is evident from the table that 1% in increase

GDP, Inflation and Exchange Rate leads to 2.311%, 0.390% and 1.126% respectively,

increase in Stock Prices (Sensex). The findings are consistent with Fama (1981, 1990) and

Chen et al. (1986) for GDP; Kessel (1956), Ioannidis et al. (2004) for Inflation; and

Mukherjee and Naka (1995) and, Nadeem and Zakir (2009) for Exchange Rate.

Whereas, the coefficient of crude oil price is negative and significant at 1%. Therefore,

crude oil prices have a significant negative relationship adversely affecting stock prices and

the findings are consistent with Miller and Ratti (2009) and Basher et al. (2012).

129

Table 5.3.4: Estimated Long Run Coefficients using ARDL Approach

(Dependent variable: LBSE) Regressors ARDL(1,0,0,0)

Coefficient t- values Prob. Values

LGDP 2.311*** 4.047 0.000

LCO -0.917*** -3.012 0.006

LCPI 0.390*** 2.060 0.050

LREER 1.126*** 3.372 0.002

LFDI -0.167 -1.356 0.187

LRIR 0.128 0.718 0.479

CONS -4.202 -2.936 0.007

Robustness Indicators

R2 0.987

Adjusted R2 0.984

F Statistics 243.364 [0.000]

D.W. Stat 2.131

Serial Correlation, F 0.537 [0.464]

Heteroskedasticity, F 0.424 [0.515]

Ramsey reset test, F 0.086 [0.769] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC).

(2) *** indicate significant at the 1 percent level of significance. Values in [#] are probability

values.

The short-run relationship of the macroeconomic variables on stock market index is

presented in Table 5.3.5. As can be seen from the table, GDP, Exchange Rate and Inflation

have a significant and positive impact on stock market index in the short run also and similar

to long-run is the situation for crude oil prices. The short run adjustment process is examined

from the ECM coefficient. The coefficient lies between 0 and -1, the equilibrium is

converging to the long run equilibrium path, is responsive to any external shocks. However,

if the value is positive, the equilibrium will be divergent from the reported values of ECM

test. The coefficient of the lagged error-correction term (-0.536) is significant at the 1% level

of significance. The coefficient implies that a deviation from the equilibrium level of stock

market index in the current period will be corrected by 53 percent in the next period to resort

the equilibrium.

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Table 5.3.5: Estimated Short Run Coefficients using ARDL Approach

(Dependent variable: LBSE)

Regressors ARDL(1,0,0,0)

Coefficient T – Ratio Prob. Values

ΔLGDP 1.238*** 4.006 0.000

ΔLCO -0.491*** -3.277 0.003

ΔLCPI 0.209* 1.749 0.092

ΔLREER 0.604** 2.183 0. 038

ΔLFDI 0.049 0.804 0.429

ΔLRIR 0.069 0.719 0.478

ΔCONS -2.251 -2.056 0.050

ECM t-1 -0.536 -3.333 0.003


R2 0.459

Adjusted R2 0.286

D.W. Stat 2.131

SE Regression 0.195

RSS 0.952

F Statistics 3.029[0.018] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC).

(2) *, ** and *** indicate significant at 10, 5 and 1 percent level of significance, respectively.

5.3.4. VECM based causality

The next step is to test for the causality between the variables, the short run and long

run granger causality test findings are reported in Table 5.3.6. The results of table 5.3.6

indicate that short run unidirectional causality running from LFDI, LGDP and LRIR to LBSE

in India. It is also observed that error correction term is statistically significant for

specification with LBSE as the dependent variable which indicate that there exist a long run

causal relationship among the variable with LBSE as the dependent variable. This result is

also confirmed by the ARDL test statistics.

Table 5.3.6: Results of Vector Error Correction Model

Dependent

variable

Sources of Causation

Short run independent variables Long run

ΔLBSE ΔLCO ΔLCPI ΔLREER ΔLFDI ΔLGDP ΔLRIR ECM(t-1)

ΔLBSE - 0.636 -1.283 -1.414 3.115**

* -2.239** 1.916* -3.906***

ΔLCO 0.198 - 0.174 -0.293 -0.407 0.389 -0.378 -0.849

ΔLCPI 0.183 -1.157 - -1.757* 0.911 0.823 0.135 0.691

ΔLREER 0.544 0.086 0.292 - -1.044 0.089 0.722 -0.402

ΔLFDI 1.590 1.792* -0.416 -0.056 - -0.396 -0.306 -0.149

ΔLGDP 0.433 0.433 -0.920 -1.651 1.632 - -0.379 -1.230

ΔLRIR -0.284 0.484 -0.579 0.694 0.655 0.242 - -1.066

*, ** and *** indicate significant at 10, 5 and 1 percent level of significance, respectively.

The robustness of the short run result are investigated with the help of diagnostic and

stability tests. The ARDL-VECM model passes the diagnostic against serial correlation,

functional misspecification and non-normal error. The cumulative sum (CUSUM) and the

131

cumulative sum of square (CUSUMSQ) tests have been employed in the present study to

investigate the stability of a long run and short run parameters. The cumulative sum

(CUSUM) and the cumulative sum of square (CUSUMSQ) plots (Figure 5.3.1) are between

critical boundaries at 5% level of significance. This confirms the stability property of a long

run and short run parameters which have an impact on the market index in case of India. This

confirms that models seem to be steady and specified appropriately.

Figure 5.3.1: Plots of Stability Test

5.3.5. Variance Decomposition (VDC) Analysis:

It is pointed out by Pesaran and Shin (2001) that the variable decomposition method

shows the contribution in one variable due to innovation shocks stemming in the forcing

variables. The variance decomposition indicates the amount of information each variable

contributes to the other variables in the autoregression. It determines how much of the

forecast error variance of each of the variables can be explained by exogenous shocks to the

other variables. The main advantage of this approach as it is insensitive to the ordering of the

variables. The results of the VDC are presented in table 5.3.7. The empirical evidence

indicates that 78.33% of stock price change is contributed by its own innovative shocks.

Further, shock in crude oil price explains the stock price by 12.73%. Foreign Direct

Investment contributes to stock prices by 2.835% and consumer price contributes 2.01%.

From this analysis, it can be referred that the movement in stock prices can be predicted from

the crude oil prices. The share of other variables is very minimal.

http://en.wikipedia.org/wiki/Variance

132

Table 5.3.7: Variance Decomposition (VDC) Analysis

Period S.E. LBSE LCO LCPI LREER LFDI LGDP LRIR

1 0.223 100.000 0.000 0.000 0.000 0.000 0.000 0.000

2 0.303 91.256 2.442 2.718 0.151 2.108 0.814 0.065

3 0.344 88.673 4.758 2.145 0.367 2.499 0.717 0.183

4 0.366 86.802 6.364 1.962 0.397 2.624 0.651 0.365

5 0.379 85.334 7.528 1.916 0.372 2.720 0.609 0.541

6 0.386 84.115 8.473 1.864 0.384 2.803 0.596 0.678

7 0.391 83.047 9.289 1.816 0.442 2.861 0.621 0.765

8 0.395 82.091 10.001 1.802 0.524 2.889 0.652 0.809

9 0.398 81.242 10.613 1.827 0.604 2.895 0.758 0.827

10 0.401 80.510 11.131 1.875 0.671 2.888 0.833 0.830

11 0.404 79.897 11.568 1.926 0.720 2.876 0.895 0.828

12 0.406 79.393 11.935 1.968 0.755 2.863 0.941 0.824

13 0.409 78.978 12.245 1.995 0.777 2.852 0.974 0.820

14 0.411 78.632 12.510 2.010 0.792 2.842 0.997 0.817

15 0.413 78.337 12.737 2.014 0.800 2.835 1.013 0.816

Cholesky Ordering: LBSE LCO LCPI LREER LFDI LGDP LRIR

133

5.4. Estimation results of the study using quarterly frequency data


between macroeconomic variables and the stock market development, by incorporating data

for quarterly frequency variables. The study uses quarterly data on the above described

variables covering the period from 1996: Q1 to 2014: Q3.




market development.

𝐿𝑀𝐶𝐴𝑃 = 𝛼0 + 𝛼1𝐿𝐺𝐷𝑃 + 𝛼2𝐿𝐹𝐷𝐼 + 𝛼3𝐿𝐹𝐼𝐼 + 𝛼4𝐿𝑇𝑂 + 휀𝑡

(5.2)


Before we proceed for ARDL estimation, we test for the stationarity of the variables

and to determine their order of integration. The test for unit root is to ensure that none of the

series in integrated at I(2). The present study uses newly developed Ng- Perron test

developed by Ng- Perron (2001). The test result is presented in Table 5.4.1. The analysis of

the unit root test results indicates that LFDI and LTO are I(0) and the remaining variables are

integrated order one (I(1)) and none of the variables are I(2) series.


Variables With trend and intercept Stationarity

Status Mza Mzt MSB MPT

LMCAP -8.321 -2.723 0.354 10.678 I (1)

ΔLMCAP -30.512 -3.796 0.225 3.426

LFDI -29.568 -3.921 0.368 3.989 I (0)

ΔLFDI -1175.319 -24.654 0.521 0.189

LFII -14.221 -2.567 0.374 4.449 I (1)

ΔLFII -31.097 -3.786 0.448 3.981

LGDP -13.224 -2.450 0.390 8.985 I (1)

ΔLGDP -25.974 -3.994 0.166 1.964

LTO -25.372 -3.679 0.248 4.679 I (0)

ΔLTO -1.467 -0.902 0.579 46.210 Source: Author’s own Calculation by using E-views 8.0

∆ denotes the first difference of the series. L implies that the variables have been transformed in natural logs.

The next step involves the selection of optimal lag length of the model. The optimal lag

length was determined by different criterion suitable to the models (Table 5.4.2) using 5

maximum lags in the model. The results of table 5.4.2 suggest that the optimal lag length is 4

based on LR, FPE and HQ.

134

Table 5.4.2: Lag Order Selection Criterion

Lag LogL LR FPE AIC SIC HQ

0 -188.926 NA 0.000 6.267 7.067 6.215

1 -7.955 322.417 2.37e-06 1.468 2.218* 1.412

2 19.245 43.342 2.65e-06 1.392 3.354 2.128

3 55.890 51.812 1.91e-06 0.741 3.661 2.960

4* 97.267 52.416* 1.07e-06* 0.199 4.110 1.428*

5 122.780 27.225 1.21e-06 0.293* 4.813 2.133 * indicates lag order selected by the criterion






5.4.3 ARDL Bounds test

After determining the order of integration and lag length, the next step is to employ

bounds test to confirm the long-run relationship among the variables. The bounds test result

confirms the long-run relationship because the calculated F-statistics are 7.5673 which are

greater than the critical value of the upper level of bounds at the 1% level of significance

(Pesaran (2001) and Narayan (2005)). This evidence gives strong indication of the existence

of a long-run relationship among the variables included in the model. Further, the estimated

statistics show that the model specification seems to pass all diagnostic tests successfully.

Table 5.4.3: ARDL bounds test results

Panel I: Bound testing to co-integration:

Estimated Equation : LMCAP = F (LFDI LFII LGDP LTO)

Indicators

Optimal lag 04








Once we established that a long-run co-integrating relationship exists, the next step is to

estimate the long-run coefficient. The estimated long-run coefficients are reported in table

5.4.4. The estimated result shows that coefficient of FDI is positive, but not significant. This

implies that FDI has not been effective in influencing stock market development in India. The

findings are consistent with Raza (2013). However the study found that the stock market is

positively related to real GDP. The coefficient of real GDP has positive impact on the Stock

135

Market and it’s significant at the 5% level. The value of coefficient implies that 1% increase

in real GDP leads to increase in the stock market by 21% on an average. The result implies

that the GDP affects the stock market indirectly through its effect on inflation, and because

investors use it as a key indicator of economic activity and future economic

prospects. Therefore, any significant change in the GDP, either up or down, can have a

significant effect on the sentiments of the investors. If investors believe the economy is

improving (and corporate earnings along with it) they are likely to be willing to pay more for

any given stock. If there is a decline in GDP (or investors expect a decline) they would only

be willing to buy a given stock for less, leading to a decline in the stock market and the result

that there exist a positive nexus between the stock market and economic growth are

consistent with the studies of Randall et al. (2000), Rousseau and Wachtel (2000), Daferighe

and Aje (2009) and Hsing (2011).

Considering the impact of trade openness, it is found the variable is significant at 1%

and has a positive impact on stock market development. This finding supports the view that

trade openness of the economy helps to attract foreign investment. This in turn increases the

activities on the stock market as firms would attempt to raise investment funds (capital) from

the stock market (Nurudeen (2009)). The FIIs are significant at 1% and has a positive impact

on market capitalization and a 1% rise in FIIs increases market capitalization by 15%. The

findings are consistent with the findings of Loomba (2012), this implies that increase in FIIs

investments brings inflow of capital and the country can have access to foreign capital for

financial development.

Table 5.4.4: Estimated Long-run Coefficients using ARDL Approach

(Dependent variable: LMCAP)



LFII 0.161** 2.586 [0.016]

LFDI 0.052 0.416 [0.731]

LGDP 0.221** 1.988 [0.055]

LTO 2.673*** 3.983 [0.000]

CONS 10.191 2.226 [0.029]


R2 0.982

Adjusted R2 0.980

F Statistics 1069.10

D.W. Stat 1.912

Serial Correlation, F 8.356[0.671]

Heteroskedasticity, F 0.551[0.698]

Ramsey reset test, F 0.094[0.715] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC).

(2) ** and *** indicate significant at 5 and 1 percent level of significance, respectively. Values in [#] are

probability values.

136

Next, the short-run dynamics can be achieved by constructing an ARDL-based Error

Correction Model (ECM). The results of short-run dynamics using the ECM version of

ARDL are reported in table 5.4.5. The short-run adjustment process is examined from the

ECM coefficient. The coefficient lies between 0 and -1, the equilibrium is converging to the

long-run equilibrium path, is responsive to any external shocks. From the reported values of

ECM test, we found that the ECMt-1 term is -0.15 and is significant at 3%, again confirming

the existence of co-integration that the derivation from long-run equilibrium path is corrected

15% per year.

Table 5.4.5: Estimated Short-run Coefficients using ARDL Approach




ΔLFII 0.035*** 4.225 [0.000]

ΔLFDI 0.015 0.057 [0.597]

ΔLGDP 0.045 0.431 [0.689]

ΔLTO 0.326*** 3.534 [0.001]

ΔCONS 1.675 2.435 [0.028]

ECM t-1 -0.159 -3.248 [0.003]


R2 0.501

Adjusted R2 0.418

D.W. Stat 1.926

SE Regression 0.225

RSS 0.008

F Statistics 8.618 [0.000] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC).

(2) *** indicate significant at the 1 percent level of significance, respectively. Values in [#] are probability

values.

The comparison of long-run coefficients with that of short-run ECM coefficients

confirms that the directions of relationships are maintained. However, the economic growth

variable which is positive and significant at the 10% level in the long-run failed to explain the

variation in stock market growth significantly in the short-run. This may be due to the fact

that investor’s behavior in the stock market regulated by long-term growth rate of GDP and

may not bother about short-term fluctuations in it. Other variables, such as FII and TO are

significantly influencing the market capitalization both in the short-run as well as in the long-

run. Here also, the coefficient of FDI is positive and insignificant.

137


The short-run and long-run granger causality test findings are reported in Table 5.4.6.

The results of table 5.4.6 indicate that short-run unidirectional causality running from LTO

variable to MCAP in India. It is also observed that error correction term is statistically

significant for specification with MCAP as the dependent variable which indicate that there

exist a long-run causal relationship among the variables with MCAP as the dependent

variable. This result is also confirmed by the ARDL test statistics.


Dependent

variable



∆LMCAP ∆LGDP ∆LTO ∆LFDI ∆LFII ECM(t-1)

∆LMCAP - 1.073 1.321 1.895 3.133*** -2.554**

∆LGDP 0.563 - 0.979 0.094 0.674 -0.381

∆LTO 2.226** 3.006*** - 1.535 3.035*** -0.986

∆LFDI 0.411 1.057 1.225 - 0.541 2.280

∆LFII 1.376 1.977 0.414 2.767* - 0.977 *** indicates 1% level of significance, ** indicates 5% level of significance

The robustness of the short-run result are investigated with the help of diagnostic and




investigate the stability of long-run and short-run parameters. This confirms the stability

property of long-run and short-run parameters. This confirms that models seem to be steady

and specified appropriate.


138

5.4.5. Variance Decomposition Analysis:

The Variance Decomposition analysis indicates the percentage of forecast error

variance in one variable that is due to errors in forecasting itself and each of the variables.

The results of Variance Decomposition are illustrated in table 5.4.7. The empirical results

show that the LMCAP explanatory has increased over the time through FDI growth variable

as the second year, 4.05% of market capitalization variable changes is explained by the

variance. However, Trade openness variable play the most important role, explaining 45%

variation in stock market capitalization in India.


Period S.E. LMCAP LGDP LTO LFDI LFII

1 0.145 100.000 0.000 0.000 0.000 0.000

2 0.298 92.157 2.094 1.676 4.112 0.096

3 0.300 88.598 2.057 2.357 6.695 0.367

4 0.305 80.855 3.014 2.314 11.126 2.781

5 0.335 75.731 3.378 2.567 16.159 2.374

6 0.357 72.599 3.592 4.724 16.524 2.599

7 0.389 67.653 3.441 8.935 16.707 3.312

8 0.407 64.498 3.291 12.569 16.460 3.383

9 0.424 61.759 2.941 15.936 16.143 3.219

10 0.456 60.019 2.718 19.109 15.215 2.998

11 0.467 58.038 2.549 22.407 14.275 2.729

12 0.479 56.224 2.394 25.258 13.641 2.672

13 0.497 54.121 2.116 28.057 13.225 2.436

14 0.514 52.131 2.091 30.949 12.547 2.345

15 0.535 50.117 1.966 33.864 11.909 2.226

16 0.547 48.303 1.850 36.514 11.451 2.132

17 0.572 46.532 1.786 38.894 10.854 1.979

18 0.588 45.134 1.671 41.227 10.319 1.901

19 0.599 43.541 1.599 43.245 9.818 1.892

20 0.616 42.259 1.567 44.912 9.299 1.879

Cholesky Ordering: LMCAP LGDP LTO LFDI LFII

139

5.5. Estimation results of the study using monthly frequency data


between macroeconomic variables and the stock prices, by incorporating data for monthly

frequency variables. Further, the study has been divided into two sub-sections, which


prices. The first sub-section of the study shows the empirical relationship between

fundamental macroeconomic variables and Sensitivity Index of Bombay Stock Exchange

(Sensex), using the monthly time series data from the April 2004 to July 2014. The second

sub-section of the study exhibits the empirical relationship empirical relationship between

fundamental macroeconomic variables and Index of National stock exchange (CNX nifty),

using the monthly time series data from the April 2004 to July 2015. Each sub-section of the

study will include model specification and data validation.

5.5.1. Relationship between macroeconomic variables and Indian stock price

The study empirically estimated the effect of fundamental macroeconomic indicators

on stock prices in India, with the help of econometric techniques. The study uses monthly

data covering the period from April 2004 to July 20149. The selection of the monthly data set

is used to capture the short run fluctuation in the variables. Most of the study in Indian

context is carried on annual data; hence this study will provide valuable information on the

dynamic relationship of stock prices and macroeconomic variables. Based on the extensive

literature review the above macroeconomic variables are selected for the study, which are

expected to have some influence on stock market performance in the present context.

5.5.1.1. Model specification



market.

𝐿𝐵𝑆𝐸 = 𝛼0 + 𝛼1𝐿𝐼𝐼𝑃 + 𝛼2𝐿𝐶𝑃𝐼 + 𝛼3𝐿𝑅𝐸𝐸𝑅 + 𝛼4𝐿𝐶𝑀𝑅 + 𝛼5𝐿𝐺𝑂𝑅 + 휀𝑡

(5.3)

5.5.1.2. Stationarity test and Lag length selection before co-integration




9 The study limits to the starting period as April 2004 to July 2014 due to the non-availability of data with

common base year on IIP and CPI prior to this period.

140

stationarity tests are presented in Table 5.5.1.1. The results show that all the variables are

non-stationary at levels. The next step is to difference the variables once in order to perform



concluding that all the variables used in this study are integrated of order one i.e. difference

stationary I(1). Therefore the study uses autoregressive distributed lag (ARDL) approach to

co-integration. In addition, it is also important to ascertain that the optimal lag order of the

model is chosen appropriately so that the error terms of the equations are not serially

correlated. Consequently, the lag order should be high enough so that the conditional ECM is

not subject to over parameterization problems (Narayan, 2005; Pesaran, 2001). The results of

these tests are presented in Table 5.5.1.2. The results of Table 5.5.1.2 suggest that the optimal

lag length is one based on both LR, FPE, SIC and HQ.

Table 5.5.1.1: Unit root test: Ng-Perron Test

Variables With trend and intercept Stationarity


LBSE -90.810 -6.722 0.074 1.068 I (1)

ΔLBSE -19.954 -3.156 0.158 4.582

LCMR -20.416 -3.192 0.156 4.478 I (1)

ΔLCMR -42.693 -4.619 0.108 2.136

LCPI -22.071 -3.314 0.150 4.172 I (1)

ΔLCPI -9.894 -2.103 0.212 9.752

LGOR -2.784 -0.852 0.306 23.938 I (1)

ΔLGOR -39.29 -4.432 0.112 2.319

LIIP -26.410 -3.579 0.135 3.770 I (1)

ΔLIIP -7.641 -1.950 0.255 11.935

LREER -6.723 -1.833 0.272 13.552 I (1)

ΔLREER -38.759 -4.401 0.113 2.355 Source: Author’s own Calculation by using E-views 8.0

∆ denotes the first difference of the series. L implies that the variables have been transformed in

natural logs.

141

Table 5.5.1.2: Lag Order Selection Criterion Lag LogL LR FPE AIC SIC HQ

0 396.298 NA 1.59e-12 -10.137 -9.954 -10.064

1 969.597 1042.363 1.39e-18 -24.093 -22.815* -23.582

2 996.843 45.291 1.77e-18 -23.866 -21.491 -22.916

3 1026.728 45.021 2.17e-18 -23.707 -20.237 -22.319

4 1071.441 73.913* 1.11e-18 -24.605 -18.943 -22.340

5 1133.304 60.391 1.88e-18 -23.933 -19.367 -22.107

6 1163.409 31.277 1.62e-18 -24.452 -17.694 -21.749

7 1211.150 42.161 1.69e-18 -24.757 -16.903 -21.615

8 1272.728 44.784 1.47e-18 -25.421 -16.472 -21.841

9 1340.677 38.827 1.41e-18 -26.251 -16.206 -22.233

10 1446.572 44.008 7.98e-19 -28.066 -16.926 -23.610

11 1605.994 41.408 2.75e-19* -31.272* -19.036 -26.378* * indicates lag order selected by the criterion






After determining the order of integration of all the variables in table 5.5.1.1, the next

step is to employ an ARDL approach to co-integration in order to determine the long run

relationship among the variables. By applying, the procedure in OLS regression for the first

difference part of the equation (5.3) and then test for the joint significance of the parameters

of the lagged level variables when added to the first regression.

5.5.1.3. ARDL Bounds test


variables in the equation (5.3) are zero. Table 5.5.1.3, reports the result of the calculated F-

Statistics and diagnostic tests of the estimated model. The result shows the calculated F-

statistics were 5.3790. Thus the calculated F-statistics turns out to be higher than the upper-

bound critical value at the 5 percent level. This suggests that there is a co-integrating

relationship among the variables included in the model, i.e. Sensex (LBSE), the Index of

Industrial Production (LIIP), Inflation (LCPI), Real Effective Exchange Rate (LREER), Call

Money Rate (LCMR) and Gold Prices (LGOR)

142

Table 5.5.1.3: ARDL Bounds test


Estimated Equation : LBSE = F (LIIP LCPI LREER LCMR LGOR)

Indicators

Optimal lag 04








The second step is to estimate the long-run and short-run estimates of ARDL test. The

long run results are illustrated in Table 5.5.1.4. The results show that the rise in IIP, Inflation

and Exchange Rate has a positive effect on stock prices. The coefficient of Index of Industrial

Production (LIIP), Inflation (LCPI) and Real Effective Exchange Rate (LREER) is

statistically significant and positive at 5%, 1% and 10% respectively. It is evident from the

table that 5% increase in IIP, a 1% increase in Inflation, and 10% increase in Exchange Rate

leads to 1.200%, 1.922%, and 1.211%, respectively, increase in Stock Prices (Sensex). The

findings are consistent with Chen et al. (1986), Maysami et al. (2004), Rahman et al. (2009),

and Ratanapakorn and Sharma, (2007) for IIP, Ioannidis et al. (2004) for Inflation and

Mukherjee and Naka (1995) for Exchange Rate. Whereas, the coefficient of Gold Price is

negative and significant at the 1% level in explaining the variation in stock prices. Therefore,

Gold Prices have a significant negative relationship adversely affecting stock prices and the

findings are consistent with Ray, S., (2012); Gupta and Reid (2013).

143

Table 5.5.1.4: Estimated Long Run Coefficients using ARDL Approach

(Dependent variable: LBSE) Regressors ARDL(1,0,0,0)


LIIP 1.2003** 2.260 [0.027]

LCPI 1.9215*** 3.353 [0.001]

LREER 1.2119* 1.758 [0.083]

LCMR -0.090 -0.756 [0.452]

LGOR -0.866*** -2.953 [0.004]

CONS -3.271 -1.237 [0.220]


R2 0.946

Adjusted R2 0.938

F Statistics 127.778[0.000]

D.W. Stat 1.899




(2) *, ** and *** indicate significant at 10, 5 and 1 percent level of significance, respectively. Values

in [#] are probability values.

The short-run relationship of the macroeconomic variables on stock market index is

presented in Table 5.5.1.5. As can be seen from the table, IIP and Inflation has a significant

and positive impact on stock market index in the short run also at 10% and 1% level,

respectively. Similar to long-run, gold prices is significantly negative at 1% in the short-run

also. The short run adjustment process is examined from the ECM coefficient. The

coefficient lies between 0 and -1, the equilibrium is converging to the long run equilibrium

path, is responsive to any external shocks. However, if the value is positive, the equilibrium

will be divergent from the reported values of ECM test. The coefficient of the lagged error-

correction term (-0.222) is significant at the 1% level of significance. The coefficient implies

that a deviation from the equilibrium level of stock market index in the current period will be

corrected by 22 percent in the next period to resort the equilibrium.

144

Table 5.5.1.5: Estimated Short Run Coefficients using ARDL Approach




ΔLIIP 0.267* 1.776 [0.080]

ΔLCPI 0.428*** 2.724 [0.008]

ΔLREER 0.270 1.625 [0.108]

ΔLCMR -0.019 -0.876 [0.383]

ΔLGOR -0.546*** -3.494 [0.001]

ΔCONS -0.729 -1.232 [0.222]

ECM t-1 -0.222 -3.238 [0.002]


R2 0.426

Adjusted R2 0.348

D.W. Stat 1.899

SE Regression 0.053

RSS 0.202



Values in [#] are probability values.

5.5.1.4. VECM based causality

The results of table 5.5.1.6 indicate that there is no short run causality running from any

of the variable to LBSE in India. It is observed that error correction term is statistically

significant for specification with LBSE as the dependent variable which indicate that there

exist a long run causal relationship among the variable with LBSE as the dependent variable.

This result is also confirmed by the ARDL test statistics.

Table 5.5.1.6: Results of Vector Error Correction Model

Dependent

variable



ΔLBSE ΔLIIP ΔLCPI ΔLREER ΔLCMR ΔLGOR ECM(t-1)

ΔLBSE - 0.535 0.667 0.870 0.689 0.703 -2.794***

ΔLIIP 5.490*** - 1.713 0.789 0.508 5.822*** -2.563***

ΔLCPI 2.331* 3.224** - 1.405 0.854 1.729 2.182

ΔLREER 0.679 0.280 1.367 - 0.203 0.332 0.145

ΔLCMR 1.543 4.677*** 1.363 0.212 - 0.509 4.848

ΔLGOR 1.136 0.702 1.241 1.026 0.311 - 0.785


The robustness of the short run result is investigated with the help of diagnostic and





(CUSUM) and the cumulative sum of square (CUSUMSQ) plots (Figure 5.5.1.1) are between

145

critical boundaries at 5% level of significance. This confirms the stability property of the long


confirms that models seem to be steady and specified appropriate.

Figure 5.5.1.1: Plots of Stability Test

5.5.1.5. Variance Decomposition (VDC) Analysis:



variables. The main advantage of this approach as it is insensitive to the ordering of the

variables. The results of the VDC are presented in table 5.5.1.7. The empirical evidence

indicates that 72.02% of stock price change is contributed by its own innovative shocks.

Further shock in Gold price explains the stock price by 12.92%. IIP contributes to stock

prices by 9.74% and inflation and exchange rate contributes 2.16% and 2.82% respectively.

The share of other call money rate is very minimal.

146

Table 5.5.1.7: Variance Decomposition (VDC) Analysis

Period S.E. LBSE LIIP LCPI LREER LCMR LGOR

1 0.059 100.00 0.000 0.000 0.000 0.000 0.000

2 0.100 97.545 1.917 0.295 0.068 0.020 0.151

3 0.125 95.908 2.976 0.506 0.279 0.196 0.131

4 0.146 94.879 3.246 0.555 0.714 0.305 0.298

5 0.170 94.009 3.639 0.706 0.920 0.346 0.376

6 0.191 92.822 4.951 0.823 0.727 0.373 0.301

7 0.207 91.810 6.032 0.741 0.701 0.408 0.306

8 0.220 91.021 6.730 0.659 0.830 0.426 0.331

9 0.232 89.664 7.622 0.615 1.147 0.416 0.533

10 0.243 87.496 8.429 0.679 1.668 0.384 1.341

11 0.253 85.134 9.061 0.883 2.104 0.355 2.460

12 0.262 82.626 9.562 1.209 2.439 0.337 3.825

13 0.272 79.997 9.826 1.563 2.689 0.335 5.586

14 0.280 77.609 9.988 1.841 2.830 0.342 7.388

15 0.288 75.629 10.041 2.051 2.900 0.350 9.026

16 0.296 74.114 9.973 2.177 2.912 0.357 10.464

17 0.302 73.068 9.895 2.211 2.889 0.354 11.581

18 0.309 72.374 9.823 2.199 2.860 0.344 12.397

19 0.315 72.020 9.737 2.164 2.821 0.332 12.923

20 0.320 71.929 9.676 2.109 2.774 0.320 13.190

Cholesky Ordering: LBSE LIIP LCPI LREER LCMR LGOR

147

5.5.2. Relationship between Fundamental Macroeconomic Variables and CNX nifty

The study empirically estimated the effect of fundamental macroeconomic variables on

stock prices (CNX Nifty) in India. The study uses monthly data covering the period from

April 2004 to July 2015.

5.5.2.1. Model Specification

The following general specification has been used in this study to empirically

examine the effect of fundamental macroeconomic factors on stock prices.

𝐿𝑁𝑆𝐸 = 𝛼0 + 𝛼1𝐿𝐼𝐼𝑃 + 𝛼2𝐿𝐹𝐼𝐼 + 𝛼3𝐿𝐺𝑂𝑅 + 𝛼4𝐿𝑇𝐵𝑅 + 𝛼5𝐿𝑊𝑃𝐼 + 𝛼5𝐿𝐶𝑂 + 𝛼6𝐿𝑅𝐸𝐸𝑅 + 휀𝑡

(5.4)

5.5.2.2. Stationarity test and Lag length selection before co-integration




stationarity tests are presented in Table 5.5.2.1. The results show that all the variables are

non-stationary at levels. The next step is to difference the variables once in order to perform


once, all the variables were confirmed to be stationary. It is, therefore, worth concluding that

all the variables used in this study are integrated of order one i.e. difference stationary I(1).

Therefore the study uses autoregressive distributed lag (ARDL) approach to co-integration. In

addition, it is also important to ascertain that the optimal lag order of the model is chosen

appropriately so that the error terms of the equations are not serially correlated.

Consequently, the lag order should be high enough so that the conditional ECM is not subject

to over parameterization problems (Narayan, 2005; Pesaran, 2001). The results of these tests

are presented in Table 5.5.2.2. The results of Table 5.5.2.2 suggest that the optimal lag length

is one based on both LR, FPE, SIC and HQ.

148

Table 5.5.2.1: Unit root test: Ng-Perron Test

Variables Without trend and intercept Stationarity


LNSE 0.576 0.429 0.744 38.514 I (1)

ΔLNSE -6.556 -1.739 0.265 3.983

LFII 0.481 1.630 9.626 51.912 I (1)

ΔLFII -54.747 -5.231 0.095 0.447

LGOR 0.828 1.514 1.828 209.246 I (1)

ΔLGOR -15.656 -2.791 0.178 1.589

LIIP -3.459 -1.243 0.359 7.068 I (1)

ΔLIIP -57.168 -5.345 0.093 0.431

LREER 0.153 0.098 0.642 28.032 I (1)

ΔLREER -53.440 -5.129 0.095 0.557

LTBR 1.457 1.558 1.070 85.539 I (1)

ΔLTBR -16.494 -2.869 0.174 1.494

LWPI 0.143 0.093 0.652 28.471 I (1)

ΔLWPI -14.298 -2.640 0.185 1.840

LCO -2.340 -1.065 0.455 10.366 I (1)

ΔLCO -23.521 -3.323 0.141 1.402 Source: Author’s own Calculation by using E-views 8.0


natural logs.

Table 5.5.2.2: Lag Order Selection Criterion Lag LogL LR FPE AIC SIC HQ

0 -281.469 NA 1.27e-08 4.522 4.701 4.595

1 725.217 1871.807 5.11e-15* -10.206* -8.602* -9.554*

2 786.204 105.775* 5.41e-15 -10.159 -7.129 -8.928

3 837.308 82.245 6.81e-15 -9.957 -5.501 -8.147

4 880.668 64.362 9.93e-15 -9.635 -3.753 -7.245

5 931.249 68.758 1.34e-14 -9.425 -2.117 -6.456

6 982.161 62.844 1.90e-14 -9.221 -0.486 -5.672

7 1041.636 65.979 2.51e-14 -9.150 1.009 -5.022

8 1097.682 55.170 3.81e-14 -9.026 2.560 -4.318 * indicates lag order selected by the criterion






After determining the order of integration of all the variables in table 5.5.2.1, the next





5.5.2.3. ARDL Bounds test


variables in the equation (5.4) are zero. Table 5.5.2.3, reports the result of the calculated F-


149


bound critical value at the 5 percent level. This suggests that there is a cointegrating

relationship among the variables included in the model, i.e. CNX nifty (LNSE), the Index of

Industrial Production (LIIP), Financial Institutional Investment (LFII), Gold (LGOR), T-Bill

Rate (LTBR), Wholesale Price Index (LWPI), Crude oil price (LCO) and Real Effective

Exchange Rate (LREER).

Table 5.5.2.3: ARDL bounds test results


Estimated Equation: LNSE = F (LIIP LFII LGOR LTBR LWPI LCO LREER)

Indicators

Optimal lag 02




Normality J-B value 0. 9011




The second step is to estimate the long and short-run estimates of ARDL test. The long

run results are illustrated in Table 5.5.2.4. The results show that the coefficient of Crude oil

prices (LCO) is statistically significant and negative at 5%. It is evident from the table that

5% increase in Crude oil price leads to 0.644% decrease in CNX nifty (LNSE). The findings

are consistent with Valadkhani et al. (2009), Hosseini et. al., (2011) (For India) and Kuwornu

(2012). The result found in this study implies that, since India is an oil importer country,

therefore, the increases in oil price would lead to increase the cost of production and,

consequently, the expected cash flow would decrease and it is also evident that the increase in

oil prices should result in higher costs and, hence, lower equity values.

Similarly, the coefficient of Inflation (LWPI) is negative and significant at 1%. It is

evident from the table that 1% increase in Inflation leads to -0.328%, decrease in CNX nifty

(LNSE). The findings of the study are consistent with Fama (1981), Mukherjee and Naka

(1995), and Maysami and Koh (2000), who have found a negative correlation between

inflation and stock prices. The negative relationship may be due to the reason that because

inflation causes the value of money to decrease and consequently the purchasing power of the

150

people decreases, which leads to a negative effect of saving and investment activities of the

stock exchange.

Table 5.5.2.4: Estimated Long Run Coefficients using ARDL Approach

(Dependent variable: LNSE) Regressors ARDL(1,0,0,0)


LIIP 0 .082 0.948 [0.345]

LFII -0.010 -0.466 [0.642]

LGOR 0.293 2.309 [0.355]

LTBR 0.228 0.838 [0.403]

LWPI -0.328*** 2.919 [0.004]

LCO -0.644** 0.928 [0.023]

LREER 0.428 0.339 [0.735]

CONS -0.840 -0.112 [0.911]


R2 0.988

Adjusted R2 0.987

F Statistics 877.934 [0.000]

D.W. Stat 1.845




(2) ** and *** indicate significant at 5 and 1 percent level of significance, respectively. Values


The short-run relationship of the macroeconomic variables on the National Stock

Exchange is presented in Table 5.5.2.5. As can be seen from the table, Inflation (LWPI) and

Crude oil price (LCO) has a significant and negative impact on CNX nifty (LNSE) in the

short run at 1% level of significance. One can say that 1% increase in inflation and crude oil

price leads to 0.021% and 0.203%, decrease in CNX nifty. This may be due to the fact that

investors are more sensitive towards the movements in crude oil price and inflation in the

short run.

Whereas, Gold (LGOR), T-bill rates (LTBR) and Real Effective Exchange Rate

(LREER) are significantly positive at 10%, 10% and 1% level, respectively, in short-run. The

positive impact of T-bill rates on the CNX nifty Index is to some extent consistent with

Kuwornu (2012), implying that investors do not view Short Term T-bill rate with the

associated interest rates as option to investment opportunities. Therefore, increases in T-bill

rates lead to increased investment in stocks, causing stock returns to rise in India. The

appreciation of the Real Effective Exchange Rate in India would attract more investors to

invest in the stock market in the short run. The short run adjustment process is examined

from the ECM coefficient. The coefficient lies between 0 and -1, the equilibrium is

151

converging to the long run equilibrium path, is responsive to any external shocks. However,

if the value is positive, the equilibrium will be divergent from the reported values of ECM

test. The coefficient of the lagged error-correction term (-0.0746) is significant at the 1%

level of significance. The coefficient implies that a deviation from the equilibrium level of

National Stock Exchange in the current period will be corrected by 7 percent in the next

period to resort the equilibrium.

Table 5.5.2.5: Estimated Short Run Coefficients using ARDL Approach

(Dependent variable: LNSE)



ΔLIIP 0.006 0.880 [0.381]

ΔLFII -0.745E-3 -0.471 [0.638]

ΔLGOR 0.0479* 1.724 [0.087]

ΔLTBR 0.0669* 1.802 [0.074]

ΔLWPI -0.0217*** 3.144 [0.002]

ΔLCO -0.2036*** 3.913 [0.000]

ΔLREER 1.391*** 5.464 [0.000]

ΔCONS -0.0623 -0.111 [0.911]

ECM t-1 -0.0746 3.106 [0.002]


R2 0.430

Adjusted R2 0.374

D.W. Stat 1.845

SE Regression 0.047

RSS 0.264


(2) * and *** indicate significant at 10 and 1 percent level of significance, respectively. Values


5.5.2.4. VECM based causality

The results of table 5.5.2.6 indicate that there exists a short-run causality running from

inflation and crude oil price to stock prices in India. Furthermore, a unidirectional causality is

also running from stock prices to gold and inflation. Thus, it is clearly observed that

bidirectional causality is running between inflation and CNX nifty index. It is also observed

that error correction term is statistically significant for specification with LNSE as the

dependent variable which indicate that there exist a long-run causal relationship between the

variable with LNSE as the dependent variable. This result is also confirmed by the ARDL test

statistics.

152

Table 5.5.2.6: Results of Vector Error Correction Model

Dependent

variable



ΔLNSE ΔLIIP ΔLFII ΔLGOR ΔLTBR ΔLWPI ΔLCO ΔLREER ECM(t-1)

ΔLNSE - 0.380 0.530 1.612 2.090 6.833** 5.613** 0.897 1.664**

ΔLIIP 3.656 - 0.567 2.673 1.094 1.729 2.793 0.714 -0.364

ΔLFII 0.799 0.389 - 0.148 0.380 3.116 0.411 1.352 0.723***

ΔLGOR 5.484** 1.504 1.577 - 1.187 1.336 0.282 0.078 -0.276

ΔLTBR 5.207* 0.860 2.689 1.492 - 1.921 1.493 0.257 -0.508

ΔLWPI 7.012** 0.024 3.813 0.037 3.690 - 6.250** 1.063 -1.817*

ΔLCO 1.200 0.204 1.779 0.738 3.265 0.321 - 0.182 -1.197

ΔLREER 2.696 7.242* 2.199 2.186 2.607 1.153 1.964 - -0.356







(CUSUM) and the cumulative sum of square (CUSUMSQ) plots (Figure 5.5.2.1) are between




Figure 5.5.2.1: Plots of Stability Test

5.5.2.5. Variance Decomposition (VDC) Analysis:







variables. The results of the VDC are presented in table 5.5.2.7. The empirical evidence


153

indicates that 71.85% of CNX nifty index change is contributed by its own innovative shocks.

Further shock in inflation explains CNX nifty index by 15.67%. Crude oil price contributes to

the CNX nifty index by 9.24%, and the results are consistent with the results of VECM. Thus,

it can be said that the most important macroeconomic variables that influence CNX nifty

index in India are inflation and crude oil prices, though they are marginal at 15.67% and

9.24% respectively. From this analysis, it can be referred that the Indian Stock Market

Returns can be predicted from the inflation and crude oil prices. The share of other variables

is very minimal.

Table 5.5.2.7: Variance Decomposition (VDC) Analysis

Period S.E. LNSE LFII LGOR LREER LTBR LIIP LWPI LCO

1 0.054 100.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

2 0.085 97.815 0.252 0.352 0.148 1.012 0.320 0.086 0.010

3 0.107 97.020 0.532 0.395 0.146 1.019 0.297 0.575 0.010

4 0.123 95.720 0.668 0.371 0.168 1.016 0.456 1.565 0.032

5 0.135 94.387 0.681 0.326 0.181 0.913 0.502 2.816 0.189

6 0.145 92.809 0.657 0.286 0.176 0.809 0.500 4.188 0.570

7 0.153 90.911 0.628 0.258 0.162 0.727 0.483 5.631 1.195

8 0.160 88.714 0.600 0.250 0.149 0.675 0.462 7.111 2.035

9 0.165 86.301 0.574 0.263 0.150 0.647 0.442 8.583 3.035

10 0.171 83.774 0.549 0.299 0.178 0.639 0.422 10.005 4.130

11 0.176 81.222 0.526 0.359 0.241 0.640 0.403 11.347 5.258

12 0.180 78.714 0.504 0.441 0.349 0.644 0.389 12.589 6.368

13 0.184 76.298 0.485 0.544 0.508 0.646 0.379 13.725 7.419

14 0.188 74.006 0.468 0.669 0.719 0.643 0.351 14.752 8.383

15 0.192 71.854 0.453 0.815 0.981 0.634 0.345 15.672 9.244

Cholesky Ordering: LNSE LFII LGOR LREER LTBR LIIP LWPI LCO

Figure 5.5.2.2: VDC analysis combined graph

0

20

40

60

80

100

2 4 6 8 10 12 14 16 18 20

LNSE LFII LGOLD

LREER LTBR LIIP

LWPI LCO

Variance Decomposition of LNSE

0

20

40

60

80

100

2 4 6 8 10 12 14 16 18 20

LNSE LFII LGOLD

LREER LTBR LIIP

LWPI LCO

Variance Decomposition of LFII

0

20

40

60

80

100

2 4 6 8 10 12 14 16 18 20

LNSE LFII LGOLD

LREER LTBR LIIP

LWPI LCO

Var iance Decomposition of LGOLD

0

20

40

60

80

2 4 6 8 10 12 14 16 18 20

LNSE LFII LGOLD

LREER LTBR LIIP

LWPI LCO

Variance Decomposition of LREER

0

20

40

60

80

100

2 4 6 8 10 12 14 16 18 20

LNSE LFII LGOLD

LREER LTBR LIIP

LWPI LCO

Variance Decomposition of LTBR

0

20

40

60

80

100

2 4 6 8 10 12 14 16 18 20

LNSE LFII LGOLD

LREER LTBR LIIP

LWPI LCO

Variance Decomposition of LIIP

0

20

40

60

80

100

2 4 6 8 10 12 14 16 18 20

LNSE LFII LGOLD

LREER LTBR LIIP

LWPI LCO

Var iance Decomposition of LWPI

0

20

40

60

80

100

2 4 6 8 10 12 14 16 18 20

LNSE LFII LGOLD

LREER LTBR LIIP

LWPI LCO

Variance Decomposition of LCO

154

5.5.2.6. Impulse Response Function (IRF)

An impulse response refers to the reaction of any dynamic system in response to some

external change. It helps to trace out the responsiveness of the dependent variables in the

VAR to shocks to each of the variables. Table 5.5.2.8 presents the estimates from the impulse

response function of stock market index as against its “own shocks” and the shocks of

Foreign Institutional Investors, gold, Real Effective Exchange Rate, T-bill rates, the Index of

Industrial Production, Inflation and crude oil prices. The result shows that the CNX nifty

index has a negative relationship with its past on the long-run. In its response to the shocks of

Index of Industrial Production, it is observed that there is a negative relationship throughout

the period, whereas, a similar relationship is observed in the case of inflation and crude oil in

the long run, except for the first three periods, i.e. it shows a positive relationship in the short

run. Further, T-bill rates show a positive relationship in the long run, except for the second

period, the result is consistent with the result of short run ARDL estimation. In its response to

the shocks of Foreign Institutional Investors, it is also observed that there is a negative

relation in second to sixth period, i.e. in the short run and thereafter it shows a positive

relationship in the long run. Furthermore, in its response to the shocks of Real Effective

Exchange rate and Gold the negative relationship starts from seventh and eighth period,

respectively, but it shows a positive relationship in the short run. Also, in its response to the

shocks of explanatory variables, CNX nifty does not respond in the first period. The

evidences in favor of the explanations given in the table are also presented in graphical

format in figure 5.5.2.3.

Table 5.5.2.8: Impulse Response Function (IRF)

Period S.E. LNSE LFII LGOR LREER LTBR LIIP LWPI LCO

1 0.054 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

2 0.064 -0.004 -0.005 0.003 0.008 -0.004 -0.002 0.000 0.000

3 0.063 -0.006 -0.004 0.002 0.006 0.003 -0.007 0.000 0.000

4 0.058 -0.006 -0.003 0.002 0.006 0.005 -0.013 -0.001 -0.001

5 0.052 -0.004 -0.001 0.002 0.003 0.004 -0.016 -0.005 -0.005

6 0.047 -0.003 -0.000 0.001 0.001 0.003 -0.019 -0.009 -0.009

7 0.041 -0.002 0.000 0.000 -0.000 0.002 -0.020 -0.012 -0.012

8 0.036 -0.002 0.001 -0.000 -0.001 0.002 -0.022 -0.015 -0.015

9 0.032 -0.002 0.002 -0.001 -0.002 0.001 -0.023 -0.017 -0.017

10 0.028 -0.001 0.003 -0.003 -0.003 0.001 -0.023 -0.019 -0.019

11 0.024 -0.001 0.004 -0.004 -0.003 0.001 -0.024 -0.020 -0.020

12 0.021 -0.001 0.005 -0.006 -0.003 0.000 -0.024 -0.021 -0.021

13 0.019 -0.001 0.006 -0.007 -0.003 0.000 -0.024 -0.021 -0.021

14 0.016 -0.000 0.007 -0.009 -0.002 0.000 -0.023 -0.021 -0.021

15 0.014 -0.000 0.007 -0.010 -0.002 0.000 -0.023 -0.020 -0.020

Cholesky Ordering: LNSE LFII LGOR LREER LTBR LIIP LWPI LCO

155

Figure: 5.5.2.3 Impulse Response Function combined graph

-.04

-.02

.00

.02

.04

.06

.08

2 4 6 8 10 12 14 16 18 20

LNSE LFII LGOLD

LREER LTBR LIIP

LWPI LCO

Response of LNSE to Cholesky

One S.D. Innovations

-0.5

0.0

0.5

1.0

1.5

2.0

2 4 6 8 10 12 14 16 18 20

LNSE LFII LGOLD

LREER LTBR LIIP

LWPI LCO

Response of LFII to Cholesky


-.02

-.01

.00

.01

.02

.03

.04

2 4 6 8 10 12 14 16 18 20

LNSE LFII LGOLD

LREER LTBR LIIP

LWPI LCO

Response of LGOLD to Cholesky


-.010

-.005

.000

.005

.010

.015

.020

2 4 6 8 10 12 14 16 18 20

LNSE LFII LGOLD

LREER LTBR LIIP

LWPI LCO

Response of LREER to Cholesky


-.04

.00

.04

.08

.12

2 4 6 8 10 12 14 16 18 20

LNSE LFII LGOLD

LREER LTBR LIIP

LWPI LCO

Response of LTBR to Cholesky


-.2

.0

.2

.4

.6

.8

2 4 6 8 10 12 14 16 18 20

LNSE LFII LGOLD

LREER LTBR LIIP

LWPI LCO

Response of LIIP to Cholesky


-.1

.0

.1

.2

.3

.4

2 4 6 8 10 12 14 16 18 20

LNSE LFII LGOLD

LREER LTBR LIIP

LWPI LCO

Response of LWPI to Cholesky


-.02

.00

.02

.04

.06

.08

.10

2 4 6 8 10 12 14 16 18 20

LNSE LFII LGOLD

LREER LTBR LIIP

LWPI LCO

Response of LCO to Cholesky


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5.6. Summary

In the present chapter of the study, with the help of modern econometric techniques, an

effort has been made to empirically investigate the relationship between stock prices or stock

market development with different sets of domestic and international macroeconomic

variables. Towards this effort different models has been formulated, using the data for

different time span and frequency, according to the need of the study. The study is

categorised into three major categories, viz.-a-viz., the first category is the empirical

estimation of the study using annual frequency data; the second category is the empirical

estimation of the study using quarterly frequency data; and the third category consist of the

study using monthly frequency data.

The first category, deals with the estimation and discussion on the relationship between

stock prices and macroeconomic variables by using data from the year 1979 to 2014. The

long-run estimates of ARDL test showed that positive and significant relationship exists

between economic growth and stock prices. It also confirms a significant and positive

influence of Exchange Rate and Inflation on stock price movements in India. However, there

exists a negative and significant relationship between crude oil price and stock prices. The

results of long run estimates of ARDL are consistent in the short run as well. The error


deviation is quite high. The result of VECM based granger causality shows that there exists a

short run unidirectional causality running from foreign direct investment, GDP and real

interest rate to BSE in India. Further, the result indicates the presence of long run causality

for the equation with the stock price as the dependent variable. The results of the VDC

analysis show that a major percentage of stock price change is its own innovative shocks.

The second category, i.e. the study with quarterly frequency data, empirically examined

the relationship between macroeconomic variables and stock market development (MCAP) in

India, data from the period 1996:Q1 to 2014:Q3. The long-run estimates of ARDL test

showed that economic growth, FIIs and Trade openness in India significantly influence

market capitalization positively. However, economic growth failed to explain the variation in

stock market growth significantly in the short-run. The results of VECM based granger

causality show that there exists long-run causality running from economic growth, trade

openness, FDI and FII in the long-run towards Stock Market Capitalization, whereas, in

short-run the change in trade openness causes a change in Stock Market Capitalization. The

result of the VDC analysis shows that trade openness is having maximum shock on stock

market capitalization.

157

The third category, i.e. the study with monthly frequency data, empirically examined

the relationship between stock prices and macroeconomic variables, using different time

period for the study and different set of macroeconomic variables, formulating different

models. The first part of the monthly study deals with the estimation and discussion on the

relationship between BSE Sensex and macroeconomic variables by using data from the

period April 2004 to July 2014. The long-run estimates of ARDL test showed that positive

and significant relationship exists between economic growth (IIP), Exchange Rate and

Inflation on stock price movements in India. Further, the study confirms negative and

significant relationship between gold prices and stock prices. The error correction model of

ARDL approach reveals that the adjustment process from the short-run deviation is slow. The

result of VECM based causality found no short run causality running from any of the

variables to BSE in India. Further, the result indicates the presence of long run causality for

the equation with the stock price as the dependent variable. The results of VDC show that a

major percentage of stock price change is its own innovative shocks.

The second part of the monthly study investigated the relationship between

fundamental macroeconomic variables and the index of National Stock Exchange (CNX

Nifty) in India, by using data from the period April 2004 to July 2015. The long-run estimates

of ARDL test showed that negative and significant relationship exists for the crude oil prices,

Inflation with stock prices. The results of the influence of both the variables on stock prices

are consistent in the short run as well. Further, for short-run the study confirms positive and

significant relationship for Gold, T-bill rates and Real Effective Exchange Rate. Furthermore,

for short-run the study confirms positive and significant relationship for Gold, T-bill rates and

Real Effective Exchange Rate. The error correction model of ARDL approach reveals that the

adjustment process from the short-run deviation is high. The result of VECM based causality

found short run causality running from Inflation and crude oil price to National Stock

Exchange in India. The results of VDC analysis and IRF show that a major percentage of

stock prices change is its own innovative shocks.

158

CHAPTER 6

Fiscal policy variables and Stock Market Development in India

6.1. Introduction

It is well said that the current and the future economic growth of the economy depend

on country’s stock market performance and the stock market performance depends on the

country’s fiscal budget. This is partly due to the notion that a large budget deficit could affect

the current and future economic growth of the nation through its effects on the stock markets.

Theoretically, it is true that when the budget of the country is in deficit, it will depress the

stock prices and undermine the investor’s confidence. Falling current investment reduces

future competitiveness of an economy. Roley and Schall (1988) investigated the potential

effects of federal deficits on the stock market and concluded that the growing budget deficit

may influence the conditions of the economy. The empirical evidence from this study

suggested that increases in the structural deficits have historically led to increase in stock

prices and the structural deficit has typically risen during the recession and then decreased

early in the subsequent expansion. In terms of the stock market, a prolonged trade deficit

could have adverse effects. If a country has been importing more goods than it is exporting

for a sustained period of time, it is essentially going into debt (much like a household would).

Over time, investors will notice the decline in spending on domestically produced goods,

which will hurt domestic producers and their stock prices. Given enough time, investors will

realize fewer investment opportunities domestically and begin to invest in foreign stock

markets, as prospects in these markets will be much better. This will lower demand in the

domestic stock market and cause that market to decline.

Hence, this chapter of the study deals with the discussion of empirical results derived

using different econometric techniques, to know the relationship between fiscal policy

variables of India along with some macroeconomic variables and the Indian stock market

development. The econometric methodologies used for estimating the empirical results of the

studies are, Ng-Perron unit root test is utilized to check the order of integration of the

variables. Lag-length selection criteria are used to determine the appropriate lag length for the

model. The long run relationship is examined by implementing the ARDL bounds testing

approach to co-integration. VECM method is used to test the short and long run causality and

variance Decomposition and Impulse Response Function are used to predict long run

exogenous shocks of the variables.

http://www.investopedia.com/terms/d/debt.asp

http://www.investopedia.com/terms/s/stockmarket.asp

http://www.investopedia.com/terms/s/stockmarket.asp

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The chapter has been segmented into four sections; the first section consists of the

extensive literature review based on the relationship between stock market development and

the fiscal and current account deficit, along with controlled macroeconomic variables; second

section encomposes the study of relationship between fiscal deficit and stock prices in India,

using yearly frequency data; the third section is composed of the studying the relationship

between twin deficit and stock market development in India, using yearly frequency data; and

the fourth section is composed of summary of the findings.



the present study focuses on the causal relationship between fiscal policy variables and stock

market development, along with some controlled macroeconomic variables. The first section

will discuss the relevant studies from overall economies, the studies related to Indian

economy will be provided in the second section.

6.2.1. Studies of overall economies other than India

Darrat (1990) employed Akaike’s final prediction error (FPE) criteria in conjunction

with multivariate Granger causality tests to examine whether changes in Canadian stock

returns are predicted by several economic variables including the money base, interest rates,

interest rate volatility, real income, inflation, exchange rates, and fiscal deficits. The

empirical study used monthly data from January 1972 to February 1987. Results indicated

that current stock prices in Canada fully incorporate all available information from monetary

policy instruments, and that stock returns are Granger-caused by lagged changes in fiscal

deficits. This conclusion held even when interest rates, interest rate volatility, real income,

inflation, monetary policy, and exchange rates are excluded from the estimation. Under the

assumption of constant expected stock returns, such findings appear inconsistent with the

stock market efficiency hypothesis.

Abdullah and Hayworth (1993) used seven macroeconomic variables to explain

fluctuations of monthly stock returns in the U.S. stock market using a vector Autoregressions,

Granger causality tests, and impulse response analysis. The macroeconomic variables were

M1, budget deficits, trade deficits, inflation, IP, short-term interest rates, and the S&P 500.

The results indicated that money growth, budget deficits, trade deficits, inflation, and both

short-term and long-term interest rates Granger-cause stock returns. Additionally, stock

returns were positively related to inflation and money growth, but, consistent with economic

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theory, stock returns were negatively related to budget deficits, trade deficits, and both short-

term and long-term interest rates.

Abdullah (1998) employed Sims (1980) forecast error variance decompositions to

analyze the effects of six macroeconomic variable changes on UK stock returns, proxied by

the London share price index. The macroeconomic variables were M1, budget deficits and

surpluses, IP, the consumer price index (CPI), and a long term interest rate. The results

suggested that money growth variability accounts for 22.82% and 19.53% of the variance in

interests’ rates and stock returns, respectively. Therefore, money growth variability

contributed to the uncertainty associated with returns on investments in stocks and other

financial assets. The other variables included in the model were statistically significant in

explaining the variance of UK stock returns.

Adrangi and Allender (1998) provide empirical evidence regarding budget deficit and

stock prices in industrialized countries such as Japan, US, France and Germany by using

Monthly data from 1974-1995. Granger causality, VAR test results showed a negative

relationship between budget deficit and equity returns in the U.S. However, in Japan, France

and Germany change in deficits do not affect the equity market returns.

Hanousek and Filer (2000) examine the possibility that newly-emerging equity markets

in Central Europe exhibit semi-strong form efficiency such that no relationship exists

between lagged values of changes in macroeconomic variables (M1, M2, exports, imports,

trade balance, foreign capital inflow, budget deficit, government debt, CPI, PPI, exchange

rate, and industrial production) and changes in equity prices using Granger causality tests.

They find that while there are connections between real economy and equity market returns

in Poland and Hungary, these links occur with lags, suggesting the possibility of profitable

trading strategies based on public information and rejecting semi-strong efficiency

hypothesis. For Czech Republic and Slovakia, the situation is more complex. In the early

years of their existence, these markets may have possessed elements of semi-strong

efficiency, with both lagged and contemporaneous relationships between real variables and

equity markets. However, these links have disappeared over time. In other words, these stock

markets appear to have become increasingly divorced from reality.

Laopodis (2007) examined the dynamic linkages among the federal budget deficit,

monetary policy and the stock market of US, by using quarterly data from 1960:Q1 to

2004:Q4. The methodology employed was Granger causality, vector autoregressions and

cointegration techniques. The empirical results generally suggest that deficits matter for the

stock market and imply a violation of the Ricardian Equivalence Proposition. Further

https://scholar.google.co.in/citations?user=yIcoTqgAAAAJ&hl=en&oi=sra

161

analyses using taxes and government spending show a higher sensitivity of the stock market

to taxes relative to spending.

Emrah Ozbay (2009) addressed the causal relationship between stock prices and

macroeconomic factors such as interest rate (Overnight Interest Rate, Treasury Bill Interest

Rate), inflation (Producer Price Index, Consumer Price Index), exchange rates, Current

Deficit to Gross Domestic Production (CD/GDP), foreign transactions, money supply and the

real economy, applying monthly data covering the period of 1998:01 to 2008:12 from

Turkey. Granger causality model is employed to explore such relationships. The results of the

study indicated that the interest rate, inflation, CD/GDP, and foreign sale do Granger cause

stock returns, while stock returns do Granger cause money supply, exchange rate, interest rate

inflation (PPI), foreign transactions. Industrial production is indicated as neither the result

variable nor the cause variable of stock price movement. Furthermore, the analysis of the

results showed that interest rates (CPI and PPI) are the negative determinants of stock prices,

while foreign transactions are the positive determinants of stock prices in Turkey.

Asaolu and Ogunmuyiwa (2010) investigated the impact of macroeconomic variables

on Average Share Price (ASP) and goes further to determine whether changes in

macroeconomic variables explain movements in stock prices in Nigeria. Granger Causality

test, Co-integration and Error Correction Method (ECM) were employed on annual time

series data from 1986-2007. Macroeconomic variables used for the study were External Debt

(ED), Interest Rate (IR), Fiscal Deficit (FD), Exchange Rate (EX), Foreign Capital Inflow

(FCI), Investment (INV), Industrial Output (INDO) and Inflation Rate (INF). The results

revealed that a weak relationship exists between ASP and macroeconomic variables in

Nigeria. The findings further showed that ASP is not a leading indicator of macroeconomic

performance in Nigeria.

Hsing, Budden and Phillips (2011) examined the macroeconomic factors that are

expected to influence the Argentine stock market index, using quarterly time series data from

1998:Q1 to 2011:Q2. The exponential GARCH model was employed for the study. The

variables included were real GDP, monetary policy, fiscal policy, exchange rate, inflation rate

and the world stock market as represented by the U.S. stock market index. It was found from

the study that the Argentine stock market index is positively associated with real GDP, the

ratio of M2 money supply to GDP, the peso/USD exchange rate and the U.S. stock market

index. And it is negatively influenced by the money market rate, government spending as a

percent of GDP and the inflation rate.

162

Yu Hsing (2011) examined the effects of selected macroeconomic variables on the

stock market index in South Africa, using exponential GARCH (Nelson, 1991) model. The

quarterly time series data from 1980:Q2 to 2010:Q3 was used. Macroeconomic variables

used were real output, the government deficit, the money supply, domestic real interest rate,

the nominal effective exchange rate, the inflation rate, the world stock market index, and the

world interest rate. The study showed that the South Africa’s stock market Index is positively

influenced by the growth rate of real GDP, the ratio of the money supply to GDP and the U.S.

stock market index and negatively affected by the ratio of the government deficit to GDP, the

domestic real interest rate, the nominal effective exchange rate, the domestic inflation rate,

and the U.S. government bond yield.

Ahmet Ozcan (2012) examined whether macroeconomic variables have a significant

relationship with the ISE industry index by using monthly data for the period from 2003 to

2010. The selected macroeconomic variables for the study include interest rates, consumer

price index, money supply, exchange rate, gold prices, oil prices, current account deficit and

export volume. The Johansen’s cointegration test was adopted to determine the impact of

selected macroeconomic variables on the ISE industry index. The result suggested that

macroeconomic variables exhibit a long run equilibrium relationship with the ISE industry

index.

Osamwonyi and Osagie (2012) attempted to determine the relationship between

macroeconomic variables and the Nigerian capital market index. Macroeconomic variables

used for the study were interest rates, inflation rates, exchange rates, fiscal deficit, GDP and

money supply, from the year 1975 to 2005 with annual frequency. Vector Error Correction

Model (VECM) was used to study the short-run dynamics as well as the long-run relationship

between the stock market index and selected macroeconomic variables. From the study it was

found that the macroeconomic variables influence stock market index in Nigeria.

Şerife and Ergun (2012) identified the effects of selected macroeconomic variables,

including inflation rate, exchange rate, interest rate, current account deficit and the

unemployment rate on stock returns of 45 companies from 11 different sectors.

Autoregressive distributed lag method was employed for the monthly data spanning from

February, 2005 to May, 2012. Results suggested that the exchange rate and interest rate are

the most significant factors in the stock price fluctuations of the companies. Stock returns of

the companies in any industry are very sensitive to the changes in exchange rate and interest

rate.

163

Osahon and Dickson (2013) investigated the effects of fiscal deficits on stock prices in

Nigeria, utilizing vector auto-regression and error-correction mechanisms (ECM) techniques

with annual time series data spanning 1984-2010. The controlled variables used for the study

were interest rate, money supply, volume of transaction, and inflation. The results showed

that fiscal deficit is negatively related to stock prices.

Luqman Safdar (2014) examined the relationship of the twin deficit with the stock

market of Pakistan by using yearly data from 1992 to 2012. Variables used for the study were

Karachi stock exchange index, current account deficit as a percentage of GDP, and Budget

deficit as a percentage of GDP. ARDL approach was used to examine the long run

relationship among variables. The result confirmed that there exists a positive relationship of

twin deficit for Pakistan and for short-run also the result remains the same.


Vuyyuri and Seshaiah (2004) studied the interaction of the budget deficit of India with

other macroeconomic variables such as Nominal effective exchange rate, GDP, CPI and

money supply (M3) giving special emphasis on the budget deficit-exchange rate relationship

using Cointegration approach and Variance Error Correction Models (VECM) for the period

1970-2002, using the annual frequency of data. The results revealed that the variables under

study are cointegrated and there is bidirectional causality between budget deficit and nominal

effective exchange rates. It was also observed that the GDP Granger causes budget deficit,

whereas budget deficit does not.

Saleem et al. (2012) studied that weather changes in deficits cause changes in stock

prices of Pakistan and India and if so, in what direction, using the Johansen Cointegration

technique and Granger Causality Test. Annual data from 1990-2010 was considered for the

study. Stock price indices under consideration were, KSE 100 index for Pakistan and BSE

200 index for India. This study suggested that high developmental expenditure in Pakistan is

the reason for long term positive causal relationship between budget deficit and stock prices

in case of Pakistan while in India a long term negative relationship is observed which is due

to high current expenditures.

Prantik and Vina (2012) studied the relationship between the real economic variables

and the capital market in Indian context, using VAR and Artificial Neural Network. The

paper considers the monthly time series data from 1994 to 2003. Macroeconomic variables

used for the study were national output, fiscal deficit, interest rate, inflation, exchange rate,

money supply, foreign institutional investment BSE Sensex. The finding showed that the

164

variables like interest rate, output, money supply, inflation rate and exchange rate has

considerable influence in the stock market movement.

Aggarwal, P. and Kumar, M. M. (2012) analyzed the relationship between stock prices

and macroeconomic variables in India and US, using monthly frequency data from January

1994 to December 2011. Nifty and S&P 500 were used to represent the stock prices of India

and US, respectively, and the macroeconomic variables used for the study include foreign

institutional investment (FII), exchange rate, gold price/(10 gm), fiscal deficit, industrial

production index (IIP), inflation (WPI), interest rate and gross domestic product (GDP).

Cointegration technique was adopted as the methodology. The results suggested that the

macroeconomic variables have a significant impact on stock prices.

Singh (2014) examined the relationship between macroeconomic variables and the

Indian stock market. The methodology employed was multivariate stepwise regression

analysis and Granger’s causality test, using monthly frequency data from January 2011 to

December 2012. The variables used for the study include the average monthly closing price

of BSE Sensex and S&P CNX Nifty and the explanatory variables were the Index of

Industrial Production (IIP), Wholesale Price Index (WPI), Money Supply (M3), Interest Rates

(IR), Trade Deficit (TD), Foreign Institutional Investment (FII), Exchange rate (ER), Crude

Oil Price (CP) and Gold Price (GP). The result showed significant impact of macroeconomic

variables on the Indian stock market. The gold prices have its negative impact on the stock

market. Further, the study proves that the Indian Stock market improves with the increase in

the inflow of foreign investment. Also, the exchange rate shows its adverse effect on the

stock market during the study period. The Granger causality test confirmed that there exists a

unidirectional causal relationship from the exchange rate to stock market. On the other hand

the causality is also running from the index to trade deficit and foreign institutional investors.

From the above review of literature it can be concluded that the studies, particularly, on

the relationship between fiscal policy variables (Fiscal Deficit and Current Account Deficit)

are very scant. Further, the study finds that there has been no study conducted while taking

into account the effects of the twin deficits, along with other controlled macroeconomic

variables on stock market development using the ARDL approach on the emerging economy

like India. This study attempts to fill this gap by exploring the effects of variations in twin

deficit and other macroeconomic variables towards stock market development in India with

the help of annual time series data.

165

6.3. Relationship between Fiscal Deficits and Stock Prices in India

The Study empirically estimated the effect of fiscal deficit and controlled

macroeconomic variables on stock prices in India. The study uses annual data covering the

period from 1988 to 2014.



the effect of fiscal deficit and other fundamental macroeconomic factors on the stock market.

𝐿𝐵𝑆𝐸𝑡 = α0 + α1𝐿𝐹𝐷𝑡 + α2𝐿𝑀3𝑡 + α3𝐿𝐶𝑃𝐼𝑡 + α4𝐿𝑅𝐼𝑅𝑡 + ε𝑡

(6.1)


Before we proceed to ARDL testing, we test for unit root of the variables to determine

their order of integration. The test for unit root is to ensure that none of the series is

integrated at I(2). In the present study, we have used Ng-Perron unit root tests. The results of

the newly developed Ng-Perron (2001) test developed by Ng-Perron are presented in Table

6.3.1. The analysis of the unit root test results indicates that the variables are integrated order

one (I(1)) and none of the variables are I(2) series10.


Variables With Trend and Intercept Stationarity

Status Mza Mzt MSB MPT

LBSE −0.504 −0.239 0.473 16.189 I (1)

ΔLBSE −11.187 −2.363 0.211 2.195

LFD −1.851 −1.981 0.252 3.120 I (1)

ΔLFD −10.896 −2.322 0.213 2.294

LM3 −0.267 −0.149 0.559 20.937 I (1)

ΔLM3 −10.130 −2.218 0.219 2.539

LCPI −2.918 −0.385 0.135 1.441 I (1)

ΔLCPI −17.659 −1.920 0.250 3.329

LRIR −2.488 −0.074 0.218 2.967 I (1)

ΔLRIR −8.930 −2.102 0.235 2.781

Source: Author’s own Calculation by using E-views 8.0. ∆ denotes the first difference of the

series. L implies that the variables have been transformed in natural logs.

The next step involves estimating the model and determining the rank, r to find the

number of co-integrating relations in our model. In the ARDL model specification, it has

been specified that the number of lags is the same for all the variables taken for the study

because all these variables are incorporated in a model as specified in Equation (6.3.1), where

LBSE is taken as dependent variable and other variables as independent. The optimal lag

10 ARDL technique is applicable irrespective of whether regressor in the model is I(0) or I(1).

166

length was determined by different criterion suitable to the models (Table 6.3.2) using two

maximum lags in the model. The aim is to choose the number of parameters, which

minimizes the value of the information criteria.

Table 6.3.2: Lag Order Selection Criterion

Lag LogL LR FPE AIC SIC HQ

0 −8.382 NA 2.20 × 10−6 1.163 1.410 1.225

1* 116.236 184.218* 4.05 ×10−10 * −7.498* −6.017* −7.126*

2 139.492 24.266 6.78×10−10 −7.347 −4.631 −6.664

* Indicates lag order selected by the criterion;

LR: sequential modified LR test statistic (each test at the 5% level);

FPE: Final prediction error;

AIC: Akaike information criterion;

SIC: Schwarz information criterion;

HQ: Hannan-Quinn information criterion.

6.3.3. ARDL Bounds Test

The paper estimates the ARDL bounds test approach to co-integration. We used AIC,

LR, SIC, HQ and FPE for selecting a minimum lag order of 1 for conditional ARDL-VECM,

by applying the procedure in OLS regression for the first difference part of the Equation (6.1)

and then testing for the joint significance of the parameters of the lagged level variables when

added to the first regression. The F-Statistics test the joint Null hypothesis that the

coefficients of lagged level variables are zero. Table 6.3.3 reports the result of the calculated

F-Statistics which are more than UCB which is at 5% (Pesaran (2001) or Narayan (2005)).

Thus the Null Hypothesis of no co-integration is rejected, implying long run co-integrating

relationship amongst the stock market index and economic growth. The estimated statistics

show that the model specification seems to pass all diagnostic tests successfully.



Estimated Equation : LBSE = F (LFD LM3 LCPI LRIR)

Indicators

Optimal lag 01








167

The results of long run estimation have been shown in Table 6.3.4, which shows that

the coefficient of FD is negative and significant at the 1% level. It means that FD has a

significant negative relationship with the stock market index. This implies that FD is

negatively affecting stock market index, which shows that with the increase in Fiscal deficit,

stock market index is decreasing. This is due to the fact that the extent of fiscal deficit and

means of financing it, influence the money supply and the interest rate in the economy. High

interest rates mean higher cost of capital for the industry, lower profits and hence lower stock

prices. The findings are consistent with Geske and Roll (1983), Laopodis (2006), Asaolu and

Ogunmuyiwa (2011), and Quayes (2010) Saleem and Yasir et al. (For India, 2012)); but the

contrast to the finding of Van Aarle, et al. (2003), Udegbunam and Oaikhenan (2012).

The coefficient of money supply has a positive impact on the Stock Market and it is

significant at the 1% level. The value of coefficient implies that a 1% increase in M3 leads to

an increase in stock market index with the fact that the increase in the money supply meaning

that money demands are increasing in anticipation of an increase in economic activity. Higher

economic activity implies higher expected profitability, which causes stock prices to rise11.

Considering the impact of inflation, it is significant at 1% and has a positive impact on a

market index. This finding supports the views of Kessel (1956) and Ioannidis et al. (2005).

The coefficient of real interest rate is positive, but not significant which shows there is no

significant relationship between LRIR and stock market index.

The results of short run dynamics using the ECM version of ARDL are reported in

Table 6.3.5. The short run adjustment process is examined from the ECM coefficient. The

coefficient of the error correction term is an adjustment coefficient capturing the proportion

of the disequilibrium in economic growth in one period which is corrected in the next period.

The coefficient generally represents the speed of adjustment towards equilibrium, that means

how quickly the equilibrium is established if the path is in disequilibrium. The larger the error

term, the earlier the economy’s return to the equilibrium rate of growth; following a shock.

The coefficient lies between 0 and −1, the equilibrium is converging to the long run

equilibrium path and is responsive to any external shocks.

11 Homa and Jaffe [52].

168

Table 6.3.4: Estimated Long-run Coefficients using ARDL Approach




LFD -1.597*** -4.201 [0.001]

LM3 2.840*** 7.226 [0.000]

LCPI 0.931*** 4.751 [0.000]

LRIR 0.005 0.040 [0.968]

CONS -1.930 -0.875 [0.394]


R2 0.980

Adjusted R2 0.971

F Statistics 112.92

D.W. Stat 2.2876



Ramsey reset test, F 0.197 [0.657] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC);

(2)*** indicates significant at the 1 percent level of significance.

Table 6.3.5: Estimated Short-run Coefficients using ARDL Approach



Coefficient t-Ratio Prob. Values

ΔLFD -0.211 -1.330 [0.200]

ΔLM3 1.453*** 3.795 [0.001]

ΔLCPI 0.274* 2.811 [0.012]

ΔLRIR 0.002 0.040 [0.968]

ΔCONS -0.987 -0.815 [0.425]

ECM t-1 -0.511 -5.547 [0.000]


R2 0.716

Adjusted R2 0.592

D.W. Stat 2.288

SE Regression 0.151

RSS 0.366

F Statistics 8.072 [0.000] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC);

(2) *** and * indicate significant at the 1% and 10% level of significance, respectively.

The comparison of long run coefficients with that of short run ECM coefficients

confirms that the directions of relationships are maintained. However, the Fiscal Deficit was

negative and significant at the 1% level in the long run and failed to explain the variation in

the stock market index significantly in the short run. This may be due to the fact that

investor’s behavior in the stock market is regulated by long term fiscal deficit and may not

bother about short term fluctuations in it. Other variables, such as M3 (1%) and CPI (10%)

are significant and positively influencing the market index both in the short run as well as in

the long run. Here also, the coefficient of LRIR is positive, but not significant in both long

run as well as short run. Table 6.3.5 also shows that the coefficient of ECM(t−1)is significant

169

at the 1% level, which indicates that the speed of adjustment for a short run to reach long run

is significant. Further, the error correction term is −0.51 with expected sign, suggesting that

when the stock price is above or below its equilibrium level, it adjusts by almost 51% per

year and the full convergence process takes about two years.


The short run and long run Granger causality test findings are reported in Table 6.3.6.

In the above table the values mentioned under the heading ECM(t−1) are indicating long run

Granger causality, whereas, the rest of the values are the values of F-test. The results of Table

6.3.6 indicate short run unidirectional causality running from LFD to LBSE in India. It is also

observed that error correction term is statistically significant for specification with LBSE as

the dependent variable which indicates that there exists a long run causal relationship among

the variable with LBSE as the dependent variable. The result is also confirmed by the ARDL

test statistics.


Dependent Variable


Short Run Independent Variables Long Run

Independent Variables

∆LBSE ∆LFD ∆LCPI ∆LM3 ∆LRIR ECM(t−1)

∆LBSE - 4.464* 0.458 0.361 0.000 −4.874*

∆LFD 0.145 - 0.132 0.667 1.403 −1.061

∆LCPI 0.063 0.062 - 0.115 0.017 −0.042

∆LM3 0.098 0.653 0.511 - 0.053 −0.505

∆LRIR 10.031* 9.702* 0.183 8.907* - −0.337

* Indicates 1% level of significance.

The robustness of the short run results are investigated with the help of diagnostic and





(CUSUM) (Figure 6.3.1) and the cumulative sum of square (CUSUMSQ) plots is between



confirms that the models seem to be steady and are specified as appropriate.

170


6.3.5. Variance Decomposition (VDC) Analysis

Brooks (2008) stated that variance decomposition accounts for the share of variations in

the endogenous variables resulting from the endogenous variables and the transmission to all

other variables in the system, because of the dynamic nature of the VAR. Hence, VDC gives

the proportion of the movements in the dependent variables that are due to their “own”

shocks, versus shocks to the other variables. It is pointed out by Pesaran and Shin (2001) that

the variable decomposition method shows the contribution in one variable due to innovation

shocks stemming in the forcing variables. The variance decomposition indicates the amount

of information each variable contributes to the other variables in the auto regression. It

determines how much of the forecast error variance of each of the variables can be explained

by exogenous shocks to the other variables. The main advantage of this approach is it is

insensitive to the ordering of the variables. The residuals generated by the VAR models are

usually contemporaneously correlated. This is because in a VAR model only lagged

endogenous variables are admitted on the right-hand side of each equation (in addition to a

constant term), and hence all the contemporaneous shocks which impact on LBSE are forced

to feed through the residuals (Kuszczak and Murray, 1986). While this may not cause a

problem in the estimation of the VAR model, the impulse responses and variance

decompositions derived from the initial estimates of the VAR model could be affected such

that any adjustment to the order in which the variables are entered in the system could

produce different results (Kuszczak and Murray, 1986). Thus, there is a need to impose some

restrictions when estimating the VAR model to identify the VDC. In this regard, a common

approach is the Cholesky decomposition, which was originally applied by Sims (1980), The

Cholesky decomposition overcomes the problem of contemporaneous relationships among

the innovations error terms within the estimated VAR model by identifying the structural

shocks such that the covariance matrix of the estimated residuals is lower triangular.

171

The ordering of the variables was done after going through the iteration process and we

have tried various ordering of variables to check the consistency of the results. The main

principal of Cholesky ordering is that the first variable should be selected such that it is the

only one with potential immediate impact on other variables. The ordering of the variables

was based on the assumption that a shock in the real interest rate would be

contemporaneously transmitted to money supply, fiscal deficit, inflation and stock prices, and

a shock in money supply would be transmitted to the fiscal deficit, which would, in turn,

affect inflation. However, this shock in money supply will not affect the interest rate variable.

Similarly, the shock in fiscal deficit would contemporaneously affect inflation and stock

prices, but not to the money supply and interest rate.

The results of the VDC are presented in Table 6.3.7. The column SE is the forecast

error of the variable to be forecast at different lengths into the future. The empirical evidence

indicates that 57.86% of stock prices change is contributed by its own innovative shocks.

Further shock in fiscal deficit explains stock prices by 21.03% and the money supply

contributes to market capitalization by 16.08%. The share of other variables is very minimal.

Thus, the result indicates that the stock prices behave exogenously. During the initial period,

the variation in changes in stock prices is caused by the stock price itself.

As time passes, the change in LBSE is contributed by fiscal deficit. However, the impact

exerted by other macroeconomic variables on stock prices is very low. Therefore, it can be

said that over the horizon of 10 years, fiscal deficit plays the most important role, explaining

21% variation in stock market prices in India.


Period S.E. LBSE LCPI LFD LM3 LRIR

1 0.193 100.000 0.000 0.000 0.000 0.000

2 0.283 84.980 0.009 12.600 0.048 2.361

3 0.333 76.863 0.313 19.045 1.508 2.269

4 0.362 70.667 1.028 21.937 4.441 1.925

5 0.380 65.830 1.737 22.716 7.835 1.880

6 0.391 62.444 2.180 22.449 10.843 2.081

7 0.398 60.313 2.346 21.888 13.100 2.350

8 0.403 59.051 2.352 21.427 14.606 2.562

9 0.406 58.306 2.320 21.165 15.531 2.676

10 0.408 57.861 2.321 21.037 16.068 2.711

Cholesky Ordering: LBSE LCPI LFD LM3 LRIR

Note: All the values of VDC are calculated using E-views 8.0.

172

6.4. Relationship between Twin Deficit and Stock Market Development in India

Recently the role of twin deficit in the economy and its potential effects on stock prices

attracted some serious consideration from academics and policymakers in both developed and

developing economies. The linkage of twin deficits is largely observed due to its important

implication as large twin deficits could affect current and future economic growth and their

imbalance could impair economic activity, undermine wealth creation and can be risky for

the well-being of the nation. Twin deficit identity is used to refer to a nation’s current account

deficit and simultaneous fiscal deficit and it is a shorthand summary for two related economic

problems, the government budget deficit and the current account (international trade) deficit.

From a policy perspective, if the rising current account deficit is indeed due to the increasing

fiscal deficit, then the external balance cannot be remedied unless the policies that address to

government deficits are not first put in place. Further, it can be said that the twin deficits

(Fiscal deficit and Current account deficit) through their effects on macroeconomic variables,

can significantly influence stock market development i.e. market capitalization. Hence, the

Study empirically estimated the effect of twin deficit and controlled macroeconomic

variables on market capitalization in India. The study uses annual data covering the period

from 1979 to 2014.



the effect of twin deficit and other controlled macroeconomic factors on market

capitalization.

𝐿𝑀𝐶𝐴𝑃 = 𝛼0 + 𝛼1𝐿𝐶𝐴𝐷 + 𝛼2𝐿𝐹𝐷 + 𝛼3𝐿𝐺𝐷𝑃 + 𝛼4𝐿𝐶𝑂 + 𝛼5𝐿𝑇𝑂 + 𝛼5𝐿𝑅𝐸𝐸𝑅 + 휀𝑡

(6.2)





stationarity tests are presented in Table 6.4.1. The results show that all the variables are non-



once, all the variables were confirmed to be stationary. It is, therefore, worth concluding that

all the variables used in this study are integrated of order one i.e. difference stationary I(1).

Therefore the study uses autoregressive distributed lag (ARDL) approach to co-integration. In

173

addition, it is also important to ascertain that the optimal lag order of the model is chosen

appropriately so that the error terms of the equations are not serially correlated.

Consequently, the lag order should be high enough so that the conditional ECM is not subject

to over parameterization problems (Narayan, 2005; Pesaran 2001). The results of these tests

are presented in Table 6.4.2. The results of Table 6.4.2 suggest that the optimal lag length is

one based on both LR, FPE, SIC and HQ.


Variables Without trend and intercept Stationarity


LMCAP 1.030 1.032 1.003 70.125 I (1)

ΔLMCAP -14.186 -2.663 0.188 1.727

LCAD -5.609 -1.636 0.292 4.478 I (1)

ΔLCAD -13.312 -2.453 0.184 2.317

LFD -10.530 -2.286 0.217 2.360 I (1)

ΔLFD -15.730 -2.780 0.177 1.647

LGDP 2.210 2.215 1.002 86.223 I (1)

ΔLGDP -15.290 -2.717 0.178 1.780

LCO -2.859 -1.172 0.410 8.501 I (1)

ΔLCO -16.391 -2.820 0.172 1.652

LTO 1.457 1.558 1.070 85.539 I (1)

ΔLTO -16.494 -2.869 0.174 1.494

LREER 0.143 0.093 0.652 28.471 I (1)

ΔLREER -14.298 -2.640 0.185 1.840 Source: Author’s own Calculation by using E-views 8.0


natural logs.

Table 6.4.2: Lag Order Selection Criterion Lag LogL LR FPE AIC SIC HQ

0 -26.42035 NA 1.79e-08 2.025476 2.342917 2.132285

1 190.3338 328.4154 7.36e-13 -8.141444 -5.601916* -7.286970

2 263.7382 80.07755* 2.59e-13* -9.620500* -4.858885 -8.018361* * indicates lag order selected by the criterion






6.4.3. ARDL Bounds test

After determining the order of integration of all the variables in table 6.4.1, the next





174


variables in the equation (6.2) are zero. Table 6.4.3, reports the result of the calculated F-



bound critical value at the 5 percent level. This suggests that there is a cointegrating

relationship among the variables included in the model, i.e. Stock Market Capitalization

(LMCAP), Current Account Deficit (LCAD), Fiscal Deficit (LFD), Real GDP (LGDP),

Crude Oil (LCO), Trade Openness (LTO) and Real Effective Exchange Rate (LREER).



Estimated Equation: LMCAP = F (LCAD LFD LGDP LCO LTO LREER)

Indicators

Optimal lag 02








The second step is to estimate the long and short-run estimates of ARDL test. The long

run results are illustrated in Table 6.4.4. The results show that the coefficient of Current

account Deficit and Crude oil are statistically significant and negative at 3% and 1%

respectively. It is evident from the table that 1% increase in CAD and 1% increase in crude

oil leads to 0.266% and 0.638%, decrease in Market Capitalization (LMCAP), respectively.

The findings for crude oil are consistent with Miller and Ratti (2009) and Basher et al.

(2012). The result found in this study implies that a prolonged trade deficit could have an

adverse effect on the stock market in the long-run. As investors notice the decline in spending

overtime on domestically produced goods which will hurt domestic producers and their stock

prices. Given enough time, investors will realize fewer investment opportunities domestically

and begun to invest in foreign stock markets, as prospects in these markets are better, this will

lower demand in the domestic market and cause stock market volume to decline.

Whereas, the coefficient of Real GDP (LGDP) and Real Effective Exchange Rate

(LREER) are positive and significant at 1%. It is evident from the table that 1% increase in

175

Real GDP and exchange rate leads to 3.265% and 1.381%, respectively, increase in Market

Capitalization (LMCAP). Therefore, GDP and Exchange rate have a significant positive

relationship favourably affecting market capitalization. The findings are consistent with Fama

(1981, 1990), Chen et al. (1986) for GDP and Nadeem and Zakir (2009) for Exchange Rate.

Fiscal deficit does not show any impact on stock prices, and the finding is consistent with

Adrangi and Allender (1998)12.

Table 6.4.4: Estimated Long Run Coefficients using ARDL Approach




LCAD -0.266** -2.245 [0.033]

LFD -0.220 -0.634 [0.531]

LGDP 3.265*** 6.579 [0.000]

LCO -0.638*** -2.688 [0.012]

LTO -0.832 -1.224 [0.232]

LREER 1.381*** 4.644 [0.000]

CONS -9.994 -7.943 [0.000]


R2 0.986

Adjusted R2 0.983

F Statistics 272.249 [0.000]

D.W. Stat 1.656




(2) ** and *** indicate significant at 5 and 1 percent level of significance, respectively. Values


The short-run relationship of the macroeconomic variables on market capitalization is

presented in Table 6.4.5. As can be seen from the table, CAD and Trade Openness (LTO) has

a significant and negative impact on market capitalization in the short run at 1% and 10%

level, respectively. One can say that 1% increase in CAD and 1% increase in trade openness

leads to 0.293% and 1.243%, decrease in Market Capitalization (LMCAP), respectively.

Whereas, GDP and Exchange rate are significantly positive at 1% and 5% level,

respectively, in short-run. The short run adjustment process is examined from the ECM

coefficient. The coefficient lies between 0 and -1, the equilibrium is converging to the long

run equilibrium path, is responsive to any external shocks. However, if the value is positive,

the equilibrium will be divergent from the reported values of ECM test. The coefficient of the

12 Adrangi and Allender (1998) examine the evidence regarding budget deficit and stock prices in industrialized

countries such as Japan, US, France and Germany. Granger causality and VAR test shows the negative relation

of the budget deficit and stock prices for US; however, in other countries deficits do not affect stock prices.

176

lagged error-correction term (-0.681) is significant at the 1% level of significance. The

coefficient implies that a deviation from the equilibrium level of market capitalization in the

current period will be corrected by 68 percent in the next period to resort the equilibrium.

Table 6.4.5: Estimated Short Run Coefficients using ARDL Approach




ΔLCAD -0.293*** -2.504 [0.019]

ΔLFD -0.471 -1.254 [0.221]

ΔLGDP 2.765*** 4.737 [0.000]

ΔLCO -0.141 -0.500 [0.621]

ΔLTO -1.243* -1.739 [0.094]

ΔLREER 0.948** 2.338 [0.027]

ΔCONS -7.328 -3.442 [0.002]

ECM t-1 -0.681 -3.274 [0.003]


R2 0.563

Adjusted R2 0.424

D.W. Stat 2.183

SE Regression 0.283

RSS 1.997



Values in [#] are probability values.


The results of table 6.4.6 indicate that there exists short-run causality running from

current account deficit, Real GDP, trade openness and crude oil to market capitalization in

India. In fact, trade openness is having a bi-directional causality with MCAP in short-run. It

is also observed that error correction term is statistically significant for specification with

LMCAP as the dependent variable which indicate that there exist a long-run causal

relationship between the variable with LMCAP as the dependent variable. This result is also

confirmed by the ARDL test statistics.


Dependent

variable



ΔLMCAP ΔLCAD ΔLFD ΔLGDP ΔLCO ΔLTO ΔLREER ECM(t-1)

ΔLMCAP - 2.437** -0.666 3.240*** -2.962** 2.390** 0.306 -7.790***

ΔLCAD -1.527 - -1.975** 0.005 2.012** 0.307 -1.192 1.921

ΔLFD -0.357 -1.866* - 1.819 0.936 -1.216 1.279 0.392

ΔLGDP -0.582 -1.123 -3.089*** - 1.418 -0.131 -1.601 0.370

ΔLCO -1.036 -2.316** -1.456 2.447** - -0.727 1.070 -0.590

ΔLTO 7.165*** 2.312** 4.390*** 3.226*** -3.521*** - 3.444*** -6.320***

ΔLREER 1.287 0.145 1.917* 0.364 -1.263 0.533 - 0.764


177






(CUSUM) and the cumulative sum of square (CUSUMSQ) plots (Figure 6.4.1) are between





6.4.5. Variance Decomposition (VDC) Analysis:







variables. The results of the VDC are presented in table 6.4.7. The empirical evidence

indicates that 64.83% of market capitalization change is contributed by its own innovative

shocks. Further shock in crude oil price explains market capitalization by 14.61%. CAD

contributes to market capitalization by 10.02%, fiscal deficit and exchange rate contributes

5.03% and 3.59% respectively. The share of other variables is very minimal.


178


Period S.E. LMCAP LCAD LFD LGDP LCO LTO LREER

1 0.313 100.000 0.000 0.000 0.000 0.000 0.000 0.000

2 0.385 77.696 6.303 3.053 1.582 10.505 0.560 0.297

3 0.427 74.314 7.135 3.986 1.618 11.795 0.564 0.585

4 0.457 71.578 7.789 4.573 1.626 12.928 0.571 0.932

5 0.480 70.031 8.155 4.857 1.590 13.527 0.553 1.284

6 0.499 68.911 8.449 4.996 1.555 13.933 0.529 1.624

7 0.516 68.074 8.702 5.052 1.525 14.200 0.502 1.941

8 0.530 67.409 8.931 5.066 1.503 14.379 0.477 2.232

9 0.544 66.863 9.141 5.059 1.488 14.498 0.455 2.494

10 0.556 66.402 9.333 5.044 1.481 14.572 0.435 2.729

11 0.567 66.005 9.507 5.030 1.481 14.616 0.417 2.940

12 0.578 65.659 9.663 5.020 1.487 14.637 0.402 3.129

13 0.588 65.352 9.800 5.018 1.499 14.641 0.389 3.299

14 0.597 65.078 9.919 5.024 1.515 14.633 0.377 3.452

15 0.606 64.830 10.022 5.039 1.534 14.615 0.366 3.591

Cholesky Ordering: LMCAP LCAD LFD LGDP LCO LTO LREER

Figure 6.4.2.: VDC analysis combined graph

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10

LMCAP LCAD LFD

LGDP LCR LTO

LEX

Variance Decomposition of LMCAP

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10

LMCAP LCAD LFD

LGDP LCR LTO

LEX

Variance Decomposition of LCAD

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10

LMCAP LCAD LFD

LGDP LCR LTO

LEX

Variance Decomposition of LFD

0

10

20

30

40

50

60

70

1 2 3 4 5 6 7 8 9 10

LMCAP LCAD LFD

LGDP LCR LTO

LEX

Variance Decomposition of LGDP

0

20

40

60

80

1 2 3 4 5 6 7 8 9 10

LMCAP LCAD LFD

LGDP LCR LTO

LEX

Variance Decomposition of LCR

0

10

20

30

40

50

60

70

1 2 3 4 5 6 7 8 9 10

LMCAP LCAD LFD

LGDP LCR LTO

LEX

Var iance Decomposition of LTO

0

10

20

30

40

50

60

1 2 3 4 5 6 7 8 9 10

LMCAP LCAD LFD

LGDP LCR LTO

LEX

Variance Decomposition of LEX

6.4.6. Impulse Response Function (IRF)

An impulse response refers to the reaction of any dynamic system in response to some

external changes. It helps to trace out the responsiveness of the dependent variables in the

VAR to shocks to each of the variables. Table 6.4.8 presents the estimates from the impulse

response function of stock market development as against its “own shocks” and the shocks of

179

current account deficit, fiscal deficit, GDP, crude oil prices, trade openness and exchange rate

over the time period. The result shows that market capitalization has a positive relationship

with its past on the long-run. In its response to the shocks of current account deficit, GDP and

exchange rate, it is observed that there is a positive relationship in the long run and reverse is

observed in the case for the shocks of fiscal deficits and crude oil prices, i.e. there is a

negative relationship in the long run throughout the period for fiscal deficit and crude oil

prices. Also, in its response to the shocks of explanatory variables, market capitalization does

not respond in the first period. The evidences in favor of the explanations given in the table

are also presented in graphical format in figure 6.4.3.

Table 6.4.8: Impulse Response Function (IRF)

Figure: 6.4.3. Impulse Response Function combined graph

-.2

-.1

.0

.1

.2

.3

.4

1 2 3 4 5 6 7 8 9 10

LMCAP LCAD LFD

LGDP LCR LTO

LEX

Response of LMCAP to Cholesky


-.2

.0

.2

.4

.6

1 2 3 4 5 6 7 8 9 10

LMCAP LCAD LFD

LGDP LCR LTO

LEX

Response of LCAD to Cholesky


-.10

-.05

.00

.05

.10

.15

.20

1 2 3 4 5 6 7 8 9 10

LMCAP LCAD LFD

LGDP LCR LTO

LEX

Response of LFD to Cholesky


-.06

-.04

-.02

.00

.02

.04

.06

1 2 3 4 5 6 7 8 9 10

LMCAP LCAD LFD

LGDP LCR LTO

LEX

Response of LGDP to Cholesky


-.10

-.05

.00

.05

.10

.15

.20

1 2 3 4 5 6 7 8 9 10

LMCAP LCAD LFD

LGDP LCR LTO

LEX

Response of LCR to Cholesky


-.06

-.04

-.02

.00

.02

.04

.06

1 2 3 4 5 6 7 8 9 10

LMCAP LCAD LFD

LGDP LCR LTO

LEX

Response of LTO to Cholesky


-.06

-.04

-.02

.00

.02

.04

1 2 3 4 5 6 7 8 9 10

LMCAP LCAD LFD

LGDP LCR LTO

LEX

Response of LEX to Cholesky


Period LMCAP LCAD LFD LGDP LCO LTO LREER

1 0.313 0.000 0.000 0.000 0.000 0.000 0.000

2 0.130 0.096 -0.067 0.048 -0.124 0.028 0.020

3 0.142 0.060 -0.052 0.024 -0.077 0.014 0.025

4 0.117 0.057 -0.047 0.021 -0.074 0.012 0.029

5 0.109 0.050 -0.040 0.016 -0.064 0.009 0.031

6 0.101 0.047 -0.035 0.014 -0.059 0.006 0.033

7 0.096 0.045 -0.031 0.013 -0.055 0.004 0.033

8 0.093 0.044 -0.028 0.013 -0.051 0.002 0.033

9 0.090 0.043 -0.026 0.013 -0.049 0.001 0.033

10 0.087 0.042 -0.025 0.013 -0.046 0.000 0.032

Cholesky Ordering: LMCAP LCAD LFD LGDP LCO LTO LREER

180

6.5. Summary

In the present chapter of the study, with the help of modern econometric techniques, an

effort has been made to empirically investigate the relationship between stock prices or stock

market development with fiscal policy variables in India, along with different sets of

domestic and international macroeconomic variables. Towards this effort two different

models has been formulated, using the data for different time span and annual frequency,

according to the need of the study and availability of the data. The first model is formulated

for the empirical estimation of the study using annual frequency data to know the relationship

between stock prices and fiscal deficit; and the second model is formulated for the empirical

estimation of the study using annual frequency data to study the relationship between stock

market development in India and twin deficits.

The first part of the study discusses the estimation results of the relationship between

BSE Sensex and fiscal deficit by employing data from the period 1988 to 2014. The test

statistics of the unit root suggest that none of the variables included in the study are I(2). The

bounds test confirms that the estimated equation and the series are co-integrated. The ARDL

results suggest a long run negative and significant relationship exists between budget deficit

and stock prices. However, the relationship does not show any significant results in the short

run. Further, the money supply and inflation in India influence stock prices positively both in

the long run as well as in the short run. The result of VECM based causality shows that there

exist a short run Granger causality running from fiscal deficit to stock price. Further, the

result indicates the presence of long run Granger causality for the equation with the stock

price as the dependent variable. The results of the VDC analysis show that the fiscal deficit

plays an important role in explaining the variation in stock prices in India.

The second part of the study discusses the estimation results of the relationship between

stock market development (MCAP) and twin deficit by applying data from the year 1979 to

2014. The long-run estimates of ARDL test confirmed the negative and significant

relationship between the current account deficit (CAD) and crude oil with stock market

capitalization. It also confirms a significant and positive influence of Real GDP and

Exchange Rate on market capitalization in India both in long-run and short-run. Further, for

short-run the study confirms negative and significant relationship between CAD and trade

openness with stock market capitalizations in India. The error correction model of ARDL

approach reveals that the adjustment process from the short-run deviation is high. The result

of VECM found short run causality running from CAD, Real GDP and crude oil to market

capitalization in India. Additionally, trade openness is having a bi-directional causality with

181

MCAP in short-run. Further, the result indicates the presence of long run causality for the

equation with a market capitalization as the dependent variable. The results of VDC analysis

and IRF show that a major percentage of market capitalization change is its own innovative

shocks.

182

CHAPTER 7

Macroeconomic Determinants of Sectoral Stock Market Development in India

7.1. Introduction

It is a proved fact that aggregate GDP affects composite stock market indexes, but

sometimes a change in aggregate GDP, for example, an increase in aggregate GDP cause

composite index to increase, but an increase in composite index does not mean that all the

sectors of the composite index or all the sector indices are increasing, a few of the sectors

cannot perform well even if the GDP of the economy is increasing, while others can

outperform the market. Further, it should also be noticed that, with the change in the GDP of

a particular sector, it is not necessary that the stock market changes, but if any of the sector

performs extremely well and attains a significant change in GDP than it can give a boost to

the composite stock index. All these phenomena can be better understood with the help of

sector wise study. Therefore, an attempt has been taken to study the impact of sectoral

contribution of GDP in explaining the variation in the sectoral stock market index. Further,

apart from sectoral GDP, few other macroeconomic variables are expected to influence the

stock prices of a specific sector. Hence, the study also attains to identify the impact of

sectoral GDP, along with certain controlled variables, on respective sectoral indices. The

study uses three different sectors, viz-a-viz, manufacturing sector index, electricity, gas and

water sector index and service sector index of BSE and the respective sectors of GDP are; (1)

manufacturing sector share in GDP, (2) electricity, gas and water sector share in GDP and (3)

service sector share in GDP. The three sectors have been chosen for the study because these

three sectors are the fastest growing sectors in India.

The service sector contributes maximum to the India’s GDP with 57% share of GDP in

2013-14, up from 15% in 1950-51.Whereas, manufacturing sector contributes about 15.1% of

India’s GDP and 50% of the India’s export, which shows that they are playing a significant

role in Indian economy. While the electricity, gas and water supply sector is also an

important part of the Indian economy from an industrial point of view, as because this is the

basic necessity of any of the industry to develop. This sector constitutes a small portion of

India’s GDP with a 2.5% share of GDP, in 2013-14, up from 0.24% in 1950-51. The three

indices (manufacturing index; electricity, gas and water supply index; and service index) are

taken according the sectoral GDP. It is a general belief that all the indices should be

positively affected by the respective GDP, because the increase in the GDP of a particular

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sector gives confidence to investors which leads to increase in the index of that particular

sector.

Thus, this chapter of the study deals with the discussion of empirical results derived

using different econometric techniques, to examine the cointegration and long-run stability

between the sectoral BSE indices with sectoral contribution in GDP along with other

controlled variables. The econometric methodologies used for estimating the empirical results

of the studies are, Ng-Perron unit root test is utilized to check the order of integration of the

variables. Lag-length selection criteria are used to determine the appropriate lag length for the

model. The long run relationship is examined by implementing the ARDL bounds testing

approach to co-integration. VECM method is used to test the short and long run causality and

the Variance Decomposition (VDC) is also used to explore the degree of exogeneity of the

variables involved in this study. For the purpose of analysis quarterly data starting from the

year 2003:Q4 to 2014:Q4 are used.



the present study focuses on the causal relationship between different sectors of stock market

and macroeconomic factors. Therefore, in this section, we will discuss the studies showing

the relationship between macroeconomic variables and different sectors of stock market. The

first section will discuss the relevant studies from overall economies, the studies related to

Indian economy will be provided in the second section.

7.2.1. Studies of overall economies other than India

Ta and Teo (1985) studied Co-movement and cointegration among sectoral stock

market indices, and observed high correlation among six Singapore sector indices in the

period 1975 to 1984 and the overall SES market return (e.g. All-S Equities Industrial and

Commercial Index, SES All-S Equities Finance Index, SES All-S Equities Property Index,

SES All-S Hotel Index, SES All-S Plantation Index and SES All-S Mining Index). Using

daily data in examining the relationships, they had concluded that sector returns were highly

correlated to each other, although such correlations did not remain stable over time.

Sun and Brannman (1994) found a single long-run relationship among the SES All-S

Equities Industrial & Commercial Index, Finance Index, Hotel Index, and Property Index by

applying annual data from 1975 to 1992. The study employed Johansen’s (1988) VECM to

examine the long-run equilibrium relationship between selected macroeconomic variables

and stock market sector indices represented on the Stock Exchange of Singapore (recently

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demutualized and renamed the Singapore Exchange (SGX)): the Finance Index, the Property

Index, and the Hotel Index. The choice of macroeconomic variables and the hypothesized

relations with the sector indices are discussed next.

Maysami, Howe and Hamzah (2004) examined the long-term equilibrium relationships

between selected macroeconomic variables and the Singapore stock market index (STI), as

well as with various Singapore Exchange Sector indices—the finance index, the property

index, and the hotel index. Monthly time series data from January 1989 to December 2001

was considered. Macroeconomic variables used for the study were interest rate (short-run),

inflation (CPI), exchange rate, industrial production and money supply (M2). The study

concluded that the Singapore’s stock market and the property index form cointegration

relationship with changes in the short and long-term interest rates, industrial production, price

levels, exchange rate and money supply.

Maysami et al. (2005) studied the existence of long-run cointegrating relationship

between stocks listed dually in the US and Singapore stock markets. In addition, they used

Johansen’s (1988) VECM, to examine the co-movement between sectoral stock indices of the

U.S. and Singapore, through examining whether the S&P 500 Electronics (Semiconductor)

Price Index leads Stock Exchange of Singapore’s Electronics Price Index. While their results

confirmed the long term cointegrating sectoral relationships, there was evidence pointing to a

short-term disequilibria in the prices of dually listed stocks, leading to the conclusion that

short-run arbitrage opportunities may exist.

Gunsel et al. (2007) performed a sectoral study on the effect of macroeconomic factors,

as well as industry specific variables, risk premia, and sectoral unanticipated dividend yields

on London Stock Exchange returns. They found evidence that the variables indentified (term

structure of interest rates, unanticipated inflation, unanticipated sectoral industrial production,

risk premium, real exchange rate, money supply and sectoral unanticipated dividend yield) all

had a significant effect on investment returns. More conclusive findings were that different

industries sometimes had opposite results for the variables used. Unexpected inflation was

found to have a significant and negative effect on the food, beverage and tobacco sectors. The

effective exchange rate had a significant and positive effect on the chemical sector, but a

negative and significant effect on the building materials and merchants, and engineering

sectors. Money supply had a positive and significant effect on the building materials and

merchants, as well as the food, beverage & tobacco sectors while a negative relationship was

found with household goods and textiles. Onemonth-lagged-term structure of interest rate

was found to have a positive and significant relationship with the construction; food,

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beverage and tobacco; oil exploration and production; and electronic and electrical equipment

sectors. Unanticipated sectoral production was found to have a negative and significant effect

on the food, beverage and tobacco and engineering industries. The causes of these differences

was attributed to the differing nature of dividend yields within the industries analysed as well

as the various exposures present in each sector to global macroeconomic factors such as the

exchange rate‟s effect on import and export revenues, while interest rate yields will have a

stronger influence over domestic lending dependant sectors such as the financial sector.

Tursoy et al. (2008) empirically tested the Arbitrage Pricing Theory (APT) and tested

13 macroeconomic variables against 11 industry portfolios of Istanbul Stock Exchange to

observe the effects of those variables on stocks’ returns, by employing data from February

2001 to September 2005 on monthly base. Macroeconomic variables used for the study were

money supply (M2), industrial production, crude oil price, consumer price index (CPI),

import, export, gold price, exchange rate, interest rate, gross domestic product (GDP), foreign

reserve, unemployment rate and market pressure index (MPI) which is built by the authors.

The empirical estimation is carried out using regression analysis. Regression results indicate

that macroeconomic factors do not have significant effect on stock returns.

Hancocks (2010) determined the effect of selected macroeconomic variables on stock

market prices of the All-Share, Financial, Mining and Retail Indices of the Johannesburg

Stock Exchange in South Africa, by applying monthly data from July 1996 to December

2008. Acroeconomic variables used for the study include interest rates (91-Day Treasury

Bill), Consumer Price Inflation, exchange rates (nominal bilateral exchange rate), money

supply (M2) and long-term interest rates (the yield on long-dated government bonds).

Methodology employed consist of cointegration and VECM approaches. The results showed

that certain macroeconomic variables had differing influences on each sector of the stock

market. Impulse Response tests indicated that the selected macroeconomic variables caused a

shock to the sectoral indices in the short-run.

Chinzara (2011) analyzed how systematic risk emanating from the macro-economy is

transmitted into stock market volatility, using augmented autoregressive Generalised

Autoregressive Conditional Heteroscedastic (AR-GARCH) and vector autoregression (VAR)

models. Aggregate stock market index and the four main sectors (Financial, industrial,

mining and general retail) and macroeconomic variables were used for the study. The study

also examined is whether the relationship between the two is bidirectional. By imposing

dummies for the 1997-1998 Asian and the 2007-2009 sub-prime financial crises,It was found

186

from the study that volatility transmission between the stock market and most of the

macroeconomic variables and the stock market is bidirectional.

Saeed, S. (2012) examined the impact of macroeconomic variables on stock returns by

applying multifactor model within an APT framework. This study consists of five

macroeconomic variables Money Supply, Exchange Rate, Industrial Production, Short Term

Interest Rate and Oil prices. Nine sectors are selected for the study on the basis of data

availablefor the Karachi Stock Exchange 100 index. These sectors are Oil and Gas, Textile

Composite, Jute, Cement, Cable and electrical Goods, Automobile, Chemical and

Pharmaceutical, Leasing and Glass and Ceramics. The closing prices of each firm, of the

respective sector are obtained for the period of ten years starting from June 2000- June 2010.

The methodology includes Ordinary Least Square techniques to analyze the impact of

macroeconomic variables on the returns. The result reveals that macro-economic variables

have a significant impact on the returns of sectors, but their contribution to bring variation in

their returns is very small. Only Short Term Interest Rate has a significant impact on returns

of various sectors where as Exchange Rate and Oil prices have a significant impact on

specific sectors like and Oil and Gas sector, Automobile and Cable and Electronics.

Hasanzadeh and Kianvand (2012) examined the effects of selected macroeconomic

variables on the stock market index in Iran, Using cointegration and Vector Error Correction

Method (VECM). Quarterly data from 1996:Q1 to 2008:Q1 was considered for the study.

Variables used for the study include Tehran Stock Index (TSI) and five macroeconomic

variables which consist of gross domestic product, nominal effective exchange rate, money

supply, gold coin price and investment in housing sector. Findings suggested that that Iran’s

stock market index is positively influenced by the growth rate of the GDP, the money supply

and negatively affected by the gold prices, the private sector investment in housing sector and

the nominal effective exchange rate.

Sharabati (2013) investigated the relationship between independent variables: ASE

market sectors on dependent variable i.e. Real GDP. The sectoral indices used for the study

include Banks, Insurances, Services and Industry sectors. The data were of the annual

frequency from 1999 to 2012. The methodology used includes correlation, simple and

multiple regressions and stepwise regression techniques. The results of the study showed that

the four sectors of the ASE market are strongly related to each other and are strongly related

to ASE general indicator. Among the four ASE sector only industrial sector showed a strong

relationship with GDP. Further, simple regression test showed that there is no effect of ASE

general indicator on the GDP. While multiple regressions showed that there is a strong effect

187

of the ASE sectors together on GDP, but results did not show any significant effect of each

sector when considering the four sectors together on GDP. Furthermore, the first stepwise

regression model showed that there is a strong positive significant effect of industry sectors

on GDP, while the second model showed that there is a strong positive significant effect of

industry sectors on GDP and there is a negative significant effect of insurance sector on GDP.


Sinha and Kohli (2013) studied the effect of exchange rate on three market indices;

BSE Sensex index, BSE IT sector index and BSE Oil & Gas sector index for monthly data

from January 2006 to March 2012. Simple regression techniques were used for the

estimation. The result revealed that no interrelation between the daily returns in the foreign

exchange and the stock market of India were found.

Tripathi, Parashar and Jaiswal (2014) examined the long term relationship between

selected external macroeconomic variables and different sectoral indices at National Stock

Exchange (NSE) India, using monthly frequency data from April 2005 to March 2013. The

methodology employed for the study was variables Multiple Regression equation model

(Galton, 1877) using SPSS-16. The macroeconomic variables, namely, Exchange Rate

(USD), Crude Oil prices, Foreign Institutional Investments, Current Account Balance and

Foreign Exchange Reserves have been used to magnify the impact of external

macroeconomic variables on different sectors of Indian economy represented by Sectoral

Indices at National Stock Exchange (NSE) viz. CNX Auto, CNX Bank, CNX Energy, CNX

FMCG and CNX IT. The results so obtained revealed a high correlation among the variables

and suggested that amongst all macroeconomic variables only except Foreign Institutional

Investment (FII) affects all sectoral indices, however, the rest of the macroeconomic variables

selectively affect different sectoral indices in India.

The main key conclusion drawn from literature review is, that, so far, no study has been

done on the relationship between sectoral stock indices and respective sectoral GDP, which

provides the investors a new insight to track the changes in a particular sector of the stock

market by analyzing the movement of sectoral GDP of that particular sector. Thus, this study

is the initiative taken in this area. Finally, after going through literature, it has been concluded

that, this study, to the best of my knowledge, will be among the first empirical studies in

India to consider the relationships between the Indian stock market and a set of

macroeconomic variables, using the ARDL and VECM approach for the analysis.

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7.3. Model Specification and Data validation:

For the study, three models are framed, in which each of the sectoral stock price indices

is placed as dependent variable and Crude Oil Price, REER, T-bill rates, Trade openness and

WPI along with respective sectoral GDP worked as independent variables. The models are

defined as:

MANI = f (GMAN, CO, REER, TBR, TO, WPI)………….. Model I;

EGWI = f (GEGW, CO, REER, TBR, TO, WPI)…………. Model II;

SERI = f (GSER, CO, REER, TBR, TO, WPI)……………. Model III

Principle component analysis is used in this study to construct the composite index of

manufacturing index; electricity, gas and water supply index; and service index.

Manufacturing index has been formulated by incorporating automobile index, consumer

durables index, capital goods index, metal index and fast moving consumer goods index.

Electricity, gas and water supply index has been formulated by incorporating oil and gas

index and power sector index. Service index has been formulated by incorporating bank

index, health care index, IPO index, information technology index and Telecom, Media, and

Telecommunications index. All the three aggregate indexes were formulated following the

guidelines of BSE.


the effect of sectoral GDP and other controlled macroeconomic factors on respective sectoral

indices.

𝐿𝑥 = 𝛼0 + 𝛼1𝑦1 + 𝛼2𝑦2 + 𝛼3𝑦3 + 𝛼4𝑦4 + 𝛼5𝑦5 + 𝛼5𝑦6 + 휀𝑡

(7.1)

Here, x is considered as the dependent variable (LMANI, LEGWI, and LSERI) and y1

(LGMAN, LGEGW, LGSER), y2 (LCO), y3 (LREER), y4 (LTBR), y5 (LTO) and y6 (LWPI)

as independent variables.

Where:

LMANI= Manufacturing sector index,

LGMAN= manufacturing sector share in GDP,

LEGWI= Electricity, gas and water index,

LGEGW= electricity, gas and water supply sector share in GDP,

LSERI= Service sector index,

LGSER= service sector share in GDP,

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All the indexes are listed on Bombay Stock Exchange (BSE)13 and are collected from

the official website of Bombay Stock Exchange. All the variables are taken in their natural

logarithm.

7.4. Stationarity test and Lag length selection before co-integration




stationarity tests are presented in Table 7.1. The results show that all the variables are non-




concluding that all the variables used in this study are integrated of order one, i.e. difference

stationary I(1), except for LMANI, LGMAN, LGSER and LWPI. Therefore the study uses

autoregressive distributed lag (ARDL) approach to co-integration. In addition, it is also

important to ascertain that the optimal lag order of the model is chosen appropriately so that

the error terms of the equations are not serially correlated. Consequently, the lag order should

be high enough so that the conditional ECM is not subject to over parameterization problems

(Narayan, 2005; Pesaran, 2001). The results of these tests are presented in Table 7.2. The

results of Table 7.2 suggest that the optimal lag length is one based on SIC.

13 National Stock Exchange (NSE) sectoral indices are not incorporated in the study due to unavailability of

sectoral data.

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Table 7.1: Unit root test: Ng-Perron Test Variables With constant and trend Stationarity


LMANI 0.448 0.296 0.659 30.823 I (1)

ΔLMANI -19.566 -3.127 0.159 1.252

LEGWI -0.719 -0.436 0.606 21.241 I (1)

ΔLEGWI -20.365 -3.188 0.156 1.212

LSERI -0.215 -0.093 0.434 15.519 I (1)

ΔLSERI -19.607 -3.125 0.159 1.268

LGMAN 1.130 0.974 0.861 54.734 I (0)

ΔLGMAN -3.362 -1.280 0.380 7.274

LGEGW -1.168 -0.464 0.397 12.057 I (1)

ΔLGEGW -11.063 -2.339 0.211 2.261

LGSER 1.757 1.549 0.881 63.651 I (0)

ΔLGSER -1.128 -0.698 0.619 19.702

LCO -1.445 -0.780 0.540 15.364 I (1)

ΔLCO -57.648 -5.265 0.091 0.669

LREER -5.578 -1.616 0.289 4.546 I (1)

ΔLREER -21.008 -3.240 0.154 1.168

LTBR -2.450 -0.899 0.367 8.926 I (1)

ΔLTBR -20.297 -3.178 0.156 1.232

LTO -3.771 -1.172 0.310 6.591 I (1)

ΔLTO -21.423 -3.272 0.152 1.146

LWPI 0.353 0.198 0.560 23.773 I (0)

ΔLWPI -11.302 -2.374 0.210 2.179 Source: Author’s own Calculation by using E-views 8.0


natural logs.

Table 7.2: Lag Order Selection Criterion Lag LogL LR FPE AIC SIC HQ

Model I 4 802.817 58.391 5.33e-21* -29.259* -20.775 -26.169*

Model II 4 851.626 62.032 4.92e-22* -31.640* -23.156 -28.550*

Model III 4 839.183 80.389* 9.03e-22* -31.033* -22.549 -27.943* * indicates lag order selected by the criterion






After determining the order of integration of all the variables in table 7.1, the next step

is to employ an ARDL approach to co-integration in order to determine the long-run




7.5. ARDL Bounds test


variables in the equation (7.1) are zero. Table 7.3, reports the result of the calculated F-

191


statistics were 9.4890, 10.3724 and 8.2299 for the model I, model II and model III

respectively. Thus the calculated F-statistics turns out to be higher than the upper-bound

critical value at the 5 percent level. This suggests that there is a co-integrating relationship

among the variables included in the models.

Table 7.3: ARDL Bounds test


Estimated Equation

Model I : LMANI = F (LGMAN LCO LREER LTBR LTO LWPI)

Model II : LEGWI= F (LGEGW LCO LREER LTBR LTO LWPI)

Model III : LSERI = F (LGSER LCO LREER LTBR LTO LWPI)

The second step is to estimate the long- and short-run estimates of ARDL test. The

long-run results are illustrated in Table 7.4. The results of the model I show that the rise in

LGMAN has a positive effect on LMANI. It is evident from the table that 1% increase

LGMAN leads to 0.345% increase in the LMANI. This is due to the fact that with the rise in

manufacturing sector share in GDP, the expectations of investors increases, which gives a

motivation to investors to invest in the shares of manufacturing sector. The investment leads

to rise in manufacturing index.

The results of the model II show that the rise in LGEGW and LWPI has a positive

effect on LEGWI. The coefficient of LGEGW and LWPI are statistically significant and

positive at 1% level. It is evident from the table that 1% increase in LGEGW and LWPI leads

to 1.043% and 0.771% respectively increase in LEGWI. The rationale behind this explains the

Fisher hypothesis (1911) for inflation. And the rise in the electricity, gas and water supply

sector share in GDP gives a boost to investors’ confidence to invest in the shares of

electricity, gas and water supply sector.

The results of the model III show that the rise in LGSER and LTBR has a positive

effect on service index. The coefficient of LGSER and LTBR are statistically significant and

positive at 1% and 10% respectively. It is evident from the table that 1% increase in LGSER

and 10% increase in LTBR leads to 0.5% and 0.065% respectively increase in the LSERI.

Indicators Model I Model II Model III

Optimal-lags 01 01 01

F – Statistics 9.4890 10.3724 8.2299

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The rationale behind this is the same as mentioned above for the rest two models for the

relation of service sector share in GDP and service index.

Table 7.4: Estimated Long-run Coefficients using ARDL Approach

(Dependent variable: LMANI, LEGWI, LSERI)

Regressors Model I Model II Model III

Coefficient t- values Coefficient t- values Coefficient t- values

LGMAN 0.345* 3.033 - - - -

LGEGW - - 1.043* 3.193 - -

LGSER - - - - 0.500** 2.164

LCO -0.032 -0.555 -0.027 -0.340 -0.117 -1.334

LREER 0.052 0.471 0.087 0.515 0.099 0.753

LTBR 0.031 1.042 0.052 0.896 0.065*** 1.713

LTO 0.116 1.606 0.052 0.603 0.134 1.504

LWPI -0.158 -1.609 0.771* 8.434 -0.431 -1.643

CONS -0.502 -0.560 3.411 3.538 -1.619 -0.876


R2 0.972 0.995 0.974

Adjusted R2 0.966 0.993 0.9690

F Statistics 157.369 636.710 169.075

D.W. Stat 2.971 -0.802 2.297

Serial Correlation, F 6.120 [0.190] 9.201 [0.056] 6.067 [0.194]

Heteroskedasticity, F 0.240 [0.624] 0.008 [0.926] 0.018 [0.891]

Ramsey reset test, F 11.464 [0.001] 1.315 [0.251] 6.109 [0.013] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC).

(2) *, ** and *** indicate significant at 1, 5 and 10 percent level of significance, respectively. Values in [#] are

probability values.

The short-run relationship of the sectoral index with respective sectoral GDP along

with some controlled variables is presented in Table 7.5. As can be seen from the table, for

the model I LGMAN, LCO and LTO has a significant and positive impact on LMANI in the

short-run at 1%, 1% and 5% level, respectively.

For the model II, unlike the long-run result, LGEGW is not significant to LEGWI in the

short-run. But LCO and LREER has a significant and positive impact on the LEGWI in the

short-run at 1% level. Whereas, LWPI is negatively significant to LEGWI at 1% level.

For the model III, LGSER, LCO and LTBR has a significant and positive impact on

LSERI in the short-run at 1%, 1% and 10% level, respectively. Whereas, LWPI is negatively

significant to LSERI at 10% level in the short-run.

The short-run adjustment process is examined from the ECM coefficient. The

coefficient lies between 0 and -1, the equilibrium is converging to the long-run equilibrium

path, is responsive to any external shocks. However, if the value is positive, the equilibrium

will be divergent from the reported values of ECM test. The coefficient of the lagged error-

correction term (-0.333), (-0.318) and (-0.215) are significant at the 1 % level of significance

for the model I, model II and model III, respectively. The coefficient implies that a deviation

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from the equilibrium level of stock market index in the current period will be corrected by

33% for model I, 31% for model II and 21% for model III, in the next period to resort the

equilibrium.

Table 7.5: Estimated Short-run Coefficients using ARDL Approach

(Dependent variable: LMANI, LEGWI, LSERI)

Regressors Model I Model II Model III

Coefficient t- values Coefficient t- values Coefficient t- values

ΔLGMAN 0.115* 2.744 - - - -

ΔLGEGW - - -0.181 -0.708 - -

ΔLGSER - - - - 0.107* 2.801

ΔLCO 0.047* 3.520 0.082* 2.668 0.039* 3.455

ΔLREER 0.017 0.449 0.239* 2.640 0.021 0.731

ΔLTBR 0.010 1.012 0.016 1.040 0.014*** 1.737

ΔLTO 0.038** 1.943 0.016 0.639 0.028 1.618

ΔLWPI -0.052 -1.474 -1.354* -3.864 -0.092*** -1.863

CONS -0.167 -0.574 1.087 1.747 -0.348 -1.070

ECMt-1 -0.333 -2.860 -0.318 -2.373 -0.215 -2.313


R2 0.647 0.606 0.665

Adjusted R2 0.566 0.470 0.588

D.W. Stat 1.431 2.109 1.455

SE Regression 0.011 0.015 0.008

RSS 0.004 0.007 0.002

F Statistics 9.186 [0.000] 7.039 [0.000] 9.944 [0.000] Note: (1) The lag order of the model is based on Schwarz Bayesian Criterion (SBC).

(2) *, ** and *** indicate significant at the 1, 5 and 10 percent level of significance, respectively. Values


7.6. VECM based Causality

The results of table 7.6 indicate that there is causality running from LGMAN to

LMANI in India, which shows that a change in manufacturing sector share in GDP causes a

change in manufacturing index. It is also observed that the error correction term is

statistically significant for specification with LMANI as the dependent variable which

indicate that there exist a long-run causal relationship among the variables with LMANI as

the dependent variable.

The results of table 7.6 (Model II) indicate that there is causality running from LGEGW

and LWPI to LEGWI in India, which shows that a change in electricity, gas and water supply

sector share in GDP and change in inflation causes a change in electricity, gas and water

index. It is also observed that the error correction term is statistically significant for

specification with LEGWI as the dependent variable which indicate that there exist a long-

run causal relationship among the variables with LEGWI as the dependent variable.

Estimation results show a unidirectional causality running from LEGWI to LTO.

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The results of table 7.6 (Model III) indicate that there is no causality running from any

of the variables to LSERI in India. It is also observed that the error correction term is also not

statistically significant for specification with LSERI as the dependent variable which indicate

that there exist no long-run causal relationship among the variables with LSERI as the

dependent variable.

Table 7.6: Results of Vector Error Correction Model Dependent

variable Sources of Causation

Short-run independent variables Long-run

Model I ΔLMANI ΔLGMAN ΔLCO ΔLREER ΔLTBR ΔLTO ΔLWPI ECM(t-1)

ΔLMANI - -2.200** 0.126 -0.300 -0.889 0.916 -1.375 -2.724*

ΔLGMAN -0.028 - -0.659 0.594 -1.211 -0.208 -0.458 0.310

ΔLCO -0.647 1.090 - -1.132 -0.938 -0.605 -3.148* -0.883

ΔLREER -0.132 1.756*** -0.714 - 0.423 -1.824*** 0.277 -0.832

ΔLTBR -0.787 2.010** 0.813 0.276 - -0.072 0.365 -3.025*

ΔLTO -0.136 0.407 2.357** 0.388 -1.310 - -1.382 0.550

ΔLWPI -0.210 -0.693 2.951* 0.113 -0.491 -1.327 - -0.471

Model II ΔLEGWI ΔLGEGW ΔLCO ΔLREER ΔLTBR ΔLTO ΔLWPI

ΔLEGWI - 1.704*** 0.492 0.289 0.441 1.074 -1.752*** -5.428*

ΔLGEGW -1.594 - -2.739* -2.187** -1.452 -1.470 -0.411 2.066

ΔLCO -1.177 -0.674 - -0.379 -0.373 0.031 -2.917* 0.170

ΔLREER 0.358 0.393 -0.645 - -0.133 -1.499 0.242 -1.013

ΔLTBR 0.914 -0.246 1.118 0.493 - 0.426 0.472 -1.827***

ΔLTO -1.893*** -0.179 2.330** 1.142 0.039 - -1.803*** 1.663

ΔLWPI -0.900 -0.420 3.013* 0.691 0.761 -0.361 - 2.147

Model III ΔLSERI ΔLGSER ΔLCO ΔLREER ΔLTBR ΔLTO ΔLWPI

ΔLSERI - -0.873 0.004 0.217 -1.296 0.659 -0.444 -0.425

ΔLGSER -0.119 - -0.378 -0.223 -1.585 -0.043 0.584 -1.943**

ΔLCO -0.439 -0.138 - -1.189 -0.928 0.044 -3.051 0.757

ΔLREER 0.678 0.884 -0.579 - 0.508 -1.671 0.388 -0.205

ΔLTBR 0.092 2.437** 0.198 0.646 - -0.423 -0.602 -3.343*

ΔLTO -0.187 -0.361 2.067** 0.107 -1.402 - -1.343 -0.032

ΔLWPI -0.588 -1.884** 3.237* 0.208 -0.174 -0.181 - -0.641


The robustness of the short-run result are investigated with the help of diagnostic and




investigate the stability of long-run and short-run parameters. The cumulative sum (CUSUM)

and the cumulative sum of square (CUSUMSQ) plots are between critical boundaries at 5%

level of significance. This confirms the stability property of long-run and short-run

parameters which have an impact on the sectoral indices in case of India. This confirms that

models seem to be steady and specified appropriate.

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7.7. Variance Decomposition (VDC) Analysis:







variables. The results of the VDC for all the models are presented in table 7.7. The empirical

evidence indicates that 39.63% of LMANI change is contributed by its own innovative

shocks. Further, shock in LGMANI explains manufacturing index by 26.22%. Shock in LCO

also explains LMANI by 23.48%, which shows that crude oil price also plays an important

role in explaining manufacturing index. The share of other variables is minimal.

The empirical evidence for model II, indicates that 35.22% of LEGWI change is

contributed by its own innovative shocks. Further, shock in LGEGW explains LEGWI by

5.21%. LCO contributes the maximum to LEGW by 43.32%.

The empirical evidence for model III, indicates that 34.45% of LSERI change is

contributed by its own innovative shocks. Further, shock in LGSER explains LSERI by

18.05%. LCO contributes the maximum to LSERI by 38.53%.

Table 7.7: Variance Decomposition (VDC) Analysis

Period S.E. LMANI LGMAN LCO LREER LTBR LTO LWPI

Model I

1 0.015 100.000 0.000 0.000 0.0000 0.000 0.000 0.000

5 0.032 54.845 19.741 22.374 0.008 0.152 2.768 0.109

10 0.037 42.114 26.777 24.579 0.661 1.754 2.831 1.280

15 0.038 39.632 26.223 23.481 1.852 3.000 2.899 2.909

Model II

LEGWI LGEGW LCO LREER LTBR LTO LWPI

1 0.013 100.000 0.000 0.000 0.000 0.000 0.000 0.000

5 0.034 47.809 7.994 34.810 2.143 1.822 5.132 0.287

10 0.043 36.389 5.477 43.123 3.235 3.626 7.956 0.191

15 0.045 35.229 5.211 43.321 3.283 3.974 8.746 0.233

Model III LSERI LGSER LCO LREER LTBR LTO LWPI

1 0.012 100.000 0.000 0.000 0.000 0.000 0.000 0.000

5 0.027 51.364 13.502 33.333 0.611 0.925 0.003 0.259

10 0.033 36.791 19.070 39.573 0.501 1.905 0.035 2.122

15 0.034 34.453 18.052 38.538 0.633 3.096 0.390 4.835

Cholesky Ordering: LSERI LGSER LCO LREER LTBR LTO LWPI

7.8. Summary

This study aims to examine the relationship between gross domestic product and stock

prices from a sectoral perspective. Precisely, an effort has been made in this study to


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investigate whether sectoral GDP, i.e. Manufacturing sector, electricity, gas and water supply

sector and service sector share in GDP affect respective sectoral stock indices in India or not.

Towards this effort, quarterly data from 2003:Q3 to 2014:Q4 for the all the variables included

in the estimation has been used. The bounds test used for the study, confirms that there exists

a long-run co-integrating the relationship between sectoral GDP and sectoral stock indices in

India. The long-run estimates of ARDL test for model I showed that positive and significant

relationship exists between the manufacturing sector share in GDP with the manufacturing

index. It also confirms that the manufacturing sector share in GDP, crude oil price and trade

openness have a significant and positive impact on the manufacturing index in the short-run.

For model II, the results show that the electricity, gas and water supply sector share in GDP

and inflation has a positive effect on electricity, gas and water supply index, unlike short-run.

Crude oil price and real effective exchange rate has a significant and positive impact on the

electricity, gas and water index in the short-run. For model III, results show that the service

sector share in GDP and T-bills rate has a positive effect on service sector index in the long-

run and in short-run as well along with crude oil price. The results suggest that sectoral

indices are affected by changes in sectoral GDP in the long-run, whereas, all the three indices

are sensitive to the change in crude oil price in the short-run. The error correction model of

ARDL approach reveals that the adjustment process from the short-run deviation is high.

the result of VECM based causality found unidirectional short-run causality running

from sectoral GDP, crude oil price, REER, T-bill rates, trade openness and WPI to respective

sectoral stock indices in India. Further, the result indicates the presence of long-run causality

for the equation with manufacturing index and electricity, gas and water supply index as the

dependent variable, but, except for the service sector index which shows no long-run

causality running from any of the independent variables. The result of VDC analysis, for all

three models, shows that a major percentage of sectoral indices are its own innovative shocks.

Other than the respective sectoral GDP, crude oil price is a common variable which is playing

a crucial role in explaining all three indices by contributing its maximum towards the shock,

hence, reflecting maximum information about the movement of the indices.

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CHAPTER 8

Summary and Policy Implications of the study

8.1. Summary and Conclusion

The relationship between macroeconomic variables and stock prices has been the focus

of both theoretical and empirical research since early nineteenth century. Since then, there

has been growing effort made by researchers to empirically estimate this relationship by

using sophisticated econometric methods (Fama, 1965; Ross, 1976; Friedman 1987;

Mishikin, 1988; Flannery and Protopapadakis (2002); and Semmler, 2006). The exisiting

empirical studies have shown the use of vast range of macroeconomic variables to examine

their influence of stock prices. The main macroeconomic variables identified by various

studies are Real Gross Domestic Product (GDP), Index of Industrial Product (IIP), Real

Effective Exchange Rate (REER), International crude oil prices, Foreign Direct Investment

(FDI), Foreign Institutional Investment (FII), Inflation (Consumer Price Index (CPI) and

Wholesale Price Index (WPI)), Real Interest Rate, Short term Interest Rate (T-bill rates and

Call Money Rates (CMR)), Money Supply (M3), Fiscal Deficit, Current Account Deficit

(CAD), Trade openness, and Gold prices. The proxy variables for stock market development

used in the study are stock market capitalization (MCAP), stock price index, market liquidity

and turnover ratio.

All the research are conducted by applying different methodologies, namely,

correlation analysis, regression analysis using OLS, ARCH and GARCH models,

cointegration techniques using EG, JJ and ARDL methods, the causality tests like bivariate

and multivariate granger causality. The studies are accomplished by using different frequency

of data viz. daily, weekly, monthly, Quarterly or annual data sets. All country specific studies

use time series data, whereas, studies with multi country uses panel data series.

Last two decades has witnessed a dramatic change in the world financial markets

particularly in the stock market due to globalization and financial sector reforms across the

world market. These changes in the macro environment influence the stock prices of a single

country.

Indian stock market has developed in terms of the number of stock exchanges, number

of listed stocks, market capitalization, trading volume, turnover of the stock exchanges,

investor’s population and price during these years. Since 1991, the Indian economy has

experienced many major reforms policy initiatives in the financial system. The opening of

capital market to foreign institutional investors, allowing Indian companies to issue equity

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abroad through Global Depository Receipts (GDRs), formation of new stock exchange NSE,

liberalization and decontrolling crude oil price are few initiatives which are expected to have

huge impact on the stock market volatility.

In the last two decades, numerous empirical studies have examined the dynamic

relationships between stock market behaviour and macroeconomic variables, particularly for

developed economies. However, research on the above relationship in developing countries,

such as Latin America, Eastern Europe, Middle East and South Asian countries are still at

infant stage. With regard to the Indian economy, little work has been done in the dynamic

relationship between stock market and macroeconomic variables. To the best of researcher’s

knowledge, there is no published work to link fiscal policy variables and stock market

development and explore the link in a sectoral stock market perspective. Further, exploring

the dynamic relationship between oil prices, gold price, FII, FDI along with other

macroeconomic variables in a multivariate setting is not explained with sophisticated

econometric techniques like Auto Regressive Distributed Lag (ARDL), Vector Error

Correction Model (VECM), Impulse Response Function (IRF) and Variance Decomposition

(VDC). Hence, the primary motive of the present work is to answer the following research

questions:

Q.1. Do the key macroeocnomic variables included in this study has long-run

cointegrating relationship with Indian stock market proxied by BSE Sensex, CNX Nifty, and

market capitalization?

Q.2. Do these key macroeconomic variables have causal relationships during the

sample period? If so, what is the direction of the causality between BSE, NSE, market

capitalization and each of these variables in long-run and short-run?

Q.3. How does the stock market development indicators respond to an external shock

from any of these variables?

Q.4. To what extent can innovation in each of the key macroeconomic variables explain

the movements in stock market variables?

Q.5. How does the sectoral stock market indices being influenced by the set of sectoral

real activity in the Indian economy?

In an effort to investigate the effect of macroeconomic variables on the stock price in

India, the study examines the role of some fundamental macroeconomic variables in

https://scholar.google.co.in/scholar?hl=en&as_sdt=0,5&q=Global+Depository+Receipts

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explaining the long-run and short-run behaviour of the Indian stock market. In particular, the

study tries to examine the long-run and short-run dynamic relationship and the direction of

causality between stock market in India with different sets of domestic and international

macroeconomic variables. Towards this effort different models has been formulated, using

the data for different time span and frequency. The empirical analysis of the thesis began with

testing the stationarity properties of the variables by applying Ng-Perron unit root test. After

testing the stationarity properties of the variables, the lag-length selection criteria is

determining in order to ascertain that the optimal lag order of the model is chosen

appropriately so that the error terms of the equations are not serially correlated. To study the

long-run and short-run cointegrating relationship among the variables ARDL bounds testing

approach is used. The error correction term ECMt-1 identifies the speed of adjustment towards

the equilibrium. Once the co-integrating relationships among the variables are identified the

direction of causality being tested with the use of VECM based Granger causality test.

Additionally, CUSUM and CUSUMQ have been employed to test the stability of the

variables. Finally, Impulse Response Function (IRF) and Variance Decomposition (VDC)

analysis were used to predict the long run and short run shocks in the model.

Existing Financial and Economic literature, such as Efficient Market Hypothesis

(EMH) and Arbitrage Pricing Theory (APT) advocates the relationship between the stock

market and macroeconomic variables. However, these theories have been silent about

determining which precise events or economic factors are likely to influence asset prices. The

macroeconomic variables selected for the study are considered on the basis of existing

literature which examines the theoretical and empirical relationship between the two. Further,

the variables are selected on the basis of availability of data with respect to the frequency and

common base year.

The study first accomplishes the empirical estimation of macroeconomic determinants

of the stock market development in India, using data for different time periods. The study is

divided into three parts as per the frequency and availability of data to capture the dynamic

movement of the stock market. The first part of the study deals with the estimation and

discussion of the relationship between BSE Sensex and economic growth, along with some

selected macroeconomic variables. The macroeconomic variables used for the study include

GDP, crude oil prices, Consumer Price Index (CPI), real effective exchange rate, Foreign

Direct investment (FDI) and real interest rate, for the period from the year 1979 to 2014. The

empirical results of Ng-Perron unit root test shows that all the variables used for the study are

stationary at level. The estimation results of ARDL test confirms significant and positive

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influence of economic growth, exchange rate and inflation on stock price movements in

India. However, there exists a negative and significant relationship between crude oil price

and stock prices. The results are consistent for both long run and short run. The error


deviation is quite high. More precisely, it is found that the ECMt-1 term is -0.536 (significant

at 1%), again confirming the existence of co-integration that the derivation from the long run

equilibrium path is corrected 53% per year. Moreover, it is found from VECM based Granger

causality test that there exists a short run unidirectional causality running from foreign direct

investment, GDP and real interest rate to BSE in India. Further, the result indicates the

presence of long run causality for the equation with the stock price as the dependent variable.

To predict the long-run and short-run shocks variance decomposition is used for the study,

the results of the VDC analysis show that a major percentage of stock price change is its own

innovative shocks. Further, the shock in crude oil prices explains stock prices by 12.73%,

hence, the movement of stock prices can be tracked by analysing the movement in crude oil

prices. Thus, it is concluded from the estimation that economic growth, exchange rate, and

inflation effects positively to the stock market and change in crude oil price effects

negatively.

The estimated results of the quarterly time series data are presented in the next section,

the stock market development is represented by market capitalization ratio (MCAP) and

macroeconomic variables quarterly time series data is used for the study. The variables used

are Market capitalization, Real Gross Domestic Product (GDP), Foreign Direct Investment

(FDI), Foreign Institutional Investment (FII) and Trade openness (TO). The data employed

covering the period from 1996: Q1 to 2014: Q3. The ARDL bounds test confirms that the

estimated equation and the series are co-integrated. The test results suggest that economic

growth, FIIs and Trade openness in India influence market capitalization positively.

Consistent results are found for FII and trade openness in short run also. The findings suggest

that openness of the economy helps to attract foreign investment. This in turn increases the

activities on the stock market as firms would attempt to raise investment funds (capital) from

the stock market, which will lead to increase in market capitalization. However, economic

growth failed to explain the variation in stock market growth significantly in the short-run.

This may be due to the fact that investor’s behaviour in the stock market regulated by long-

term growth rate of GDP and may not bother about short-term fluctuations in it. The error


deviation is low. More precisely, it is found that the ECMt-1 term is -0.159 (significant at 1%),

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again confirming the existence of cointegration that the derivation from the long run

equilibrium path is corrected 15% per quarter. The results of VECM based Granger causality

shows that there exists long-run causality running from four independent variables (GDP,

TO, FDI and FII) in the long-run towards Stock Market Capitalization (MCAP), whereas, in

short-run the change in trade openness causes a change in Stock Market Capitalization,

whereas a change in stock market capitalization will cause a change in Foreign Institutional

Investment. The results of VDC analysis shows that out of the all of exogenous variables

used for the study, trade openness is having maximum shock on stock market capitalization.

Moreover, the study also focuses on short-run dynamic relationship between

macroeconomic variables and the stock prices, by incorporating data for monthly frequency

of the variables. Further, the monthly study has been divided into two sections, which


prices (BSE Sensex and CNX Nifty). The first section of the study highlights the relationship

between fundamental macroeconomic variables and Sensitivity Index of Bombay Stock

Exchange (BSE Sensex), using the monthly time series data from the April 2004 to July

2014. The independent variables used for the study are Real Effective Exchange Rate

(REER), Index of Industrial Production (IIP), Consumer Price Index (CPI), Call Money Rates

(CMR) and Gold price (GOR). The ARDL bounds test confirms the existence of long-run

cointegrating relationship between different macroeconomic variables and stock prices in

India. The long-run estimates of ARDL test showed a significant and positive influence of

economic growth (IIP), Exchange Rate and Inflation on stock prices. Further, the study

confirms negative and significant relationship between gold prices and stock prices in India

because gold is a substitute investment avenue for Indian investors. As the gold price rises,

Indian investors tend to invest less in stocks, causing stock prices to fall. The results for IIP,

Inflation and Gold prices are consistent in short-run also. The error correction model of

ARDL approach reveals that the adjustment process from the short-run deviation is slow.

More precisely, it is found that the ECMt-1 term is -0.222 (significant at 1%), again

confirming the existence of co-integration that the derivation from the long run equilibrium

path is corrected 22% per year. The VECM based granger causality result shows that there is

no short run causality running from any of the variables to BSE in India. Further, the result

indicates the presence of long run causality for the equation with the stock price as the

dependent variable. The results of VDC analysis shows that out of the all of exogenous

variables used for the study, Gold price is having maximum shock on stock prices.

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The second section of the empirical study focuses on the relationship between

fundamental macroeconomic variables and Index of National stock exchange (NSE), using

the monthly time series data from the April 2004 to July 2015. The variables used for the

study are CNX Nifty, Index of Industrial Production (IIP), Foreign Institutional Investment

(FII), Gold price (GOR), Treasury bills rate (TBR), Wholesale Price Index (WPI),

International Crude Oil price (CO) and Real Effective Exchange Rate (REER). The ARDL

bounds test confirms that there exists a long-run co-integrating relationship between different

macroeconomic variables and the stock prices in India. The long-run estimates of ARDL test

showed a negative and significant effect of crude oil prices, Inflation (WPI) on stock prices.

The results of the influence of both the variables on stock prices are consistent in the short

run as well. Further, for short-run the study confirms positive and significant relationship for

Gold, T-bill rates (TBR) and Real Effective Exchange Rate (REER). The error correction

model of ARDL approach reveals that the adjustment process from the short-run deviation is

high. More precisely, it is found that the ECMt-1 term is -0.0746 (1%), again confirming the

existence of cointegration that the derivation from the long run equilibrium path is corrected

7% per month. The VECM based Granger causality test found short run causality running

from Inflation and crude oil price to National Stock Exchange in India. Additionally, a

unidirectional causality is also running from national stock exchange to gold and inflation.

Hence, it is observed that bidirectional causality is running between Inflation and CNX nifty

index. Further, the result indicates the presence of long run causality for the equation with a

CNX nifty index as the dependent variable. To predict the long-run and short-run shocks

variance decomposition is used for the study, the results of VDC analysis shows that the

shock in inflation and crude oil explains stock prices by 15.67% and 9.244%, respectively.

The results of IRF show that in its response to the shocks of IIP it is observed that there is a

negative relationship in the long run.

The next empirical chapter presents the relationship between the fiscal policy variables

and stock market development in India, with the use of annual frequency data for the

variables. The study for the relationship between fiscal policy variables and stock market

development has been divided into two sections, the first section of which contains the

estimation results of the relationship between BSE Sensex and fiscal deficit, along with some

controlled macroeconomic variables. The macroeconomic variables used for the study

include money supply (M3), consumer price index (CPI) and real interest rate (RIR), for the

period from the year 1988 to 2014. The test statistics of the unit root suggest that none of the

variables included in the study are I(2). The ARDL results suggest a long run negative and

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significant relationship exists between budget deficit and stock prices. However, the

relationship does not show any significant results in the short run. The findings imply that, in

a country when the budget is in deficit, it will depress the stock prices and undermine the

investor’s confidence, so the firm’s ability to get capital on favorable terms will be

diminished in the long run. Further, as the deficit increases, future tax burden, interest rates,

and the dollar value increases, leading to decrease in corporate profits because of weak

domestic as well as export revenues. So, sales decrease which ultimately lowers net earnings,

thus decreasing equity prices. These findings are analogous with the work of Adrangi and

Allender (1998); Salem and Yasir et al. (2012). However, investors are indifferent to the

short run fluctuations in the fiscal deficits. The money supply and inflation in India influence

stock prices positively both in the long run as well as in the short run. The results of VECM

based Granger causality test suggests that there exists a short run causality running from

fiscal deficit to stock price. Further, the result indicates the presence of long run causality for

the equation with the stock price as the dependent variable. The results of VDC analysis show

that the fiscal deficit plays an important role in explaining the variation in stock prices in

India.

The second part of the study on the relationship between fiscal policy variables and

stock market development in India, discusses the estimation results of the relationship

between stock market development (MCAP) and twin deficit, along with other

macroeconomic variables. The study uses Current Account Deficit (CAD) and Fiscal Deficit

(FD) as the fiscal policy variables. The other macroeconomic variables used for the study

include Gross Domestic Product (GDP), crude oil prices (CO), trade openness (TO) and real

effective exchange rate (REER), for the period from the year 1979 to 2014. The long-run

estimates of ARDL test showed that negative and significant relationship exists between the

current account deficit (CAD) and crude oil with stock market capitalization. It also confirms

a significant and positive influence of Real GDP and Exchange Rate on market capitalization

in India both in long-run and short-run. Further, for short-run the study confirms negative and

significant relationship between CAD and trade openness with stock market capitalizations in

India. The error correction model of ARDL approach reveals that the adjustment process

from the short-run deviation is high. More precisely, it is found that the ECMt-1 term is -0.681

(significant at 1%), again confirming the existence of cointegration that the derivation from

the long run equilibrium path is corrected 68% per year. The results of VECM based Granger

causality found short run causality running from CAD, Real GDP, trade openness and crude

oil to market capitalization in India. In fact, trade openness is having a bi-directional causality

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with MCAP in short-run. Further, the result indicates the presence of long run causality for

the equation with a market capitalization as the dependent variable. To predict the long-run

and short-run shocks variance decomposition is used for the study, the results of VDC

analysis shows that the shock in crude oil price explains market capitalization by 14.61%,

CAD and fiscal deficit contributes to market capitalization by 10.02% and 5.03%,

respectively. The results of IRF shows that in its response to the shocks of current account

deficit, GDP and exchange rate, it is observed that there is a positive relationship in the long

run and reverse is observed in the case for the shocks of fiscal deficits and crude oil prices,

i.e. there is a negative relationship in the long run throughout the period.

The last empirical chapter contains the relationship between macroeconomic variables

and stock at sectoral level by employing quarterly data covering the period from 2003:Q4 to

2014:Q4. The main variables used for the study include Manufacturing sector index,

electricity, gas and water supply sector index, service sector index, contribution of GDP in

manufacturing sector, contribution of GDP in electricity, gas and water supply sector,

contribution of GDP in service sector, and the other macroeconomic variables used for the

study are Crude Oil Price (CO), Real Effective Exchange Rate (REER), T-bill rates (TBR),

Trade openness (TO) and Wholesale Price Index (WPI), a proxy for inflation. Principle

component analysis is used in this study to construct the composite index of manufacturing

index; electricity, gas and water supply index; and service index. For the purpose of study,

three models has been framed, in which each of the sectoral stock price indices is placed as

dependent variable; and Crude Oil Price, REER, T-bill rates, Trade openness and WPI along

with respective sectoral GDP worked as independent variables. The bounds test used for the

study, confirms that there exists a long-run co-integrating the relationship between sectoral

GDP and sectoral stock indices in India. The long-run estimates of ARDL test for the model I

(Manufacturing sector index and share of manufacturing sector in GDP) showed that positive

and significant relationship exists between the manufacturing sector share in GDP with the

manufacturing index. It also confirms that the manufacturing sector share in GDP, crude oil

price and trade openness have a significant and positive impact on the manufacturing index in

the short-run. For model II (Electricity, Gas and Water supply sector index and share of

Electricity, Gas and Water supply sector in GDP), the results show that the electricity, gas

and water supply sector share in GDP and inflation has a positive effect on electricity, gas

and water supply index, unlike short-run. Crude oil price and real effective exchange rate has

a significant and positive impact on the electricity, gas and water index in the short-run. For

model III (Service sector index and share of Service sector in GDP), results show that the

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service sector share in GDP and T-bills rate has a positive effect on service sector index in

the long-run and in short-run as well along with crude oil price. The results suggest that

sectoral indices are affected by changes in sectoral GDP in the long-run, whereas, all the

three indices are sensitive to the change in crude oil price in the short-run. The error


deviation is high. More precisely, it is found that the ECMt-1 term is (-0.333), (-0.318) and (-

0.215). These terms are significant at 1%, for all three models, again confirming the existence

of cointegration that the derivation from the long-run equilibrium path is corrected 33%, 31%

and 21%, respectively, per quarter. The results of VECM based Granger causality test

suggests a unidirectional short-run causality running from sectoral GDP, crude oil price,

REER, T-bill rates, trade openness and WPI to respective sectoral stock indices in India.

Further, the result indicates the presence of long-run causality for the equation with

manufacturing index and electricity, gas and water supply index as the dependent variable,

but, except for the service sector index which shows no long-run causality running from any

of the independent variables. To predict the long-run and short-run shocks variance

decomposition is used for the study, the result of VDC analysis, for all three models, show

that a major percentage of sectoral indices are its own innovative shocks. Other than the

respective sectoral GDP, crude oil price is a common variable which is playing a crucial role

in explaining all three indices by contributing its maximum towards the shock, hence,

reflecting maximum information about the movement of the indices.

From the above mentioned empirical results of the relationship between

macroeconomic variables and stock price development it has been concluded that the effect

of economic growth (GDP in yearly study and IIP in monthly study) in almost all the studies

is positive. From the next observation of the empirical results, we conclude that there exist a

positive relationship between real effective exchange rate and stock prices; the positive

influence of exchange rate on stock price movements is favorable for export based countries.

The influence of inflation also comes out to be positive which proves Fisher (1911)

hypothesis, according to him, shares, hedged against inflation in the sense that an increase in

expected inflation leads to a proportional change in nominal share returns. The findings of

our study are contradictory to the findings of Fama (1981). The findings seem to suggest that

investors in making better portfolio decisions should perhaps view shares as long-term

holdings against inflation’s loss of purchasing power. While coming to the fiscal policy

variables, it has been concluded from the empirical results that fiscal deficit and current

account defict are negatively affecting stock market development process in India. It has also

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been analysed that current account deficit and fiscal deficit along with the international crude

oil prices plays an important role in in explaining the variation in stock market development

in India.

All the international macroeconomic variables used in the study viz. trade openness,

Foreign Institutional Investors, Foreign Direct Investment, International gold prices and

International crude oil prices are significantly influencing the stock market development in

India. Variables like trade openness, FDI and FII are positively influencing the stock market

development in the long-run, whereas the variables like international crude oil prices and

gold prices are negatively influencing stock market development in the long run.

Further, considering the empirical results of sectoral study, it has been found that all the

sectoral indices are having a significant long-run relationship with the share of that particular

sector in GDP of the nation.

Thus, the estimated results of the study indicate that the Indian stock market is sensitive

to changes in macroeconomic fundamentals in the long run. However, in the short run also

few of the macroeconomic variables affect stock prices. Further, the stock prices are

relatively exogenous in relation to most of the macroeconomic variables selected for the

study, as major percentage of the variation in the forecast of the Indian stock prices is

attributable to its own shocks. This may be due to the fact that speculative trading continues

to dominate the Indian stock market. The results of the study suggest a positive impact of

macroeconomic variables on the stock market development in India. Therefore, in order to

facilitate economic growth, macroeconomic development is solely desirable in developing

countries like India. Moreover, it is also true that the informed and sensible investor in India

can attain super normal profit, by tracking the historical data of stock market and the change

in macroeconomic variables. This may help the investors to formulate a profitable strategy to

for trading and making profitable decisions.

8.2. Policy Implications of the study

The stock market plays an important role in the financial and economic development of

a country. Therefore, an open, disciplined, transparent and regulated securities market is

considered as the essential element for the economic development of a country. Hence, the

government must have to play a positive role in reinforcing the stock market operations. The

government should formulate some policies for protecting and safeguarding investor’s

interest against all possible insecurities related to investment, this measure will help in

building investor’s confidence. Such a policy for investor’s protection will not only attract

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domestic investors, but it will also help to increase foreign inflows. This section of the study

intends to suggest some policy recommendations for domestic as well as foreign investors,

stock market regulators, policy makers and stock market analysts. Investors and stock market

analysts could forecast stock prices and earn profits. Stock market regulators could take

initiatives for the accountability of companies to prevent manipulation of stock prices and to

educate layman investors for stock market and encourage them to invest in stocks. Policy

makers should be acquainted of these macroeconomic effects on stock market and help them

to take efficient and effective decisions.

The implications of the present study are multifaceted and the findings of the study

implies that, the relationship between economic growth and stock market development found

to be positive, this could have been due to various reasons including pure coincidence, the

working of the wealth effect, the stock market acting as a predictor of GDP or that the stock

market does not move of its own accord but rather remains in line with physical production

conditions. The stock prices and GDP are related because changes in information about the

future course of GDP cause prices to change in the stock market today (Carlstrom, et al.

(2002)). GDP is the most crucial economic indicator which tells us about the health of our

economy. Higher economic activity implies higher expected profitability, which causes stock

prices to rise. Therefore, the stock markets can be flourished with economic growth of the

nation, because it plays a significant positive role in the developments of capital markets of

India. In a country, when the real GDP will raise it will help stock prices to increase and

boost up the investor’s confidence, with the growing economy. It can help companies and

investors decide on, what investment strategies they should adopt. It also guides the policy

makers for taking decisions for formulating and implementing the effective policies. Steps

should be taken to develop export based businesses, to promote economic growth of the

country. Therefore, the authorities concerned should formulate such a policy, so as to support

stock market by promoting economic growth.

The relationship between real exchange rate and stock market development comes out

to be postitive and this relationship may be useful because devaluation of domestic currency

increase export, hence improve the cash flow and divide payoffs for firms that rely on exports

in India. This relationship may also be useful for portfolio managers interested in global asset

allocation or investors trying to hedge against foreign exchange risk. The positive impact of

real effective exchange rate on Indian stock market suggests that, in order to develop the

stock market in India, the exchange rate should be managed carefully by keeping in view the

elasticity of exports and imports that leads to stability in the stock market.

208

World oil price is a powerful exogenous variable which influences the stock market and

the findings imply that increase in crude oil prices leads to decreased stock prices, creating an

unfavorable investment climate. But up to some extent that the negative impact of oil prices

can be mitigated, only if the uses of alternative energy resources are facilitated. Therefore,

the rising crude oil prices should serve as the reminder for policy makers to monitor and

control its effects on economic conditions.

The empirical data also suggests that, attracting foreign capital inflows (both Foreign

Direct Investment (FDI) and Foreign Institutional Investment (FII)) and promoting trade

openness can facilitate further investment and easier means of raising capital to support the

activities of the stock markets, which will lead to increased economic activity. Foreign capital

inflow is an important determinant is an important determinant of stock market development

in India. Hence, more liberalized policies in context of foreign capital inflows, must be

formulated so as to ensure more liquidity in the stock market in India, as a result the Indian

capital markets become more attractive for the foreign investors of major economies of the

world.

As per the empirical results the inflation is showing both positive (for yearly data

estimation) and negative impact (for monthly data estimation) on stock prices. Therefore,

keeping in view long run and short run frequency of data, appropriate policies should be

formulated for balancing the inflation in the country. The positive relationship may be due to

the reason that the stock market returns may provide an effective hedge against inflation in

India. This is explained by the significant and positive relationship between inflation and

stock prices as the Fisher (1930) hypothesis postulates. This also implies that investors in

making better portfolio decisions should perhaps view shares as long-term holdings against

inflation’s loss of purchasing power. The study also suggests that suitable policy measures

should be taken by the proficient authorities for the purpose of controlling inflation, which

ultimately leads to the control of volatility of the stock market. By implementing appropriate

monetary policies and setting appropriate fiscal measures, the Indian government will be in

the situation to control and regulate the rate of inflation, to promote a healthy growth of the

stock markets in India. Therefore, the study suggests that the financial regulators and

policymakers should consider the effect of these fundamental macroeconomic variables while

formulating fiscal and economic policies.

The result suggests a negative impact of fiscal deficit on the stock prices in India.

Hence, the government must adopt appropriate policies to improve budget deficit. A stable

government with stable policies can help in achieving confidence among foreign and

209

domestic investors. If the government seriously targets these variables, the stock market will

develop resulting in the financial development of the country.

The findings imply that increase in current account deficit leads to decrease stock

prices; therefore, the rising deficits should serve as the reminder for policy makers to monitor

and control its effects on economic conditions. The policymakers should take corrective

measures to curb the deficits up to the level which is acceptable for current economic

conditions. The concerned authorities should promote bilateral trade and should formulate a

tax structure which benefits export businesses to reduce the gap of export and import thus

reducing the current account deficit.

The finding also implies that, the increase in gold prices, gives an alternative and

uncontroversial safe investment during the time of financial crisis as it allows its holder to

resell it without loss at any time especially in the financial markets collapse.

It is believed that the effects of macroeconomic variables on the profitability of

different sectors vary depending on their sensitivity to these variables or it can be said that

every sector is sensitive to the changes in particular macroeconomic variables. For example,

capital-intensive industries (such as banking sector industries or other non-banking financial

firms) are likely to be more sensitive to interest rate changes. Similarly, the earnings of

sectors such as retail and tourism are more likely to be affected by a slowdown in economic

activity. Another perspective of the sectoral study shows that some secoTrs are immune to

the changes in the aggregate macroeconomic variables. For example, the slowdown of the

economy is less likely to affect sectors, such as consumer staples or health industries that

produce goods and services that are essential to consumers. Therefore, the empirical results

of the sectoral study imply that the investors should follow the changes in the sectoral

contribution of GDP, to predict the movement of the shares of that particular sector.

8.3. Contribution of the study

The present study on the relationship between macroeconomic variables and stock

prices has been extensive for many developed economies. However, the study in the context

of emerging economies like India is limited and orthodox in nature. Findings in the present

thesis provide a broad understanding on the dynamic relationship between macroeconomic

variables and the Indian stock market. The study attempted to discuss theoretical hypothesis

on the relationship between macroeconomic variables and stock market development; and

compares it with empirical evidences from previous research works. The present study adds

several primary contributions to the exisiting literature in this field.

210

First, it extends the literature by examining the relationship between macroeconomic

variables and stock prices in the context of emerging Indian economy and intends to be a

basic coverage for further research; and the study helps in observing possible time series

correlation between the Indian stock market prices and domestic and international

macroeconomic factors to enhance investors portfolio understanding and evaluation in terms

of the sensitivity of respective stock market prices to the systematic effect of the selected

macroeconomic factors.

The study contributes to the literature with the addition of fiscal policy variables (Fiscal

Deficit and Current Account Deficit) and their relationship with the stock market

development in India. the study focuses on the relationship between fiscal deficit and stock

market development, which will help the policy makers and regulators to formulate the

appropriate policies in order to improve the conditions of fiscal deficit. Further, the study is

first to attempt the empirical relationship between twin deficit and stock market development

in India.

The study is first to attempt the empirical relationship between sectoral index and

sectoral contribution of GDP. This relationship will help investors, portfolio managers, policy

makers and financial regulators to track the movement in a particular sector index, due to the

changes in the share of GDP of that particular sector. This sectoral study will specifically

contribute in concentrating on the shares of a particular sector so as to get a better insight of

the performance of stocks of that particular sector. Thus, the sectoral analysis of stock market

provides better insight about the performance of the market to both the investors and the

regulators. Sectoral analysis is a better approach for both investors as well as regulators. In a

sectoral study the impact of macroeconomic factors is studied on various sectors. The

performance of different sectors in same economic conditions is different. This gives an idea

of risk diversification to investors and enables them to design well diversified portfolios. The

relationship of sectoral GDP with respective sectoral indices is a matter of interest to

investors, institutions, researchers and policy makers.

The study also contributes by including the techniques like Impulse Response Function

(IRF) and Variance Decomposition (VDC) in the study. These are one of the essential tools

for interpreting VAR model results. The IRF allows us to examine the current and future

behavior of a variable that following a shock to another variable within the system. Whereas,

the VDC determines the relative importance of each innovation to the variables in the system.

Both the techniques help in predictin the responsiveness of variables towards the shock in

other variables.

211

The findings of the present study, helps the investors and portfolio managers to make

effective investment decisions, because the knowledge of this inter-relationship between

macroeconomic variables and stock prices provides a better understanding of portfolio

structure. Further, the study also provides an insight to investors and portfolio managers in

making an evaluation for the improvement of overall portfolio design, which ultimately leads

to a better risk diversification strategy and more return. Therefore, it can be said that this

study is significant for investors and portfolio managers. Similarly, the study is expected to

offer some insights to financial regulators and policy makers in terms of formulating

economic and financial policies. A specific precondition of this type of relationship, where

the change in a particular variable can influence the change in another variable, may help the

government agencies in designing economic policies so as to encourage more capital inflows

into the capital market, which leads to economic development of the country. Moreover, the

results provide an opportunity for risk diversification in Indian stock market. Since the stock

returns of different industries behave differently in similar economic conditions so investors

should analyze the nature of industry before making an investment decision. The results can

help investors and portfolio managers in extending their understanding of the risk return

relationship as well as pricing of macroeconomic risk.

Apart from identifying and relating the movement in stock market with the changes in

macroeconomic variables, the present study sheds some light by providing better

understanding on the depth of the stock market activities, especially in an emerging market

like India. Therefore, this study identifies the speed of adjustment towards the long-run

equilibrium by estimating the error correction term.

The study applies different modern econometric methods that may provide insight for

the exisiting literature about the sensitivity of the analysis to the methods employed. Further,

the study employs ARDL techniques to address the cointegration among variables in both

long-run and short-run, since the traditional econometric techniques does not provide enough

scope to capture both long-run and short-run cointegrating relationship among

macroeconomic variables and stock prices.

Thus, conducting such study is worth for the emerging economies like India, as the

study provides a better way of understanding the movement of stock prices through

identification and validation of the effects of macroeconomic variables on the stock market

performance, both on aggregate basis and on sectoral basis. Thus, more efficient risk

measurement and management models can be formulated with a greater confidence in the

decision making process for investments in the stock market.

212

8.4. Limitations of the study

This research has attempted to address a number of issues regarding the relationship

between macroeconomic variables and stock prices in India, by framing different estimation

models as per the availability of the data. Although the thesis has made every attempt to

provide a comprehensive and detailed analysis of the relationship between two, some

limitations remain. These limitations are discussed in this section of the concluding chapter.

One of the limitations of the research is the use of Index of Industrial Production

(IIP) as a proxy for economic activity, due to unavailability of GDP in monthly frequency

data. Further, while conducting the study on the relationship between BSE Sensex and

macroeconomic variables on monthly data, CPI has been used as the proxy for inflation and

the data period taken for the study was starting from April 2004, due to unavailability of CPI

data in common base year prior to this period.

The study has not considered the effect of changes in the monetary policy and fiscal

policy on the movement of stock prices. Furthermore, the study has not incorporated the

effect of stock prices of other major economies on the Indian stock prices. The external

variables like changes in the federal rates which can influence the foreign inflows of the

country, which ultimately effects stock prices, has also not been taken into account.

8.5. Scope for further studies

The study suggests further scope for the research to increase the understanding about

the dynamic relationship between the macroeconomic variables and stock prices in India.

Further research may either eliminate some of the limitations or expand the scope of

relationship already done in the present thesis.

Future work might re-examined the issues addressed in this thesis using a relatively

more comprehensive data set (i) including more recent share price data; and (ii) the data of

major leading stock indices of developed economis can also be included. This research would

be particularly valuable as a more recent time period and inclusion of share prices of

developed economies will give a better insight to predict the movement of share prices by

tracking the changes in leading share markets of the world. Examining how the developed

markets of the UK and the US affect the emerging markets like India could be valuable.

The current thesis focuses exclusively on the time series data of Indian economy, but

the further studies can be done by considering panel data incorporating similar

macroeconomic variables for more countries of south asian region. This would help in

213

examining why various domestic and global factors are important in various countries of the

region by performing a research on panel data.

The current thesis focuses on the relationship between macroeconomic variables and

stock prices without considering the impact of any global financial crisis, which can give a

better understanding of the global economic scenario with respect to major events occurred in

the economy.

The research can be further extended by considering the impact of selected

macroeconomic variables along with other important economic determinants like

employment rate, education level, political conditions; which are not included in the analysis.

Moreover, the research can also be extended to analyse the stock market volatility with the

help of GARCH family model, by incorporating the set of macroeconomic variables used in

the present study.

The present research focused on sectoral index and its relationship with respective

sectoral GDP, but the research can further be extended by including some more sectors like

infrastructure sector and agriculture sector.

This thesis used the same set of macroeconomic variables to test for the relationships

on the Sector indices. It may be useful for future studies to include other economic variables

that might affect each sector specifically. It is also recommended to work out research that

compares results with other developing countries’ under similar assessment and

measurement.

Finally, the sectoral research can further be segmented to industry level, because the

research at industry level may help the investors to understand the response of the shares of

that industry due to the changes in external economic environment.

214

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Reports

ENS Economic Bureau - The Indian Express, May 2015.

Annual Reports: Ministry of Finance, Government of India

Center for Budget and Governance Accountability (CBGA), India

Annual reports and monthly bulletin: Reserve Bank of India

Websites

www.sebi.gov.in

www.bseindia.com

www.nseindia.com

www.dipp.nic.in

http://goldprice.org/

http://indianexpress.com/agency/ens-economic-bureau/

https://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0CBwQFjAAahUKEwi4t_G3gIjIAhUFGJQKHbArCVU&url=http%3A%2F%2Ffinmin.nic.in%2Freports%2Fannualreport.asp&usg=AFQjCNENWdlk2oBygD5zaz1sW8hhDvNPhA&sig2=QDhEE-MBz8Mhl5t7U_dBuw&bvm=bv.103073922,d.c2E

http://www.sebi.gov.in/

http://www.bseindia.com/

http://www.nseindia.com/

http://www.dipp.nic.in/

239

Appendix

B. Lag length selection criteria

A standard problem in time series analysis is the choice of an appropriate model to

represent the data. This is a common problem when a statistical model contains many

variables. According to Parzen (1982), statistical data modelling is a field of statistical

reasoning that seeks to fit models to data without knowing what the “true” model is or might

be. Consequently, one seeks to learn the model and study the quality of the model by a

process which is called statistical model identification or evaluation. In recent years, in the

literature, the necessity of introducing the concept of model selection or model evaluation has

been recognized. Sclove (1994) describes model selection as the choice of selecting the best

model(s) from a set of models and the different type of models that one compares and selects

can be characterized according to the number of lags, the different number of explanatory

variables and other factors. Also, there is presently a great deal of interest in simple criteria

represented by the parsimony of parameters for choosing one of a set of competing models to

describe a given data set. As discussed in Stone (1981), parsimony can take into account a

variety of attributes of the selected model. One such attribute is the cost of measuring the

models that required implementing the model and a second attribute is the complexity of the

selected model. The general principle is that for a given level of accuracy, a simpler or a

more parsimonious model is preferable to a more complex one.

This study focuses on four well-known model selection criteria to determine the order

of the model and each of these criteria is discussed in the literature that follows. The four

criteria are Akaike’s information criterion, Schwarz’s information criterion, Hannan-Quinn’s

information criterion and Final Prediction Error. In this study, these criteria are used to

analyze simulated data from a theoretical cointegrated model. The criterion which identifies

the correct model most often is identified as the most appropriate criterion.

The four well-known information criteria that are used in this research follow a similar

format to the general information criterion (GIC) and the formula of the GIC is illustrated

below. The first term of the GIC measures the lack of fit of the model and the second term is

a penalty function for the number of parameters in the model. The lack of fit of the model

involves a measure of the lack of parsimony or complexity of the model. One of the issues

that lead to model complexity is the number of parameters incorporated in the model.

𝐺𝐼𝐶 = −2log (𝐿𝑘) + 𝑃𝑘 (a)

240

Where: Lk is the likelihood value of the k-th model

Pk is the penalty for the k-th model

A.1. Akaike’s information criterion

During the last three decades, Akaike’s information criterion (AIC) has had an

important impact on statistical model evaluation problems. AIC has been developed for the

identification of an optimal and parsimonious model in data analysis for a class of competing

models which take model complexity into account. The introduction of AIC furthered the

recognition of the importance of good modelling statistics. The model selection strategy of

AIC has the objective of selecting a model based on simply minimizing the Kullback-Leibler

discrepancy between the unknown (true) and the approximating data based models. The

finding of the true model can be very complex and may require a great amount of time, since

the model may incorporate an infinite number of parameters. Therefore, obtaining a true

model is not an ideal manner to represent the recorded data, but rather allow for the best

approximating model and that is what AIC does.

𝐴𝐼𝐶(𝑝) = 𝑙𝑛|∑| +2𝑘2𝑝

𝑇 (a.1)

Where:

k = the number of variables in the model

p = the number of lag terms in the model

T = the number of observations used

𝑙𝑛|∑| = the estimated covariance matrix of the fitted multivariate model taken from

Lutkepohl (1985) and Gonzalo and Pitarakis (1998) and it consists of two measurement

terms. The first term (i.e. 𝑙𝑛|∑|) measures the inaccuracy or poorness of fit of the model. The

second term (i.e. 2𝑘2𝑝

𝑇 ) measures the complexity or the penalty due to the increase of

unreliability in the first term which depends upon the number of parameters used to fit the

data.

Consequently, when there are several competing models the parameters within the

models are estimated by the method of maximum likelihood and the values of the AIC are

computed and compared to find a model with the minimum value of AIC. This approach is

called the minimum AIC procedure and the model with the minimum AIC value is called the

minimum AIC estimator and is chosen to be the best model. For us the best model is the one

with the least complexity, or equivalent, the highest information gain. In applying AIC, the

241

emphasis is on comparing the goodness of fit of various models with an allowance made for

parsimony.

A.2. Schwarz’s information criterion

This model selection criterion is used when a true model exists and has a finite and

small dimension that does not increase with sample size. This criterion does not receive any

benefit from the theory of Kullback-Leibler discrepancy, but is derived based on a Bayesian

viewpoint. The best fitting true model is chosen from the list of candidate models as the one

that has the lowest Schwarz’s information criterion (SIC) value.

Lutkepohl (1985) performed a comparison of several information criteria used for

determining the order of a vector autoregressive process for different sample sizes. The result

indicated that the Schwarz’s information criterion estimated the order of an autoregressive

process correctly most often and estimated correctly more often when the sample size

increased. Lutkepohl suggested that the Schwarz’s information criterion and the Hannan-

Quinn’s criterion were the most parsimonious criteria as these two criteria produced the

smallest average squared forecasting error and estimated the order of an autoregressive

process correctly more often. The criterion developed by Schwarz is often referred to as SIC,

Bayesian information criterion (BIC) or even Schwarz Bayesian criterion (SBC).

𝑆𝐼𝐶(𝑝) = 𝑙𝑛|∑| +𝑘2𝑝ln(𝑇)

𝑇 (a.2)

Where:




𝑙𝑛|∑| = the estimated covariance matrix of the fitted multivariate model

A.3. Hannan-Quinn’s information criterion

Hannan and Quinn (1979) provide a brief discussion on methods used for the

determining the order of an autoregressive model. They realized that a method such as

Shibata’s information criterion was inconsistent in the estimation of the order of the

autoregressive model. Hannan and Quinn (1979) claimed that the best-known rule for

estimating the true order of an autoregression was to make use of the method developed by

Akaike (1969). They followed a similar estimation procedure where the method was strongly

consistent for estimating the order of the autoregression. This model is called the Hannan-

242

Quinn’s information criterion (HQ) and it has been used in analysis by Lutkepohl (1985),

Quinn (1980) and Gonzalo and Pitarakis (1998).

Lutkepohl (1985) illustrated in his analysis that the method developed by Hannan and

Quinn was consistent in the estimation of the true order of an autoregressive process. This

was established when performing a comparison with other consistent criteria of various

sample sizes. Lutkepohl suggested that the Schwarz’s information criterion and Hannan-

Quinn’s information criterion were the best criteria when one was interested in forecasting

(minimizing the mean square forecasting error) or estimating the order of a finite order vector

autoregressive model. Quinn (1980) extended the procedure developed by HQ to the larger

dimension case. This larger dimension case was referred to as the multivariate autoregressive

process. This procedure was developed in such a way that it has been strongly consistent just

as in the situation of a univariate autoregression. During the same period, Hannan (1980)

extended the original work of HQ by determining the order of an autoregressive moving

average process.

𝐻𝑄(𝑝) = 𝑙𝑛|∑| +2𝑘2𝑝lnln𝑇

𝑇 (a.3)

Where:





A.4. Final Prediction Error

Akaike (1969) provided a brief discussion on the practical use of the Final Prediction

Error (FPE) in determining the order of an autoregressive model. The practical application of

the FPE is to estimate the FPE of each autoregressive model within a prescribed sufficiently

wide range of possible orders and to select the one that gives the minimum of the estimates.

Akaike (1969) claimed that by seeking the minimum of FPE, we would be able to arrive at an

autoregressive model of an order that did not have a significant bias and simultaneously did

not have a large mean square prediction error.

In research published during 1969, Akaike performed a comparison of three types of

predictors that were used for model selection. These predictors were the original minimum

FPE, the modified version denoted by the minimizing (FPE)1/4 and the FPE proposed by

Anderson (1963) for the decision of the order of a Gaussian autoregressive process. These

243

three predictors were compared based on various simulated time series models, the predictor

that indicated the true model most often was the one selected. The results showed that for

practical applications, the original procedure, minimum FPE, was the best procedure to use

for model comparison. Lutkepohl (1985) also compared several types of information criteria

and found that the predictor FPE had a tendency to over-estimate the order of an

autoregressive process. In addition, the criteria FPE, AIC and Shibata all had a tendency to

obtain the same number of lag terms for large sample sizes.

𝐹𝑃𝐸(𝑝) = |∑| + (𝑇+𝑝𝑘2+1

𝑇−𝑝𝑘2−1)𝑘2

(a.4)

ln𝐹𝑃𝐸(𝑝) = ln|∑| + 𝑘2ln (𝑇+𝑝𝑘2+1

𝑇−𝑝𝑘2−1)𝑘2

(a.5)

Where:





244

List of Publications and Presentations

Publications from the Ph. D. thesis

“Fiscal Deficits and Stock Prices in India: Empirical Evidence”, International Journal of

Financial Studies, 2015, Vol. 3, No.3, pp. 393-410.

"Cointegration and Causality between Macroeconomic variables and Stock Prices: Empirical

Analysis from Indian Economy", Business and Economic Research, 2015, Vol. 5, No. 2, pp.

327-245.

"Examining the Relationship between Sectoral Stock Market Indices and Sectoral Gross

Domestic Product: An Empirical Evidence from India”, Global Journal of Management and

Business Research, 2015, Vol. 15, No. 9, pp. 14-26.

“Dynamic Relations between macroeconomic variables and Indian Stock Price: An

application of ARDL bounds testing approach”, Asian Economic and Financial Review, Vol.

5, No. 10, pp. 1119-1133.

“Macroeconomic determinants of Stock market development: Empirical evidence from

India”, Business Perspectives, 2015, Vol. 14, No. 2, pp. 36-50.

“Macroeconomic Variables and Stock market development in India: An application of ARDL

bounds testing approach", Empirical Economics Letters, 2015, Vol. 14, No.7, pp. 705-718.

“Causal Relationship between Stock market Indices and macroeconomic Variables:

Empirical Evidences from India”, International Journal of Multidisciplinary Research, 2013,

Vol. 2, No. 3, pp.114-117.

“An Empirical Analysis of the Relationship between Stock Market Indices And

Macroeconomic Variables: Evidences from India”, International Academic Research Journal

of Economic and Finance, 2013, Vo. 2, No.1.

Conference Papers:

“Fiscal Deficits and Stock Prices in India: An Empirical Evidence”, Paper presented

in 4th International Conference on Applied Econometrics, IBS Hyderabad, 20-21 March

2014, Hyderabad, India

“Sensitivity of Stock Market Indices to Oil Prices, Exchange Rate and Economic Growth:

Evidence from Industrial Sub-sectors in India”, Paper presented in the National Seminar on

Econometric application in Management at Central University of Rajasthan during 20 – 21

November, 2013.

245

Brief Biography of the Candidate

Pooja Joshi is currently pursuing Ph. D. At the Department of Economics and Finance at

BITS, Pilani Campus in the area of Financial Economics. Her Ph. D. Thesis is entitled

“Relationship between Macroeconomic Variables and Stock Market Development: Evidences

from the Indian Economy”. She has received a first class Master’s degree in Management,

from Rajasthan Technical University in 2008. She has more than three years of teaching and

research experience. She is active in research and authored a number of research papers in

international and national journals. Her research interest include Financial Economics,

Macroeconomics, Business Economics, Capital Markets and Financial management

246

Brief Biography of the Supervisor

Prof. A.K. Giri is an Associate Professor of Macroeconomics and Monetry Economics, at

Department of Economics and Finance, Birla Institute of Technology and Science (BITS-

Pilani), Pilani. He is currently the Head of the Department of Economics and Finance. He has

received a first class Master’s and M. Phil. In Economics, from Department of Economics,

Central University, Hyderabad and a Doctorate in Macro-Monetry Economics from the same

University in 1998. His research interest include Macroeconomics, Monetry Economics,

Financial Economics, and Economics of Growth and Development. He has more than 16

years of experience in teaching and research in Economics at postgraduate level. He has

authored a number of research papers in international and national journals and conference

proceedings.

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