Causal Relationship between Stock market and Real Economy in India using Granger Causality test

CAUSAL RELATIONSHIP BETWEEN STOCK MARKET AND

REAL ECONOMY IN INDIA

A PROJECT REPORT SUBMITTED IN

PARTIAL FULFILLMENT OF THE

DEGREE OF BACHELORS OF MANAGEMENT STUDIES

SUBMITTED BY:

SAMYAK CHAUDHARY

HAHEED SUKHDEV COLLEGE OF BUSINESS STUDIES

APRIL 2016

2

CERTIFICATE

This is to certify that the project report entitled “Causal Relationship between Stock Market and

Real Economy of India” is the project work carried out by Samyak Chaudhary at Shaheed

Sukhdev College of Business Studies for partial fulfillment of BMS.

This report has not been submitted to any other organization for the award & any other Degree

/Diploma.

Samyak Chaudhary

3

TABLE OF CONTENTS

Chapter no. Title of the Chapters Page no.

ACKNOWLEDGEMENT 6

ABSTRACT 7

1 INTRODUCTION 8

1.1 Scope of the research 9

2 THEORETICAL PERSPECTIVE 10

3 LITERATURE REVIEW 13

4 RESEARCH METHODOLOGY 15

4.1 Data Collected for the tests 15

5 DATA ANALYSIS 19

5.1 QUARTERLY SERIES ANALYSIS 20

5.2 ANNUAL DATA ANALYSIS 23

6 FINDINGS AND CONCLUSIONS 25

7 LIMITATIONS OF THE PROJECT 27

8 FURTHER SCOPE 27

9 APPENDIX 30

10 REFERENCES 34

4

LIST OF TABLES AND FIGURES

Numbers Tables and figures Page no.

Table: 1 Second difference stationarity result GDP quarterly 20

Figure: 1 Level correlogram result GDP quarterly 20

Figure: 2 Second difference correlogram result GDP quarterly 21

Table: 2 Lag length Criteria result GDP and Sensex quarterly (lag2) 21

Table: 3 Lag length Criteria result GDP and Sensex quarterly (lag3)

21

Table: 4 Serial correlation LM test GDP and Sensex quarterly

22

Table: 5 Granger Causality result GDP and Sensex quarterly 22

Table: 6 Granger Causality result GDP and Nifty50 quarterly 22

Table: 7 Granger Causality result GDP and Sensex Yearly 23

Table: 8 Granger Causality result GDP and Nifty50 yearly

24

Table: 9 ADF test results 25

Table:10 Lag terms Results 25

Table 11: Granger Causality Test Results 25

Table 12: Market Capitalization % of GDP 28

Table 13: Market Capitalization and GDP (current billion US$) 29

5

ACKNOWLEDGEMENT

I wish to express my sincere thanks to Dr. Poonam Verma, Principal of SSCBS, for

providing me with all the necessary facilities for the research.

I place on record, my sincere thanks to Mr. Neeraj Kumar Sehrawat, Assistant professor,

SSCBS, for the continues encouragement and support as a faculty guide.

I take this opportunity to express gratitude to all of the Department faculty members for

their help and support. I also thank my parents for the unceasing encouragement, support and

attention.

I also place on record, my sense of gratitude to my colleague, who directly or indirectly, has lent

their hand in this venture.

Samyak Chaudhary

6

ABSTRACT

This study tries to find out the direction of causal relationship between stock market and the real

economy of India by applying Granger Causality test on Nifty 50 and BSE Sensex data from the

period 1996-2014 and 1990-2014 respectively with real GDP values both, quarterly and

annually. By applying ADF Unit root test to check stationarity, Vector Autoregression (VAR) to

find out optimum lag structure and finally Granger causality test, the report finds out that there is

no statistically significant Granger Causal relationship between stock market and the real

economy of India for quarterly periods and a unidirectional Granger causality from real GDP to

market indices for annual figures. They may have some other causal relation which is not

explained by the Granger test assumptions or they might still be independent of each other in

India.

Being a very interesting issue to determine the cause and effect relation between stock market

and economy, this study explains the theories related to such a relationship and also emphasize

on the fact that why considering real economy for such a study is beneficial.

The results of this report show that stock market and real economy of India Granger Cause each

other but in very different scenarios and thus motivates to assess all the different scenarios. The

yearly direction for causality is from real GDP to market indices. Hence, it says that either of the

variables can be explained by their own historical values or by some exogenous variable, and

also by one another but only for yearly figures.

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1. INTRODUCTION

The stock market and economy are very common terms that we hear every day and are one of the

strongest entities based on which the entire future of a country depends. They affect lives of

millions of people and make to the news headlines every day. Also, it is interesting for any

researcher or academician to ponder over the causal relationship between these two entities. The

prime aim of this research is to evaluate the causal relationship between stock market and real

economy of India and most importantly find out the direction of causality.

This topic may appear generic yet is widely debated as one cannot claim to know the true

relation between stock market and economy. There exist many theories that offer arguments and

counter-arguments to this issue. Also, many studies have been conducted related to this, so this

issue is very interesting to study upon. One can be curious to know whether it is a good economy

that causes a better performing stock market or is it vice-versa. Hence, this can be considered as

the motivation to conduct this study and effort is made to discuss the theoretical aspects of this

issue along with statistically studying the relationship using the popular method of Granger

causality test to know the direction of causality.

This topic may also help to re-evaluate the causal relationship between these two entities and can

motivate academicians to work upon a model in which stock market performance can be

considered an indicator of good economic conditions or vice-versa if such direction of causality

prevails.

Also, the use of Real economy is an important addition to this report. Based on the previous

researches either macroeconomic variables such as inflation, industrial production, CPI etc. or

nominal GDP was used. By using real GDP at factor cost, the report tries to capture the actual

output value of goods and services after adjusting for inflation.

Real economy may be the part of the economy which actually takes into account produced goods

and services. So it excludes the effects of inflation or deflation. So if prices are assumed to not

go up, then this gives a realistic measure of assessing the growth or the output. Real GDP is

assumed to be the variable that can most represent real economy.

8

As per Bart Hobijn and Charles Steindel (CURRENT ISSUES IN ECONOMICS AND

FINANCE , Volume 15, Number 7, 2009) GDP, especially real GDP, is considered the central

measure of overall economic activity primarily because its long and short run movements are

correlated with many factors. An important example is that the real GDP growth is closely

associated over the long run with the improvement in living standards. Subsequently, GDP

growth measured in current dollar prices and the long-run growth of the tax base are closely

correlated and thus affect tax revenues.

1.1 Scope of the research

The prime objective is to identify direction of Granger causal relationship between stock market

and real economy. This is important to note that the report only theoretically explains the

relationship between the entities based on previous literature, and only finds the presence and the

direction of the causality to know what causes what. This is done on the basis of considering that

there indeed exists a relationship between the two entities, believing the theories to be true and

only discusses them for basic understanding. So the scope is limited to the extent of determining

the direction of causality and not empirically providing a relationship for the Indian entities.

Scope of the study is limited to Indian context and data is therefore is completely based on

availability. Nifty5o was not available before 1990 and GDP values were not for period before

1988 (1960 on World Bank). So it is highly dependent on the short duration data available.

Also, as proxy for real economy is taken as Real GDP and other macro economic factors that

affect stock prices are not considered for analysis. This is done just to simplify the study and to

show the causal link between two major variables of interest using the Granger test. It is believed

that real GDP is the most appropriate indicator of economy as sometimes GDP growth is

synonymous with economic growth. By doing this, the result will be easier to interpret and

conclusion will be straightforward.

9

2. THEORETICAL PERSPECTIVE

Financial markets are crucial for the foundation of a stable financial system of a country. Also,

financial system is an integral part of the country’s economy. Many domestic as well as

international factors directly or indirectly affect the performance of the stock market.

Volatility in stock prices raises the interest of the general public and policy-makers into stock

market environment and increase the focus on its impact on the economy. When prices rise

sharply, it raises fears of a stock market bubble or it may happen just due to inflationary

pressures. A collapse in share prices has the ability to cause immense economic damage, a

famous stance being of the stock market crash of 1929 which acted as a key factor in causing the

Great Depression in 1930s.

However, we cannot take instances from history to simply say that this always happen, as daily

movements in the stock market can also have insignificant impact on economy. A fall in share

prices does not necessarily cause an economic downturn.

The fall in share prices may affect the average consumers as the people with money invested in

stocks experience a decline in the value of their invest when stock prices plummet. So their

wealth decreases. Karl E. Case, John M. Quigley, and Robert J. Shiller (2005) it is the causal

effect of exogenous changes in wealth upon consumption behavior.

Ricardo M. Sousa (WEALTH EFFECTS ON CONSUMPTION EVIDENCE FROM THE

EURO AREA, 2009) even concludes that financial wealth effects are relatively large and

statistically significant than Housing wealth effects. But the effect should be given too much

importance as it strongly incorporates behavioral component that may vary significantly. And

investor spending pattern may be independent of share prices.

Equity markets and its news are given great amount of importance and sometimes economic

conditions are inferred from it. But to what extent theoretical models apply to practical cause and

effect, which may still remain in dark.

One consequence of 1990s liberalization has been a significant increase in the inflows of foreign

capital into India. But for countries implementing financial liberalization for adjusting the

10

economic scenario like India during 1990s, the first step is the loosening of controls on the

inflow of foreign capital, accompanied by a shift to a more liberalized exchange rate regime5.

This may make it more susceptible to bad times of foreign markets.

Nevertheless, given that the importance of the equity markets within the Indian financial system

is expected to increase significantly in the longer term, the impact of stock price movements on

the macroeconomic development may also increase.

However, all in all we can say that share prices may appear to be a part of the economic

movement, because if condition of economy can affect the prices of goods, services or other

entities, then shares to maybe get covered under the same umbrella. Or maybe stock prices under

certain conditions pull the economy to one direction.

The more important issue is to find out why it is essential to find a decent and logical relation

between stock market and economy. It is because if economy causes the stock market movement,

then macroeconomic indicators become really helpful for an investor. On the other hand, if

opposite happens, then we can consider share prices as an economic indicator and can predict an

economic slowdown or recession.

Theories for the relationship:

Stock price is the discounted present value of a firm’s payout in an economy, as suggested by

economic theory under Dividend Discount model (Gordon). As mentioned by Gevit Duca

(2007), if the firm’s payout that is profit portion which is earned due to real economic activity,

then we can say that this payout is a function of economic activity and such relationship should

prevail. Equity earnings and cash flows are naturally correlated with economic activity and the

business cycle, as per Paulo Maio and Dennis Philip (2013)1. They further suggest that equity

discount rates needed to determine equity risk premium are related to systematic common risk

factors for which macroeconomic variables are often selected. Thus, current stock prices should

be related to future economic activity through the cash-flow channel.

There are other theoretical propositions about how stock prices may have a direct affect on

economic output, further strengthening the link in the relationship between these two variables.

11

The first theory is explained by Tobin’s Q which was suggested by Tobin (1969). Tobin’s Q is

the ratio of share prices and the replacement cost of 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.

So relatively, the cost of capital appears to be low as the numerator increases. As cost of making

investment appears to be low, investment expenditure increases and thus technically aggregate

economic output should increase. This occurs because investment would be easier through debt

(which is cheaper) and no new shares are offered in a situation of a high share price.

The second of the relationship was suggested by Modigliani (1971) from the consumer’s point of

view. The basic premise of his theory is the impact of wealth on consumption. A permanent

increase in security prices results in an increase in the individual’s wealth (wealth effect).

Through the permanent income increase hypothesis, Modigliani postulated that consumers

smooth consumption in order to maximize their utility. This increase in permanent income will

allow them to consume more and to re-adjust their consumption levels upwards.

The third possibility is when stock prices impact output which is referred to as the financial

accelerator (Bernanke and Gertler, 1989; Kiyotaki and Moore, 1997). It focuses on the impact

that stock prices have on firms’ balance sheets. The ability of firms to borrow depends

substantially on the collateral they can pledge. The collateral value firms can offer increases in

scenarios where their stock price value increases as firms can pledge their stocks itself. Thus,

higher credit can be raised which can be used for investment purposes and subsequently triggers

a growth in economic activity.

So the basic premise of the first and the third theories is that if by any means cost of credit

decreases, investment increases and further assumes that a good investment will definitely

increase in aggregate output for the whole economy.

12

3. LITERATURE REVIEW

Gevit Duca (2007) conducted a Granger causal test of stock market prices and GDP in

developed market economies which revealed that these two tend to move together over time and

have statistically significant causal relationship in some countries. The question is also raised as

to what is the reason for such a relationship. It explains many popular theories about this

relationship which my report also incorporates and uses as a base. This paper employs the

Granger causality test in order to examine causality direction. The focus of the paper is on long-

term trends with evidence presented from top five stock markets in the world in terms of market

capitalization. He found that a unidirectional causality prevails from stock index to GDP in US,

UK, Japan and France, but no causality was found in Germany.

Samveg Patel (2012) in his study tries to investigate the effect of macroeconomic determinants

on the performance of the Indian Stock Market using monthly data over the period January 1991

to December 2011. The study is done for eight macroeconomic variables, namely, Interest Rate,

Inflation, Exchange Rate, Index of Industrial Production, Money Supply, Gold Price, Silver Price

& Oil Price, and two stock market indices namely Sensex and S&P CNX Nifty. It applies

Augmented Dickey Fuller Unit root test, Johansen Cointegration test, Granger Causality test and

Vector Error Correction Model (VECM) and found that the study found that Interest Rate is I

(0); Sensex, Nifty, Exchange Rate, Index of Industrial Production, Gold Price, Silver Price and

Oil Price are I (1); and Inflation and Money Supply is I (2). It also found the long run

relationship between macroeconomic variables and stock market indices. The study also revealed

the causality from exchange rate to stock market indices to IIP and Oil Price.

A study done by ABOUDOU Maman Tachiwou (2009) incorporates a different approach from

the above papers. This examines the causal relationship between stock market development and

economic growth. The geographical area selected is the West African Monetary Union economy

over the last decade. The techniques applied are unit–root tests and the long–run Granger

noncausality test proposed by Toda and Yamamoto (1995). The results say that there is strong

causal flow from the stock market development to economic growth. A unidirectional causal

relationship is also observed between real market capitalization ratio and economic growth.

13

Another paper by Adwin Surja Atmadja (2005) seeks to examine the Granger-causality among

stock prices indices and macroeconomic variables in five ASEAN countries, Indonesia;

Malaysia; the Philippines; Singapore; and Thailand with particular attention to the 1997 Asian

financial crisis and period onwards. Monthly data is used to find that there were few Granger

causalities found between the country’s stock price index and macroeconomic variables and

concludes that the stock markets do not seem to have played a significant role in most of these

countries’ economies. Atmadja also says that macroeconomic variables are unlikely to be

appropriate indicators to predict the future behavior of other macroeconomic variables.

The paper by Pramod Kumar Naik, Puja Padhi investigates the same objectives for BSE

Sensex and five macroeconomic variables. Johansen’s co-integration and vector error correction

model are used to explore the long-run equilibrium. The analysis reveals that macroeconomic

variables and the stock market index are co-integrated and, hence, a long-run equilibrium

relationship exists between them. They say that the stock prices positively relate to the money

supply and industrial production but negatively to inflation.

14

4. RESEARCH METHODOLOGY

The objective of this dissertation is to find out whether real Indian economy and the Indian Stock

Market indices namely, Nifty50 and BSE Sensex Granger cause each other or not.

The two variables considered in this study are real GDP and stock market indices that can be

considered as the dummies, with the relationship between them being tested by the Granger

Causality test.

Real GDP is the GDP value at constant prices, taking prices of base years as reference. This can

act as a proxy for real economy.

Calculation of Real GDP is done on the basis of this formula

Real GDP at FC= Nominal GDP at FC*Real GDP index (base=2010 prices)/100

Data Collected for the tests

This study is mainly conducted on Indian data using the Indian GDP values and Indian stock

market indices. This is to apply this research in the Indian market scenario.

It uses both quarterly and annual data for real GDP and Market indices.

The quarterly period is from year 1996 Q4 to 2015 Q3.

The annual period is from the year 1990 to 2014.

The CNX BSE Sensex data has been obtained from BSE official website, whereas, Nifty50 data

is taken from NSE Website (from1990 to 1995) and Investing.com (from 1996 to 2015)

Nominal GDP at Factor Cost in Indian Rupees and the Real GDP index with base as 2010 prices

taken as 100 are the necessary data for real GDP collected from IMF database (International

Financial Statistics)

All GDP values are in Billion Rs.

15

Process

The data used is tested for stationarity. For this, augmented Dickey Fuller Test is applied.

Non-stationary series is converted to stationary at required differences.

Also, serial autocorrelation is also check alongside this and to remove it if it is there,

intercept and trend is ignored for the series and another subsequent difference is taken.

After that, the number of lagged terms included in all Granger tests conducted is going to

be determined on the basis of the VAR lag length criteria. Schwarz Information Criterion

is given preference.

Autocorrelation LM tests are applied to further select the best lag that removes serial

correlation also.

Furthermore, Granger Tests are applied first to see whether real GDP Granger causes

each of the Stock Market Indices and vice-versa is also checked.

Also, GDP is tested with each Index, one at a time. There are two indices taken for this research

(BSE Sensex and Nifty50)

1. Null Hypothesis is Stock index do not Granger cause GDP

2. Alternate Hypothesis is Stock index Granger cause GDP

Nifty50 and BSE sensex are the two most important and popular stock market indices of India,

which I believe, truly represent the Stock market of India.

16

Tests Used

The three step process to conduct this research is as follows:

ADF Unit root test to test stationarity: A Unit root test is conducted to test the stationarity of a

time series using an autoregressive model. Augmented Dickey-Fuller (ADF) has a null

hypothesis that rho = 1 or the series has a unit root.

They are first observed at level, and then p-value is checked. If p-value is significant, then we

select the series for further analysis otherwise, we check stationarity at first difference and so on.

We also begin with a simple assumption that the series might have both constant and trend.

However, p-value remains the criteria for judging irrespective of other values.

VAR to select optimum lag structure: Vector autoregressive (VAR) models are widely used in

forecasting. It computes various criteria to select the lag order of an unrestricted VAR. It

prompts to specify the maximum lag to “test” for and we compare the modified LR statistics to

the 5% critical values starting from the maximum lag, and decreasing the lag one at a time until

we first get a rejection. The alternative lag order from the first rejected test is marked with an

asterisk (if no test rejects, the minimum lag will be marked with an asterisk).The determination

of the lag length can be used in Granger Causality test.

Autocorrelation LM test: Test for Autocorrelation in the residuals in the VAR window. It reports

the multivariate LM test statistics for residual serial correlation up to the specified order. This is

done because no serial correlation is a basic assumption of Granger and this helps if the lag

selection criterion is showing a tie for 2 or more lag values at the time of VAR Lag structure

selection.

H0: no serial correlation is present

H1: serial correlation is present

Granger Causality Test: As regression analysis deals with the dependence of one variable on the

other and correlation tells the degree of such relationship, but they do not explain cause and

effect. The Granger (1969) approach to the question of whether x causes y is to see how much of

17

the current y values can be explained by past values of y and then to see whether adding lagged

values of x can improve the explanation.

Y is said to be Granger-caused by x if previous lagged values of x explain the current values of y

significantly better than Y’s own previous lagged values.

EViews runs bivariate regressions of the form:

The null hypothesis is that x does not Granger-cause y in the first regression and

that y does not Granger-cause x in the second regression.

It is important to note that the statement “x Granger causes y” does not imply that y is the effect

or the result of x. Granger causality measures precedence and information held but does not by

itself indicate causality in the more common use of the term.

18

5. DATA ANALYSIS

First, we collect the data in MS Excel. From here, we export to Eviews for analysis. Level of

significance for all the tests is a standard 5% and P-value selection criterion,

If p-value < α, then enough proof to reject H0, accept Alternate hypothesis

If p-value > α, then not enough roof to reject H0, accept Alternate hypothesis

0

10,000

20,000

30,000

40,000

50,000

1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

GDPquarter

Nifty50

BSEsensex

0

4,000

8,000

12,000

16,000

20,000

24,000

28,000

90 92 94 96 98 00 02 04 06 08 10 12 14

GDPannual

Nifty50annual

BSEsensex

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5.1 QUARTERLY SERIES ANALYSIS

1. ADF Unit root Test:

For GDP quarterly: Stationarity at second difference, with no intercept and trend

Null Hypothesis: D(GDPQUARTER,2) has a unit root

Exogenous: None

Lag Length: 4 (Automatic - based on SIC, mailbag=11)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -8.173229 0.0000

Test critical values: 1% level -2.598907

5% level -1.945596

10% level -1.613719

Table: 1 second difference stationarity result GDP quarterly

There was a presence of partial correlation, as shown by Correlogram. So, GDP quarterly series

was taken at second difference without any intercept and trend. Even, though GDP at first

difference with intercept and trend was stationary, still to remove serial correlation, another

difference had to be taken and intercept and trend had to be excluded.

Figure: 1 level correlogram result GDP quarterly

New series generated as d2gdpquarter=d(gdpquarter,2)

For new series, correlogram is below which shows autocorrelation to be removed.

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Figure: 2 second difference correlogram result GDP quarterly

For Sensex quarterly:

Series is stationary at first difference, with intercept and trend. But series at first difference is

having serial correlation. So another difference is taken at no intercept and trend. So finally

below result is for second difference.

At second difference, serial correlation is removed results of which are shown in the next figure.

For Nifty50 quarterly:

Series made stationary at second difference, with no intercept and trend to reduce serial

correlation.

For GDP and Sensex: lag length criteria optimum at both 2nd

and 3rd

lag

Lag LogL LR FPE AIC SC HQ

0 -1275.643 NA 8.88e+12 35.49008 35.55332 35.51526

1 -1270.816 9.252211 8.67e+12 35.46710 35.65683 35.54263

2 -1239.270 58.71054* 4.04e+12* 34.70194* 35.01814* 34.82782*



0 -1258.873 NA 9.12e+12 35.51754 35.58127 35.54288

1 -1254.147 9.052070 8.94e+12 35.49709 35.68831 35.57313

2 -1223.046 57.82085 4.17e+12 34.73370 35.05238 34.86043

3 -1172.427 91.25630* 1.12e+12* 33.42049* 33.86665* 33.59792*


21

So we apply Serial correlation LM test

VAR Residual Serial Correlation LM Tests

Null Hypothesis: no serial correlation at lag order h

Included observations: 72

Lags LM-Stat Prob

1 88.22238 0.0000

2 6.627193 0.1570

3 26.50936 0.0000

Table: 4 serial correlation LM test GDP and Sensex quarterly

Lag 2 selected as it accepts null hypothesis which means no serial correlation. Thus, we select

lag 2 for Granger Test.

Pairwise Granger Causality Tests

Sample: 1996Q4 2015Q3

Lags: 2

Null Hypothesis: Obs F-Statistic Prob.

D2BSESENSEX does not Granger Cause D2GDPQUARTER 72 2.41119 0.0975

D2GDPQUARTER does not Granger Cause D2BSESENSEX 1.15921 0.3199

Table: 5 Granger Causality result GDP and Sensex quarterly

As both null hypotheses could not be rejected, thus no Granger causality exists.

GDP and Nifty50

Similar solution for Nifty50 also, Optimum lag = 2. No serial correlation for lag 2 as p-value

rejects alternate. Thus lag 2 is selected here as well.

Lags: 2


D2NIFTY50 does not Granger Cause D2GDPQUARTER 72 2.38673 0.0997

D2GDPQUARTER does not Granger Cause D2NIFTY50 0.85568 0.4296

Table: 6 Granger Causality result GDP and Nifty50 quarterly

No significant relationship in this test, so no Granger Causation.

22

5.2 ANNUAL DATA ANALYSIS

ADF test: GDP Annual (Annual GDP is Stationary at 3rd

difference.)

At 3rd

difference, there is no autocorrelation present, hence, GDP in 3rd

difference is ready for

testing.

Sensex annual

Annual Sensex Stationary at first differences only, subsequently, no autocorrelation at first

difference.

Nifty 50

Annual Nifty50 also stationary at first difference. No subsequent Autocorrelation.

After this, the data is ready for lag selection.

GDP and Sensex annual

There are 2 optimum possible values of lag i.e. 1 and 2. As per LM test, no decision can be

made.

In case of a tie, lag that appeared in the process earlier is selected( in this case, lag 2)


Sample: 1990 2014

Lags: 2


DBSESENSEX does not Granger Cause D3GDPANNUAL 20 0.41221 0.6695

D3GDPANNUAL does not Granger Cause DBSESENSEX 5.54492 0.0157

Table: 7 Granger Causality result GDP and Sensex Yearly

p-value is significant for second hypothesis, and there exists a unidirectional causality from GDP

to BSE sensex.

Here also, the decision of optimum lag is made on the basis of the one that appeared earlier in the

process.

23

Optimum lag is 2


Sample: 1990 2014

Lags: 2


DNIFTY50ANNUAL does not Granger Cause

D3GDPANNUAL 20 0.41595 0.6671

D3GDPANNUAL does not Granger Cause DNIFTY50ANNUAL 5.33656 0.0178

Table: 8 Granger Causality result GDP and Nifty50 yearly

Here also, p-value is significant for second hypothesis, and there exists a unidirectional causality

from GDP to BSE sensex.

24

6. FINDINGS AND CONCLUSIONS

ADF Unit root test Results

difference

Quarterly

data

Annual

Data

GDP 2nd 3rd

BSE

SENSEX

2nd 1st

Nifty50 2nd 1st

Table: 9 ADF test results Table: 10 Lag terms Results

Quarterly p-

value

Status Annually p-

Value

Status

BSE sensex does not Granger

cause GDP

0.0975 TRUE BSE sensex does not Granger

cause GDP

0.6695 TRUE

GDP does not Granger cause

BSE sensex

0.3199 TRUE GDP does not Granger cause

BSE sensex

0.0157 FALSE

Nifty50 does not Granger

cause GDP

0.0997 TRUE Nifty50 does not Granger

cause GDP

0.6671 TRUE

GDP does not Granger cause

Nifty50

0.4296 TRUE GDP does not Granger cause

Nifty50

0.0178 FALSE

Table 11: Granger Causality Test Results

Table11 reports the p-value for the Granger causality tests between the market indices and GDP

and between the GDP and the stock market for both data types under consideration.

In case of Annual Data sets there was a Unidirectional Causality from real GDP to both stock

indices.

Lag terms

Quarterly

data

Annual Data

BSE and

GDP

2 BSE and

GDP

2

Nifty and

GDP

2 Nifty and

GDP

2

25

For the quarterly data set, no significant causality results were obtained due to which we

conclude that when considering quarterly, Granger Causation does not occur. However, annual

data shows Granger Causation and that too unidirectional.

One interpretation can be that the previous yearly values of GDP explain a stronger relationship

with market indices rather than previous values of market indices themselves. As both the

indices are showing same results, we can say that the result is consistent in capturing the stock

market variable for the study, as the type of index does not matter(amongst these 2 only). And

GDP precede market indices on annual basis as GDP takes a year’s time to actually have some

effect.

Thus we conclude that the increase in the volume of real output produced in the economy,

especially when at constant price, is there, then stock prices are expected to appreciate whose

effect may be experienced after a year.

As per the Base Paper of Gevit Duca (2007)in the case of the US, the bivariate test results

suggested the presence of a unidirectional causality from the Dow-Jones stock index to GDP. In

other words, in the US, stock price movements cause movements in GDP. But the scenario is

completely opposite annually for India. Moreover, the base research was done on quarterly data.

One suggested reason can be the mismatch of the data. The data for US market was quarterly for

over 100 years. But Indian quarterly data appeared to be scarce. A similar tendency emerged for

the UK where the leading stock index, namely the FTSE 100, Granger causes GDP. But this is

not happening in India.

Another suggestion here is that one can better predict Nifty50 index and BSE Sensex Index

yearly using real economic GDP. Real vs nominal can also be a cause for variation as base

research uses nominal GDP values.

26

7. LIMITATIONS OF THE PROJECT

This study fails to explain how strong the relationship is and how strongly GDP growth causes

stock market movement.

Real GDP may not be the only indicator of real economy of India. As this report does not

incorporate other indicators, hence, we can say that the relationship is between GDP and the

indices only and not the true real economy and indices. Similarly, we have only considered

popular indices that are a representative of the Indian stock market.

Data constraint is a major problem, as real GDP data is limited. Quarterly data seemed

unavailable before the 1996 period and annual data before 1960. Also, the years after Indian

Independence are less compared to country like US or UK. Thus, data too was bound to be less.

The analysis of BSE Sensex was done with annual GDP data and Nifty50 was done on quarterly

basis. The rationale was to have as many observations as we can. However, this may incorporate

some inconsistencies about which cannot be explained.

We still cannot say for sure what is the cause and effect relationship between real GDP and stock

market of India.

8. FURTHER SCOPE The results indicate that there does not appear to be any causality from GDP to the stock index.

Gevit Duca argues that the relative smallness of the market (comparing the market capitalization

of India with US in figure ), may suggest a lack of causality between the stock market and the

economy since a small stock market implies that stock price movements have a potentially

smaller impact on aggregate household wealth, than is the case in other countries where the ratio

of market capitalization to GDP is higher. However, we argue that if the current results are

actually correct, then why the quarterly GDP and stock indices are not related at all. This can be

further researched that why wealth effect is not significant in India. Also, for annual figures, it is

still not enough to explain, thus a proper explanation of this effect needs to be covered.

27

Also, as unidirectional causality has been found from GDP to indices, now further research can

be done to appropriately explain the true reasons for the existence of the Granger relationships.

Lastly, one may obtain different results for India if nominal GDP would have been used in place

of real GDP. So further researches can use other macroeconomic variables or nominal GDP to

investigate the results.

Graphs show that India may be very low in the figures as compared to US markets and the

market cap to GDP ratio is very high, but compared to other countries like France, Japan, for

which Duca found significant values, India is no way different from them.

So this offers an opportunity to conduct this research to other countries as well in Real GDP

terms and then results can be compared.

Countries US Japan France China India

Market

Capitalization

26330.59 4377.99 2085.90 6004.95 1558.30

GDP 17419.00 4601.46 2829.19 10354.83 2048.52

Table 12: Market Capitalization and GDP (current billion US$)

Source: World Bank database

0.00

5000.00

10000.00

15000.00

20000.00

25000.00

30000.00

US Japan France China India

Market Capitalization and GDP (current billion US$)

Market Capitalization

GDP

28

Countries US Japan France China India

Market Capitalization %

of GDP

151.1601653 95.14357 73.72761 57.99175 76.06963

Table 13: Market Capitalization % of GDP

Source: World Bank database

0

20

40

60

80

100

120

140

160

US Japan France China India

Market Capitalization % of GDP

Market Capitalization % of GDP

29

9. APPENDIX

For Sensex quarterly: ADF Results

t-Statistic Prob.*



5% level -1.945389

10% level -1.613838

For Nifty50 quarterly

t-Statistic Prob.*



5% level -1.945389

10% level -1.613838

Table 3:

30

GDP and Nifty50


0 -1191.515 NA 8.58e+11 33.15319 33.21643 33.17837

1 -1185.739 11.06980 8.16e+11 33.10387 33.29359 33.17940

2 -1155.085 57.05116* 3.90e+11* 32.36347* 32.67968* 32.48935*

Lags LM-Stat Prob

1 88.97537 0.0000

2 7.107530 0.1303

3 26.49234 0.0000

Annual Data Analysis

t-Statistic Prob.*



5% level -1.959071

10% level -1.607456

Sensex annual

t-Statistic Prob.*



5% level -1.956406

10% level -1.608495

31

Nifty 50

t-Statistic Prob.*



5% level -1.956406

10% level -1.608495

GDP and Sensex annual


0 -348.0916 NA 5.49e+12 35.00916 35.10873 35.02859

1 -337.4617 18.07070 2.84e+12 34.34617 34.64489 34.40449

2 -324.7454 19.07453* 1.21e+12* 33.47454* 33.97241* 33.57173*


0 -364.4880 NA 4.94e+12 34.90362 35.00310 34.92521

1 -353.3260 19.13482* 2.51e+12* 34.22152* 34.51996* 34.28629*

Lags LM-Stat Prob

1 1.943917 0.7461

2 5.043514 0.2829

3 7.552821 0.1094

32

GDP and Nifty50 annual


0 -324.0403 NA 4.95e+11 32.60403 32.70360 32.62347

1 -312.7392 19.21188 2.40e+11 31.87392 32.17264 31.93223

2 -300.6326 18.15992* 1.08e+11* 31.06326* 31.56112* 31.16045*


0 -339.2382 NA 4.46e+11 32.49887 32.59835 32.52046

1 -327.3705 20.34458* 2.12e+11* 31.74957* 32.04801* 31.81434*

Lags LM-Stat Prob

1 2.389563 0.6645

2 5.138375 0.2734

3 7.687445 0.1037

33

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Causal Relationship between Stock market and Real Economy in India using Granger Causality test