Karla Neha Factorsgold and stock market impact

RIJBFA Volume 1, Issue 4(April 2012) ISSN: 2277 100X

Journal of Radix International Educational and Research Consortium

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FACTORS CONTRIBUTING TO FLUCTUATIONS IN BSE SENSEX- AN

EMPIRICAL STUDY

Ms. Neha Kalra Dr. Rajesh Bagga

Assistant Professor Associate Professor and Offg. Director

Apeejay Institute of Management Apeejay Institute of Management Jalandhar

Jalandhar

Mr. Ashish Arora

Assistant Professor

Guru Nanak Dev University College

Jalandhar

Abstract

The BSE benchmark SENSEX has moved between positive and negative terrain in a span

of few years witnessing both the massive crash of January 2008 as well as record

inflows from Foreign Institutional Investors (FIIs) trying to reallocate their funds from

risky emerging markets to stable developed markets. Apart from the global

uncertainty, there were various other interrelated factors on the domestic front that

had a significant impact on the Indian stock market. These factors include the

A Journal of Radix International Educational and

Research Consortium

RIJBFA

RADIX INTERNATIONAL JOURNAL OF BANKING, FINANCE AND ACCOUNTING



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fluctuations in the Gold prices, oil prices and dollar prices, withdrawals of foreign

equity in the form of FDI and FPI and finally changes in the CRR, call money rate and

inflation rate. This study makes an attempt to examine the impact of these factors on

the Indian stock market by taking the month end prices for these variables from

secondary sources for a period starting from January 2008 to October 2011. The

research uses analytical techniques of correlation and multiple regression and for this

purpose, SPSS has been meticulously used. Empirical results show that there exists a

positive and a significant correlation of gold prices and oil prices with SENSEX and a

negative relation between dollar prices and SENSEX. The results of the regression

analysis show that Gold Prices is the variable that accounts for maximum variance in

fluctuations in SENSEX index, followed by Dollar Prices and Oil Prices. The results also

indicate that no significant correlation has been found between Inflation Rate, Foreign

Direct Investment, Call Money Rate with SENSEX.

INTRODUCTION

The Indian Stock market has been through a lot of phases in a span of few years and

the investors have had their share of surprises too. The SENSEX crash of January 2008

swept with it a large number of small scale investors while registering a record dip of

2062 points in a day. The major cause of this crash was attributed to the recession in

the global economies, especially with the US dollar losing its strength to the Indian

rupee. A large amount of equity in the form of shares was floated in the Indian

economy as an impact of Foreign Institutional Investors (FIIs) withdrawing their

money from the Indian markets. In 2009 the market was in a recovery mode, in 2010 it

consolidated. After maintaining a range of 17,500-20,500 for more than a year, the

SENSEX finally crashed. This crash was triggered by major global events, such as the



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S&P downgrading US debt from AAA to AA+, concern about the AAA rating status of

French debt, sovereign debt crisis spreading to bigger Euro zone economies like Italy,

Greece and Spain. Hence, several global indices, like the Dow Jones Industrial Average

(US), DAX (Germany), CAC (France), and FTSE (UK), broke their major supports.

The Bombay Stock Exchange (BSE) is known to be the oldest exchange in Asia. The

Bombay Stock Exchange developed the BSE SENSEX in 1986, giving the BSE a means to

measure overall performance of the exchange. The SENSEX is the benchmark for the

Indian Stock exchange, which captures the price movement. It is considered to be the

pulse of the Indian stock markets. Theoretically, the rising SENSEX is an indicator of

economic growth and is considered good for the market. However there are various

factors affecting the rise and fall of the SENSEX. Few of them are:

Gold Price: The gold rate in todays market depends entirely on the demand and

availability of the metal. Gold prices hit its all-time high of $1,895 an ounce in

September 2011. Investors were worried about both the U.S. debt crisis and the

eurozone crisis. It seemed neither the dollar nor the euro were safe investments.

When other investments look too risky, gold always looks like a good hedge. It is not

possible to state that golds value changes as a result of activity within the stock

market and it is also not possible to state that the level of the stock market changes

as a result of activity in the gold market. But the historical evidence is

overwhelming. Over the long-term, gold and stocks tend to move in opposite

directions. This has been born out in research done by the World Gold Council for

decades which shows that there is indeed a negative long-term correlation between

gold and stocks, as measured by all of the major stock indices, namely the Dow

Jones Industrial Average, the Standard & Poors 500, and the Wilshire 5000.



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Whether the stocks being compared to gold are large blue chips or small, aggressive

growth companies, the correlation to gold is still negative over the long-term.

Oil Price: Oil is one of the most precious commodities on earth and is available only

in limited amounts. Crude oil is the basic form of oil from which is used to extract

other useful form of oils like petroleum, diesel, jet-fuel after refining. Companies

involved in oil production are exploration and production (E &P) companies (back-

end) and refining and marketing companies (front-end). In India, ONGC and Oil

India are the leading front-end players while IOC, HPCL, BPCL and Reliance are major

back-end players. There are a number of reasons leading to a rise in the oil prices

like, a weak dollar. As oil exporting nations get money in terms of dollar for their oil,

their profits decreases as dollar becomes weak. So, to protect their margins, they

increase oil cost. Also the prices of crude oil are determined by the demand and

supply gaps. Higher growth in developing countries like India and China increases

demand for oil thereby leading to a price rise. Lastly, war between an oil exporting

nation and an oil importing nation (like US and Iran).

Oil prices have significant impact on financial markets. Initially stock market rises in

tandem with oil prices as it is the economic growth which is creating more demand

for oil in the first place. Because of this increased demand, oil prices are increasing

(sometimes they increase because of just speculation which is a dangerous situation

and a warning signal). But if oil prices keep on increasing and sustain at higher

values for a longer period of times, it will have detrimental effects on the economy.

Higher the oil price increase and longer the higher prices are sustained, the bigger

the macro economic impact.

Dollar Price: When it comes to the US being a consumer, it has one of the largest

appetites in the world. To keep up its demand for consumption, its imports are huge

when compared to exports. This created pressure since there were more payments



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in dollars than receipt of any other currency, which made the supply of the dollar

greater for imports payment and less receipt of foreign currency from exports. This

resulted in the depreciation of the dollars value, which again caused more outflow

of dollar for import payments. This created a state of inflation and made

consumables costlier to US. To control inflation US resorted to increase in interest

rates to cool down pressure on demand side of consumption. This factor along with

recession in all other sectors, particularly real estate, is causing the mighty US dollar

to shake.

Until the 70s and 80s India aimed at to be self-reliant by concentrating more on

imports and allowing very little exports to cover import costs. However, this could

not last long because the oil price rise in the 1970s and 80s created a big gap in

Indias balance of payment. Balance of payment (BOP) of any country is the balance

resulting from the flow of payments/receipts between an individual country and all

other countries as a result of import/exports happening between an individual

country, in our case India and rest of the world. This gap widened during Iraqs

attempt to take over Kuwait. Thereafter, exports also contributed to FX reserve

along with Foreign Direct Investment into the Indian economy and reduced the BOP

gap. Indian rupee appreciation against dollar impacted the Indian economy heavily.

Foreign Direct Investment: India has emerged as the preferred destination for many

foreign international enterprises due to constructive factors such as high economic

growth, fast population growth, English speaking people, and lower costs for

workers. Indias inward investment rule went through a series of changes since

economic reforms were escorted in two decades back. Inward investments have

been constantly rising since the sharp drop witnessed in 2009, following the global

financial crisis. The FDI inflow was much better compared to last year. During the

April-September period, FDI went up by about 74 per cent to $19.13 billion from



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$11 billion in the year-ago period. This has been majorly because of the

liberalization of the FDI in retail, with 51% FDI in multi brand retail and 100% FDI in

single-brand retail. It is expected to bring about modern infrastructure that would

help to boost the productivity of the organised retail sector in India.

Foreign Portfolio Investment: Foreign Portfolio Investments in the form of ADRs,

GDRs, FCCB and FIIs are the manner in which foreign investors can invest in the

Indian stock market. Foreign Institutional Investors including institutions such as

Pension Funds, Mutual Funds, Investment Trusts, Asset Management or their power

of attorney holders are invited to invest in all the securities traded on the Primary

and Secondary markets, including the equity and other securities/instruments of

companies which are listed/to be listed on the stock exchanges in India including

the OTC Exchange of India. Since the opening up of Indias capital markets, the FII

activity has been on a constant rise. FII are extremely keen to invest in the BRIC

countries or Brazil, Russia, India and China. With the meltdown in the eurozone, due

to debt crisis, foreign investors have been selling having an impact on the Indian

economy and markets. Apart from this inflation, rising interest rates, corruption

issue, pending bills, rollback in FDI in Retail has given excuse to press sell button to

FII in India.

Cash Reserve Ratio: CRR is the rate of the money the banks used to keep with the

RBI for security without any interest. The increase in interest rate will directly

impact the housing, experts, banks, automobile sell figure etc. in the short term.

Since market is more sensitive towards the short term reactions it leads to the fall in

the above sectors. The real jerk is felt in the monthly sell figure and turnover of the

above mentioned sectors. Continuous increase in the CRR impacts the quarterly

profitability of the above sectors. Further, if it is inline with the increase in the

interest rate in the cash deposits then it will directly impact the stock market since



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the big money will flow out from the high risk sector to the low risk sector resulting

low participation in the market. The Reserve Bank of India has been cutting cash

reserve requirements, indicating a policy shift towards reviving growth after nearly

two years of fighting inflation.

Inflation: Recent volatility of domestic staple prices appear higher than that

prevailing before the 2008 global food price crisis. Average food price volatility for a

sample of 26 low income countries has been higher over the past year than it was in

2006/07. Price volatility creates additional risks and is a particular burden for low

income producers who are least able to hedge against these fluctuations, as well as

for poor consumers. Increased volatility tends to lead to greater government

intervention in agricultural markets often with sizeable fiscal costs. In developing

country context, inflation tolerance in India is fairly low. And within the overall

inflation, food price inflation is least tolerated as bulk of the population spend

majority of their income on food items. During the last decade, food price inflation

exceeded the headline inflation measured by wholesale price index since around

the end of 2005, barring the period September 2007 to September 2008. This gap

has become all the more glaring in the more recent time. Currently, this continuing

rise in food price inflation has become a major cause for concern for policy makers

in India.

Call Money Rate: Call money market is the most sensitive part of money market, in

which a good number of players from banking sector as well as the non-bank

financial sector actively participate on a regular basis. The head offices, after

meeting their usual liquidity requirement, invest the surplus funds in the call money

market. The NCBs are the main source of fund in the call money market. The cost of

funds for FCBs is very low as compared to the local banks but they prefer to

preserve the excess reserve rather than lend in the interbank money market mainly



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because of lack of confidence. The NBFIs are now participating in the inter-bank

money market in both borrowing and lending but they borrow more than lending.

Therefore, they play an important role in the interbank money market.

Foreign Exchange Reserves: Foreign exchange reserves include foreign exchange

and gold, Special Drawing Rights and International Monetary Fund reserve positions

held by central banks and monetary authorities. India's accumulation of foreign

exchange reserves has been increasing in recent years. The country's primary

sources of foreign exchange reserves have been capital flows and portfolio inflows.

High foreign exchange reserves are often seen as a strength indicating the backing a

currency has. On the other side of the coin, however, holding of huge foreign

exchange reserves also indicates the lack of confidence on the global financial

architecture.

The advent of floating exchange rate in 1973, reforms of financial markets in the

early 1990s and the Asian currency crisis of 1997-98 have jointly made a strong pitch

for the dynamic linkage between stock and foreign exchange market. Both the

markets are considered as the most sensitive segment of the financial markets

because the impact of any such deviation is associated with policy variables as well

as macroeconomic variables. However, in the case of foreign exchange market, the

impact is direct whereas in the case of stock market there is an indirect impact.

REVIEW OF LITERATURE

In the past decades, many researchers attempted to predict various determinants of

stock markets but due to shortage of time & inability to cover all the past studies,

some of the studies have been considered in this section that has provided a base for

this research.



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Yucel and Kurt

(2002) examined the foreign exchange exposure of Turkish companies

in the study for a sample of 152 companies listed in stanbul Stock Exchange. The

findings revealed that 11.8 % of sample firms had a positive and significant economic

exposure for the examined period. The proportion and mean exposure coefficient

were high for exporter companies compare to non-exporter and oveall sample. The

results from the inclusion of market return to the model do not reveal significant

difference in the economic exposure of the companies. Akhtaruzzaman, Akhter and

Masuduzzaman (2005) investigated the call money market in Bangladesh and the

results revealed that in most cases, whenever excess reserve fell, the rate of interest in

call money market rose and vice versa. Liao and Chen (2005) examined the

relationship among oil prices, gold prices, and individual Industrial Sub-Indices instead

of the popular Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX).

The authors believed that commodity prices should have different degrees of

influences to individual industries instead of the whole market. According to previous

researches, stock returns have leptokurtic, volatility clustering, and volatility

asymmetric characteristics; this research further applied the TGARCH models to

described the relationship among oil prices, gold prices, and individual Industrial Sub-

Indices. It was concluded that the fluctuations in oil prices influenced both the

Electronic Industrial Sub-Indices and the Rubber Industrial Sub-Indices. The

correlations among oil prices and the Electronic Industrial Sub-Indices and the Rubber

Industrial Sub-Indices were positive. Further, the Chemical Industrial Sub-Indices,

Cement Industrial Sub-Indices, Automobile Industrial Sub-Indices, Food Industrial Sub-

Indices, and Textiles Industrial Sub-Indices were influenced by fluctuations in gold

prices.



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Gnsel and ukur (2007) investigated the performance of the Arbitrage Pricing Theory

(APT) in London Stock Exchange for the period of 1980-1993 as monthly. The

researcher developed seven prespecified macroeconomic variables. The term

structure of interest rate, the risk premium, the exchange rate, the money supply and

unanticipated inflation were similar to those derived in Chen, Roll and Ross

(1986).Using OLS technique, the study has demonstrated that there were some big

differences among industries. Before interpreting the OLS results, the serial correlation

problem was discussed by using Durbin Waltson Statistics. D-W statistics showed that

there was no evidence for positive or negative serial correlation. Subair and Salihu

(2007) also investigated the effects of exchange rate volatility on the Nigeria stock

markets. It was found that the exchange rate volatility generated via GARCH process

exerted a stronger negative impact on the Nigeria stock markets. However the rate of

inflation and interest rate did not have long run relationship with stock market

capitalization since the major participant in the market is government. Based on this it

was recommended that a coordinated monetary and fiscal policy should be put in

place to check mate the fluctuation of exchange rate in order to deepen the depth of

the Stock Market.

Ahmed (2008) investigated the nature of the causal relationships between stock prices

and the key macro economic variables representing real and financial sector of the

Indian economy for the period March, 1995 to March, 2007 using quarterly data.

These variables are the index of industrial production, exports, foreign direct

investment, money supply, exchange rate, interest rate, NSE Nifty and BSE SENSEX in

India. The study indicated that stock prices in India lead economic activity except

movement in interest rate. Interest rate seemed to lead the stock prices. The study

indicated that Indian stock market seemed to be driven not only by actual



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performance but also by expected potential performances. The study revealed that

the movement of stock prices is not only the outcome of behaviour of key macro

economic variables but it is also one of the causes of movement in other macro

dimension in the economy.

Gogineni (2008) explored the reaction of the US stock market as a whole and of

different industries to daily oil price changes. It was found that the direction and

magnitude of the markets reaction to oil price changes depended on the magnitude

of the price changes. Oil price changes most likely caused by supply shocks had a

negative impact while oil price changes most likely caused by shifts in aggregate

demand had a positive impact on the same day market returns. Nair (2008) has

examined the macroeconomic determinants of stock market development in India

over 1993-94 to 2006-07empirically. Cointegration and error correction modelling was

used for the analysis. The results showed that there is long run relationship between

all the macroeconomic variables used and stock market development. The variables

exchange rate, inflation and Foreign Institutional Investment (FII) were found to have

no significant influence on stock market development in India.

Adam and Tweneboah (2009) used multivariate cointegration and error correction

model, to examine the impact of Foreign Direct Investment (FDI) on the stock market

development in Ghana. The results indicated that there existed a long-run relationship

between FDI, nominal exchange rate and stock market development in Ghana. It was

found that a shock to FDI significantly influenced the development of stock market in

Ghana. Further, Mohammad,Hussain, Jalil and Ali (2009) explored the correlation

among the macroeconomics variables and share prices of KSE (Karachi Stock Exchange)

in context of Pakistan. The study considered several quarterly data for different



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macroeconomics variables are as foreign exchange reserve, foreign exchange rate,

industrial production index (IPI), whole sale price index (WPI), gross fixed capital

formation (GFCF) and broad money M2. The result showed that after the reforms in

1991 the influence of foreign exchange rate and foreign exchange reserve significantly

affected the stock prices, while other variables like IPI and GFCF are insignificantly

affect stock prices.

Hye, Wasti, Khatoon and Imran (2009) used the robust time series tools in order to

estimate Pakistans money demand function for the period 1971:1-2006:4. It was

found that there were four cointegrating vectors in money demand, interest rate,

economic activity, inflation, stock prices and exchange rate. The results revealed that

stock price had positively and statistically significant wealth effect and exchange rate

insignificantly effected the money demand in the long run. But in the short run the

inflation had a negative and significant effect on money demand. Aliyu (2010) applied

the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to

assess the impact of inflation on stock market returns and volatility using monthly time

series data from the two West African countries, that is, Nigeria and Ghana. In

addition, the impact of asymmetric shocks was investigated using the QGARCH model

developed by Sentana (1995), in both countries. Results for Nigeria showed weak

support for the hypothesis that bad news exerted more adverse effect on stock market

volatility than good news of the same magnitude, while a strong opposite case holds

for Ghana. Furthermore, inflation rate and its three month average were found to

have significant effect on stock market volatility in the two countries.

Wang and Huang (2010) analysed the daily data and employed time series method to

explore the impacts of fluctuations in crude oil price, gold price, and exchange rates of



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the US dollar vs. Various currencies on the stock price indices of the United States,

Germany, Japan, Taiwan, and China respectively, as well as the long and short-term

correlations among these variables. The empirical results showed that there exist co-

integrations among fluctuations in oil price, gold price and exchange rates of the dollar

vs. various currencies, and the stock markets in Germany, Japan, Taiwan and China.

This indicated that there existed long-term stable relationships among these variables.

Whereas there was no co-integration relationship among these variables and the U.S.

stock market indices. Ghosh, Roy, Bandyopadhyay and Choudhuri (2010) examined the

primary factors responsible for affecting Bombay Stock Exchange (BSE) in India. The

paper investigated the relative influence of the factors affecting BSE and thereby

categorizing them. With the help of multiple regression model and applying Factor

analysis the primary factors were traced out. The relationship between BSE SENSEX

and some other important economical factors like, Oil prices, Gold price, Cash Reserve

Ratio, Food price inflation, Dollar price, Foreign Capital Inflows has been estimated

taking into consideration the Multicollinearity problem among different independent

variables and attempted to eliminate it. The results revealed that dollar price along

with Factor 1i.e; External Reserve and Factor score 2i.e; Inflation inertia are

significantly affecting BSE SENSEX. The fluctuations in SENSEX due to Oil and CRR are

significant. Any rise in Oil price will create inflation inertia which will generate

stochasticity in SENSEX. The External reserves taken together will act as resource

generating Factor in attracting Foreign Capital inflows, which will make SENSEX more

sensitive. Gupta (2011) examined the relationship between Indian stock market and

FIIs investment in India and found that both, Indian stock market and FIIs influenced

each other; however, their timing of influence was different. Ray (2012) assessed the

relationship between foreign exchange reserves of India and BSE market capitalization

on the basis of annual data from the year 1990-91 to2010-11. This study uses simple



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linear regression model, unit root test, granger causality test to measure the

relationship between foreign exchange reserves of India and BSE market capitalization.

The results depicts that foreign exchange reserves of India has positive impact on BSE

Stock Market capitalization.

The Perusal of literature revealed that many studies have been conducted in the past

focusing on one or two of the factors determining stock market all over the world. The

present study investigates into the fluctuation in the Indian stock market in the past

four years and analyses the factors which directly or indirectly affect the Indian Stock

Market. The study of factors affecting their investment enables investor in taking

decision regarding their investment.

OBJECTIVES OF THE STUDY

The current study has been carried out to achieve the following objectives:

Identify the various factors affecting the Indian Stock Market (SENSEX).

Investigate into the relationship between the various factors and SENSEX.

Identify variables having significant impact on SENSEX.

HYPOTHESES FORMULATION

In line with these objectives, certain hypotheses were formulated which are as follows:

Wang and Huang (2010) opined that there exist co-integrations among fluctuations in

oil price, gold price and exchange rates of the dollar vs. various currencies, and the



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stock markets in Germany, Japan, Taiwan and China but no co-integration relationship

was established with the U.S. stock market indices. Liao and Chen (2005) also

examined the relationship among oil prices, gold prices, and individual Industrial Sub-

Indices instead of the popular Taiwan Stock Exchange Capitalization Weighted Stock

Index (TAIEX).The results revealed that correlations among oil prices and the Electronic

Industrial Sub-Indices and the Rubber Industrial Sub-Indices were positive. Based on

these results the following hypothesis was framed:

H01: There is no significant impact of Gold Prices on the SENSEX.

Gogineni (2008) explored the reaction of the US stock market as a whole and of

different industries to daily oil price changes. It was found that oil price changes most

likely caused by supply shocks had a negative impact while oil price changes most

likely caused by shifts in aggregate demand had a positive impact on the same day

market returns. Based on these results the following hypothesis was framed:

H02: There is no significant impact of Oil Prices on the SENSEX.

Yucel

and Kurt

(2002) examined the foreign exchange exposure of Turkish companies

in the study for a sample of 152 companies listed in stanbul Stock Exchange. The

findings revealed a positive and significant economic exposure for the examined

period but the results from the inclusion of market return to the model do not reveal

significant difference in the economic exposure of the companies. Based on these

results the following hypothesis was framed:



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H03: There is no significant impact of Dollar Prices on the SENSEX.

Gnsel and ukur (2007) investigated the performance of London Stock Exchange to

examine the impact of interest rate, the risk premium, the exchange rate, the money

supply and unanticipated inflation. Durbin Waltson Statistics showed that there was

no evidence for positive or negative serial correlation. Aliyu (2010) also assessed the

impact of inflation on stock market returns and volatility The results showed weak

support for the hypothesis that bad news exerted more adverse effect on stock market

volatility than good news of the same magnitude, while a strong opposite case holds

for Ghana. Furthermore, inflation rate and its three month average were found to

have significant effect on stock market volatility. Based on these results the following

hypothesis was framed:

H04: There is no significant impact of Inflation Rate on the SENSEX.

Ray (2012) assessed the relationship between foreign exchange reserves of India and

BSE market capitalization on the basis of annual data from the year 1990-91 to2010-

11. The results depicts that foreign exchange reserves of India has positive impact on

BSE Stock Market capitalization. Mohammad, Hussain, Jalil and Ali (2009) explored the

correlation among the macroeconomics variables like foreign exchange reserve,

foreign exchange rate, industrial production index (IPI), whole sale price index (WPI),

gross fixed capital formation (GFCF) and broad money (M2) and share prices of KSE

(Karachi Stock Exchange) in context of Pakistan. The result showed that after the

reforms in 1991 the influence of foreign exchange rate and foreign exchange reserve

significantly affected the stock prices, while other variables like IPI and GFCF are



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insignificantly affect stock prices. Based on these results the following hypothesis was

framed:

H05: There is no significant impact of holding of Foreign Exchange Reserves on the

SENSEX.

Ahmed (2008) investigated the nature of the causal relationships between stock prices

and the key macro economic variables like industrial production, exports, foreign

direct investment, money supply, exchange rate, interest rate, NSE Nifty and BSE

SENSEX in India. The study indicated that stock prices in India lead economic activity

except movement in interest rate. Interest rate seemed to lead the stock prices. The

study revealed that the movement of stock prices is not only the outcome of

behaviour of key macro economic variables but it is also one of the causes of

movement in other macro dimension in the economy. Adam and Tweneboah (2009)

also examined the impact of Foreign Direct Investment (FDI) on the stock market

development in Ghana. The results indicated that there existed a long-run relationship

between FDI, nominal exchange rate and stock market development in Ghana. It was

found that a shock to FDI significantly influenced the development of stock market in

Ghana. Based on these results the following hypothesis was framed:

H06: There is no significant impact of FDI on the Indian Stock Market SENSEX.

Gupta (2011) examined the relationship between Indian stock market and FIIs

investment in India and found that both, Indian stock market and FIIs influenced each

other; however, their timing of influence was different. Nair (2008) also examined the

macroeconomic determinants of stock market development in India over 1993-94 to



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2006-07empirically. The variables exchange rate, inflation and Foreign Institutional

Investment (FII) were found to have no significant influence on stock market

development in India. Based on these results the following hypothesis was framed:

H07: There is no significant impact of FPI on the SENSEX.

Akhtaruzzaman, Akhter and Masuduzzaman (2005) investigated the call money market

in Bangladesh and the results revealed that in most cases, whenever excess reserve

fell, the rate of interest in call money market rose and vice versa. Based on these

results the following hypothesis was framed:

H08: There is no significant impact of fluctuations in call money rate on the SENSEX.

DATA BASE AND METHODOLOGY

The current study is a descriptive research, as it involves a description of the state of

affairs as it exists at present. The data for the variables used in the research has been

collected from secondary sources for a period starting from January 2008 to October

2011. Month end prices have been collected for all the variables from

www.bseindia.com and www.rbi.com.



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Table 1- Description of dependent and independent variables

Variable Name Study period and

Frequency

Data Source

INDEPENDENT VARIABLES

Gold Prices Jan 2008 Oct 2010

Month end prices

World Gold Council

Oil Prices Jan 2008 Oct 2010

Month end prices

New York Stock

Exchange

Dollar Prices Jan 2008 Oct 2010

Month end prices

Reserve Bank of India

Cash Reserve Ratio(CRR) Jan 2008 Oct 2010

Month end prices


Inflation Jan 2008 Oct 2010

Month end prices


Call Money Rate Jan 2008 Oct 2010

Month end prices


Foreign Direct

Investment(FDI)

Jan 2008 Oct 2010

Month end prices


Foreign Portfolio

Investment(FPI)

Jan 2008 Oct 2010

Month end prices


Foreign Exchange

Reserves(Forex)

Jan 2008 Oct 2010

Month end prices


DEPENDENT VARIABLE

SENSEX Jan 2008 Oct 2010

Month end prices

BSE India



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STATISTICAL TOOLS AND TECHNIQUES OF ANALYSIS

In order to analyse the data, Correlation and Multiple Regression approach have been

used. For this purpose, SPSS has been meticulously used. Here SENSEX has been taken

as the dependent or the criterion variable and Gold Prices, Oil Prices, Dollar Prices,

CRR, Inflation Rate, Call Money Rate, FDI, FPI and Forex have been taken as the

independent or the predictor variables.

CORRELATION ANALYSIS

The Bivariate Correlations procedure computes the pairwise associations for a set of variables and displays the results in a matrix. It is useful for determining the strength and direction of the association between two scale or ordinal variables. Under this Pearson correlation coefficients have been computed which measure the degree of linear association between two variables.



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RESULTS

Table 2 shows the Pearson correlation coefficients of SENSEX with other variables.

According to Pearsons correlation the correlation coefficient of gold price and SENSEX

is 0.532 and oil price and SENSEX is 0.492 at 0.01 level of significance. Further the

correlation coefficients of dollar price and SENSEX is -0.298 at 0.05 level of

significance. The Pearson correlation of SENSEX with other variables FDI, FPI, CRR, Call

Money Rates, Inflation and Forex are insignificant.

The correlation table shows correlation coefficients ranging in value from 1 (a perfect

negative relationship) and +1 (a perfect positive relationship). A value of 0 indicates no

linear relationship. Correlation coefficients significant at the 0.05 level are identified

with a single asterisk, and those significant at the 0.01 level are identified with two

asterisks. It has been found that Gold price and Oil price have a significant and fairly

strong positive correlation with SENSEX, i.e., a rise in the gold or oil prices results in

corresponding rise in SENSEX. The results also show that there exists a negative

correlation between dollar price and SENSEX, i.e., a rise in the dollar prices results in a

corresponding fall in SENSEX. Further, except these three variables, all other variables

are less correlated to SENSEX.

The table also shows that independent variables are highly correlated to each other

like Gold Prices are positively correlated with Dollar at 0.05 and Forex at 0.01 level of

significance. Similarly, Oil Prices are positively correlated with CRR and Call Money

Rate and with Forex at 0.01 level of significance. This indicates that they are not

independent of each other and only one or two cannot be used to predict the

dependent variables i.e., SENSEX. So further regression is used which is useful in



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eliminating some of the independent variables. Some of them being correlated with

other variables do not add any value to regression model.

REGRESSION ANALYSIS

Multiple Regression Analysis is a statistical technique which analyses the linear

relationship between a dependent variable and multiple independent variables by

estimating coefficients for the equation for a straight line. The linear regression model

assumes that there is a linear, or "straight line," relationship between the dependent

variable and each predictor variable.

RESULTS

SENSEX AS THE DEPENDENT VARIABLE

In order to find out the contribution of variables on the performance of the stock

market, Stepwise regression was run where SENSEX was taken as dependent variable.

Stepwise regression analysis is procedure in which the predictor variables enter or are

removed from the regression equation one at a time. The purpose of this procedure is

to select from a large number of predictor variables a subset of variables that account

for most of the variation in the dependent or the criterion variable. The table present

the results of Stepwise regression analysis.



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Table 3- Variables Entered/Removeda

Model Variables Entered Variable

s

Remove

d

Method

1 GoldPrices . Stepwise (Criteria: Probability-of-F-to-enter = .100).

2 DollarPrices . Stepwise (Criteria: Probability-of-F-to-enter = .100).

3 OilPrices . Stepwise (Criteria: Probability-of-F-to-enter = .100).

4 CRR . Stepwise (Criteria: Probability-of-F-to-enter = .100).

5 Forex . Stepwise (Criteria: Probability-of-F-to-enter = .100).

6 FPI . Stepwise (Criteria: Probability-of-F-to-enter = .100).

a. Dependent Variable: SENSEX

Table 3 shows that six variables being Gold Prices, Dollar Prices, Oil Prices, CRR, Forex

and Foreign Portfolio Investment (FPI) are entered. Here, Gold Prices being the

entering variable in first model of stepwise regression at p-level of 0.05 percent, very

close to significance at 95% confidence level and other variables being Dollar Prices,

Oil Prices, CRR, Forex and Foreign Portfolio Investment (FPI) entering respectively in



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the second, third, fouth, fifth and sixth model at p-level of 0.05 percent, very close to

significance at 95% confidence level. The following tables sum up the results of

regression analysis.

Table 4 Model Summary

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

1 .532a .283 .267 .85636

2 .722b .522 .499 .70761

3 .762c .581 .551 .67025

4 .849d .721 .694 .55287

5 .882e .778 .750 .49955

6 .896f .803 .773 .47648

a. Predictors: (Constant), GoldPrices

b. Predictors: (Constant), GoldPrices, DollarPrices

c. Predictors: (Constant), GoldPrices, DollarPrices, OilPrices

d. Predictors: (Constant), GoldPrices, DollarPrices, OilPrices, CRR

e. Predictors: (Constant), GoldPrices, DollarPrices, OilPrices, CRR, Forex

f. Predictors: (Constant), GoldPrices, DollarPrices, OilPrices, CRR, Forex,

FPI

Table 4 presents the regression results when SENSEX is taken as the dependent

variable and other variables as predictors and the first stepwise regression is run. It

can be seen that in model 1, Gold Prices is the first variable to enter the equation and

accounts for 28 percent variance in SENSEX as shown by R2. This is increased to 52



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percent when Dollar Prices enter the equation in model 2 and increased to 58 percent,

72 percent, 77 percent and lastly 80 percent when Oil Prices, CRR, Forex and Foreign

Portfolio Investment enter the equation in further models. Adjusted R2 for model 1 is

.0.267 and model 2 is 0.499.

Table 5- ANOVAg

Model Sum of

Squares

df Mean

Square

F Sig.

1 Regressio

n

12.729 1 12.729 17.357 .000a

Residual 32.267 44 .733

Total 44.996 45

2 Regressio

n

23.466 2 11.733 23.432 .000b

Residual 21.531 43 .501

Total 44.996 45

3 Regressio

n

26.128 3 8.709 19.387 .000c

Residual 18.868 42 .449

Total 44.996 45

4 Regressio

n

32.464 4 8.116 26.552 .000d

Residual 12.532 41 .306

Total 44.996 45

5 Regressio

n

35.014 5 7.003 28.061 .000e



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Residual 9.982 40 .250

Total 44.996 45

6 Regressio

n

36.142 6 6.024 26.532 .000f

Residual 8.854 39 .227

Total 44.996 45

a. Predictors: (Constant), GoldPrices

b. Predictors: (Constant), GoldPrices, DollarPrices

c. Predictors: (Constant), GoldPrices, DollarPrices, OilPrices

d. Predictors: (Constant), GoldPrices, DollarPrices, OilPrices, CRR

e. Predictors: (Constant), GoldPrices, DollarPrices, OilPrices, CRR, Forex

f. Predictors: (Constant), GoldPrices, DollarPrices, OilPrices, CRR, Forex,

FPI

g. Dependent Variable: SENSEX

The Anova table gives an F- test value to determine whether the model is a good fit for

the data. It gives F-test values, one for each model of the procedure. For the initial

model 1, F-ratio is 17.35 and is highly significant at less than 1% level of significance.

Further all the models had F-ratio significant at less than 1% level of significance.



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Table 6- Coefficientsa

Model Unstandardized

Coefficients

Standardize

d

Coefficients

t Sig.

B Std.

Error

Beta

1 (Constant) .000 .126 .000 .999

GoldPrices .532 .128 .532 4.166 .000

2 (Constant) .046 .105 .435 .666

GoldPrices .692 .111 .693 6.236 .000

DollarPrices -.508 .110 -.514 -4.631 .000

3 (Constant) .036 .099 .361 .720

GoldPrices .617 .110 .617 5.634 .000

DollarPrices -.401 .113 -.405 -3.547 .001

OilPrices .267 .110 .267 2.435 .019

4 (Constant) .142 .085 1.665 .103

GoldPrices .396 .103 .396 3.860 .000

DollarPrices -.697 .114 -.705 -6.132 .000

OilPrices .716 .134 .716 5.349 .000

CRR -.665 .146 -.760 -4.553 .000

5 (Constant) .117 .077 1.509 .139

GoldPrices .520 .100 .520 5.173 .000

DollarPrices -.574 .110 -.581 -5.234 .000

OilPrices .778 .123 .778 6.352 .000

CRR -.547 .137 -.625 -3.993 .000



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Forex -.307 .096 -.307 -3.197 .003

6 (Constant) .111 .074 1.508 .140

GoldPrices .519 .096 .519 5.415 .000

DollarPrices -.564 .105 -.570 -5.387 .000

OilPrices .759 .117 .759 6.482 .000

CRR -.510 .132 -.583 -3.869 .000

Forex -.285 .092 -.285 -3.090 .004

FPI .162 .073 .162 2.229 .032

a. Dependent Variable: SENSEX

Table 6 displays the coefficients, where the unstandardized beta coefficients show

how strongly the independent variable is associated with the dependent variable. And

when we add the standard error to it, we get standardized beta coefficients which

provide us with more comparable results. Further a large value indicates that a unit

change in this predictor variable has a large effect on the criterion variable. The t and

Sig (p) values give a rough indication of the impact of each predictor variable a big

absolute t value and small p value suggests that a predictor variable is having a large

impact on the criterion variable. Here the t-values for Gold Prices, Dollar Prices and

CRR are significant at less than 1% level of significance and oil prices, Forex and FPI are

significant at less than 5% level of significance.



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Table 7- Excluded Variablesg

Model Beta In T Sig. Partial

Correlation

Collinearit

y Statistics

Tolerance

1 OilPrices .420a 3.686 .001 .490 .976

DollarPrices -.514a -4.631 .000 -.577 .902

CRR .268a 2.056 .046 .299 .895

Inflation -.003a -.026 .979 -.004 .981

CallMoneyRate -.072a -.557 .580 -.085 1.000

FDI .135a 1.049 .300 .158 .984

FPI .193a 1.537 .132 .228 .999

Forex -.223a -1.617 .113 -.239 .825

2 OilPrices .267b 2.435 .019 .352 .827

CRR -.103b -.707 .484 -.108 .532

Inflation .154b 1.398 .170 .211 .897

CallMoneyRate -.173b -1.641 .108 -.245 .962

FDI .000b .002 .998 .000 .911

FPI .218b 2.144 .038 .314 .997

Forex -.199b -1.750 .087 -.261 .824

3 CRR -.760c -4.553 .000 -.579 .244

Inflation .234c 2.247 .030 .331 .840

CallMoneyRate -.313c -3.120 .003 -.438 .823

FDI -.034c -.318 .752 -.050 .895

FPI .242c 2.568 .014 .372 .988



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Forex -.410c -3.796 .000 -.510 .648

4 Inflation .128d 1.374 .177 .212 .771

CallMoneyRate -.049d -.386 .702 -.061 .426

FDI -.106d -1.207 .234 -.188 .868

FPI .187d 2.341 .024 .347 .963

Forex -.307d -3.197 .003 -.451 .601

5 Inflation .064e .725 .473 .115 .723

CallMoneyRate -.050e -.435 .666 -.070 .426

FDI -.046e -.557 .581 -.089 .815

FPI .162e 2.229 .032 .336 .951

6 Inflation .045f .534 .596 .086 .716

CallMoneyRate -.030f -.269 .789 -.044 .423

FDI -.021f -.258 .798 -.042 .797

a. Predictors in the Model: (Constant), GoldPrices

b. Predictors in the Model: (Constant), GoldPrices, DollarPrices

c. Predictors in the Model: (Constant), GoldPrices, DollarPrices, OilPrices

d. Predictors in the Model: (Constant), GoldPrices, DollarPrices, OilPrices, CRR

e. Predictors in the Model: (Constant), GoldPrices, DollarPrices, OilPrices, CRR,

Forex

f. Predictors in the Model: (Constant), GoldPrices, DollarPrices, OilPrices, CRR,

Forex, FPI

g. Dependent Variable: SENSEX

Table 7 produces the list of variables that have been excluded. In model 1, Dollar

Prices, Oil Prices, CRR, Call Money Rate, Inflation Rate, FDI, FPI and Forex have been



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excluded. And in model 2, Oil Prices, CRR, Call Money Rate, Inflation Rate, FDI, FPI and

Forex have been excluded.

The study makes an attempt to find out the relationship between BSE SENSEX and

some other important economical factors in which some significant relationships have

been established. The research used statistical methods to analyse monthly basis

database of different economical variables. Finally some significant relationships of

those variables with BSE SENSEX have been identified. The correlation matrix shows a

positive and a significant positive relationship between gold prices and oil prices and a

negative relation between dollar prices and SENSEX, thus rejecting the null hypotheses

(H01, H02 and H03). When SENSEX is taken as the dependent variable and other variables

as predictors and the first stepwise regression is run, it was seen that Gold Prices is the

variable that accounts for 28 percent variance in SENSEX. Followed by Gold Prices are

the Dollar Prices, both of which combined account for a variance of 52 percent. It has

also been found that CRR, Forex and FPI have also been contributing to fluctuations in

the SENSEX, thus rejecting the null hypotheses (H05 and H07). Further, there has no

significant impact of Inflation Rate, Foreign Direct Investment and Call Money Rate on

SENSEX, resulting in acceptance of the null hypotheses (H04, H06 and H08).

CONCLUSION

The Indian Stock market has been through a lot of phases in a span of few due to

various reasons like the SENSEX crash of January 2008 which was attributed to the

recession in the global economies. In 2009 the market was in a recovery mode, but in

2010 it consolidated its position. Yet again the SENSEX crashed. This crash was



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triggered by major global events, such as the S&P downgrading US debt from AAA to

AA+, concern about the AAA rating status of French debt, sovereign debt crisis

spreading to bigger Euro zone economies like Italy, Greece and Spain. Hence, several

global indices, like the Dow Jones Industrial Average (US), DAX (Germany), CAC

(France), and FTSE (UK), broke their major supports. Apart from the global uncertainty,

there were various other interrelated factors on the domestic front that had a

significant impact on the Indian stock market. These factors include the fluctuations in

the Gold prices, oil prices and dollar prices, withdrawals of foreign equity in the form

of FDI and FPI and finally changes in the CRR, call money rate and inflation. In this

study an attempt has been made to analyse the impact of these factors on the Indian

stock market by taking the month end prices for these variables from secondary

sources for a period starting from January 2008 to October 2011. In order to analyse

the data, Correlation and Multiple Regression approach have been used. For this

purpose, SPSS has been meticulously used.

The results reveal some significant relationships of these variables with BSE SENSEX.

The correlation matrix shows a positive and a significant relationship of gold prices and

oil prices with SENSEX, and a negative relation between dollar prices and SENSEX. The

results of the regression analysis reveal that Gold Prices is the variable that accounts

for 28 percent variance in SENSEX. Followed by Gold Prices are the Dollar Prices, both

of which combined account for a variance of 52 percent. It has also been found that

CRR, Forex and FPI have also been contributing to fluctuations in the SENSEX. Lastly,

no significant correlation has been found between Inflation Rate, Foreign Direct

Investment, Call Money Rate and SENSEX. Moreover, these three variables have

accounted for the least variance in SENSEX. Several other factors like Government

Policies, political turbulence and social variables affects fluctuations in BSE SENSEX



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which can be analyzed statistically in future studies. Due to the constraint on data base

the impact of political factors and turbulence on BSE SENSEX have not been

considered.

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