0011 Relationship Between Stock Market and Macro Economic Variables in India

Relationship between Stock Market and Macroeconomic Variables in India 1

Running Head: RELATIONSHIP BETWEEN STOCK MARKET AND MACROECONOMIC

VARIABLES IN INDIA

Relationship between Stock Market and Macroeconomic Variables in India

[Name of the Writer]

[Name of the Institution]


Abstract

Over the past few years, Bombay Stock Exchange (BSE) has obtained huge consideration from

Indian media. Its unexpected rise and fall has also been a lime light for media. A few Indian

economists have also predicted it as an indicator for India economic development and

industrial growth. This research paper has been designed to clarify the relationship with the

focus on informal relationship among BSE and a few other actual financial variables by using

the below techniques:

• Correlation

• Descriptive statistics

• Unit root stationarity tests

• Granger causality

The data used for this thesis is from 2000 to 2010 to evaluate all the major financial factors,

e.g. BSE, stock treasury, GDP, bank price, consumer price index (CPI), gross domestic money

structure, national investments, and extensive money. Some of the econometric tactics are also

applied like, unit root tests conducted to verify stationarity and after that Granger causality has

also been applied to view an informal linkage among them. The outcomes of these tests are

vague and mixed. This means that there do exists an informal linkage but its one-sided which

moves from BSE to a few real financial variables. All the tests reveal that the BSE is an

important factor for Indian economy.


Table of Contents

Abstract....................................................................................................................................................2

Chapter I: INTRODUCTION...................................................................................................................4

Overview..............................................................................................................................................4

Current Situation..................................................................................................................................5

Structure of the Dissertation................................................................................................................9

Research Questions..............................................................................................................................9

CHAPTER II: LITERATURE REVIEW...............................................................................................10

Introduction........................................................................................................................................10

First School of Thought.......................................................................................................................11

Second School of Thought..................................................................................................................12

Third School of Thought.....................................................................................................................14

Issues and Challenges.........................................................................................................................21

CHAPTER III: METHODLOGY...........................................................................................................22

Granger Causality Test........................................................................................................................25

Reliability and validity of the study.....................................................................................................26

Summary............................................................................................................................................44

CHAPTER V: CONCLUSION, POLICY IMPLICATIONS AND LIMITATIONS..............................46

Conclusion..........................................................................................................................................46

Policy Implications..............................................................................................................................47

Limitations..........................................................................................................................................47

Scope for Future Research..................................................................................................................48

REFERENCES.......................................................................................................................................49

APPENDIX............................................................................................................................................55


Relationship between Stock Market and Macroeconomic Variables in India

Chapter I: INTRODUCTION

Overview

Investment is dependent on human behavior. Keynes (1936) elaborates which sort of

behavior humans adopt while investing particularly in capital market. Usually people get in use

with their “animal feelings” and “flock mentality”. Economic and social and political

environment also affects the opinion of people and they force them to think several times

before investing. This is the reason one cannot forget the fact that the value of economic

activities and information nourished to the market is vital. Here we are going to discuss the

tendency of animal spirit in India. The propensity of animal spirit in India is very much high-

flying. The major reason for this is that only 2% of the population of India participates in such

activities. The medium to get knowledge of market and accuracy in information is not available

easily. This forces people to act as per their nature rather than their logic. The core theme of

this analysis is the study of interrelation between real economic variables and capital market

variables. The significance of this relationship has got marvelous recognition in the past due to

roller-coaster ride of the BSE. A few academes also claim that it is not greatly shored up by the

financial basics.

To determine the character of these basics in studying the stock prices, different people

have done different researches. Amidst of these thoughts, some fundamentalists have tailored

theory of Efficient Market Hypothesis (EMH). This theory was further extended by Fama in

1971 to narrow and categorize such business centers on the foundation of their response and

data nourished to them in feeble, partially built and well-built markets. Another theory which is

Popular Model Theory shows a different perspective in general. This theory explains the

qualitative clarification of price which shows that most of the people proceed inappropriately to


the information which they get and easily accessible information is not included in stock

market price as Efficient Market Hypothesis verifies which is also much analogous to Keynes

model.

These days, everyone in the world is running after word money (finance). Nothing is

possible without money and economy and other financial operations including growth of this

whole world are also dependent on the same. Without finance, no one can turn the wheel of

economy at 360 degree, because each and every transaction needs money at its core. Amadou

(2007). In the past, there was a system in use, which is barter system. To avoid its

complications, ‘money’- component of finance was introduced and as then, money has been the

most precious thing of the world. It has become a need for every individual to fulfill the

requirements. Money is the core object needed to establish a business. The most valuable

source of this sort of money is the post laissez-faireization period is stock markets. In today’s

world, each and every individual can witness the appearance and acceptance of the capital

markets in the period of Globalization. This is considered to be highly regarded as an award for

the globalization years to under developed regions to enlarge and reinforce their nitty-gritty as

their financial crisis is gratified to an extent by these stock markets. Capital markets are also

considered to be a profitable platform for firms to get financing for their new or forthcoming

projects and moreover for people as a prospect to invest with chances of risk but huge profits.

The establishment of such capital market is the most important aspect for companies and

individuals. It is also a valuable state of the prerequisite of economy on the degree required in a

contemporary varied economy. The subsistence of such markets persuades the existence of

such arguments shaped in the hoisting of money can be transmitted.

Current Situation


“Thus if the ‘animal spirits’ are dimmed and the spontaneous optimism falters, leaving

us to depend on nothing but a mathematical expectation, enterprise will fade and die;-though

fears of loss may have basis no more reasonable than hopes of profit had before. . . . . . . But

individual initiative will be adequate only when reasonable calculation is supplemented and

supported by animal spirits. . . . . . . .”

– Keynes (1936).

In the above quote, Keynes (1936) has very bluntly and accurately explained the

reasons undercover of the current situation along with the entire humankind is getting

exaggerated in our day in 1936 merely in one of his leading models. A few economists have

also accepted in an extremely enjoyable method which it is not the object that the worth of our

productivity has decreased or lowered but just only the animal feelings are fainted and what we

were expecting is that the value of our capital market has boosted is wrong in comparison with

the primordial epoch and regard as added rise in actual value improbable. And every financial

system requires such “animal spirits” or the positive behavior along with the estimated threats

and reserves to became visible and outshine and development.

Since her independence, India was following the socialistic outline for growth, but after

the 1990’s financial crisis, India had to make some strict financial improvements as proposed

by World Bank. The most vital factors of that package were financial liberalization. This

financial change cemented a new way for India to boost its economy and make developments

and gave India impulsive environment to the financial markets specifically in language of BSE

BSE which is supposed to be solitary of the major components of India’s fiscal hike. These

days, capital markets has considered to be the most valuable source to transform domestic

savings to upcoming productive projects and provides a chance to the country to develop.

Brigham & (2008)


According to a survey conducted in India, around two percent of the general populace

is directly caught up in capital markets but when anything happens to such financial markets, it

is the whole population which get affected, which clearly shows the correlation, impact and

importance of these capital institutions and actual economy not only on the ground intensity

but also bottomless within the main rank. In these days, newspapers are bombarded with such

sort of news and major newspapers also issue supplement for financial news. Due to such

releases, there is a little doubt that the macroeconomic news which is vital to capital market

will be affected. In recent years, the whole financial market of the world had crashed and with

the recession in capital market, a gap has been established which lead to decrease real

economic fundamentals.

Consequently, it also boosts the value of this study because the focal point of this

downturn is considered to be the capital markets and if we consider it the other way, a nation

can become economically strong by considering such markets. So, it can easily be said by any

person that it seems to be that the stock prices will go up and would result in the formation of

some talented analysts of upcoming values of macroeconomic pointers similar to productivity

growth and price increases. If one takes this thoughts then this would definitely result in arose

of two more questions that what will make the market create hyper boom and incorporation of

capital market with other marketplaces. Agrawal (2008)

The most suitable reply to be given to the earliest query is that the flow of information

rise which has crowded the market, for instance, media (commerce news channels). Another

rebellion that shocked the market is Information Technology. The shortage of skilled people in

IT in the US has also become highly required after stocks due to their fantastic dough take

home power.


The other question which is creating a fuss is regarding the connection of such capital

markets with the actual financial system. Different analysts have also studied regarding this

theory and worked on this linkage. Ando and Modigliani (1963) developed a theory called life

cycle theory which is based on the linkage between stock prices and actual spending. The

theory talks about the individuals’ decisions and states that people mark their expenditure

verdicts on the conventional life span earning, division of which might be detained in capital

connecting to capital cost variation to variation in using up expenditure. Beck & Levine (2001

& 2008)

In the same manner, the linkage between capital costs and investment spending is

supported by q theory. The q theory is designed by James Tobin (1969) to analyze the effect of

stock prices on investment spending, where q stand for fraction of total retail worth of

comapnies to the substitute value of their on hand capital market at recent stock prices. In

addition, we had also discussed EMH model. With this research we have concluded that none

of these theories fit into the actual and recent image of stock markets. Some fit partially but no

one is perfectly related. To analyze such issues, there should be more research to be done on

this for better understanding and the below paragraph communicates it in an improved way.

“We should not conclude from this that everything depends on the waves of irrational

psychology. On the contrary, the state of long-term expectation is often steady, and, even when

it is not, the other factors exert their compensating effects. We are merely reminding ourselves

that human decisions affecting the future, whether personal or political or economic, can’t

depend on strict mathematical expectation, since the basis for making such calculation does not

exist, and that it is not innate urge to activity which makes the wheels go round, our rational

selves choosing between the alternatives as best we are able, calculating where we can, but

often falling back for our motive on whim or sentiment or chance.” Keynes (1936).


Structure of the Dissertation

This study is stepped into five chapters and this is to be pointed that the register worth

has been taken from all the factors. The first chapter thoroughly gives the overview of the

current situation. The second chapter is based on the literatures related to our study. The third

chapter comprises of a bunch of points and statistics and method considered in this analysis.

The fourth chapter will explain the estimations and result analysis. Chapter five exemplifies the

results, policy insinuations and boundaries of this analysis effort.

Research Questions

1. What is the relationship between stock market and macroeconomic variables in India?

2. Is there an informal relationship between capital market and real economy?

3. What is the influence of investment on financial health of India?


CHAPTER II: LITERATURE REVIEW

Introduction

The word economics has emerged to be the most powerful word. Each and every

individual is running after money and always try to earn as much as the one can within the

shortest possible span. Without finance, no one can imagine running any sort of business and it

has become an essential which runs the businesses and all the major features begin and finish at

it. In recent world, the quickest and trouble-free way to earn money for new and challenging

projects is to publicize or to enter into the stock markets where a little investment can make the

wise firms accelerate and helps these firms to become giants amongst the other big names.

Chauvet (2009) There are around two percent of the entire inhabitants in India which is caught

up in stock markets but the whole population in India gets affected either directly or indirectly

if anything good or bad occurs in such markets. This clearly shows that Capital Markets and

Actual Economy both have strong and vital link on not just only the lesser stage but also on the

higher stage. A number of studies and researches have been done in this regard but the

outcomes are vague. The reason for this is that most of the researchers have found a tough

bilateral linkage among capital markets and actual economy. On the other hand some

researches have entirely rejected this analysis that stock markets and real economy are

correlated.

To study more deeply we can distribute numerous theories in 3 schools of thoughts on

the ground of the literature review: first school of thought says that there is no linkage between

capital market and real financial factors. The second school of thought has analyzed that there

is an informal link among stock market and macro economic variable. The last one which is

third school of thought promulgates an unclear belief that there is a relation between the two

but not positively in both short and long run. Pindyck (2004)


This research is comprised of some on hand literature which has been reviewed relating

to the above problems. Below we have discussed the overall findings of different researchers.

First School of Thought

Chowhan, P.K. et al. (2000) The first school of thought attempted to obtain reasons for

hurly-burly in capital market in small period in India considering BSE as the major indicator.

In the period from 2008-2000, capital markets in India had shown irregular and unstable

activities which does not go with the information provided to them. Due to the severe up and

down in the stock prices, the investor confidence resulted in turmoil in the markets. The school

of thought tried to explain that what could be the actual causes at the back of instability and

what made Efficient Market Hypothesis (EMH) by Fama can’t explain the reason. The

researchers attempted to find out the reason of such huge rise in BSE stock value. On October

2008, BSE was at 2761 and in February 2000 it went at 6000, which shows 117% appraisal in

just 15 months, which is not powerfully maintained by basic financial factors in this era

because Indian economy boosted just only 5.9% in 2009-2000. On the other hand, the

corporate profit raised by 32%while the accumulated growth rate for industrial production

during Apr-Dec 2009 was reported to be 6.2%. The rate of inflation in September 2008 was

8.8% while in 2009-2000 it has also jumped down to 2.9%. This research shows that long run

financial factors such fluctuations in stock prices. India’s GDP in addition did not prove any

hike in the past years. Not only India was affected with this situation but also other countries as

well. The economists found some reasons to create a boost in Indian stock markets, they are:

• Information hike;

• IT impact;

• Internet fable;


• Feedback outcome;

• Cultural alteration.

Another economist Sarkar, P. (2007) states that if there exists any significant

correlation between growth and capital accretion, they have to use yearly information on

numerous financial factors like, insignificant and actual share price, capital market turnover,

companies which are stock market members, fixed stock structure and enlargement of actual

GDP and production results. Despite, all the researchers tell the same thing from 2000-1951 till

2005, there are no correlation actual and capital market variables neither in short nor in long

run. In addition, Sarkar also researched new movements and came to a conclusion that most of

the financial variables are unstable and comprised of an upswing movement in mid 1970s.

The tactics used in this thesis to get the best possible results is Unit Root tests. These

tests are used to obtain a fixed and worthy degeneration analysis. Besides these studies, OLS

and MLE approaches are also utilized for determining the sequence of auto-linkage of the

remaining and handling with it. To estimate long-term and short-term relationships in a better

manner, ECM and Autoregressive Distributed Lag (ADRL) respectively are used in this thesis.

Shiller, R. (1990), an economist of University of Yale, had researched and tried to

compare the normal & meager compound Capital Price Index from 1871-2000. He concluded

that the capital price instability is not compared by the profits.

Second School of Thought

In 2001, Black tried to elaborate the interlinkage between the U.S. capital costs and real

economic variables. Black gathered 54 year periodical data and along with it used VAR model

supported by hypothetical structure to study relationship between capital prices and macro

financial variables. With the lime light on actual results and considering current value


approach, Black found that the basic price-output ratio and the basic capital price in the light of

various suppositions relating to time variation of income, and to contrast such values to the real

data.

Black studies 3 situations, in the first one, he initiated supposing that the individuals

expect fixed return as being the wealth holder and then support this supposition by first,

permitting the undisruptive rate to differ in due course and next the risk payment to be time

changeable. Although there is a variation in models results, most mean that the capital market

has comparatively been overestimated in comparison with its value expected from growth

rates. According to reports, the ratio of stock market capitalization with GDP has boosted to be

thrice as compared to last twenty five years in the US. From this figure, in mid 1970s less than

30% and in 1990s it was 80%. The point which is to be noted here is that it does not only seem

that the capital market has risen in 1990s but its link with the actual finances has turned out to

be well-built and due to which it got accredited. As per the records, capital market has been

associated with actual financial variables by numerous techniques, out of which one of them is

asset costing perception in which Arbitrage Pricing Theory is second-hand as structure to

analyze the impacts of actual economic measures on capital costs concentrating on the question

that will the risk allied with a few real financial variables is replicated in probable asset

takings. On the other hand, there also exists expenditure-CAPM study of expenditure which

deliberates on a particular real factor power. There are other researches also conducted to

analyze the relation between capital prices and investment when stock prices hide the actual

economy which can be doled out with. In recent times, a lot of researches have aroused

analyzing the mutual linkage between capital worth and actual financial factors with VAR

models as the structure, devoid of any particular hypothetical formation.


Bulmash (2003) has done a very distinctive analysis to elaborate the interaction

between company investments and stock markets. He also studied that how investments affect

stock markets and vice versa. This theory also reflects how investments respond faster than

customers in capital markets. In his previous researches, Bulmash showed that what makes one

capital market to get boosted just by the difference in returns of economies. And finally results

in unity of this income fetch the stock into association in the long term. It has also been proved

that what makes these stock markets covert into analysts of the future financial flows. It has

also showed the linkage between capital investment markets and actual financial variables

through a system that value of capital market will boost when:

o Firms increase their investment to enlarge their operations which will increase

the GDP.

o With the increase in capital, actual capital also amplifies because buyers also

raise their expenses which accelerate GDP.

o Such points will result in worth formation which will lead to real economy.

Third School of Thought

A renowned economist, Mustafa in 2007 has done an analysis on the linkage between

capital market and actual financial market in Pakistan. For his study, he took a number of

economic variables like, per capita GDP, productivity growth and capital market liquidity,

volume of capital market highlighting the financial Market. He took up co-amalgamation and

Error Correction Model method to set up the pragmatic link, if any among the two from 1980-

2004.

The probable outcomes points that the activities in the capital market of Pakistan

elaborate the per capita GDP and productivity growth for small period only. On the other side,


financial growth variables elaborates capital market factors in nearby as well as in long term

which shows that the enlargement of capital market relies on by and large development of

economy in Pakistan. The economy of Pakistan had not been affected by the acceleration in

Karachi Stock Exchange which signifies that the elevated instability is not out of the ordinary

of the up-and-coming stock markets. The previous theories related to this subject used stock

prices as capital market movement sign and utilization, price rises, industrial output, financial

flow, rate of interest as real financial factor. Mustafa’s theory is quite different from other’s

work because the variables are different as used by others. The analyst have come to a

conclusion on the basis of empirical results that in Pakistan the stock market is way behind and

it needs to develop to be vital in the economy of Pakistan with the help of other financial

institutions. In any nation’s economic growth, capital markets play an important role but

capital market is inactive in the growth of a nation unless it is in its emergent stage.

Another big name amongst economists is Hussain, F (2010). He also studied about the

linkage among stock price and actual economy of Pakistan. He gathered data from 1959 till

2004 or 2005. After getting data, he distributed it in 2 halves, which are pre and post

liberalization and with the help of this activity he analyzed the linkage between stock price and

actual economy by applying different econometric tactics like ECM, Engle-Granger co-

integrating regressions and Augmented Dickey Fuller (ADF) Unit Root tests. The delay period

was decided in all the cases on the foundation of the two, i.e., Final Prediction Error and

Akaike Information Criteria (AIC). By applying such techniques Hussain examined that there

exists long term linkage between capital worth and actual financial factors.

On the subject of the causal section, he has brought into being unilateral grounds from

actual market to stock costs. This highlights that the stock markets in Pakistan are yet in a

developing stage. They have not got developed to influence the actual variables of economy


and because of that they cannot be considered as most important sign of the monetary activity.

It means that Government of Pakistan can utilize actual sector to power the capital market.

Another study observes that in India, the incorporation between Foreign Exchange and

Stock market in liberalization period. The researchers tried their level best to find out the link

flanked by Foreign Exchange and Stock market by using goods market approach and portfolio

balance approach. To narrow the research, they have used a number of econometric techniques

Granger’s causality test in VAR structure, which is supported by F-Test to test this theory; and

to examine such chains for immobility, ADF Unit Root Test is useful. One more econometric

test is applied which is Gweke’s Measures for the degree of business center amalgamation. But

the point is that this study gave new outcomes which are totally different. The previous

examinations reveal that there exists an informal linkage among income in foreign exchange

and stock markets. While as per the final analysis, there is a high extent of assimilation among

foreign exchange and capital markets and there is still two dimensional as well as simultaneous

informal linkages between foreign exchange and stock markets. Figlewski (2001)

Out of one of the many various kinds of papers written by Brenner, M., et al. (2010)

have studied the nearby prevision and reaction of U.S. stocks, treasury bonds, and commercial

bond places to the initial public announcement of major macroeconomic bulletin on

employment, inflation, and interest rates.

Four essential set of queries have been examined under this study. These are listed

below:

• Whether these traded assets in the markets are more responsive to instability

prior to release of these public announcements or less impulsive in nature afterwards?

• Are various asset class influenced in a different way by these public

announcements?


• Is the prevailing degree of correlation between various assets being affected by

these macroeconomic announcements?

• Do the influences of such public announcements occur solely due to their

unanticipated mechanism or is the reaction happening in congruence to the predictable

information?

In response to the above stated queries, they have under taken a number of day to day,

incessantly compounded surplus waiting-phase profits on the three major asset categories,

namely; stocks, reserves bonds, and shared bonds. Koutsoyiannis (2004)

What differentiates this research in contrast to the prior researches of its various types is

the investigation of impact of major macroeconomic news on the cooperative allocation of

returns in three different financial souks. Surveys and potential data have also been used to dig

out the unforeseen mechanism of this information. Furthermore affect of these news bulletins

on both the profits generated out of the three categories of assets, as well as their instability and

connection is also a part of the analysis.

To understand the flexibility of univariate GARCH model instead of containing the

intricacy of the multivariate GARCH model, Dynamic Conditional Correlation (DCC) model

by Engle (2002) has been taken under study.

In view of this research they have come up to the conclusion that the information

pertaining to the macroeconomic bulletins is considered to have a rather statistically

momentous and economically considerable influence over the financial markets of US. Also

this significant impact differs to a great extent across asset categories. Therefore it analyzes a

multifaceted picture of interaction between the returns on asset in propinquity of public

announcements or bulletins. In short a powerful correlation exists amid the actual economy and

money markets of the U.S.


With the help of a bivariate GARCH framework, Sarkar, A., et al. (2009), have been

able to analyze whether the possibility of positive provisional relationship exists among capital

profits and spending or not, even though the existence of absolute correlation is not evident in

the case of G7 countries. A monthly statistics of approximately forty years have been

considered for the United States and for other countries, quarterly data has been taken into

consideration. They have witnessed strong evidence with respect to the existence of positive

and considerable uncertain link among advancement in spending increase and capital profits.

The hypothesis could not be verified and have been disregarded incase of 6 of the G7 countries

as the correlation appears to be constant in nature for them.

In relation to the above they have reached to a conclusion which proposes that the

response of the policy incase when the stock market’s performance is signifying better results

than expected, may need to have a stronger policy than the usual. But when the market

conditions turn out to be on the extreme that is either in a positive condition or a negative one,

additional effects on the policy shouldn’t have an impact by them.In this manner they have

worked hard to establish a signifying linkage among capital market and actual economy and

what effect does the capital market has on the actual economy.

Another economist, Chauvet, M. (2009), has compiled his work upon establishing an

active linkage among capital market variations and the trade cycle. It is of the believe that the

movements in the stock market is being reflected by the various positions held up by the

participants of the market on the basis of their assessment pertaining to the present condition of

the economy. With the help of the on hand financial variables, in this thesis, the researcher has

tried to explore the likelihood of envisaging the key rotating spots of the business series. A

model has been proposed by Chauvet (2009) that facilitates in generating the predicted key

rotating spots of the business series with the help of the trade cycle factor. It also assists in


anticipating these predicted turning points pertaining to the trade cycle with the help of the

stock market factor. The author; Chauvet, M. (2009) also suggests in this paper, an indicator of

the stock market named as the stock market indicator (SMI). With the help of various series of

financials, the stock market indicator (SMI) financially assists in the anticipation of essential

rotating spots of the business series better in comparison to its individual components. This

indicates that the SMI model is by far a better alternative to be used as a tool in anticipating the

essential turning points of the trade cycle. Besides that, SMI can also be evaluated by the

month end, which highlights the latest information for that particular month of the year. In

contrast the CLI model only highlights information pertaining to the preceding month.

Therefore the underlying structure suggested by Chauvet, M. (2009), is utilized to study and

explore the characteristics of the stock market activities in predicting the trade cycles,

particularly the beginning of the recession as a result of which it establishes a strong correlation

and one way causal relationship between stock market and the real economy. Monthly data has

been taken into consideration by the author from 1954-1994, in studying the economic and

financial variables. The name of the economic variables used is: industrial production,

manufacturing and trade sales from 1982, non-agricultural civilian employment, and the sum of

individual income minus the transfer of payments from 1987. The author has taken into

account a number of other variables for the stock market factor, these variables reveal general

facts and figures pertaining to the current situation of the financial environment, for example;

the amendments in the S&P 500 PE ratio, surplus stock returns, 3-month Treasury bill rate and

S&P 500 dividend yield. A two state Markov process has to b followed by each of the factors

discussed above. These factors depict various phases of the trade cycle. All of the factors are of

the permission to change asynchronously in due course of time.


Brenner, M., et al. (2010) throughout writing this paper have analyzed and examined

the deep seated roots that are linked to the financial markets and the real economy. They have

also examined the first public news pertaining to information of US macro economy to the

short term anticipation and reaction of US stock, Treasury and Corporate Bond. The focus has

been primarily placed upon studying the influence of these announcements on not just only on

the different levels of those asset returns, but also upon the unpredictability and cooperative

changes of those asset returns.

The procedure of how the price arrangement takes place in relation to the

macroeconomic news for the three major segments of the market that is – stocks, government

bonds and corporate bonds nad has also been explored by them.

While carrying out the research on the underlying variables they have come across 4

important issues:

• In relation to the first release of the announcements, what impact would these

announcements have on the asset returns and the volatility of these asset returns?

• Does the affect of these announcements differ in their impact in different ways

for the various asset classes?

• Is the current degree of correlation that exists between different asset classes are

significantly affected by this news?

• Is the reaction to the predicted information or the unexpected components of

these news driving the impact created by these news releases?

Having the flexibility of univariate GARCH model as opposed to the complex nature of

the multivariate GARCH specifications, DCC model has helped resolve the underlying issues

mentioned above.


The conclusion reached by Brenner (2010) highlights the significant impact of

macroeconomic announcements on the US financial markets. Brenner also highlights that the

impact across the asset classes varies to a large extent.

Issues and Challenges

After a thorough analysis of the various studies, we have stumbled upon a number of

doubtful results as depicted by the three schools of thoughts. Therefore while conducting this

research analysis; we have to sustain the status quo in light of the new challenges that are to be

confronted. On the other hand we are also confined to limited access to Indian economic

statistics due to inaccessibility of the facts and figures.

In order to cover up every facet of real economy and the stock exchange, the selection

of variables have to be carried out in a persistently cautious manner. Having said that, we

shouldn’t lose track of the fundamental purpose of carrying out this research, which is to figure

out the interconnection and informal relationship among the variables under study.

We will be discussing a number of set of issues and objectives in the subsequent

chapter, in view of the above mentioned issues concerning the topic under study.


CHAPTER III: METHODLOGY

Research Design

The selected research design for this study is Quantitative research technique. Which is

the analysis of different aspects that can be easily measurable and quantifiable, such as

consumption, shopping, etc? Often used with numeric data. Is that commercial research is to

determine and specify the relationships among variables that influence a phenomenon, so you

can put in clear cause and effect relationships between these variables. Can be expressed in

mathematical language, sometimes in a qualitative way.

Objectives

The purpose of this study is analyzing the correlation among the stock market and the

real economic changes.

The purpose of this study is listed as follows:

To estimate the correlation and causality among stock market index, GDP

and the real economic variants.

To find out the nature of causal association among the stock market and the

real economic variants, either it is unilateral or bilateral.

Find out the effect of the stock markets and the real economic variants on each

other.

Data Se l ec t i on

Yearly data from 1950-51 onwards to 2007-08 has been taken under study in order to

study the variables such as GDP (Gross Domestic Product, bank rate, consumer price index


(CPI), domestic savings, gross domestic capital formation (GDCF), and monetary ratio M3

(broad money).

The data for the above mentioned macroeconomic variants has been taken from the

Statistics on Indian Economy kept by the Reserve Bank of India (RBI). The data for GDP has

also been taken from an on-line data source; the International Financial Statistics kept by the

International Monetary Fund.

Methodology Adopted

In light of the pre determined set of objectives to be achieved, various set of techniques

have been selected. First and foremost, to carry out the research objectives, evocative

statistics like descriptive findings of the dataset are carried out to demonstrate the nature and

basic features of the variants studied in this research. Correlation is the second step

leading towards the accomplishment of the set of objectives mentioned in this

study. It would also support in stating any association between the stock exchange and

t h e macroeconomic variants. Furthermore the formal analysis is carried out by exploring

the arbitrary statistical properties of the variables with the help of Unit Root Test, to assess

the immobility of the variables. In relation to this study, the most extensively used methods

are Augmented Dickey Fuller (ADF) (1979) and Phillips-Perron (PP) (1988) test. If the

unit root problem is not evident after examining the variables then Granger

causality can be anticipated.

We would now be discussing the two widely considered methods to assess the

arbitrary statistical properties of the variants under study.

Unit Root Test


The first step to implement co-integration procedure is to determine the order of

integration for multivariate series Co-integration requires that the series must he non-stationary

and integrated of the same order. There are several variations of the unit root test the

Augmented Dckey-FuIIer (1979. 1987), Phillip-Perron (1988), and Kwiatkowski. Schmidt and

Shin (1992).

The Augmented Dickcy- Fuller (ADE) test has been the most popular test used to check

data stationarity in empirical research. This test is applied in higher-order models and models

where the error terms are serially correlated. The ADF tests are based on the following three

regression models:

1. Without Constant and trend

2. With Contant

3. with Constant and Trend

Where Yt is the series being tested with, α is a constant, t represents a time series, and δ is the

lag truncation parameter.

The ADF is achieved under the assumption that a unit root exists, the null hypothesis of

unit root (p=0) and the alternate hypothesis states that the series are stationary (p<0). If the

calculated statistics is higher than the critical value, we do not reject the null hypothesis, and

consider variable is non-stationary; if null hypothesis is rejected, then the variable is considered


to be stationary. If it is determined that a series is stationary, the co-integration is not

appropriate, and another technique such as Least Square Model can be used.

Granger Causality Test

The essence of VAR models is as follows: We propose a system of equations, with

many equations as a series to analyze or predict, but in which no distinction between

endogenous and exogenous variables. Thus, each variable is explained by the delay of itself (as

in an AR model) and lags of other variables. Is set then a set of autoregressive equations or, if

you do so, a vector autoregressive (VAR).

The general expression of VAR model would be given by the following specification:

where t is a vector g with the object of prediction variables (call them explained), xt is a vector

of k variables that explain in addition to the above, the alpha and beta coefficients are matrices

of coefficients to be estimated, and epsilon is a vector of disturbances random (one per

equation), each of which individually satisfies the assumption of white noise (homoskedasticity

and no autocorrelation), and including the assumption of homoskedasticity meet international

equations.

This study determines whether there are causal links in the direction of Granger

between stock indexes and key macroeconomic aggregates. If these links exist, it is particularly

interesting to determine in which direction they will: is it for clues that "cause" aggregates or is

it vice versa? The direction of causation may give rise to new implications in the world of

finance for example.

Indeed, if we can demonstrate that macroeconomic aggregates "cause" changes in

market indices, then it is obvious that the analysis of these aggregates in determining policy


investment on the derivatives markets may be useful. In another vein, it would also be

interesting to get the opposite result to know whether stock indexes, which "causes" the main

aggregates macroeconomic.

In this case, it would be interesting to see how extent the central authorities could

benefit from information provided by changes in market indices to determine their own fiscal

policies or monetary. In all cases, it is extremely exciting to engage in this type of link Granger

causality depending on such variables as from our extensive research, nothing has been

published about it. The track is still probably totally blank and we hope to obtain conclusive

results to be worthy of our role as pioneers in this field.

Reliability and validity of the study

For a research study to provide evidence adequate, consistent and relevant information

it provides must be both reliable and valid. In secondary research, the notion of reliability has a

special meaning, which is narrower than the general application of the term audit. To be

reliable, the measure must be consistent from one individual to another in the investigation,

regardless of the medium and regardless of the time. So we can make general statements, it is

essential that information is consistent.

Validity is to explain how the research data are relevant to the finding and are

sufficiently accurate and complete to support the conclusion. It is generally said that the

validity answers the question: "Do we measure what we want to measure?”For example, we

may ask employees if the terms of play" fair. “Their answer cannot express their opinion on the

terms as such, but on results, the fact that they were promoted or not.

Reliability requires the use of instruments of uniform data collection and investigative

procedures which are designed to improve consistency. To ensure that information is relevant,


it takes careful planning, so that information is clearly linked to the objectives of the audit and

is collected from individuals who are best able to provide it.


CHAPTER IV: FINDINGS & DISCUSSION

Introduction

In order to explore the effect of macroeconomic variable on the stock market in India,

it is important to understand the economic environment in which the stock market exists. This

chapter provides a brief overview of the economic structure of India, exploring the role of

consumer price index in the economy as well as some economic indicators such as GDP, Bank

rate, Domestic savings, Gross Domestic Capital Formation (GDCF), and Broad money or M3.

Trends of all Macroeconomic and Stock Market Variables

Figure 4.1: GDP


Figure 4.2: Per capita CDCF

Figure 4.3: Domestic savings


Figure 4.4: Bank rate

Figure 4.5: Money supply

Figure 4.6: CPI

The above sequence charts show that a cyclic and an increasing trend is present in the

data set. So first we have to minimize the trend of the data set by using time series techniques


then we apply unit stationarity test on the selected data set.

Descriptive Statistics

Table 4.1: Descriptive Statistics

Variable s Bank

Rate

Domestic

Savings

GDCF GDP M3 CPI

Mea 0.827 4.374 5.139 5.82 4.66 1.30752

Median 0.845 4.372 5.151 5.777 4.561 1.253

Max 1.065 6.158 6.022 6.45 6.47 2.122

Min 0.477 2.926 4.362 5.35 3.32 0.591

S.D. 0.182 0.989 0.427 0.31 1.01 0.508

Skewness -0.309 0.157 0.133 0.32 0.25 0.109

Kurt 1.713 1.759 2.213 1.98 1.69 1.589

JB 4.841 3.889 1.638 3.40 4.61 4.837

Prob 0.088 0.143 0.440 0.18 0.09 0.089

From above table of descriptive statistics it is observed that variation is present in the

dataset as the value of standard deviation is high.

Correlation Analysis

Table 4.2: Correlation


Variables Bank

Rate

Domestic

Savings

GDCF GDP M3 CPI

Bank 1.000 0.715 0.683 0.661 0.694 0.740

Domestic 0.715 1.000 0.990 0.995 0.997 0.995

GDCF 0.683 0.990 1.000 0.991 0.985 0.975GDP 0.661 0.995 0.991 1.000 0.994 0.987M3 0.694 0.997 0.982 0.994 1.000 0.990CPI 0.740 0.995 0.976 0.987 0.996 1.000

From above Correlation matrix table it is observed that the value of pearson correlation is

close to 1so it represents a strong association among the selected macroeconomic variables. It

can be said that variation in one variable may affect the other variable as direct association is

present among the variables.

Stationarity and Causality Analysis

In the previous section, we discussed the variables that will be used in studying the

association between stock market prices and macroeconomic variables, and in this section we

will examine the time series properties of the data.

In order to implement cointegration between the time series. We need to pretest the

variables for their order of integration. It is necessary to show that they integrated in the same

order.

For each of the variable, the Augmented Dickey-Fuller (ADF) unit root test was used

with constant and with constant and time trend for all variables in their levels and then in their

first difference using a low lags based on the Schwarz Information Criterion (SIC). In this test,


the null hypothesis Ho: α=0 is that the variable under study contains a unit root against the

alternative that it does not contain a Unit root. Therefore, the failure to reject the null hypothesis

means that the variable is non-stationary, while the rejection of the null hypothesis reflects the

absence of a unit root, which means that the variable is stationary, if the null hypothesis is not

rejected for the variable in its level and rejected for the variable in its first difference, then we

can conclude that the variable is integrated I (1). if the null hypothesis is rejected For the variable

in its level then it can be said the variable is stationary I (0).

The Auginerned Dickey-Fuller test results are shown in Table 4.3 and 4.4 with the

constant and in Table 4.3 and 4.4 for the case where we have constant and time trend. Comparing

the ADF t-values of the level series with 1 and 5% critical value, which is reposted at the bottom

of each table, the results suggest that all market indices as well as macroeconomic variables are I

(1), where the first differences arc integrated of order zero I(0). We fail to reject the null

hypothesis of the existence of a unit root in levels, but reject the same null hypothesis in the first

difference of the series.

Table 4.5: Granger Causality Test

Pairwise Granger Causality Tests

Date: 03/28/11 Time: 15:00

Sample: 1961 2009

Lags: 2

Null Hypothesis: Obs

F-

Statistic Prob.

CPI does not Granger Cause GDP 57 0.32113 0.7271

GDP does not Granger Cause CPI 0.05736 0.9443


Bank rate does not Granger Cause GDP 57 2.03935 0.1428

GDP does not Granger Cause Bank rate 0.58156 0.5635

GDCF does not Granger Cause GDP 57 7.95952 0.0012

GDP does not Granger Cause GDCF 0.70882 0.4980

Broad money does not Granger Cause

GDP 57 0.39121 0.6787

GDP does not Granger Cause Broad money 1.87081 0.1666

Bank rate does not Granger Cause CPI 57 0.42104 0.6591

CPI does not Granger Cause Bank rate 2.00113 0.1479

GDCF does not Granger Cause CPI 57 0.73388 0.4861

CPI does not Granger Cause GDCF 0.44071 0.6465

Broad money does not Granger Cause

CPI 57 0.18089 0.8352

CPI does not Granger Cause Broad money 1.58079 0.2178

The test of Granger causality confirms the results of the estimation model VEC, only the

rate of GDP growth because changes in the CPI underlying with a probability associated with

the F-statistic of 0.05 below the threshold of 10%. All other assumptions of non-causality with

the index of underlying price are accepted at the 10%. Regarding the causality of changes in CPI

with the rate of GDP growth and with changes in the exchange rate, the test confirms the null


hypotheses of no causality, which support the conclusion of the estimation of this model: the

fluctuations in CPI, Bank rate, Domestic savings, Gross Domestic Capital Formation (GDCF),

and Broad money or M3 does not generate second-round effects via the rate of GDP growth or

fluctuations in exchange rate rupees dollar

Discussion

Stock markets play an important role in the financial sector of each economy. A healthy

stock market can promote economic growth by stabilizing the financial sector and providing an

important investment channel that contributes to attract domestic and foreign capital. Fama

(1981) showed that there is a strong association between stock prices and industrial production as

well as gross national product. In related work, Chang and Pinegar (1989) also concluded that

there is a close association between the stock market and the domestic economic activity, despite

the importance of stock markets in the world’s economies, stock markets remain unstable. For

example, the famous 1929 market decline in the United States prior to the great depression.

Other examples include the share market crash in the United States in 1987, which sent shock

waves throughout the world’s financial market, and, more recently, the crises of the Southeast

Asian stock markets in 1997. These crashes and accompanying swings have raised the question

what, if anything can be done to moderate volatility in stock prices. A debate has been

established concerning the design of monetary policy and possible intervention actions by the

central banks to prevent a stock market crisis (Dhakal, Kandil, and Sharrna, 2003).

An understanding of the determinants of stock market movement is an essential goal, not

only for economists or financial analysts, but also of government offices. Most of the empirical


studies regarding the determinants of stock market movements have been centered on two

contradicting theories: the quantity theory of money and the efficient market hypothesis.

The quantity theory of money has played a large role in determining the association

between money supply and various economic variables. On the other hand, in the efficient

market hypothesis, new information is rapidly incorporated into the prices of assets held in the

market, so current asset prices reflect all currently available information.

March 2008 fiscal year rate of about 9 percent. 6.7 percent growth rate is relatively better

because being told the financial analysts expressed fears of financial crisis bad economy and

growth rates were estimated to be between 6 and 6.5 percent were expressed. March quarter

growth estimate of 5.2 percent rather than 5.8 was.

Is believed that the economy has passed the worst phase and the reason that the recent

interest rate cuts by the Reserve Bank is not that blew life into the economy can be. Indications

are that the estimated time before there are signs of improvement in the Indian economy.

Economic growth projected over the effect of having appeared on the stock exchange and

the Bombay Stock Exchange CPI index climbed 370 points reached 14,667. Last quarter of the

fiscal year ended in 2009, 5.8 percent growth rate is estimated.

India's gross domestic product of financial derivatives can be decomposed in this sector

the impact of a brief explanation. In the short term to promote economic growth and the

country's international trade, these investments. This is actually a (usually through mergers and

acquisitions) International Bank shed light as the leading consumer finance imports, especially

automobiles, mobile phones and other consumer electronics products quickly exactly the same

entrance. India in 2000, less than one million mobile phone users have up to 60 million mobile

phone users. In the mobile communications companies are now part or all of the foreign


investment, and Japanese car manufacturers in India has doubled in the last five years of

automobile production. In oil imports, oil imports will increase low-cost expansion to cover the

deficit increase. This increase in consumption should lead to inflation in the short term, it is

through our research that. Similarly, exports compared to faster import growth means that, short-

term foreign direct investment is not in the country's export earnings improved significantly.

Foreign direct investment, in this case, it is possible to replace domestic investment.

Between human capital and long-term negative correlation between the technology

without any significant foreign investment from the overflow of the indicators of the presence of

impact. According to the Palestinian side of the technical level of the staff did not keep up with

financial derivatives, growth rate of the rich scientific and technological content. During 1992-

2006, investment policy reforms and foreign direct investment stage, overall labor productivity

growth rate of 1.7 per cent a moderate flow increase. In addition, foreign investment in India,

especially in recent years has been concentrated in high-tech capital-intensive services, such as

oil and gas exploration, financial services and telecommunications sectors do not create many

jobs. In the main labor-intensive agriculture, mainly unskilled and semi-skilled service sector

employment, labor skills, traffic has not changed much. Thus, economic growth reflects the

modern and the traditional differences between industries.

Some economists believe that a good policy by increasing the incentives and punishment

policy errors, in order to promote the free flow of transnational capital, more stringent

macroeconomic policies to reduce the frequency of wrong decisions. This requires observation,

if this has been the situation in India.

The first answer is a qualified affirmative. The Government has reformed the procedures

related to business and industry to simplify regulations and reduce the bureaucratic obstacles.


The results of India, more business than some other countries in the region friendly, although

there is still much work to do.

On the contrary, the consequences for the general public are so useful. Foreign M & A

tends to focus on juicy, immediately profitable, but the largest in the industry for the country's

long-term investment sector is often little concern about potential. The Government is going

through access to its cheap and abundant hydropower and thermal power potential foreign

investment difficult and expensive, but soon the oil-based power plants to install the proposition

not come easily. Similarly, the Bank has attracted a lot of the credit boom of foreign direct

investment inflows, especially since the early 21st century, as more and more consumers, the

bank interest rate differential more than seven percent, and most of the regional economy, such

as textiles, agriculture, and transportation investment still missing. This foreign investment has a

positive economic impact, but not many people can benefit. Not much work has been produced,

nor is it an ordinary life, raise standards. Who is to fight inflation by the consumer boom is the

rise in the majority of the poor poorer. In a country, a window less than a dollar a third of the

population lives, this non-job creation, capital-intensive investment, the gap between rich and

poor countries to further expand the have-nots name of the poverty line. There are also some

good examples, foreign investment, profit repatriation and off investors in the first year the total

investment.

Another important finding is that so far the causal association between exports, foreign

direct investment and GDP, we are in the sequence diagram graph shows the association between

cause and effect between the time series analysis of these three variables may does not produce a

useful general rule economic policy information.


Finally, we can identify that current literature tends to emphasize as foreign direct

investment to GDP growth, the development of individual capital or financial contributions.

individual capital, and for that matter, financial development, may be important to estimate a

regression or economic growth, the impact of foreign direct investment flows, is considered by

Borensztein, De Gregorio, and Lee (2008) and horses Shi and Lensink shown determination

(2003). Though, the rationale of this paper is not to estimate such a side effect, which inevitably

caused a problem of the endogenous variables. Instead, we aim in this paper is to test the foreign

direct investment and gross domestic product, the causal association, as well as export.

Therefore, we can also indicate that our sheet data examination also showed that the predictable

foreign direct investment in gross domestic product resulting openly or ultimately, throughout

exports, therefore, our analysis shows that exports are likely is a fine alternative, if not

corresponding, individual and capital or foreign direct investment and GDP association between

financial developments.

Research on multiple foreign stock market prices and macroeconomic variables

One of the most comprehensive studies into the dynamic linkage of stock prices and

macroeconomic factors for emerging economies was conducted by Muradoglu, Taskin, and

Bigan (2000). Muradoglu et al. noted past research had provided evidence for the effect of a

number of macroeconomic variables on stock market returns which were assumed to be

unidirectional from macroeconomic variables to stock returns. Exchange rates had been shown to

influence stock prices through the terms of trade effect where the depreciation of domestic

currency increased the volume of exports, leading to higher cash flows and stock prices for

domestic companies, provided the demand for export goods were elastic. Also, the association


between inflation and stock returns has proven to be more controversial, with empirical studies

showing a negative association between the two, through the effect of inflation on interest rates

and the discount rate. Given an increase in inflation is expected to increase the nominal risk-free

rate, this in turn will raise the discount rate used in valuating stocks, leading to inverse stock

valuations if cash flows do not adjust in the same direction. Lastly, Muradoglu et al. noted

previous research had shown the level of real economic activity, reflected in a production index,

was expected to have a positive impact on stock prices, through increased future cash flows.

As earlier research into the linkages of stock market returns and macroeconomic

variables had focused on one country versus a group of countries and assumed the respective

relationships to be unidirectional, Muradoglu et al. focused their study on the causal association

between macroeconomic variables and stock market returns for 19 emerging markets (defined by

the World Bank’s International Finance Corporation (IFC) index as any market belonging to

low- and middle-income, less-developed countries with the implication they all have the

potential for development) and tested for the direction of the underlying causal association using

monthly time series data. The IFC index attempts to cover 70 percent of market capitalization

and is calculated for all of the 19 emerging markets in a similar fashion, making international

comparisons possible. For each country (Argentina, Brazil, Chile, Colombia, Greece, India,

Indonesia, Jordan, South Korea, Malaysia, Mexico, Nigeria, Pakistan, Philippines, Portugal,

Thailand, Turkey, Venezuela, and Zimbabwe) the stock returns (R), exchange rates (FX), and

interest rates (I) were assumed to be linear in a set of local and global information variables,

whereas inflation (INF) and industrial production (PROD) 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 (SNP), which

represents the world market portfolio and controls for the degree of market liberalization. Local

information variables arc returns on country indices (R), exchange rates (FX) interest rates (I),

inflation (INF), and an industrial production index (PROD). Both the local stock market returns

(R) from the IFC index and the S&P 500 index (Datastream) were calculated using the first-order

differencing of their logarithmic levels. The data for the macroeconomic variables were obtained

from the International Monetary Fund (International Financial Statistics or IFS database) and are

the first-order differences of logarithmic values to account for growth rates, except for interest

rates. Interest rates (I), in percentages, were obtained from the monthly computed value of time

deposit rates used by each respective country and the inflation (INF) variables were computed

from the consumer price index of each country. The exchange rates (FX) were defined as the

national currency per special drawing rights (SDR) and captured the effect of a basket of

currencies on the stock market, instead of a single foreign currency.

Real economic activity (PROD) was represented and measured by the industrial

production index of each country and as a measure of general economic activity which proxies

for the gross domestic product (GDP).

The focus of the study conducted by Muradoglu et al. was to investigate the causal

association between stock returns and macroeconomic variables in emerging markets.

Muradoglu et al. found for the 19 emerging economies in their sample, inflation and interest

rates in Argentina and Brazil, and only interest rates in Pakistan and Zimbabwe, Granger-caused

their respective stock returns. In countries such as Brazil, Colombia, Greece, Korea, Mexico, and

Nigeria, exchange rates preceded stock returns and only in Colombia, Mexico, and Portugal did

domestic stock returns follow the S&P 500 index. To determine the precedence among the


macroeconomic variables, the left-side of their equation was replaced by one of the

macroeconomic variables, and by testing the joint significance of the coefficient of the lagged

values of the other macroeconomic variables and stock returns (without the lagged S&P 500

index because of the expectation this index would not affect domestic macroeconomic variables).

The precedence testing done by Muradoglu et al. showed domestic stock returns Granger-caused

domestic inflation in Argentina, Jordan, and Zimbabwe, and interest rates in Argentina, Korea,

and Mexico, Also, the real sector and domestic production followed stock returns in countries

such as India and Mexico, with exchange rates also being Granger-caused by stock returns in the

latter country.

The results of their study was important because: (I) out of the 19 countries, 12 of the

emerging markets exhibited any type of causal relationships with stock returns (Argentina,

Brazil, Colombia, and Mexico from the southern Western hemisphere; Portugal and Greece from

Europe; South Korea form the Pacific Rim: Jordan. Pakistan, and India from Asia and Nigeria

and Zimbabwe from Africa) indicating leading economic countries in their respective

geographical areas and with higher per capita income than other lower-income developing

countries, start the liberalization process earlier and are less insulated from global markets; (2)

only Argentina and Mexico exhibited bi-directional causality between stock market returns and

macroeconomic variables due to their being the top two countries in terms of the level of foreign

equity holdings which can be traced back to their respective market liberalization increasing

capital flows and becoming integrated in to the global market; (3) eight of the countries, beside

Argentina and Mexico, were observed to exhibit unidirectional causality with U.S. returns, and

macroeconomic variables to stock market returns: Colombia (U.S. returns and foreign exchange

rates), Brazil (inflation, interest rates. and foreign exchange rates), Portugal (U.S. returns),


Greece/Korea/Nigeria (foreign exchange rates), and Pakistan/Zimbabwe (interest rates); and (4)

in four countries beside Argentina and Mexico, unidirectional causality was observed from stock

returns to macroeconomic variables either as a lead-lag association or the size of the stock

market: Jordan/Zimbabwe (inflation), Korea (interest rates), and India (industrial production).

The generalized results from this study show a two-way interaction between stock returns and

macroeconomic variables (weak efficient market hypothesis) is mainly due to the size of the

respective stock markets and their integration into the world market through various measures of

financial liberalization.

Another comprehensive study of the association between stock market returns and

macroeconomic variables was conducted across a group of countries by

Wongbangpo and Sharma (2002).

Wongbango and Sharma observed the positive hypothesized association between growth

in output, as measured by GNP, and equity prices for the five ASEAN countries of the study. A

negative association between stock prices and interest rates, as hypothesized, was observed for

the Philippines, India, and Thailand. However, this negative interaction was not observed for

Indonesia and Malaysia. High inflation in Indonesia and the Philippines influenced the negative

association between stock prices and the money supply, while the money growth in Malaysia,

India, and Thailand provoked a positive effect on equity prices. Competition in the world

exporting market was seen to explain the observed positive interrelationship between the

exchange rate and stock prices for Indonesia, Malaysia, and the Philippines and adversely

effecting asset prices for India and Thailand, Overall, they observed the goods market and money

market variables were fundamental determinants of share price values for the five emerging

ASEAN nations of Indonesia, Malaysia, Philippines, India, and Thailand.


The volatility of interest rates is critical for asset pricing and revolves around the discount

rate and the computation of the present value of asset prices. As with the empirical results

obtained by Abdullah and Hayworth (2003) and Chen et al. (1986), the hypothesized

interrelationship between interest rates and equity prices was negative. An increase in interest

rates raises the required rate of return, which in turn inversely affects the value of the asset.

Representing opportunity cost, the nominal interest rate will motivate investors to reallocate their

asset portfolio allocation and substitute equity shares for other assets in the portfolio. Also, an

increase in interest rates can reduce corporate profitability by rising financing costs and

discourage merger, acquisition, and buyout activity.

Summary

To determine the association between stock market returns and the underlying

macroeconomic variables of gross domestic product, consumer price index, Ml money supply,

bank rate, and GDCF for the BSE economies, this study used the vector autoregressive (VAR)

model which is a dynamic Granger Causality model, assuming the data to be observed are

equally spaced in time intervals and there is no feedback between the response series and the

input series. The Granger Causality test was used to test the hypothesis of stock prices being

positively related to GDP and negatively related to CPI, the bank rate, and GDCF, and to

determine the exact association between lagged stock prices and the M I money supply. The

study found not all of the hypothesized relationships were consistent for each of the BSE, and the

underlying relationships would change over time, owing to their respective economic conditions

and. responses to economic changes. The intention of this study was to serve as a primer in

exploring the association between stock market prices and macroeconomic variables for BSE.


It is recommended further research into the determination of stock market prices for the

BSE include such variables as dividend yield, term structure, and risk premium to further the

understanding of the determination of stock market prices.


CHAPTER V: CONCLUSION, POLICY IMPLICATIONS AND LIMITATIONS

Conclusion

Capital markets and actual economy are vital components of development. Some analysts

say that both these variables are two sides of a coin without any one, the other one is useless.

Stock markets and real economy both are the basic factors for the development of economy of a

nation. The value of finance has been admitted by researchers from Classical to Keynesians and

Post-Keynesians. Development of any industry or a country is totally dependent on the stock

markets and real economy. And this is the reason nations give such importance to these

variables. The linkage between capital market and actual economy is very well recognized in the

developed nations, the reason for this recognition is that most of the population linked to such

markets are very well aware of such markets. Stocks markets are dependent on the investments

of the public and these small investments allow industries to grow and become a power

economy. The purpose of this study is to route the association and causal relation between capital

market and actual economy in India. The outcomes of this thesis are unclear and mixed. The

reason behind this is that there is certainly strong correlation between capital market and actual

economy, but Granger causality is common in the midst of just handful variables. The outcomes

from this research reflect that Indian economy is still in a developing stage over the previous 6

years.

The core purpose of this analysis is to expose the casual relationship among stock market

and real economy. As per the analysis, there is a well-built relationship amongst the stock market

and real economic variables and still some statistics shows that there is a huge enlargement in

capital market variables than real economic variables. This shows that the hike in capital market

is not much pampered by the real economy. There is still a casual behavior, which has been


analyzed with the help of this research, between actual economic variables and capital market

variables. This casual relationship strengthens the problem that Indian capital market is in a

childhood stage because the impact of stock market in India is less as compared to other

developed nations. Other the other side, the impact of actual financial variables is zero on capital

market index.

To analyze these issues, data from 2000 till 2010 has been used as the basic indicator

variable. The outcomes found from this analysis are diversified and unclear because the

relationship between all the variables utilized is high. All of the variables are moving in the same

direction, but in case of casual relationship, such sequence is not maintained.

Another reason is that not much of the population of India is involved in stock markets

only 2 % people are involved in such businesses which are the core reason why Indian economic

health is weaker as compared to other nations.

Policy Implications

This has been made very clear from this research that if the government wants to bring

change or to boost real sector of the economy, it should not neglect stock markets. This also

clarifies that the Indian stock market has not become that much stable to be the indicator of

financial growth. This thesis also indentifies that if the financial factors are not affecting the

capital market so there are definitely a few factors which impacts it, and these variables are

required to be bring into being and scrutinized to analyze this whole affect series entirely.

Limitations


The research indicates a number of limitations. The very first thing is that due to time

issue, the researchers were unable to do thorough analysis on this issue. The second who limits

this research is the lack of data accessibility due to which it was impossible to take more capital

market factors for instance market capitalization ratio, number of companies registered on the

exchange, etc.

Scope for Future Research

The outcome of this thesis is found to be ambiguous so this allows the researchers to go

ahead and do more detailed research on comparisons of linkage amidst the capital market and

actual economic variables. This also has to be clarified that how capital market and actual

economy are interrelated to eachother. If this is done, it could the governments to grow a lot and

to achieve the desired results related to economy.


REFERENCES

Agrawal, Anupam. (2008). “Indian Economy”, Pratiyogita Darpan 1-88.

Ahmad et al., 2004 Ahmad, M. H., Alam, S., & Butt, M. S. (2004). Foreign direct investment,

exports and domestic output in Pakistan. Paper presented at the 19th Annual General

Meeting, PIDE, Quaid-e-Azam University, Islamabad.

Alici and Ucal, 2003 Alici, A. A., & Ucal, M. S. (2003). Foreign direct investment, exports and

output growth of Turkey: Causality analysis. Paper Presented at the European Trade

Study Group (ETSG) Fifth Annual Conference, Universidad Carlos III de Madrid,

Madrid.

Amadou, N.R.S. (2007). “Capital Account Convertibility and Risk Management in India”. IMF

Working Paper 97. 4-12.

Baltagi, 2001 B.H. Baltagi, Econometric analysis of panel data (2nd ed.), John Wiley and Sons,

New York (2001).

Basu et al., 2003 P. Basu, C. Chakraborty and D. Reagle, Liberalization, FDI, and growth in

developing countries: A panel cointegration approaches, Economic Inquiry 41 (3) (2003),

pp. 510–516.

Beck, T., Levine, R. (2001). “Stock Markets, Banks and Growth: Correlation or Causality”.

SSRN.

Black, A. et al. (2001). “US Stock Prices and Macro Economic Fundamentals”. Aberdeen

Working Papers.01-3

Borensztein et al., 2008 E. Borensztein, J. De Gregorio and J.-W. Lee, How does foreign direct

investment affect economic growth?, Journal of International Economics 45 (2008), pp.

115–135.


Brenner, M., Pasquariello, P., Subrahmanyam, M. (2006). “Financial Markets and the Macro

Economy”. SSRN.

Brigham, E.F., Houston, J.F. (2008). “Fundamentals of Financial Management”, Tenth edition,

Cengage Learning India Private Limited. New Delhi.

Bulmash, S.B., (2003). “The interaction between the stock market and business sector

investments and the economy”, SSRN.

Carkovic and Levine, 2005 M. Carkovic and R. Levine, Does foreign direct investment

accelerate economic growth?. In: T.H. Moran, E.M. Graham and M. Blomstrom,

Editors, Does foreign direct investment promote development?, Institute of International

Economics, Washington, DC (2005), pp. 195–220.

Chauvet. M., (2009). “Stock Market Fluctuations and Business Cycle”, SSRN.

Cho, 2005 Cho, K. (2005). Studies on knowledge spillovers, trade, and foreign direct investment

—Theory and empirics, Ph.D. Thesis, Boulder, CO: Department of Economics,

University of Colorado at Boulder.

Chowan. P.K., et al. (2000). “Volatility in Indian Stock Markets”, Xavier Institute of

Management.

Chowdhury and Mavrotas, 2006 A. Chowdhury and G. Mavrotas, FDI and growth: What causes

what?,World Economy 29 (1) (2006), pp. 9–19.

Christopoulos and Tsionas, 2003 D.K. Christopoulos and E.G. Tsionas, A reassessment of

balance of payments constrained growth: Results from panel unit root and panel

cointegration tests, International Economic Journal 17 (3.) (2003), pp. 39–54.


Cuadros et al., 2004 A. Cuadros, V. Orts and M. Alguacil, Openness and growth: Re-examining

foreign direct investment, trade and output linkages in Latin America, The Journal of

Development Studies 40 (4) (2004), pp. 167–192.

Davidson and MacKinnon, 2004 R. Davidson and J.G. MacKinnon, Econometrics theory and

methods, Oxford University Press, New York (2004).

de Mello, 1997 L.R. de Mello Jr., Foreign direct investment in developing countries and growth:

A selective survey, The Journal of Development Studies 34 (1) (1997), pp. 1–34.

de Mello, 2009 L.R. de Mello Jr., Foreign direct investment-led growth: Evidence from time

series and panel data, Oxford Economic Papers 51 (1) (2009), pp. 133–151.

Demirguc-Kunt, A. (2005). “Stock Market Development and Financial Intermediaries”. Policy

Research Working Paper 14. 1-64.

Dickey and Fuller, 1979 D.A. Dickey and W.A. Fuller, Distribution of the estimators

for autoregressivetime series with a unit root, Journal of the American Statistical

Association 74 (366) (1979), pp. 427–431.

Dickey and Fuller, 1981 D.A. Dickey and W.A. Fuller, Likelihood ratio statistics

for autoregressive time series with a unit root, Econometrica 49 (4) (1981 July), pp.

1057–1072.

Dornbusch, R., Fischer, S.; Startz, R. (2005). “Macroeconomics”. Ninth Edition. Tata McGraw

Hill. New Delhi.

Dritsaki et al., 2004 M. Dritsaki, C. Dritsaki and A. Adamopoulos, A causal association between

trade, foreign direct investment, and economic growth for Greece, American Journal of

Applied Sciences 1 (3) (2004), pp. 230–235.


Edwards, 2003 S. Edwards, Openness, trade liberalization, and growth in developing

countries, Journal of Economic Literature 32 (3) (2003), pp. 1358–1393.

Elliott et al., 1996 G. Elliott, T.J. Rothenberg and J.H. Stock, Efficient tests for

an autoregressive unit root, Econometrica 64 (4) (1996), pp. 813–836.

EViews, 2005 EViews 5.1. (2005). Irvine, CA: Quantitative Micro Software, LLC.

Fan, 2002 Fan, E. X. (2002). Technological spillovers from foreign direct investment–A survey.

Asian Development Bank ERD Working Paper Series No. 33.

Figlewski, S. (2001). “Forecasting Volatility”. Financial Markets, Instruments and Institutions

6 .1-88.

Frankel and Romer, 2009 J.A. Frankel and D. Romer, Does trade cause growth?, American

Economic Review 89 (2009), pp. 379–399.

Frankel et al., 1996 Frankel, J. A., Romer, D., & Cyrus, T. (1996). Trade and growth in East

Asian countries: Cause and effects? NBER Working Paper No. 5732.

Giles and Williams, 2000 J.A. Giles and C.L. Williams, Export-led growth: A survey of the

empirical literature and some non-causality results. Part I, Journal of International Trade

and Economic Development 9 (3) (2000), pp. 261–337.

Gray, 2008 H.P. Gray, International trade and foreign direct investment: The interface. In: J.H.

Dunning, Editor, Globalization, trade and foreign direct investment, Elsevier, Oxford

(2008), pp. 19–27.

Greenaway and Morgan, 2008 D. Greenaway and W. Morgan, Trade orientation and economic

development: Theory and evidence. In: D. Sapsford and J.R. Chen, Editors, Development

economics and policy, The conference volume to celebrate the 85th birthday of Professor

Sir Hans Singer, St. Martin Press, New York, NY (2008) [Chapter 9].


Greene, 2003 W.H. Greene, Econometric analysis (5th ed.), Prentice Hall, Upper Saddle River,

NJ (2003).

Gujrati, D.N., Sangeetha (2008). “Basic Econometrics”. Fourth edition. Tata-McGraw Hill.

Humpe, A., Peter, M.; (2009). “Can macroeconomic variables explain longterm stock market

movements? A comparison of the US and Japan”. Applied Financial Economics 19. 111-

119.

Husain, F. (2006). “Stock Prices, Real Sector and the Causal Analysis:The Case of Pakitan”.

Journal of Management and Social Sciences.2.179-185

Jha, R. (1996). “Inflation Targeting in India: Issues and Prospects”. Research School of Pacific

and Studies Journal 02. 28-68.

Juntilla, J., Korhonen M. (2008). “Utilizing financial market information in forecasting real

growth, inflation and real exchange rate”. SSRN.

Kakani, R.K., Chatterjee, T. (2007). “An Alternate Perspective on the Bull Run in Indian

Markets”. SPJ Centre of Management working paper 7. 1-13.

Kanakaraj, A., Singh, B.K.; Alex, D. (2008). “Stock Prices, Micro Reasons and Macro Economy

in India: What do data say between 1997-2007”. Fox Working Paper 3. 1-17.

Keynes. J.M. (1936). “The General Theory of Employment, Interest and Money”, Harcourt

Brace, New York.

Koutsoyiannis, A. (2004). “Modern Microeconomics”, Second edition, Macmillan Press

Limited, London.

Levine, R., Loayza, N.; Beck, T. (2008). “Financial Intermediation and Growth: Causality and

Causes”. SSRN.


Mishra, S.K., Puri, V.K. (2004). “Indian Economy”, Twenty second edition, Himalaya

Publishing House, Mumbai.

Nath, G.C., Samanta, G.P. (2000). “Integration between FOREX and Capital Markets in India:

An Empirical Exploration”. Applied Financial Economics 7. 25-35.

Pearce, D.W. (1992). “Macmillan Dictionary of Modern Economics”, Fourth edition, The

Macmillan Press Limited, London.

Pindyck. R.S. (2004). “Microeconomics”, Fifth edition, Pearson Education Limited, Singapore.

Sarkar, P. (2005). “Stock Market, Capital Accumulation and Growth in India since 1950”,

SSRN.

Sarkar, S., Sarkar, T. (2005). “Reforming Financial Markets in India: a Strategic Approach”.

SSRN. 960532. 1-17.

Sidhu, H.S. (2006). “Outlines of Indian Capital Market”, Ludhiana Stock Exchange Limited,

Ludhiana.

Srivastava, R., Misra, A. (2008). “Financial Management”, Oxford University Press, New Delhi.

Stonier, A.W., Hague, D.C. (1977). “A Textbook of Economic Theory”, Fourth Edition. The

English Language Book Society and Longman Group Limited, London.

Vatnick, S. (2008). “Financial Reforms and Emerging Markets”. Financial Markets, Instruments

and Institutions 17. 109-120.


APPENDIX

Table 4.3: Unit Root (ADF) Tests for variables

Variables ADF Statistic

Bank Rate 3.820713*

Domestic Savings 7.300512*

Gross Domestic Capital Formation 5.587253*

Gross Domestic Product 3.454179**

Consumer Price Index 5.093995*

Money supply 3.807223*

Note: *and ** signify stationarity at 1% and 5% level of significance

Table 4.4: Unit Root (PP) Tests for variables

Variables Phillips-Perron Statistic

Bank Rate 7.027718*

Domestic Savings 6.474774*

Gross Domestic Capital Formation 8.714410*

Gross Domestic Product 3.496084**

Consumer Price Index 5.734517*

Money supply 4.786433*

Note: *and ** signify stationarity at 1% and 5% level of significance

Documents

0011 Relationship Between Stock Market and Macro Economic Variables in India