Impact of Macroeconomic Variables on Stock Prices : Empirical Evidence from South Asian Countries

IMPACT OF MACROECONOMIC VARIABLES ON STOCK PRICES : EMPIRICAL EVIDENCE FROM SOUTH ASIAN COUNTRIES

DEPARTMENT OF BANKING AND FINANCE

A Project Submitted to the Department of Banking and Finance In Partial Fulfillment of the requirement for the Degree of BBA Banking and Finance

ABSTRACT

Stock prices play a vital role in the economy growth Movement in the stock prices strongly effect in economy growth. A collapse in stock prices has cause of economy disruption. The objective is to investigate the impact of exchange rate and oil prices on stock prices of south Asian countries. The study based on monthly data from period July 1997 to July 2013. Exchange rate and oil price are independent variables while stock price is dependent variable. Descriptive statistics, correlation analysis and regression model applied for analysis. In Regression analysis oil prices show positive significant impact on Pakistan, Afghanistan, Maldives and Indians stock prices. The impact of exchange rate on stock prices of Bangladesh, Srilanka, Maldives is positive significant but exchange rate effect inversely on Afghanistan stock prices. Oil prices have negative significant impact on Colombo and Dhaka stock exchange prices. Although the significant relationship mostly not exist between dependent (Stock prices) and independent variables (Exchange and oil prices) future researcher should include domestic country variables to check the impact on stock exchange prices.

Keywords: Exchange Rates, Oil Prices, Stock Prices, Descriptive Statistics, Regression Analysis. Pakistan, Bangladesh, Afghanistan, Srilanka, Maldives, India

DECLARATION

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Project Title: IMPACT OF MACRO ECONOMIC VARIABLES ON STOCK PRICES: EMPIRICAL EVIDENCE FROM SOUTH ASIAN COUNTRIES

DEDICATION

We want to dedicate this research work to our Parents, whose belief and encouragement motivate us to complete of work on time. Their prayers and support always being a source of guidance in the turbulence mode of our lives and give us the strength to face the ups and down in life

ACKNOWLEDGEMENT

All the praises and acclamation for Almighty Allah The benevolent that knows mysteries and secrets of universe and His Holy Prophet (S.A.W) Whose blessings enabled me to pursue higher goals of life and Whose teachings have served as beacon for the humanity in the hours of despair and darkness.

We are heartily thankful to our supervisor, whose encouragement, guidance and support from the initial to the final level enabled us to develop an understanding of the subject. He has left no stone unturned during the supervision of this project. I would also like to acknowledge my dear friends for their encouragement and cooperation.

Last but not least, ordinary words of appreciation do not cover my parent’s true love and their guidance at every corner of my life. Their keen interest, prayers, and encouragement have been a very strong support for me and enabled me to finish my project in time. I also owe my thanks to all others who encouraged and helped me throughout my research work.

CHAPTER: 1
- INTRODUCTION
- 1.1 Background of The Study
- 1.2 Objective of the study
- 1.3 Research questioner
- 1.4 Organization of the study
CHAPTER: 2
- LITERATURE REVIEW
CHAPTER: 3
- DATA & RESEARCH METHODOLOGY
- 3.1 Data
- 3.2 Explanation of variables
- 3.3 Theoretical Framework
- 3.4 Research Methodology
Chapter: 4
- ANALYSIS AND INTERPRETATION
CHAPTER: 5
CONCLUSION POLICY IMPLICATIONS AND FUTURE DIRECTIONS
Conclusion
Policy Implication
Future Direction
References

LIST OF TABLES & FIGURES

Table-1A	Descriptive Statistics ………………….……………………………….. 15
Table-1B	Correlation Analysis ……….………………………………………………….16
Table-1C	Regression Analysis ……………………………………………………………..17
Table-2A	Descriptive Statistics …………………………………………………….18
Table-2B	Correlation Analysis………………………………………………………19
Table-2C	Regression Analysis…………………………………………………………..19
Table-3A	Descriptive Statistics ……………………………………………………..20
Table-3B	Correlation Analysis ……………………………………………………………21
Table-3C	Regression Analysis …………………………………………………………….21
Table-4A	Descriptive Statistics ………….………………………………………………..23
Table-4B	Correlation Analysis…………………………..……………………………24
Table-4C	Regression Analysis……………………………..………………………24
Table-5A	Descriptive Statistics……………………………..……………………..25

Table-5B Correlation Analysis………………………………………………..26

Table-5C Regression Analysis……………………………………………….26

Table-6A Descriptive Statistics……………………………………………….28

Table-6B Correlation Analysis……………………………………………….29

Table-6C Regression Analysis…………………………………………….…29

ABBREVIATIONS

ARIMA= Auto Regressive Integrated Moving Average
ER= Exchange Rate
FDI= Foreign Direct Investment
GDP= Gross Domestic Product
INT= Interest Rate
KSE= Karachi Stock Exchange
OP= Oil Prices

CHAPTER I

INTRODUCTION

BACKGROUND OF THE STUDY

In current economy it is founded that the developed countries are facing short fall of buyers and this short fall can be reduced by gathering investors from emerging economies. The capital market has an excessive role in the economic progress and improvement because it carries the funds from that person which has enough funds to that person which is needy. The main body of study is to check the effect of macroeconomic variables on stock market return of South Asian countries. These countries are developing economies collected their resources as the ratio of the two fifth of the total gross domestic product of all growing economies. Stock prices, oil prices and exchange rate have specific effect on financial markets.

To check the relation among stock return and macroeconomic variable two important theories are used one is capital asset pricing model and other is arbitrage pricing theory.

According to Opfer and Bessler (2004) different economic variables affect the stock prices so to check the effect of variables following models have been developed. Regression model and Box-Jenkins ARIMA model are used. The stock prices show the economic condition so the relationship between macroeconomic variables and stock price is significant. The stock market has important role because with the help of these markets investor can make guess about the economy condition and investor can also make idea that either he should invest at that time or he should delay. By doing this investor can avoids from loss.

When macroeconomic variables changed they affect the equity stock earnings and stock price. Because market and economy are connect with each other. Exchange rate and oil prices are key macroeconomic variables changes the performance of the stock market (Lobo, 2000). Activities of monetary establishments have a major impact on stock prices and variation of interest rates is good or bad sign for investors. Stock market and exchange rate also keep a relation. Foreign investors affected when local currency becomes stronger and they transformed their profit into weaker currency then they bear loss. Exchange rate has adverse connection with stock prices. Stock prices reduces when exchange rate increase or decrease it has good effect on stock market. A quick increase in inflation also disturbs negatively the act of the stock market.

1.2) OBJECTIVE OF STUDY

The objective of the study is to examine the impact of macroeconomic variables on stock prices of South Asians countries.

1.3) RESEARCH QUESTION

Does a macroeconomic variable have impact on the stock prices of south Asian countries?

1.4) ORGANIZATION OF THE STUDY

In first chapter discus the introduction about macroeconomic variables and their impact on stock prices. Literature review discussed in second chapter. Third chapter related with the data and research methodology. Empirical results and discussion is chapter no four.

Chapter no five based on conclusion.

CHAPTER II

LITERATURE REVIEW

Gay, (2011) evaluates the connection among stock price, oil price and exchange rate of China, Russia, Brazil and India. Stock price is dependent variable while oil price and exchange rate are independent variables. Monthly data is used to check the influence of these variables. The model which used is Box Jenkins Auto Regressive Integrated Moving Average model. Result show that the oil price and exchange rate have insignificant relationship with stock prices.

Momani and Alsharari (2012) check out the impact macroeconomic factor which is national product, interest rate and money Supply on stock prices. Dependent variable is share prices general index an independent variable are interest rate, money supply, industrial production index, Gross national product. Multi regression model is used to find out impact of variables on share prices. They founded that there is important relationship between share price and interest rate, money supply, gross national product and industrial production index.

Asaolu and Ogunmuyiwa (2011) elaborate the influence of macroeconomic variables on stock price and average share price. The models which used are Co integration and error correction method, Augmented Dickey Fuller test and Granger causality test. Result indicates that there is weak relationship among macroeconomic variables and share price.

Kasman, (2003) analyze the relationship among exchange rate and stock prices. Two main variables which are used in this study are stock prices and exchange rate. Cointegration model is used to check the connection of stock price and exchange rate. The result recommends there is mixing affiliation amid stock prices with exchange rate. Stock exchange rate and Indices of Istanbul stock exchange moves together in long run.

Quadir, (2012) examine the influence of macroeconomic variables of Industrial production and T. bill interest rate on Dhaka stock exchange. Autoregressive integrated moving average model is used for time series data. A positive relationship find out among industrial production and interest rate with stock returns. The impact of industrial production index and Treasury bill interest rate on stock returns was found statistically insignificant.

Lijuan, (2010) uses data of Chines securities regulatory commission people bank of China. The independent variable which is used by the researcher is Shanghai composite index. Regression model is used to check the impact of macroeconomic variables. The analysis shows that corporate goods price, exchange rate, Consumer confidence index, Macroeconomic prosperity index and interest rate these variables affected by the change in stock price. Stock price is the result of many factors and it changes all the times.

Kemboi and Tarus (2012) analyze that a connection exist among macroeconomic elements and stock market. The macroeconomic factor such as stock market liquidity, income level, and banking sector development are important variable of development. Secondary data is used and composed from different market of Kenya. Two models are used: one is Johansen and Juselius cointegration modelsecond is Rossell model to test the proposed study. The result suggests that income level and banking sector and market liquidity shows positive impact on stock price.

Singh, (2010) examines the affiliation among three important factors that are exchange rate, Wholesale price index and stock price. Unit root test and Granger causality test is use to check the impression. For present study use Granger causality and for first step analysis used unit root test. The consequences that have been found are mixed and unclear as there is definitely strong connection among wholesale price index and Bharat stock exchange and index of industrial production.

Agrawal, Srivastav, and Srivastava (2010) find out relationship among exchange rate and stock returns. Different model which are apply in this article are Unit root test and Granger Causality test, Augmented Dickey Fuller, and Normality test. The outcome show that the constant relationship among the two variables.

Bildi and Elekdag (2004)explain the stockholder, regulators and instructors in policy circles to shift the increasing instability by using the price restrictions on financial marketplaces. The variable is price limit. Two models are used: one Garch mode and second Serial correlation analysis. The outcome designates that volatility has reduced after the increase in price limit both for cross section of stocks and overall index.

Chue and Cook (2008) examine the exposure of developing marketplace corporations to variations in their national exchange rates. The instrumental variables method examines the entire exposure of a business to exchange rate actions. The model identifies the entire exposure of a firm exchange rate movement.

Bellavite And Pellegrini (2010) investigate about the links among macroeconomic index of corruption and European industrial stock revenues in order to measure the impact of dishonesty in the acts of listed European industrial companies. The technique which is used in this study is the Fama andFrench (1993) three factor model approach. The result shows that Maximum capitalization companies display a negative and not statistically important relation.

Sharma, Singh and Sanjeet (2011) discover out the effect of macroeconomic variables on Gross National Income, Gross Domestic Product growth, wholesale price index and Rate of interest and Consumer Price Index, in India and Sri Lanka. The models which used in this article are Vector auto regression and Variance decomposition, Cointegration test, Unit root test. The outcome is not pure because different model are used and each model display different result.

Kirui, Wawire and Onono (2014) examine the effect on stock market of macroeconomic variables. Different variables are used which are stock market return, Inflation, Gross Domestic Product and exchange rate. The Threshold Generalized Autoregressive Conditional Heteroscedasticity model is used. The result presented a negative link among exchange rate and stock market return.

Hussin, Muhammad, Abu, and Awang, (2012) discuss the link among macroeconomic variables and development Islamic stock. The variables which are used in this research are Islamic bank rate, Exchange rate, Industrial production index. Vector Autoregressive model is use. Values shows that a long run equal relation among macroeconomic variables and Islamic share price.

Hussain, Aamir and Mumtaz (2012) investigate the short run and long run association among KSE and macroeconomic factor. Independent variables are exports, Rate of interest, money supply, Wholesale price index, exchange rate, imports, and Foreign exchange reserve. Stock price is dependent variable. The techniques are Granger causality test, unit root test, vector error correction model and Augmented Dicky fuller test. According To researchers views suitable monetary measure should adopt to control inflation.

Imdad, Hassan and Ali (2011) explain short run and long run dynamic link among stock returns and macroeconomic factor. Macroeconomic variable which used in this research are exchange rate, Treasury bill, industrial production and Reserves, money supply. Methods which uses in this research are Bivariate Cointegration and Causality test Johansen and Juselius Multivariate. Result indicates no significant connection among KSE stock prices and macroeconomic factor.

Mohammad, Hussain and Ali (2009) examine the link among share price of KSE and macroeconomic variable. The variables are wholesale price index, foreign exchange reserve, industrial production index, foreign exchange rate. The different models which used are Auto regressive integrated moving average model, descriptive statistics, and unit root test. The results in show that macroeconomic variables have solid effect on stock market return.

Imdadullah, (2012) explain major aspect of economy that are exchange rate and inflation and interest rate and their impact on stock price of Karachi stock exchange. The independent variables are exchange rate, interest rate and inflation. While the dependent variable is stock returns. The model which used is multi regression model. The result indicate that a weak difference in the dependent variable due to independent variable.

Butt,(2010) checks the stock market return of particular macroeconomic and industry variables. The variables are industrial production, risk free rate of return, consumer price index, money supply, and exchange rate. Three different models used to check the variable connection that is Garch model, descriptive statistics, unit root test. Consequence shows that stock return is diverse at industry level and firm level.

Farooq and Nasir (2013) observe the effect of stock market instability on exchange rate. Two variable used in this inquiry one is stock market return and other is exchange rate. The autoregressive conditional hetero skedasticity model is used. The outcomes specify that in stock market of Pakistan changeability exists because of Euro, Dollar, and Pounds exchange rate.

CHAPTER III

DATA AND RESEARCH METHODOLOGY

3.1) DATA.

We are going to check the effect of exchange rate and oil prices on stock prices. Exchange rate and oil prices are independent variables while stock price is dependent variable. Monthly data of oil price, exchange rate and stock prices is used.

We collected data from three websites from period 1997 to 2014. We collect monthly data of stock price from yahoo finance. Second website is OANDA from this website we collect exchange rate on monthly basis. And from the official website of inflation data we take crude oil prices. Following countries involved in our research.

Pakistan, Bangladesh, Afghanistan, Srilanka, Maldives and India

3.2) EXPLANATION OF VARIABLES

Dependent Variable

Stock Price

Stock prices show the overall condition of economy. Stock prices show that what is current price of share of listed companies and what is the current situation of economy.

Independent Variables

Exchange rate and oil price are independent variables that are used in this study. We have control over independent variables and we can manipulate them by dependent variables.

Exchange rate

Exchange rate play very important role for country. Exchange rate is that rate in which currency of one country compare to the currency of other country or one unit of domestic currency replaced with foreign currency is called exchange rate. If increase in foreign currency and decrease in domestic currency then this will increase the export of the domestic country hence it will also enhance the firm dividend and also cash inflow.

Crude Oil Price

Oil price is independent variable. The oil price which is used in our research is crude oil price.

3.3) THEORETICAL FRAMEWORK

To check the effect on stock market return of macroeconomic factor different theoretical frame work applies one is arbitrage pricing theory which is developed by Ross (1976) and other is semi strong market by Fama (1970). The both theories are used to illustrate the relation of stock market return with macroeconomic variables.

Arbitrage Pricing Theory

Arbitrage pricing theory explains the relationship between returns of a single asset and returns of portfolio with a linear arrangement of different independent macroeconomic variables. This theory is developed by Stephen Ross in 1976.

Semi Strong Market Theory

This theory developed by Eugene Fama in 1970. This theory explains that the asset prices show all the information that is available for the public. Therefore investor with extra information might have advantage on the market. If any price difference then it rapidly find and stock market adjust it.

Figure No 3.1Theoretical framework of Ex.Rate, Oil prices and Exchange rate

Theoretical framework explains the impact of independent variables on dependent variable. Stock price is dependent variable while oil price and exchange rate are independent variables. Theoretical Framework also the relationship of oil prices, exchange rate and stock prices.

3.4) RESEARCH METHODOLOGY

Descriptive Statistics

To check the connection between variables econometric technique is used. But at first stage check the statistical behavior and to check the behavior descriptive statistics apply. Mean, Standard deviation, Median, Skewness and Kurtosis and Maximum and minimum range descriptive statistics are applied to check these or to check the distribution of data. Descriptive statistics are supportive to make a judgment about the performance of time series analysis.

Correlation Analysis

The Correlation results shows the Significant and Insignificant relation between Independent variables (exchange rate, cruid oil prices) and Dependent variables (stock prices).

Regression Analysis

Regression model is used to check the impact among the dependent variable and independent variables. Stock price is dependent variable while oil price and exchange rate are independent variable. Monthly data is used.

CHAPTER IV

ANALYSIS AND INTERPRETATION

The analysis consists of South Asian countries to check the Impact of Macroeconomic variables (Exchange Rate, Oil Prices) on Stock exchange prices. We find the impact on stock price by regression analysis. The interpretation of results describes the basis of Separate Country.

PAKISTAN

Descriptive statistics

Measures of central tendency include the mean, median and mode, while measures of variability include the standard deviation (or variance), the minimum and maximum variables, kurtosis and skewness.

Table No.1A Descriptive Statistics

	Exchange Rate	KSE 100 Index	Oil prices
Mean	0.015673	7195.629	54.66668
Median	0.016400	7104.650	49.83000
Maximum	0.024700	23312.77	133.9300
Minimum	0.009900	841.7000	11.28000
Std. Dev.	0.003403	5339.611	30.76880
Skewness	0.223272	0.484917	0.398131
Kurtosis	2.776750	2.358002	2.002954
Jarque-Bera	2.004319	10.87827	13.09292
Probability	0.367086	0.004343	0.001435
Sum	3.024900	1388756.	10550.67
Sum Sq. Dev.	0.002224	5.47E+09	181770.1
Observations	193	193	193

In descriptive statistics the mean value of stock prices is 7196% which show the positive impact on profit and economy growth. The mean value of oil prices is 55% it shows that the oil prices influence the stock price the standard deviation of oil price is 31% which show the risk. But the exchange rate value is 1.5% if the mean value is greater than 10% then it show the influence on stock price the standard deviation of exchange rate is 0%. The table shows the normality of data.

Correlation Analysis

The Correlation results shows the Significant and Insignificant relation between Independent variables (exchange rate, cruid oil prices) and Dependent variables (stock prices).

Table No.1B Correlation Analysis

	Exchange Rate	KSE 100 index	Oil prices
Exchange Rate	1.000000
KSE 100 index	-0.711436	1.000000
Oil prices	-0.747389	0.904497	1.000000

There is negative insignificant relation between exchange rate and oil stock prices and the relation between exchange rate and oil prices is also negative insignificant. The relationship between stock prices and oil prices is positive insignificant.

Regression Analysis

The study consists of two Independent variables exchange rate crude oil prices and one dependent variable is stock prices.

Table No.1C Regression Results

Variable	Coefficient	Std. Error	t-Statistics	Probability
C	1157.133	1501.330	0.770739	0.4418
Oil prices	146.5575	8.018230	18.27803	0.0000
Exchange rate	-125904.9	72488.05	-1.736906	0.0840
R-Squared	Ad. R-Squared	F-Statistics	P(F-Statistics)	Durbin-W.Stat
0.820958	0.819074	435.6031	0.000000	0.152399

The oil prices have significant positive impact on stock prices the probability value of oil price is less than 5% we can reject the null hypothesis. It means the variable oil prices can affect the stock exchange prices. Other independent variable exchange rate probability value is more than 5% so we can accept the null hypothesis. It means the exchange rate cannot affect the stock prices. The Durbin-Watson statistics shows no significant impact on stock prices at the level of 1 and 5%.

R-squared is 0.820958 it means 82% the dependent variable variation occur by the two in independent variables and remaining 18% variation occur by the other factor. Probability (F-statistics) value is less than 5% So that we can reject null hypothesis. It means the both Independent variables can influence stock prices.

BANGLADESH

Table No.2A Descriptive Statistics

	Exchange Rate	Stock prices	Oil prices
Mean	0.016081	6.344679	54.66668
Median	0.015500	5.425000	49.83000
Maximum	0.022800	18.80000	133.9300
Minimum	0.011800	2.340000	11.28000
Std. Dev.	0.002827	3.312750	30.76880
Skewness	0.641876	1.275061	0.398131
Kurtosis	2.433904	4.498338	2.002954
Jarque-Bera	15.82988	56.86291	13.09292
Probability	0.000365	0.000000	0.001435
Sum	3.103700	989.7700	10550.67
Sum Sq. Dev.	0.001535	1701.018	181770.1

The mean value of stock prices is 6% and the oil price value is 55% the oil prices influence the stock prices and the standard deviation 31% which is constant. Skewness, kurtosis and Jarque-Bera show positive value it means the data is normally distributed.

Table No.2B Correlation Analysis

	Exchange Rate	Stock prices	Oil prices
Exchange Rate	1.000000
Stock prices	0.611444	1.000000
Oil prices	-0.838954	-0.466968	1.000000

There is positive insignificant relationship between exchange rate and oil prices and the relationship between exchange rate and oil prices is negative insignificant. The stock prices and oil prices have a negative insignificant relation.

Table No.2C Regression Analysis

Variable	Coefficient		Std. Error		t-Statistics	Probability
Oil prices	-0.029447		0.006376		-4.618314	0.0000
Exchange Rate	551.0141		29.22484		18.85431	0.0000
R-Squared		0.319748		Adj. R-Squared		0.315330

The oil prices have a negative significant impact on stock prices if oil prices increase stock prices decrease and vice versa. The impact of exchange rate on stock prices is positive significant. The probability value of both independent variables (exchange rate, crude oil prices) is 0.0000 so we reject the null hypothesis. R-squarer value is 31.53% it means Dhaka stock exchange influence 31.53% by independent variables and 68.47% influence by other factor.

AFGHANISTAN

Table No.3A Descriptive Statistics

	Exchange Rate	Stock Prices	Oil prices
Mean	0.013932	15.11025	54.66668
Median	0.019900	13.54000	49.83000
Maximum	0.023400	43.16000	133.9300
Minimum	0.000200	4.890000	11.28000
Std. Dev.	0.010017	6.819973	30.76880
Skewness	-0.608434	1.796458	0.398131
Kurtosis	1.450401	6.645696	2.002954
Jarque-Bera	31.21794	175.7592	13.09292
Probability	0.000000	0.000000	0.001435
Sum	2.688900	2432.750	10550.67
Sum Sq. Dev.	0.019265	7441.925	181770.1
Observations	193	161	193

The Mean value of exchange rate and crude oil prices is 1.4% and 55% and the standard deviation value of both variables is 0.010017 and 30.76880 respectively. The Mean and standard deviation value of dependent variables are 15.11025 and 6.819973. Exchange rate and oil prices influence the stock prices. Standard deviation is 55% which show high risk. The data is not normally distributed because skewness value is in negative form.

Table No.3B Correlation Analysis

	Exchange Rate	Stock prices	Oil prices
Exchange Rate	1.000000
Stock prices	-0.283734	1.000000
Oil prices	0.541311	0.032766	1.000000

The relationship between exchange rate and stock prices of Afghanistan stock exchange is negative Insignificant and the relationship among exchange rate and crude oil prices is positive insignificant. There is positive significant relation between stock prices and oil prices.

Table No.3C Regression Analysis

Variable	Coefficient	Std. Error	t-Statistics	Probability
C	16.789210	1.291077	13.00403	0.0000
Oil prices	0.063114	0.021136	2.986143	0.0033
Exchange rate	-335.3749	69.42478	-4.830767	0.0000
R-Squared	Ad. R-Squared	F-Statistics	P(F-Statistics)	Durbin-W.Stat
0.129626	0.118609	11.76560	0.000017	0.104594

The impact of oil prices on stock prices is positive significant it shows that the oil prices influence on stock prices but the impact of exchange rate on stock prices is negative significant. The probability value of Afghanistan exchange rate and oil prices is 0.0000 and 0.0033 which is less than 5% so we can reject the null hypothesis and accept the alternatives. R-Squared value is 0.129626 it mean 12% Dhaka stock exchange jointly effect by independent variables and rest by other economy factor. Probability (F-statistic) is 0.000017 so we reject the null hypothesis. It means the independent variables can jointly influence on dependent variable.

SRILANKA

Table No.4A Descriptive statistics

	Stock Prices	Exchange Rate	Oil prices
Mean	13.96907	0.010545	54.66668
Median	11.96000	0.009800	49.83000
Maximum	27.79000	0.017000	133.9300
Minimum	1.220000	0.007500	11.28000
Std. Dev.	8.289713	0.002365	30.76880
Skewness	0.162841	1.179732	0.398131
Kurtosis	1.382725	3.394293	2.002954
Jarque-Bera	13.38143	46.01875	13.09292
Probability	0.001242	0.000000	0.001435
Sum	1648.350	2.035200	10550.67
Sum Sq. Dev.	8040.164	0.001074	181770.1
Observations	118	193	193

Oil prices influence the stock prices there is positive relation between stock prices and oil prices the Mean value of oil prices is 55% and stock prices is 14%.Skewness, kurtosis, and Jarque-Bera show that the data is normally distributed.

Table No.4B Correlation Analysis

	Stock prices	Exchange Rate	Oil prices
Stock prices	1.000000
Exchange Rate	0.667712	1.000000
Oil Prices	-0.467862	-0.560654	1.000000

The stock prices and exchange rate shows positive insignificant relation in the results and the stock prices and oil prices shows negative insignificant relationship. The relationship between exchange rate and oil prices is also negative insignificant.

Table No.4C Regression Analysis

Variable	Coefficient		Std. Error		t-Statistics		Probability
Oil prices	-0.140148		0.022806		-6.145145		0.0000
Exchange rate	2700.011		194.5996		13.87470		0.0000
R-Squared		0.368864		Adj. R-Squared		0.363426

The oil prices have negative significant impact on stock prices but the exchange rate has positive significant impact on stock prices. The probability value of exchange rate and oil prices is 0.0000 which is less than 5% so we reject the null hypothesis it mean both independent variable jointly influence on stock prices. R-squared value is 36.89%. Independent variables influence 36.89% on Colombo stock exchange and remaining 63.11% effect by other economic factor.

MALDIVES

Table No.5ADescriptive Statistics

	Exchange Rate	Stock prices	Oil prices
Mean	0.078383	30.97855	54.66668
Median	0.077800	28.72000	49.83000
Maximum	0.091200	46.10000	133.9300
Minimum	0.063500	19.83000	11.28000
Std. Dev.	0.006936	6.538819	30.76880
Skewness	-0.753594	0.713818	0.398131
Kurtosis	3.076514	2.492794	2.002954
Sum	15.12790	3624.490	10550.67
Sum Sq. Dev.	0.009236	4959.714	181770.1
Observations	193	117	193

The Mean value of stock prices is 31% which shows that the stock prices growing in the economy. The oil prices influence stock prices but exchange rate not influence. Skewness, kurtosis shows that the data is not normally distributed.

Table No.5B Correlation Analysis

	Exchange Rate	Stock prices	Oil prices
Exchange Rate	1.000000
Stock prices	0.466225	1.000000
Oil prices	-0.504740	0.010241	1.000000

The relationship between exchange rate and Maldives stock exchange prices is positive insignificant and the relationship among exchange rate and oil prices is negative insignificant. The relation between stock prices and oil prices is positive significant.

Table No.5C Regression Analysis

Variable	Coefficient	Std. Error	t-Statistics	Probability
C	-31.13325	9.064016	-3.434819	0.0008
Oil prices	0.094455	0.0266052	3.625652	0.0004
Exchange rate	741.1847	106.4925	6.959970	0.0000
R-Squared	Ad. R-Squared	F-Statistics	P(F-Statistics)	Durbin-W.Stat
0.298281	0.285970	24.22911	0.000000	0.102574

The exchange rate and oil prices have negative significant impact on stock prices. The probability value of Maldives exchange rate and oil prices is less than 5% so we can reject null hypothesis. R-squares is 0.298281 it means Maldives stock exchange influence 29.83% of exchange rate and oil prices and 70.13% effect by other variables. Probability (F-statistics) is 0.00000 so we reject null hypothesis. It means the independent variables influence dependent variable.

INDIA

Table No.6A Descriptive Statistics

	Exchange Rate	Stock Prices	Oil Prices
Mean	0.021965	10955.19	54.66668
Median	0.021900	10609.25	49.83000
Maximum	0.028000	20509.09	133.9300
Minimum	0.016700	2811.600	11.28000
Std. Dev.	0.001984	6151.306	30.76880
Skewness	0.239284	0.049411	0.398131
Kurtosis	3.780010	1.384777	2.002954
Jarque-Bera	6.734432	17.78542	13.09292
Probability	0.034486	0.000137	0.001435
Sum	4.239200	1785696.	10550.67
Sum Sq. Dev.	0.000756	6.13E+09	181770.1
Observations	193	163	193

Indian stock exchange growing in the country the minimum and maximum value show that the exchange prices fluctuate in our study period. The standard deviation of stock prices is high which show risk. The oil prices influence on stock prices. The data is normally distributed.

Table No.6B Correlation Analysis

	Exchange Rate	Stock prices	Oil prices
Exchange Rate	1.000000
Stock prices	-0.086608	1.000000
Oil prices	-0.026939	0.898866	1.000000

There is negative insignificant relation between exchange rate and stock prices. The relationship between exchange rate and oil prices is negative significant and the relation among stock prices and oil prices is positive insignificant.

Table No.6C Regression Analysis

Variable	Coefficient	Std. Error	t-Statistics	Probability
C	3958.347	2747.784	1.440559	0.1517
Oil prices	193.3039	7.390993	26.15398	0.0000
Exchange rate	-227581.1	125032.8	-1.820172	0.0706
R-Squared	Ad. R-Squared	F-Statistics	P(F-Statistics)	Durbin-W.Stat
0.811855	0.809504	345.2047	0.000000	0.229765

The oil prices positively influence on stock prices. The probability of exchange rate is 0.0706 so we cannot reject null hypothesis It Means Indian exchange rate cannot influence Indian stock exchange prices. The probability of oil prices is 0.0000 so we can reject null hypothesis. It means oil prices effect on stock prices.

R-squared value is 0.809503 it means Indian stock exchange influence 80.95% by exchange rate and oil prices. Probability (F-statistics) is 0.0000 we can reject null hypothesis.

CHAPTER V

CONCLUSION, POLICY IMPLICATION AND FUTURE DIRECTIONS

CONCLUSION

Stock prices play a vital role in the economy growth Movement in the stock prices strongly effect in economy growth. A collapse in stock prices has cause of economy disruption.

This paper examines the impact of Macroeconomics variables (exchange rate, oil prices) on stock prices for the period of July 1997 to July 2013. The results shows the relationship of Macro-Economic variables exchange rate and oil prices on stock market prices of Pakistan, Bangladesh, Afghanistan, Srilanka, Maldives and India are not significant Only Afghanistan and Maldives stock exchange prices and oil prices relation is positive significant. The relationship between exchange rate and oil prices is negative significant. Other domestic and international variables (e.g. trade balance, inflation, gold prices, interest rate, dividend yield) may be show a positive significant relation with stock exchange prices.

In Regression analysis oil prices shows positive significant impact on Pakistan, Afghanistan, Maldives and Indian stock exchange prices. The impact of exchange rate on stock exchange prices of Bangladesh, Srilanka, Maldives is positive significant but exchange rate effect inversely on Afghanistan stock prices. Oil prices have negative significant impact on Colombo and Dhaka stock exchange.

Although the significant relationship mostly not exist between dependent (Stock prices) and independent variables (Exchange and oil prices) future researcher should include domestic country variables to check the impact on stock exchange prices.

POLICY IMPLICATIONS

The Mostly results are significant and policy implication could be effective. The investor should check the International variables impact while investing in stock exchange. The investor should make decision according Domestic variables (Inflation rate, interest rate, GDP rate) as well as International variables.

FUTURE DIRECTIONS

Although the instructive values of Independent Variables (exchange rate, Oil prices) and Dependent variable (Stock Market Prices) are mostly significant. The future researcher can select those variables which influence on stock market returns of both domestic and International level. The researcher can also select domestic macro-economic variables like Inflation rate, Interest rate, Gross domestic product rate for getting more significant results. The researcher can use different model for test to get more reliable results. The researcher can use this independent variable (exchange rate, oil price) for research in developed countries.

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Note: The stock prices data of Nepal and Bhutan not available in website.

Impact of Macroeconomic Variables on Stock Prices