IMPACT OF MACROECONOMIC VARIABLE ON STOCK MARKET RETURN AND ITS VOLATILITY

Transcription

1 7 IMPACT OF MACROECONOMIC VARIABLE ON STOCK MARKET RETURN AND ITS VOLATILITY 7.1 Introduction: In the recent past, worldwide there have been certain changes in the economic policies of a no. of countries. The concept of globalization and liberalization has integrated the world into a single market. The era of information technology has brought revolution in the field of research and innovation by providing the data base on various aspects of the economy. Above all the use of various software applications in the field of capital markets helps its users to reach at conclusion with high speed. The macro economic variables changes promptly whenever there is some favorable or unfavorable news in the market, the news like war, terrorist attacks, strikes, unstable governments etc. gives a negative impact on the decision of investors and hence the slow economic growth. On the other hand the news of declaring liberal economic policies, ensuring safety and security, robust infrastructure by govt. leads to the positive impact over investment decisions and hence the economic growth and prosperity of the nation. 154

2 The capital market has become very efficient with the advent and use of various software applications for data analysis. The Efficient Market Hypothesis (EMH) theory states that an efficient market is one in which stock prices changes rapidly as and when some new information is received. Several studies have found correlation between changes in world economy and macro economic variables, these studies also suggested that the stock market indices are very sensitive towards change in the macro economic variables like inflation rate, Bank rate, FII s, Interest rate etc. The developing countries are generally noticed with a common characteristic i.e. their saving attitude, it leads to availability of funds for further investment and hence high production which robusts the economy year by year, thereby making a continuous rise in GDP. The understanding of behavior of macroeconomic variables, which affects the stock market indices, is very useful for policy makers, investors, traders and all other stake holders. 7.2 Data: This study is restricted to India only because of non availability of the sufficient data in case other selected developing countries Brazil, Russia, China, Mexico.This study establishes the relationship between Indian stock market and macroeconomic variable. BSE Sensex is used in this study as a proxy for the Indian stock market and macroeconomic variable consist Exchange rate, Balance of Trade, average call money market rate, inflation rate, industrial production, 3 months treasury bill yield to maturity, money supply, Gold rate, MSCI, 3 months treasury bill rate of US market, Volume of BSE, Volatility of BSE, Foreign Institutional Investment and Mutual Fund. These macro variables played a major role in Indian economy. Data of 14 macroeconomic variables is collected from the website of Reserve Bank of India, indiastats.com, msci.com, website of Federal Reserve Bank, website of Bombay stock exchange and moneycontrol.com. Stock prices are collected from the website of BSE. The monthly data is used in this study from January 2000 to May

3 7.3 Method of Analysis: To accomplish this objective unit root test, co-integration test, multiple regression, VAR Granger causality test, Variance decomposition test, Impulse response, have been applied. Detail explanation of these tests is given in the chapter of research methodology. 7.4 Empirical Findings: Descriptive Statistics: The summary statistics for BSE sensex and others macroeconomic variable are given in Table-1. All returns are calculated as percentage change in the monthly closing prices. Then mean of the BSE sensex is The mean is highest in case of foreign institutional investment whereas it is lowest in case of mutual fund. 3 months Treasury bill yield to maturity in India, balance of trade and mutual fund are having negative value of mean. The standard deviation indicates that mutual fund, foreign institutional investment,balance of trade, average call money market rate and 3 months treasury bill rate of US market are relatively more volatile compared to BSE sensex,3 months treasury bill yield to maturity in India, gold rate, industrial production,msci,bse trading volume, money supply, exchange rate, inflation rate. The kurtosis for all the variable is more than 3,it means the frequency distribution assign a higher probability to returns around zero as well as very high positive and negative returns. The Jarque-Bera statistic for all the variable shows that distributions is not normal. 156

4 Table 7.1 : Descriptive Statistics of Macroeconomic Variable For the Period Jnuary 2000 to May 2010 Std. Variables Mean Dev. Skewness Kurtosis Jarque-Bera Probability BSE sensex months treasury bill rate of Federal bank , months treasury bill rate of India Average Call Money Market Rate , Balance of trade , Foreign institutional investment , Gold rate Industrial production Mutual fund , Morgan Stanley Composite Index BSE Trading volume Money supply Exchange rate volatility of BSE WPI UNIT ROOT TEST: The study here employed the ADF and PP unit root test to check the stationary of the concerned time series. It is seen from the table the entire variable are stationary at level as their ADF and PP test statistics are more than critical values. So the null hypothesis is rejected and data is found to be stationary. Therefore now VAR Granger causality test, multiple regression and GARCH model can be applied which requires the data to be stationary in order to avoid getting spurious results. 157

5 Table 7. 2 : Result 0f ADF and PP unit root test ADF PP Variable With Intercept With Trend & intercept With Intercept With Trend & intercept BSE sensex months treasury bill rate of Federal bank months treasury bill rate of India Average call money market rate Balance of trade Foreign institutional investment Gold rate Industrial production Mutual fund MSCI BSE Trading volume Money supply Exchange rate volatility of BSE WPI Critical Values 1% level of significance % level of significance % level of significance Note: * Rejection of null hypothesis at 5 per cent level of significance. Johansen s co-integration test: The Result of Johansen s co-integration test are shown in table 4 which explain whether there is any long term relationship between dependent variable and independent variable. The table reveals that variables average call money market rate,foreign institutional investment, mutual fund, MSCI,trading volume of BSE, money supply, exchange rate, volatility of BSE contribute to the co integration system as their calculated values are more than their critical values. So both the Eigen value and Trace statistics shows that there is long term relationship between BSE stock market and the abovementioned macroeconomic variable. But the same is not true with 3 months treasury bill of US, 3 months treasury bill of india, balance of trade, gold rate, industrial production and inflation rate. 158

6 Variable 3 months treasury bill rate of Federal bank 3 months treasury bill rate of India Average Call Money Market Rate Balance of trade Foreign institutional investment Gold rate Industrial production Mutual fund Morgan Stanley Composite Index BSE Trading volume Money supply Exchange rate volatility of BSE WPI * denotes rejection of the hypothesis at the 0.05 level able 7.3 : Results of Johansen's Co-integration Test Hypothesized No.of Trace 5% critical Prob Max-Eigen 5% critical CE(S) statistics value. Statistic value 0 None * At most None * At most None * At most 1 * None * At most None * At most 1 * None * At most None * At most None * At most 1 * None * At most 1 * None * At most 1 * None * At most 1 * None * At most 1 * None * At most 1 * None * At most

7 VAR Granger Causality test: The table represents the result of VAR Granger causality test between macroeconomic variable and Indian stock market. It shows that there is unidirectional causality between BSE sensex and industrial production, BSE sensex and inflation rate, morgan Stanley composite index of developed market and BSE sensex. This reveals that developed market affects the Indian stock market. It is also seen that BSE sensex can be used as leading indicator for the change in industrial production and inflation rate. It is observed that there is bidirectional relationship between BSE sensex and 3 months treasury bill rate of Federal bank, BSE sensex and 3 months treasury bill rate of India. Table shows that variables average call money market rate, balance of trade, foreign institutional investment, gold rate,mutual fund, BSE trading volume,money supply, exchange rate, volatility of BSE does not cause BSE sensex and not affected by BSE sensex. Therefore variables 3 months treasury bill of Federal bank, 3 months treasury bill of india and morgan Stanley index of 24 developed market can be used as leading indicator for the performance of Indian stock market. 160

8 Table 7.4: Results of VAR Granger Causality test Null Hypothesis: Wald statistic P-value BSE sensex return does not cause 3 months treasury bill rate of Federal bank months treasury bill rate of Federal bank does not cause BSE sensex return BSE sensex return does not cause 3 months treasury Bill rate of India months treasury bill rate of India does not cause BSE sensex return BSE sensex return does not cause average call money market rate Average call money market rate does not cause BSE sensex return BSE sensex return does not cause Balance of Trade Balance of Trade does not cause BSE sensex return BSE sensex return does not cause Foreign Institutional Investment Foreign Institutional investment does not cause BSE sensex return BSE sensex return does not cause Gold Rate Gold Rate does not cause BSE sensex return BSE sensex return does not cause Industrial Production Industrial Production does not cause BSE sensex return BSE sensex return does not cause Mutual Fund Mutual Fund does not cause BSE sensex return BSE sensex return does not cause MSCI MSCI does not cause BSE sensex return BSE sensex return does not cause BSE trading volume BSE trading volume does not cause BSE sensex return BSE sensex return does not cause money supply Money supply does not cause BSE sensex return BSE sensex return does not cause exchange rate Exchange rate does not cause BSE sensex return BSE sensex return does not cause volatility of BSE Volatility of BSE does not cause BSE sensex return BSE sensex return does not cause WPI WPI does not cause BSE sensex return Variance Decomposition: Variance decomposition explains the percentage of forecast variance due to each innovation in bivariate VAR framework. The table indicates that BSE sensex explains nearly 88% of its own forecast variance while remaining 12% variance of sensex are explained by 3 months treasury bill rate of federal bank and 3 months treasury bill rate of federal bank explain 84% of its own 161

9 variance while BSE sensex explain remaining 16% of variances. It means both the variable cause each other changes. BSE sensex explain about 4% of the variance of 3 months treasury bill rate of india whereas 3 months treasury bill rate of india explain 3% of variance of BSE sensex,it means both the variable affect each other. In the case of BSE sensex and industrial production, BSE sensex explain only 1% of the variances of the industrial production and industrial production explain about 6% of the variance of BSE sensex. It means industrial production cause BSE sensex changes. BSE sensex and MSCI shows that sensex explain only 10% of variance of MSCI while MSCI explain about 40% of the variance. It strongly shows that MSCI cause the sensex changes. Sensex explain 3% of the variance of inflation while at the same lag inflation explains 8% of the variance of sensex. It means both the variable cause each. Table 7.5: Results of Variance decomposition of BSE sensex and other macro variables Variance decomposition lags BSE sensex 3 months treasury bill rate of federal bank BSE Sensex months treasury bill rate of federal bank BSE sensex 3 months treasury bill yield to maturity BSE sensex months treasury bill yield to maturity BSE Sensex Industrial production BSE Sensex Industrial production BSE Sensex MSCI BSE Sensex MSCI

10 BSE Sensex WPI BSE Sensex WPI Impulse Response: The impulse response traces the responsiveness of the dependent variable in the VAR to shocks to each of the endogenous variables. So, for each variable from each equation of the VAR separately, a unit shock is applied to the error, and the effects upon the VAR system over time are noted. The ordering of the endogenous variables may affect the results of impulse response; hence the generalized impulses are considered for the analysis in order to neutralize the ordering effect. It is observed that BSE sensex respond positively to foreign institutional investment, average call money market rate, balance of trade, BSE rading volume and MSCI particularly up to 3 lags but it respond to others variable in opposite direction. 163

11 Fig-7.1 Impulse Response to a shock in BSE sensex, Foreign Institutional Investment, Mutual Fund, Exchange Rate, Balance of Trade, ACMMR, WPI, Industrial Production, 3-month Treasury bill, Money Supply, Gold Rate 10g, MSCI23DM, 3-months Treasury bill of Federal bank, Trading volume of BSE, Volatility of BSE. Response to Cholesky One S.D. Innovations ± 2 S.E. Response of SER01 to SER01 Response of SER01 to SER02 Response of SER01 to SER03 Response of SER01 to SER Response of SER01 to SER05 Response of SER01 to SER06 Response of SER01 to SER07 Response of SER01 to SER Response of SER01 to SER09 Response of SER01 to SER10 Response of SER01 to SER11 Response of SER01 to SER Response of SER01 to SER13 Response of SER01 to SER14 Response of SER01 to SER Result of Regression Model: Results of regression equation is presented in the table 3 which indicates that only two variables are statistically significant with BSE return at the 5% level of significance. MSCI represents the return of the developed market which has the positive relationship with BSE return. it means whenever there is any positive movement in the return of the developed market then BSE return will also move in the positive direction. Whereas WPI shows inverse relationship with the BSE 164

12 return, if there is any increase in the inflation then BSE return will respond negatively. The entire variables are explaining 50% of the return of the BSE, as value of R 2 IS.505. Value of Durbin- Waston statistics is which show the absence of autocorrelation. It means the model is good and fit for the data. Table 7.6: Result of Regression Model t- Variable Coefficient Std. Error Statistic Prob. Constant Federal bank interest rate Indian interest rate average call money market rate Balance of Trade Foreign Institutional Investment Gold rate Industrial Production Mutual Fund MSCI* Volume of BSE Money Supply Exchange Rate Volatility of BSE Wholesale Price Index* R-squared Mean dependent var Adjusted R-squared S.D. dependent var 00 S.E. of regression 00 Akaike info criterion Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic) 00 * Significant at 5 percent level of significance GARCH model: It is observed from the table that no one variable is having statistically significant impact on the volatility of BSE return. The. The coefficient of the GARCH term is larger than the ARCH term 165

13 which indicates that effect of past volatility is higher than the recent past information. The total of the ARCH term and GARCH term is less than 1 which presents that model is perfectly structured. Table 7.7: Result of the GARCH model Variable Coefficient Std. Error z- Statistic Prob. Constant ARCH GARCH Federal Interest rate Indian Interset rate average call money market rate Balance of trade Foreign institutional investment Gold rate Industrial Production Mutual Fund MSCI Volume of BSE Money supply Exchange rate Volatility of BSE Wholesale price index * Significant at 5 percent level of significance 7.5 Conclusion: In today globalization scenario where Indian stock market getting integrated with world markets, it has become important to understand the fundamental macroeconomic variable affecting the market at domestic and global level. This study made an effort to examining the impact of macroeconomic variable on stock market return and stock market volatility. It is found that developed market have a significant bearing on the Indian capital market and work as a leading 166

14 indicator for the Indian capital market. MSCI explain the 40% of the variance of the BSE sensex return. MSCI shows a positive relationship with BSE sensex. Treasury bill interest rate of federal bank and India also also cause change in BSE return. The impact of the inflation is also statistically significant while explaining the BSE return. Some other variable like FII, exchange rate, money supply, average call money market rate, gold rate, volume and volatility of BSE have no significant impact on the the Indian capital market. In the case of volatility no one variable is found to be statistically significant. The finding that the foreign institutional investment has been a very insignificant factor in moving the stock market is very surprising because it is contrary to the common perception that it is the foreign portfolio investment that moves the stock market. The finding can be considered as a reassurance for domestic market s strength. It becomes a crucial input for our policy makers as well as the regulators. 167