Impact of Macroeconomic Variables on Indian Stock Markets

INTRODUCTION

Indian economy has changed after the post liberalization in terms of micro and macro indicators. The changing patterns of macroeconomic indicators are influencing the overall stock market as well as the individual stock. It‘s very difficult to predict the exact degree of change in the stock market but it is possible to investigate the extent to which various factors affect the stock markets. The participant interested in the stock market keeps watch on the various indicators/events which have impact on the stock market returns. Therefore various models have developed to explain the stock market returns. The first model Capital Asses Pricing Model was developed by Sharpe in 1964. But the model has considered only one variable to explain the variation in the dependent variable. After that arbitrage pricing theory (APT) has developed that considers the multiple variables for determining the stock returns. The expected stock return is a linear combination of responsiveness to each variable‘s beta. The present study investigates the long run and short term relationship between the macroeconomic variables and the BSE Sensex.

Ray Prantik and Vani Vina (2002) evaluated the relationship between the real economic variables and the movement of the stock market. The researcher used the macroeconomic variable namely Index of Industrial Production, Whole Sale Price Index, SBI prime lending rate, FII in capital market, Fiscal Deficit and money stock. Data for the period of 1994 to 2003 examined with the Artificial Neural Network and Vector Auto Regressive Model. The Study indicated that real economic variables affect the stock market in the post economic reform era in India and also explain that some macroeconomic variable doesn‘t affect the stock market. Robert D. Gay, Jr. (2008) explicated the relationship between stock market index and the macroeconomic variables of oil price and exchange rate for Brazil, India, China and Russia. To analyze the data monthly average of respected stock market indices, oil prices and exchange for a period of 1999 to 2006 is examined. The Box Jenkins Autoregressive Integrated moving average (ARIMA) time series process was used to examine the relationship between the dependent variable (Stock Market Index) to the independent variables (Exchange rate and oil price). It was found that the effect of international macroeconomic factors of exchange rate and oil price on the stock market exchange index of Brazil, Russia, China and India did not reveal a significant relationship. D.V.Lokeswar Reddy (2011) analyzed the relationship between Inflation Rate, GDP and Interest Rates with respect to stock market returns. The researcher analyzed the data of 20 yrs from 1990-91 to 2009-10. He has used correlation, Regression, Standard Deviation techniques of statistics for analyzing the data. The finding of the researcher indicates that the increased RDGP has a positive relation and a reduction in inflation and interest rate indicate the increase in stock returns. Flows and the SENSEX (Bombay stock market index). The Researcher has taken the data for three years i.e.2010-2012 for the inflow of FII‘s investment and movement in the Bombay stock exchange index (Sensex). To analyzed the data researcher used Correlation, Linear Regression Model and also test the significant difference between FII and SENSEX through application of T test. The researchers found that there is momentous impact of FII on the BSE SENSEX and there is significant relationship between the variables of FII investment made by FII indicatively differs in terms of equity and debt segment. Syed Tabassum Sultana and S. Paradhasaradhi (2012) examined the patterns and trends of foreign capital inflow and outflow in India in the form of FDI and FII. The researcher further investigated the impact of FDI and FII on Indian stock Market (Nifty and Sensex). The Researcher analyzed the data of 11years starting from 2001 to 2011 in order to analyze the data Multi Regression OLS model and correlation statistical techniques were used. The Study finds that there is a high degree of positive correlation between FDI and Nifty. Also finds that there is moderate positive correlation between FII and SENSEX. Pramod Kumar Naik and Puja Padhi (2012) analyzed the relationship between BSE Sensex and various macroeconomic variables. The researchers used Time series analysis, Johansen‘s co-integration model and vector correction model to explore the long run and short run equilibrium relationship between the stock market index and macroeconomic variables. The study reveals that stock market index, money supply and industrial index of production (IIP) are positively related or exchange rate and short term interest rate are insignificant in determining stock price. Inflation rate is negatively correlated with the stock market index (BSE SENSEX). Vanita Tripathi and Ritika Seth(2014) explained the short and long run causal relation between stock market performance and six macroeconomic variable using monthly data by applying a variety of statistical economic techniques (these are correlation, factor analysis regression, ARCH Test, Garanger Casuality test, Bi-variate test, Multivariate Johansen Co-integration test, VAR and impulse response analysis). The researcher has analyzed the monthly data from july1997 to June 2011. The study finds that there is significant correlation among stock market and macroeconomic factors except the exchange rate, the granger causality results shows that the few variables are causing the changes in each other out of which exchange rate is the only variable that is granger causing BSE Sensex. exchange rate, interest rate, export, inflation using the sample from January 1996 to September 2013. The sample has analyzed using the cointegration, VECM technique. The finding of the study suggested that there is no long run relationship between the macroeconomic variable and Kuala Lumpur stock exchange while the interest rate has influenced on the performance of stock market. Hsing, Y.(2014) examined the relationship between Romania stock exchange and the macroeconomic variables i.e. GDP, M2, real interest rate, nominal effective exchange rate, inflation rate, stock market index of US using the quarterly data from 2001 to 2010. The GARCH model was used to analyze the sample data. The findings of the study suggested that the Romania stock market has significant influenced by industrial production. The US stock market is negatively influenced by the interest rate, inflation rate, bond yield and government borrowing to GDP.

Variables: The present study identified the macroeconomic indicators by review of literature and through the economic theory. The macroeconomic indicators included in the study are consumer price index, exchange rate, foreign exchange reserve, interest rate(repo rate) and money supply (M2). Inflation Rate (LCPI): The inflation rate affects the savings and interest rate that will lead to change in the stock prices. More saving attracts the more investment that increases the stock price. Similarly to control over the inflation rate regularity authorities increase the interest rate that will lead to reduction in the stock prices. Exchange Rate (LER): The exchange rate affects the stock prices because deprecation in the currency leads to higher cost of import of raw material, technology, machinery and equipment. The higher cost has adverse affect on the firm‘s profitability and stock prices. Interest Rate (LIR): The interest rate has negative impact on the stock market because in case of higher interest rate people would prefer to invest in banks instead of stock market. Less investment in stock market leads to decrease in the stock prices or less interest rate attracts more investment in the stock market. Foreign Exchange Reserves (LFER): A country which has high amount of foreign exchange reserves has more financial stability and also the value of the national currency would be high in the international market. The financial stability attracts

Money Supply (LMS): Increase in money supply lead to enhancement of business turnover so as to increase the profitability of the business concern. But increase money supply also impact the inflation rate thus money supply has multiple impact on the stock market. BSE Sensex (LBSE): BSE sensex is an index comprising of 30 stock of large companies calculating using the free float market capitalisation. BSE Sensex includes the shares of various sectors therefore it reflect the overall market. Data: the sample data from has been retrieved from the various sources like CEIC database, investing.com, yahoo finance, rbi website etc.

For the purpose of studying Cointegration, a stepwise procedure needs to be followed. The following analytical tools have been used for the purpose: 1. Unit Root Tests (ADF) 2. Johansen‘s cointegration testing 3. Trace test 4. Max Eigenvalue test 6. Unrestricted VAR 7. VAR Granger Causality Test The Johansen Cointegration process is a maximum likelihood method that determines the number of cointegrating vectors in a non-stationary time series Vector Autoregression (VAR) with restrictions imposed, known as a vector error correction model (VECM). Johansen‘s estimation model is as follows: Where : = (nx1) vector of all the non-stationary indices in the study = (nxn) matrix of coefficients α= (nxr) matrix of error correction coeffecients where r is the number of cointegrating relationshios in the variables so that 0

Table 1 represents the descriptive statistics of the log transformed sample data. The Jarque Bera test results shows that the none of the variables following the normal distribution.

The table 2 represents that none of the variable is stationary at level and all of them are stationary at first difference so all the research has carry out on the first difference

LBSE = 1.38LER-1.097LFER+0.40LMS-0.44LCPI-0.338LIR

The result of the Johansen Cointegration test exhibits that the long term relationship exist between the bse sensex and macroeconomic variables. The coefficient of the various variable indicated their elasticity on the bse sensex. The table 5 exhibits that all the coefficient are statistically signigicant except the money supply. In case of CPI 1% change in the CPI reduce the stock price by 0.44%. The 1% change in the exchange rate postiviely influence the stock price by 1.38% further 1% change in the stock market brings negative 1.09% and 0.33% change in the foreign exchange reserve and interest rate resptively.

Table 6 exhibits that the speed of adjustment towards the equilibrium is found negative and

The result of granger causality test which is mentioned in the table 8 explains the short run causality running from macroeconomic variable to the stock market. The interest rate has bidirectional causality running from each other. In case of inflation rate (CPI) unidirectional causality is found which is from BSE to the CPI. The exchange rate has impact on the BSE Sensex. The foreign exchange reserves are impacted by the change in the BSE Sensex. In Case of money supply the change in the money supply has influenced over the BSE.

The present study investigated the long term and short run relationship between the stock market and various macroeconomic variables. The BSE Sensex has been taken as benchmark for the stock market and repo rate for the interest rate. The macroeconomic variables included in the study are money supply, interest rate, inflation rate, exchange rate and foreign exchange reserves. The study finds that the long term relationship exist between the macroeconomic variables and stock market except the money supply. The short run causality also exist in between the macroeconomic variables and stock market. The bidirectional causality is found in between the interest rate and BSE Sensex.

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Research Scholar, University School of Management, Kurukshetra University, Kurukshetra