Int. J. Mgmt Res. & Bus. Strat. 2014 Ruta Khaparde and Anjali Bhute, 2014 ISSN 2319-345X www.ijmrbs.com Vol. 3, No. 1, January 2014 2014 IJMRBS. All Rights Reserved ROLE OF MACROECONOMIC PERFORMANCE ON STOCK MARKET VOLATILITY: AN INDIAN PERSPECTIVE Ruta Khaparde 1 * and Anjali Bhute 2 *Corresponding Author: Ruta Khaparde ruta_khaparde@yahoo.com Volatility of return in India became more prominent due to some crisis which challenged the trust and interest of the smaller investor and raised some microstructure issue. Investors lose heavily on account of the volatility that exists in the market. It's difficult to assume factors responsible for such an erratic behavior of market, since market is surrounded by innumerable macroeconomic factors, regulatory bodies, investors, other participants and their emotions and sentiments. There are several parameters on the basis of which the macroeconomic performance of a country is judged and analyzed. The macroeconomic structure of a country consists of innumerable factors which are always thought to represent the activity and growth performance of an economy. The present study is based on studying the impact of certain macroeconomic variables on the stock market performance by uncovering the extent they are significant in causing fluctuations to the stock market indices. For this purpose the variables which are selected to represent the macroeconomic performance are growth in gross domestic product, investment by Foreign Institutional Investors (FII's), exchange rate, inflation and export made by the country. The reason behind selecting these variables is, they are easily understood by common investors and are easily available to masses. BSE Sensex is used as a proxy for stock market performance. A simple multiple regression model is applied and the outcome has given some very significant perspectives. Keywords: Stock Market Performance, Macroeconomic Performance, Volatility, Macroeconomic Variable INTRODUCTION The issue of volatility has gain prominence in the emerging markets like India as it influences the return distribution. Volatility of return in India became more prominent due to some crisis which challenged the trust and interest of the smaller investor and raised some microstructure issue. Investors lose heavily on account of the volatility that exists in the market. The boom and bust in the stock prices may be on account of several 1 Bharati Vidyapeeth s Institute of Management Studies and Research, Sector 8, CBD Belapur, Navi Mumbai, (MH). 2 Pillai Institute of Management Studies and Research, Sector 16, New Panvel, Navi Mumbai, (MH) 410206. 48
micro and macro determinants but retail investors are the one that lose all their investments on account of market fluctuations. To measure the extent of risk involved in an investment, one has to check for the extent of volatility. It could be said that volatility is the most basic statistical measure of risk. It can be used to measure the market risk of a single instrument or an entire portfolio of instruments. The volatility of an asset indicates the variability of its returns. In day to day practice, volatility is calculated for all sorts of random financial variables such as stock prices, interest rates, exchange rates, the market value of a portfolio, etc. Most academics studies have defined volatility in term of statistical measure of the variability of the stock price changes. These measures are useful because they correspond to standard measure of risk in theories of portfolio selection and asset pricing. Many investors seems more apt to define volatility as episode of extreme or rapid price movements within certain days even if these incident do not noticeably affect measure of volatility calculated over longer time periods. A substantial difference in stock market volatility was found across countries by Xuejing Xing (2004) Thirty seven international markets were studied to find out whether there are any factors acting as significant explanatory power to cross-sectional market volatility differences. Education level of the investors alone as an explanatory factor can explain over 36% of cross country variation of market volatility. It is implied that the better educated the investors in the market are, the less volatile the market is. There were also evidences suggesting that market industry concentration, the relative market size and the number of firms listed may also be significantly related to cross sectional market volatility. More concentrated, smaller and markets with less number of firms listed are more volatile. Indian stock market is not efficient and investors can improve their returns by timing their investment (Ash Narayan Sah, 2007). The presence of seasonality was tested and proved in Nifty and Nifty junior returns considering the S&P CNX Nifty as the representative of stock market in India. Gangadhar and Reddy (2009) investigated the varying perceptions about the volatility of the Indian stock prices and analyzed the reasons for volatility. The degree of volatility of indices of NSE and BSE where compared with a view to identify attributable common factors as well as to suggest the measures for the reduction of their volatility. The market indices of BSE and NSE taken for the study purpose were S&P CNX Nifty, CNX Nifty Junior, CNX Midcap 200, S&P CNX 500, Sensex, BSE 100 index and BSE 500 index, respectively. The volatility of stock indices was measured using standard deviation and coefficient of variation which showed that there was less volatility in the stock indices of NSE as compared to BSE. The least volatile being the Nifty of NSE and the most is the Sensex of BSE. In case of market capitalization, NSE was more volatile than BSE. There has been an abnormal decline over the decade in the number of companies listed and the share of NSE has increased significantly and equating with that of BSE listed companies. Mala and Reddy (2007) conducted the test of volatility in Fiji s stock market and found that seven out of sixteen firms listed on Fiji s stock exchange are volatile. It is observed that the level of volatility present in Fiji s stock market is for the firms which are sensitive to Government regulations, liquidity is low and IPO s are under priced. Moreover when the stock returns were regressed with the interest rate variable, it indicated a significant role of 49
interest rate on the volatility of stock returns. Interest rate tends to increase in emerging economies, impacting the stock return volatility. Relationship between the stock market performance and the real time activity was researched by Choi et al. (1999) in which they examined the relationship between industrial production growth rates and real stock prices and lagged real stock returns for the G-7 countries. It was found that there is a long run equilibrium relationship between the log levels of industrial production and real stock prices. The research study also pointed towards a correlation between growths of industrial production and lagged real stock returns for all countries except Italy. The relationship between money and stock prices in developed countries was investigated by Morley (2000) and he also studied whether deregulation during the 1980s and 1990s have affected it. To determine whether a long run equilibrium relationship exists between stock prices and various macro economic variables in both stock markets based and bank based economies he applied Cointegration and Granger causality tests. It was found that there is strong bi-directional causality between money and stock prices in both types of economies. It was also found that the causality runs predominantly from stock prices to money, supporting the view that stock prices are an important determinant of both narrow and broad definitions of money. It was concluded that it is the nature of the financial system rather than the extent of deregulation that determines the relationship between stock prices and money supply. Shahid Ahmed (2008) had investigated the caused relationship between stock indices and the key economic variables. The analysis consisted of the variables index of industrial production, exports, foreign direct investment, money supply, exchange rate, interest rate, NSE Nifty and BSE Sensex for the period 1995 to 2007. The study explored the long run and short run causal relationship and revealed that movement in NSE does not have effect on exchange rate and IIP while movement in BSE Sensex seems to cause these variables. In short run also NSE Nifty causes exchange rate, IIP and money supply while interest rate and FDI causes Nifty followed by the same result for Sensex. Crashes of Indian stock market leads to massive wealth destructions and retail investors are also been put to losses due to a market slump. Few such crashes to be noted where Sensex had fallen more than 750 points and Nifty for more than 350 points are: 1. Crash of Sensex on 21/01/2008 by 1744 points. 2. Crash of Sensex on 24/10/2008 by 1100 points. 3. Crash of Sensex on 17/03/2008 by 951 points. 4. Crash of Sensex on 22/01/2008 by 875 points. 5. Crash of Sensex on 06/07/2009 by 869 points. 6. Crash of Sensex on 18/05/2006 by 827 points. 7. Crash of Sensex on 10/10/2008 by 800 points. 8. Crash of Sensex on 17/12/2007 by 770 points. All the crashes left thousands of investors losing millions of rupees. Even from the table below we can see that an investor if invested Rs. 12, 00,000 would be left with only Rs. 50,351 on the day of lowest valuation. 50
Table 1: High-low Range of Share Prices and Resultant Loss Highest Date of Lowest Date of Investment at Value of Investment Loss Price Highest Price Price Lowest Price Highest Price at Lowest price Penta Media 2344 Jan.2000 1.35 May.2011 1,00,000 58 99942 Graphics DSQ Software 2870 Feb.2000 5.25 Aug.2005 1,00,000 183 99817 Silverline 1395 Feb.2000 3.01 May.2011 1,00,000 216 99784 Technology Himachal Futuristic 2553 April.2000 5.95 March.2009 1,00,000 234 99767 Communication Ltd. Satyam Computers 723 May.2008 11.50 Jan.2009 1,00,000 1590 98409 Pyramid 551 Dec.2007 9 Jan.2011 1,00,000 1634 98366 Saimira Theatre SEL 735 Aug.2008 11.50 May.2011 1,00,000 2245 97755 Manufacturing BSEL Infra 119 Jan.2008 6.30 May.2011 1,00,000 3151 96849 Mirc Electronics 218 Dec.2007 12.60 June 2011 1,00,000 5779 94221 Punj Loyd 589 Dec.2007 55 May.2011 1,00,000 9338 90663 Aster Silicate 399 July.2010 16 Aug.2011 1,00,000 11278 88722 DB Reality 478 70 May.2011 1,00,000 14645 85355 Note: All the crashes left thousands of investors losing millions of rupees. Even from the table below we can see that an investor if invested Rs. 12, 00,000 would be left with only Rs. 50,351 on the day of lowest valuation. Source: www.bseindia.com It s difficult to assume factors responsible for such an erratic behavior of market since market is surrounded by innumerable macroeconomic factors, regulatory bodies, investors, other participants and their emotions and sentiments. Common investors always prefer to invest in the stock market over the fixed income options in order to get the returns which are over and above the fixed rate as well as to beat the inflation affecting the real interest rate. The stock market encourages savings by providing additional financial instruments for household s investable funds, which meets their risk preferences and liquidity needs better. This research will be of great help to the investors who can always evaluate their investment preferences in the light of macroeconomic performance. The remaining part of the paper is organized as follows: Section 2 talks about the methodology and data. Section 3 discusses the principal results obtained. The paper ends with some concluding observations in Section 4. DATA AND METHODOLOGY The present focuses on the Indian economy spanning from the year 2003 to 2013. Any study on stock market development should preferably be based on daily (or monthly) frequency, given 51
the dynamic nature of the market. For this very reason the monthly data for the variables is taken. The data is collected through RBI database available online. Exchange rate: Monthly average of exchange rate to dollar is taken. Inflation: Monthly average for WPI is taken for inflation. (base year 2004-05) FII s: Monthly figure of NET FII s is taken. Export: Monthly figure of export is taken GDP: Monthly figure of GDP is estimated by division of the quarterly data on the basis of IIP (Gen) of that particular period. First of all stationarity of the time series data is checked, by Augmented Dickey-Fuller (ADF) unit roots test, using EViews. To examine the impact of macro economic variables on stock performance indices, the Ordinary Least Square estimation technique is applied after all data was converted in to log (except FII data). Before applying this, correlation matrix is obtained to see if the independent variables are correlated between themselves which can give the problem of multicollinearity. RESULTS AND DISCUSSION The analysis regarding descriptive statistics of exchange rate, FII s, inflation, stock index and exports is explained in the first table. For the descriptive statistics the return changes of the variables have been taken. Table 2 shows the descriptive statistics figures of the variables. The mean value is highest for FII return changes with a negative change of 68.58 and the standard deviation is also highest for the same 290.64. The lowest mean is for exchange rate and the lowest standard deviation is of inflation. From the correlation matrix (Table 3), it is observed that exchange rate is negatively correlated with stock index and a positive significant correlation exist between GDP and exports. Table 3 presents the stationarity test for the independent variables. The ADF test checks the stationarity of the variables which is an essential condition to get a best fit model. Table 2: Descriptive Statistics RCEXRATE RCFII RCGDP RCWPI RCEXP Mean 0.135826-68.58096 1.513003 0.516133 2.458957 Median -0.113352-37.38037 0.970201 0.476644 1.657933 Maximum 6.786134 602.9473 14.94048 2.578566 29.59000 Minimum -4.265207-1827.145-15.67481-1.891253-29.97175 Std. Dev. 1.994059 290.6426 6.429675 0.661980 11.73429 Skewness 0.743840-2.416331-0.139281-0.099851 0.040453 Kurtosis 4.344830 14.96699 3.213681 4.673501 3.226188 Jarque-Bera 19.94124 825.8775 0.611146 14.08408 0.286129 Probability 0.000047 0.000000 0.736701 0.000874 0.866698 52
Table 3: Correlation Matrix RCSENSEX RCEXRATE RCEXP RCFII RCGDP RCSENSEX 1.000000-0.549234-0.066915 0.081770 0.006077 RCEXRATE -0.549234 1.000000 0.123604-0.040820 0.041597 RCEXP -0.066915 0.123604 1.000000 0.101200 0.579293 RCFII 0.081770-0.040820 0.101200 1.000000 0.112197 RCGDP 0.006077 0.041597 0.579293 0.112197 1.000000 Table 4: ADF Test Variables Sensex FII WPI EXP EXCRATE ADF Test Statistic -4.4274-10.2883-5.7558-6.6579-7.5825 Critical T value at 1% -3.4875-3.4865-3.4891-3.4875-3.4865 Critical T value at 5% -2.8865-2.8860-2.8871-2.8865-2.8860 Critical T value at 10% -2.5801-2.5799-2.5805-2.5801-2.5799 The Augmented Dickey Fuller Unit Root Test is conducted for intercept and all the variables are found to be stationary even at 1% critical level. Tables 4 and 5 show the regression statistics and test statistics for the multiple regression test. The R Square value is quite high at 0.89 which Table 5: Regression Statistics R Square 0.899 Adjusted R Square 0.888 Standard Error 0.165 Observations 119 Table 6: Test Statistics Variable Coeffi. SE T stat P value LOGEXCRATE -2.725435 0.242898-11.22048 0.0000 LOGWPI 0.559905 0.523710 1.069111 0.2873 LOGGDP 1.048970 0.218344 4.804203 0.0000 RCFII 3.50E-06 5.31E-05 0.065887 0.9476 indicates that the multiple regression model has very strong power of test with 16% of standard error also as per Table 6 it is observed that exchange rate and GDP have significant T stat of 11.22 and 4.80 in the negative and positive direction. CONCLUSION The macro economic variables always have a significant impact on stock market. A simple estimation of the interrelationship between stock market performance and macroeconomic 53
performance of a country will be of great help to the investors and policy makers. The change in GDP rate and in exchange rate show its significance on stock market performance. While exchange rate has got a negative impact on the stock index, GDP impacts in a positive way. It was observed that FII s and inflation do not significantly impact stock market performance. The study can progress further with other macroeconomic factors and also by testing for the lead- lag effect and autoregressive equations. REFERENCES 1. Ash Narayan Sah (2009), Stock Market Seasonality: A Study of the Indian Stock Market, the Study paper accessed at http:/ /papers.ssrn.com/sol3/papers.cfm? abstract_id=873968. Acessed on 13/08/13 2. Choi Jongmoo, Hauser and Kopecky J Kenneth (1999), Does the Stock Market Predict Real Activity? Time Series Evidence from the G-7 Countries, in the Journal of Banking and Finance, Vol. 23, No. 12, pp. 1771-1792. 3. Morley B and Pentecost J E (2000), Common Trends and Cycles in G-7 Countries Exchange Rates and Stock Prices, in the Applied Economic Letters, Vol. 7, pp. 7-10. 4. Rajni Mala and Mahendra Reddy (2007), Measuring Stock Market Volatility in an Emerging Economy in International Research Journal of Finance and Economics, Issue 8, pp. 126-133 5. Shahid Ahmed (2008), Aggregate Economic Variables and Stock Markets in India, in International Research Journal of Finance and Economics, No. 14, pp. 143-163. 6. Gangadhar V and Naresh Reddy G (2009), Stock Market Volatility: A Comparative Analysis of NSE and BSE, in Finance India, Vol. XXIII, No. 4, pp. 1335-1356 7. Xuejing Xing (2004), Why Does Stock Market Volatility Differ across Countries? Evidence from Thirty Seven International Markets, in International Journal of Business, Vol. 9, No. 1, pp. 133-156. 54