Stock Market And Macroeconomic Variables In India - Free Essay Example

Sample details Pages: 15 Words: 4496 Downloads: 10 Date added: 2017/06/26 Category Statistics Essay Did you like this example? ABSTRACT The present paper is aimed at studying the nature of the causal relationship between stock prices and macroeconomic aggregates in India, if any. By applying the techniques of unità ¢Ã¢â€š ¬Ã¢â‚¬Å"root tests, cointegration and the Granger test the causal relationships between the NSE Index à ¢Ã¢â€š ¬Ã‹Å"Niftyà ¢Ã¢â€š ¬Ã¢â€ž ¢ and the macroeconomic variables, viz., Real effective economic rate (REER), Foreign Exchange Reserve (FER), and Balance of Trade (BoT), Foreign Direct Investment (FDI), Index of industrial production (IIP), Wholesale price index (WPI) using monthly data for the period 1st April 2006 to 31st March 2010 have been studied. The major findings of the study are (i) there is no cointegration between Nifty and all other variables except Wholesale price index (WPI) as per Johansen Cointegration test. Therefore causal relationship between such macro economic variables having no cointegration with nifty is not established. (ii) Nifty does not Granger Cause WPI and WPI also does not Granger Cause Nifty. Don’t waste time! Our writers will create an original "Stock Market And Macroeconomic Variables In India" essay for you Create order Key Words: Granger Causality, Macroeconomic Variables, Cointegration, Stock Prices JEL Classification: G1, E4 Introduction The movement of stock indices is highly disposed to the changes in rudiments of the economy and to the changes in future prospects expectations. These expectations are influenced by the micro and macro fundamentals which may be formed either logically or adaptively on economic fundamentals, as well as by many subjective factors which are unpredictable and also non quantifiable. It is believed that domestic economic fundamentals play seminal role in the performance of stock market. However, in the era of globalisation and integration of world economies, domestic economic variables are also subject to change due to the policies adopted and expected to be adopted by other countries or some global events. The common external factors influencing the stock return are stock prices in global economy, the interest rate, foreign investment and the exchange rate. For illustration, capital inflows and outflows are not determined by domestic interest rate alone but also by the changes in the inte rest rate by major economies in the world. Recently, it is experienced that contagion from the US sub prime crisis has played significant movement in the capital markets across the world as foreign hedge funds unwind their positions in various markets. Other burning example in India is the appreciation of Indian currency due to increased inflow of foreign exchange. It has resulted in a decline in the stock prices of major export oriented companies especially in Information technology and textile sectors. The modern financial theory concentrates upon systematic factors as sources of risk and contemplates that the long run return on an individual asset must replicate the changes in such systematic factors. This implies that securities market has an important relationship with real and financial sectors of the economy. This relationship is generally viewed in two ways. The first relationship considers the stock market as a leading indicator of the economic activity in the country, wher eas the second relationship focuses on the possible impact the stock market might have on aggregate demand, predominantly through aggregate consumption and investment. The first case states that stock market leads economic activity, whereas the second case suggests that it follows economic activity. Knowledge of the sensitivity of stock market to macro economic behaviour of key variables and vice-versa is important in many areas of investments and finance. This research may be helpful to comprehend this relationship. Since the decade of 1990 in India, a number of measures have been adopted for economic liberalization of the country. Coupled with this various other steps have also been taken to strengthen the stock market such as opening of the stock markets to international investors, increase in the regulatory power of SEBI, reforms in the capital markets, trading in derivatives, etc. These measures have resulted in noteworthy improvements in the size and depth of stock markets in India and they are beginning to play their due role. Presently, the movement in stock market in our country is viewed and analysed carefully by a large number of global players. An understanding of the macro dynamics of Indian stock market can be valuable for traders, investors and also for the policy makers of the country. Results of the study may help in diagnosing whether the movement of stock market is the result of some other variables or it is one of the causes of movement in other macro variable in the economy. The study also expects to explore whether the movement of stock market is associated with the economy. In this context, the purpose of this paper is to explore such causal relations for India for the period of 2006 to 2010. The complete paper is organised in the four sections. Section I provides review of selected literature on the causal relationship between stock prices and macro economic variables. Section II discusses the data and explains the methodology for testing the stationarity, the existence of cointegration, and the direction of causality if any. Section III reports the results and their interpretation. Finally, Section IV deals with the concluding remarks. I. Review of Literature Many empirical studies have been conducted to study the causal relationship between stock market and macro economic variables. In retrospect of the literature, a number of hypotheses support the existence of a causal relation between stock prices and economic variables. Ma and Kao [1990] unearthed that a currency appreciation has a negative effect on the domestic stock market for an export-dominant country and a favourable effect on the domestic stock market for an import-dominant country, which appears to be consistent with the goods market theory. Bahmani and Sohrabian [1992] establish a bi-directional causality between stock prices (Standard Poors 500 index) and the effective exchange rate of the dollar in the short period of time. However, co-integration analysis did not reveal any long run relationship between the two variables. Abdalla and Murinde [1996] in their research study the relationship between exchange rates and stock prices in the emerging financial markets of India, Korea, Pakistan and the Philippines. As per their study granger causality tests results show uni-directional causality from exchange rates to stock prices in all the sample countries, except Philippines. Ajayi and Mougoue [1996], show significant interactions between foreign exchange and stock markets by using daily data for eight countries, while Abdalla and Murinde [1996] document that a countryà ¢Ã¢â€š ¬Ã¢â€ž ¢s monthly exchange rates tends to lead its stock prices but not the other way around. Pan, et al. [1999] examined the causal relationship between stock prices and exchange rates with the help of daily market data and found that the exchange rates Granger-cause stock prices with less significant causal relations from stock prices to exchange rate. They also find that the causal relationship has been stronger in the aftermath of the Asian crisis. Malliaris and Urrutia [1991] observed that the performance of the stock market might be used as a leading indicator for real economic activities in the United States. For the United Kingdom, Thornton [1993] also found that stock returns tend to lead real income. In related work and Chang and Pinegar [1989] also concluded that there is a close relationship between stock market and the domestic economic activity. Chen, Roll, and Ross [1986], Bodie [1976], Fama [1981], Geske and Roll [1983], Pearce and Roley [1983], Pearce [1985], James et. al. [1985], and Stulz [1986] and many papers have tried to show empirical associations between macroeconomic variables and security returns. Bodie [1976], Fama [1981], Geske and Roll [1983], Pearce and Roley [1983] and Pearce [1985] document that inflation and money growth has an inverse impact on equity market. Many experts however believe that positive effects will outweigh the negative effects and stock prices will eventually rise due to growth of money supply [Mukherjee and Naka, 1995]. Mukherjee and Naka [1995] reveal in their study that cointegration relation existed and positive relationship was found between the Japanese industrial production and stock return. However, Cutler, Poterba, and Summers [1989] (CPS) find that Industrial Production growth is significantly positively correlated with real stock returns over the period from 1926 to 1986, except the 1946- 85 sub-period. In context of developing countries Mustafa, K et al. [2007] have done a study to investigate the empirical relationship between the stock market and real economy in Pakistan economy by taking up various variables like per capita GDP, output growth to represent the Real economy and stock market liquidity, size of stock market representing the Stock Market. Cointegration and Error Correction Model Technique has been adopted to establish the empirical relation, if any between the two from the time period 1980- 2004. Husain, F. [2006] examined the causal relationship between stock price and real sector variables of Pakistan economy, using annual data from 1959-60 to 2004-05. It studied the causal relationship between them using various econometric techniques like ECM, Engle-Granger co integrating regressions and Augmented Dickey Fuller (ADF) Unit Root tests. The study indicates the presence of a long run relationship between the stock prices and real sector variables. More recently, Humpe, A., et al. [2009] have tried to relate the macro economic variables with long term stock market movements in US and Japan within the framework of a standard discounted value model by using monthly data over 40 years. A cointegration analysis has been applied to model the long term relationship between the industrial production, money supply, the consumer price index, long term interest rates and stock prices in US and Japan. The authors have found a significant relation between the macro economic variables and stock market in the long run. In Indian context, Abhay Pethe and Ajit Karnik [2000] has investigated the inter à ¢Ã¢â€š ¬Ã¢â‚¬Å" relationships between stock prices and important macroeconomic variables, viz., exchange rate of rupee vis-ÃÆ'  -vis the dollar, prime lending rate, narrow money supply, and index of industrial production. The analysis and discussion are situated in the context of macroeconomic changes, especially in the financial sector, that have been taking place in India since the early 1990s. Chakradhara Panda, et al. [2001] explored the causal relations and vibrant interactions among monetary policy, real activity, expected inflation and stock market returns in the post liberalization period by using a vectorà ¢Ã¢â€š ¬Ã¢â‚¬Å"autoregression (VAR) approach. The major findings of their study are (i) expected inflation and real activity do affect stock returns, (ii) monetary policy loses its explanatory power for stock returns when expected inflation and real activity are present in the system, ( iii) the relationships of monetary policy, expected inflation and real activity with stock returns lack consistency, (iv) there is no causal linkage between expected inflation and real activity. Bhattacharya and Mukherjee [2002] studied the nature of the causal relationship between stock prices and macro aggregates for the period of 1992-93 to 2000- 2001. The results of their study show that there is no causal relationship between stock price and macro economic variables like national income, money supply, and interest rate but there exists a two way causation between stock price and rate of inflation. Their result further points that index of industrial production lead the stock price. Kanakaraj, A. et al. [2008] have examined the trend of stock prices and various macro economic variables between the time periods 1997-2007. They have tried to explore upon and answer that if the recent stock market boom can be explained in the terms of macro economic fundamentals and have concluded by recommending a strong relationship between the two. As per the review of the literature there is no unanimity with regard to the causal relationship between key macro between key macro economic variables and stock prices. This relationship is different in different stock markets and time horizons in the literature. This paper makes an attempt to add to the existing literature by providing robust result which is based on more than one technique, about causal links for a period of 4 years monthly data. II. Empirical Methodology and Data For drawing useful inferences time series analysis must be based on stationary data series. Generally a data series is said to be stationary if its mean and variance are constant (non-changing) over a given period of time and the value of covariance between two time periods depends only on the distance or lag amid the two time periods and not on the actual time at which the covariance is computed. The correlation between a series and its lagged values are assumed to depend only on the length of the lag and not when the series started. This property is known as stationarity and any series obeying this is called a stationary time series. To test the stationarity of a series three unit root tests have been applied. Stationarity of the time series has been tested by using Augmented Dickey Fuller (ADF) and Phillips Perron (PP) tests. [Dickey and Fuller (1979, 1981), Gujarati (2003), Phillips and Perron (1988), Enders (1995)]. For testing null hypothesis of stationarity, KPSS test has also been applied for robustness [Kwiatkowski, Phillips, Schmidt. and Shin (1992)]. Augmented Dickey Fuller (ADF) Test Augmented Dickey-Fuller (ADF) test has been carried out which is the modified version of Dickey Fuller (DF) test. ADF makes a parametric correction in the original DF test for higher-order correlation by assuming that the series follows an AR (p) process. The ADF approach controls for higher-order correlation by adding lagged difference terms of the dependent variable to the right-hand side of the regression. The Augmented Dickey-Fuller test specification used here is as given below: p à ¢Ã‹â€ Ã¢â‚¬  yt = ÃŽÂ ±0 + ÃŽÂ »yt-1+ ÃŽÂ £ÃƒÅ½Ã‚ ³ià ¢Ã‹â€ Ã¢â‚¬  yt-i +ut (I) i=1 Phillips-Perron (PP) Test Phillips and Perron (1988) adopt a nonparametric method for controlling higher-order serial correlation in a series. The test regression for the Phillips-Perron (PP) test is the AR (1) process. The ADF test amends for higher order serial correlation by adding lagged differenced terms on the right-hand side and the PP test makes a correction to the t-statistic of the coefficient from the AR(1) regression to adjust the serial correlation in ut. The correction is nonparametric in nature. The important plus of Phillips-Perron test is that it is free from parametric errors. Phillips-Perron test allows the disturbances to be weakly dependent and heterogeneously distributed. In view of this, PP values have also been checked for stationarity. KPSS Test A major criticism of the ADF unit root testing procedure is that it cannot differentiate between unit root and near unit root processes especially when using short samples of data. This prompted the use of the KPSS test, where the null is of stationarity against the alternative of a unit root. This guarantees that the alternative will be accepted (null rejected) only when there is strong evidence for (against) it [Kwiatkowski, Phillips, Schmidt. and Shin (1992)]. Co-integration Test Using non-stationary series, cointegration analysis has been used to examine whether there is any long run equilibrium relationship. For instance, when non-stationary series are used in regression analysis, one as a dependent variable and another as an independent variable, statistical inference become tricky [Granger and Newbold, 1974]. If two variables are cointegrated, they would on average, not drift apart over a period of time this concept provides insight into the long-run relationship between the two variables and testing for the cointegration between two variables. In the present case, Johansenà ¢Ã¢â€š ¬Ã¢â€ž ¢s Maximum Likelihood procedure for Cointegration has been applied. Granger Causality Test The dynamic linkage is examined using the concept of Grangerà ¢Ã¢â€š ¬Ã¢â€ž ¢s causality test (1969, 1988). Granger causality test is applied on a stationary series. This test analyses the fact that between two given factors which one is the causing one and which factor is getting affected by another. The test is based on following two regression equations: n n Yt = ÃŽÂ £ ÃŽÂ ±i Xt-i+ ÃŽÂ £ ÃŽÂ ²j Yt-j+ u1t __________________________________ (II) i=1 j=1 n n Xt = ÃŽÂ £ ÃŽÂ »i Xt-i+ ÃŽÂ £ ÃŽÂ ´j Yt-j+ u2t __________________________________ (III) i=1 j=1 In the two equations given above it has been assumed that disturbances u1t and u2t are not correlated with each other. Equation (II) postulates that current Y is related to its own past values as that of X and next equation (III) postulates a similar behaviour of X. There are following four possibilities of cause and effect: Unidirectional causality from X to Y is indicated if the estimated coefficients on the lagged X in equation (II) are statistically different from Zero as a group (i.e. ÃŽÂ £ÃƒÅ½Ã‚ ±i à ¢Ã¢â‚¬ °Ã‚   0) and the set of estimated coefficients on the lagged Y in equation (II) is not statistically different from zero (i.e. ÃŽÂ £ÃƒÅ½Ã‚ ´j à ¢Ã¢â‚¬ °Ã‚   0). Unidirectional causality from Y to X is indicated if the estimated coefficients on the lagged X in equation (III) are statistically different from Zero as a group (i.e. ÃŽÂ £ÃƒÅ½Ã‚ ±i à ¢Ã¢â‚¬ °Ã‚   0) and the set of estimated coefficients on the lagged Y in equation (III) is statistically different from zero (i.e. ÃŽÂ £ÃƒÅ½Ã‚ ´j à ¢Ã¢â‚¬ °Ã‚   0). Feedback, or bilateral causality is suggested when the sets of X and Y coefficients are statistically significant different from zero in both the regression equations. Independence is suggested when the sets of X and Y coefficients are not statistically significant in both the cases. Lag-Length Criteria Determination of the lag length of an autoregressive process is one of the most difficult tasks in applying econometrics techniques. To overcome this difficulty various lag length selection criteria (Akaike Information Criterion, Schwarz Information Criterion, Hannan-Quinn Criterion, Final Prediction Error, Corrected version of AIC) have been proposed in the literature. Asghar and Irum have compared Akaike Information Criterion, Schwarz Information Criterion, Hannan-Quinn Criterion, Final Prediction Error, Corrected version of AIC for lag length selection for three different cases that is under normal errors, under non-normal errors and under structural break by using Monte Carlo simulation. The study shows that the performance of all these criteria improves with an increase in the sample size. For sample size of 30, although AIC and FPE have the highest probability of correct estimation but all other criteria also perform very well. For sample size equal to 60, probability of correct estimation for HQC is highest but AIC and SIC also has probability of correct estimation close to that of HQC. For large sample size (120 or greater) performance of SIC is the best. This shows that AIC and FPE are efficient but not asymptotically consistent where as SIC, AIC and HQC are asymptotically consistent criteria. Liew and Khim [2004] have carried out this stud y for both normal and non-normal errors. They found that HQC is the best for large samples. In the present study lag length is determined on the basis of Hannan-Quinn Information Criteria. III. Empirical Analysis The descriptive statistics for all four variables are calculated and presented in table 1. These variables are Real Effective Economic Rate, Balance of Trade, Foreign Exchange Reserve and NSE Nifty. The skewness coefficient, in excess of unity is taken to be fairly extreme [Chou 1969]. High or low kurtosis value indicates extreme leptokurtic or extreme platy-kurtic [Parkinson 1987]. Generally values for zero skewness and kurtosis at 3 represents that the observed distribution is normally distributed. It is seen that the frequency distribution of the above mentioned variables are not normal. Jarque-Bera statistics also indicates that the frequency distribution of the underlying series does not fit normal distribution. Further, the coefficient of variance indicates that the Foreign Direct Investment, Balance of Trade, Foreign Exchange Rate and Nifty are relatively more volatile in comparison to Index of Industrial Production, Wholesale Price Index and Real Effective Exchange Rate. Table 1: Descriptive Statistics BOT FDI FER IIP NIFTY_CL REER WPI   Mean -33750.19   8658.104   1046881.   273.6208   4205.306   97.51708   224.6375   Median -29714.00   7836.500   1166866.   269.2500   4305.400   97.56500   226.5500   Maximum -15376.00   22529.00   1301645.   347.3000   6144.350   106.0900   250.5000   Minimum -69925.00   2405.000   690730.0   225.2000   2674.600   87.48000   199.0000   Std. Dev.   14217.27   4646.714   214050.1   26.96276   872.2687   5.679449   15.35988   Skewness -0.759591   0.869245 -0.452362   0.620278   0.098875 -0.074652   0.116621 Co-eff. of Variance -42.125 53.66896 20.44646 9.854061 20.7421 5.824056 6.83763   Kurtosis   2.751454   3.416896   1.530345   3.330791   2.359419   1.856711   1.689008   Jarque-Bera   4.739378   6.392298   5.956823   3.296808   0.898900   2.658803   3.546204   Probability   0.093510   0.040919   0.050874   0.192357   0.637979   0.264636   0.169805   Sum -1620009.   415589.0   50250309   13133.80   201854.7   4680.820   10782.60   Sum Sq. Dev.   9.50E+09   1.01E+09   2.15E+12   34168.56   35760076   1516.039   11088.51   Observations   48   48   48   48   48   48   48 The first and simplest type of test one can apply to check for stationarity is to actually plot the time series and look for evidence of trend in mean, variance, autocorrelation and seasonality. If any such patterns are present then these are signs of non-stationarity. The seven time series displayed in figure-1 exhibit different such patterns. Foreign Exchange Reserve, Index of Industrial Production and Wholesale Price Index seem to exhibit a trend in the mean since they have a clear upward slope. In fact, sustained upward or downward sloping patterns (linear or non-linear) are signs of a non-constant mean. The time series on Balance of Trade, Nifty and Real Effective Economic Rate in the figure contain an obvious trend in both mean and variance. This is a sign of non-stationarity. Figure 1: Dataset Graph Apart from visual inspection, formal test for stationarity is essential to opt for appropriate methodological structure. As a first step, we tested all the variables (Balance of Trade, Foreign Exchange Reserve, Foreign Direct Investment, Nifty, Real Effective Economic Rate, Index of Industrial Production and Wholesale price index) for stationarity by applying ADF, PP unit root test and KPSS stationarity test. The result of ADF, PP and KPSS statistics are given in table-2. On the basis of ADF statistics and PP test, all the series are found to be non-stationary at levels except Foreign Direct Investment which is significant at one percent. Further, ADF statistics and PP test rejects null hypotheses of unit root in case of first differences for all the variables. In the end, KPSS test is also applied which has a null hypothesis that series is stationarity. In this case, all variables are non stationary in levels (except nifty) and stationary in first differences. Assuming all the varia bles are non-stationary at levels and stationary at first differences on the basis of ADF, PP, KPSS tests and visual inspections, Johansenà ¢Ã¢â€š ¬Ã¢â€ž ¢s approach of cointegration and Granger causality test have been applied. Table 2: Unit Root Test Variables Null Hypothesis: Variable is non-stationary Null Hypothesis: Variable is non-stationary Null Hypothesis: Variable is stationary Augmented Dicky Fuller Test Statistic Phillips-Perron Test Statistic Kwiatkowski-Phillips- Schmidt-Shin test statistic Level First Difference Level First Difference Level First Difference t- statistic p-value t- statistic p-value t- statistic p-value t- statistic p-value LM-Stat. LM-Stat. BOT -2.389654    0.1500 -7.779047* 0.0000* -2.389654    0.1500 -7.724768* 0.0000* 0.515649** 0.043815 FER -1.795759 0.3781   -5.191802* 0.0001* -1.651677    0.4488 -5.360630* 0.0000* 0.759324* 0.341829 NIFTY_CL -1.638304    0.4554 -6.201501* 0.0000* -1.727747    0.4110 -6.205292* 0.0000* 0.145549 0.086579 REER -0.958878    0.7602 -5.515513* 0.0000* -1.236302 0.6510 -5.591141* 0.0000* 0.422529*** 0.184626 IIP 0.234639 0.9719 -8.117466* 0.0000* -1.213731 0.6609 -13.32941* 0.0000* 0.823505* 0.133640 WPI -0.812230 0.8061 -3.547469** 0.0109** -0.756054 0.8220 -3.643894* 0.0085* 0.860559* 0.046077 FDI -3.962301 0.0035* -10.05718* 0.0000* -3.955949 0.0035* -10.26543* 0.0000* 0.378648*** 0.065507 Asymptotic critical values*: 1% Level -3.48 -3.48 0.74 5% Level -2.88 -2.88 0.46 10% Level -2.57 -2.57 0.35 Figure 2: Dataset Graph To explore whether there is any long-run relationship between Indian stock markets and macro economic variables such as exports, exchange rate, index of industrial production, foreign direct investment, interest rate and money supply, Johansenà ¢Ã¢â€š ¬Ã¢â€ž ¢s cointegration test has been applied. The number of lags in cointegration analysis is chosen on the basis of Hannan-Quinn Information Criterion. Before discussing the results, it is important to discuss what it implies when two variables are cointegrated and when they are not. When two variables are cointegrated, it implies that the two time series cannot wander off in opposite directions for very long without coming back to a mean distance eventually. But it does not mean that on a daily basis the two series have to move in synchrony at all. When two series are not cointegrated it implies that the two time series can wander off in opposite directions for very long without coming back to a mean distance eventually. As is concluded by unit root tests that all the variables considered except the Foreign Direct Investment (FDI) are I(1), while the FDI is I(0). So for the testing of cointegration among the variables, the FDI is dropped from the further analysis. Results indicate that Nifty and Wholesale Price Index may be cointegrated in the long run as the results vary depending on the varying assumption about trend and intercept. However, all other variables and Nifty are not cointegrated in the long run under all assumptions. In case of Balance of Trade Nifty, Foreign Exchange Reserve Nifty, Real Effective Exchange Rate Nifty and Index of Industrial production à ¢Ã¢â€š ¬Ã¢â‚¬Å" Nifty, there is no evidence of co-integration. (See table-3). Table 3: Johansen Co-Integration Test: Nifty and Other Macro Variables (Number of Cointegrating Relations by Model) Data Trend: None None Linear Linear Quadratic Test Type No Intercept Intercept Intercept Intercept Intercept No Trend No Trend No Trend Trend Trend NIFTY_CL à ¢Ã¢â€š ¬Ã¢â‚¬Å" BOT(1) Trace Max Eig 0 0 0 0 0 0 0 0 0 0 NIFTY_CL à ¢Ã¢â€š ¬Ã¢â‚¬Å" FER(1) Trace Max Eig 0 0 0 0 0 0 0 0 0 0 NIFTY_CL à ¢Ã¢â€š ¬Ã¢â‚¬Å" REER(1) Trace Max Eig 0 0 0 0 0 0 0 0 0 0 NIFTY_CL à ¢Ã¢â€š ¬Ã¢â‚¬Å" IIP(2) Trace Max Eig 0 0 0 0 0 0 0 0 0 0 NIFTY_CL à ¢Ã¢â€š ¬Ã¢â‚¬Å" WPI(2) Trace Max Eig 0 0 0 0 0 0 1 1 2 2*Critical values based on MacKinnon-Haug-Michelis (1999) **Appropriate lag is given in parentheses on the basis of Hannan-Quinn Information Criteria Table 4: Pairwise Granger Causality Tests Lags: 2   Null Hypothesis: Obs F-Statistic Prob.     D(WPI) does not Granger Cause D(NIFTY_CL)   45   0.38604 0.6822   D(NIFTY_CL) does not Granger Cause D(WPI)   0.99976 0.3770 Since there is no evidence of cointegration in the macro economic variables and Nifty series the test of Granger Causality is not applied between Nifty and such variables except Wholesale Price Index which is cointegrated with Nifty under the model of Linear Trend Intercept and Quadratic Trend Intercept. The test results in table 4 suggest that we fail to reject the null hypothesis of Granger non-causality from WPI to NIFTY_CL as well as the null hypothesis of Granger non-causality from NIFTY_CL to WPI. The results suggest that the NSE Index Nifty neither leads Wholesale Price Index nor Wholesale Price Index lead the Nifty. This implies that the stock market cannot be used as a leading indicator for future growth in wholesale price index in India. IV. Concluding Remarks The purpose of the present study is to explore the relationships between stock prices and the key macro variables representing real and financial sector of the Indian economy. These variables are the index of industrial production, foreign exchange reserves, foreign direct investment, balance of trade, real effective exchange rate, wholesale price index and NSE Nifty. The present analysis is based on monthly data from April, 2006 to March, 2010. Although there seems to be a significant relationship between macro economic variables and stock market but results of our study show that stock market boom is not much supported by the real economic fundamentals. Even there is no sign of causality between the variables which are integrated of same order which further concretizes the issue that stock markets in India are in their childhood phase as their impact on real economic variables is less as that in developed countries and moreover effect of real economic variables is almost nil on stock market index in case of causality. To solve this problem monthly data was used from April 2006 to March 2010 and the basic and believed to be à ¢Ã¢â€š ¬Ã…“indicatorà ¢Ã¢â€š ¬? variables were used and studied and analysed by first applying the basic statistical and analytical tools such as unit root test, cointegration and finally Granger causality. The results shows that series of variables used are not stationary at levels but at first difference. Further, there is no evidence of cointegration among the economic indicators chosen and Indian stock market except with inflation (Wholesale Price Index). Granger Causality test was applied between the two variables found integrated of same level I(1) i.e. Nifty and WPI. The analysis pointed that there are no sign of causality between the two variables and neither Nifty Granger causes WPI nor WPI causes Nifty. Thus implying that real sector is not causing the vibes in stock market and even the volatility in it is due to some other external factors and not these real economic factors. Adding to it, is one more reason that just 2 to 3% of the Indian population invests in stock market which makes it not so good representative of the Indian financial health.