abhinav

ABHINAV International Monthly Refereed Journal of Research in Management & Technology Special Issue ISSN – 2320-0073

IMPACT OF WPI, EXCHANGE RATE, GOLD RATE, FOREX AND MARKET CAPITALIZATION ON NSE RETURNS *Amitabha Maheshwari **Dr. Nischay Upamannyu ***Pooja Bhakuni ****Abhijeet Saban Assistant Professor, Prestige Institute of Management, Gwalior, India ****Student, Prestige Institute of Management, Gwalior, India

ABSTRACT This research studies the long and short run relationships among macroeconomic variables and NSE stock market returns in Indian Stock Exchange Index as an extraordinary case, to search answers for the following questions: What macroeconomic factors drive the performance of NSE Stock Index Returns? How does WPI, Exchange rates Gold rate, FOREX and Market Capitalization of NSE impact the Returns of NSE index? To check the stationary of variables we are applying ADF and PP test. To show the impact of macroeconomic variables on NSE returns we are applying least square test. LS method is applied to estimate the long term relationship between the macroeconomic variables and check the residuals by applying LM Residual correlogram Arch and white heteroskedasticity test and check the Granger causality of the variables. Keywords: Exchange Rate, Market Capitalization of NSE, Gold Rate, Wholesale Price Index, Foreign Exchange Reserve INTRODUCTION This paper has been developed to show the relation between various macro variables viz. ER, MKTNSE, GR, WPI, FOREX and stock returns, and their impact on NSE stock returns. It has been found that there are various factors which affect the growth of stock market. Based on the previous researches made it is assumed that there is a positive impact of all the variables viz. ER, MKTNSE, WPI, FOREX on NSE stock returns except the GR. The studies previously made have shown the impact of exchange rate on the NSE returns. It is assumed that the investors tend to invest more when market is low with the expectation of future market growth. OBJECTIVES 

To identify the various macro variables which have impact on the stock returns



To identify the impact of these variables on the stock returns



To generate a new topic this would further help the researchers to conduct a research



To study the cause and effect relationship between macroeconomic variable and returns of NSE Nifty 50.



To explore the major macroeconomic variables



To study is there any correlation between stock price and macroeconomic variables.

CONCEPTUAL FRAMEWORK The paper has been developed to check the integration between various macro variables and their impact on the NSE returns. The variables namely gold rate, exchange rate, WPI, FOREX and market capitalization of NSE has been taken in our study and are defined as follows:

Proceedings of National Conference on Trends in Management, Engineering & Technology


Gold Rate - Gold has been one of the best performing assets over the last year. As prices have reached the heights and maintain record levels. India produces around 2 tons of gold a year against the imports of 900 tons. World gold council report says that India stands today as the world’s largest single market for gold consumption. The domestic gold price in India is continuously increasing due to certain qualities of gold (security, liquidity, diversified portfolio, used by the RBI for BOP adjustment). In India gold prices influenced by the international market that is why it is important factor of financial market. Gold is considered as a safe haven whenever the market becomes very volatile. The impact of gold price changes on NSE returns is negative, (1) the more money investors are putting into gold, the less money investors are putting in the business and productive investment. In other words money invested in gold does not create output or employment in the county economy. (2) As India imports its gold, the more money investors put into gold generate wider gap between import and export. (3) Investment in gold is a sign of low confidence in the economy. As long as gold price are increasing and investors are fearful about economic prospects the impact on the overall economy will be negative. Exchange Rate - In finance, an exchange rate between two currencies is the rate at which one currency will be exchanged for another. It is also regarded as the value of one country’s currency in terms of another currency. The exchange rates of the rupee are influenced by the balance of trade deficit, the balance of payments deficit and also the foreign exchange reserve of the country. The excess of import over export is called balance of trade deficit. The balance of payment deficit represents he net difference payable on account of all transactions such as trade, services and capital transactions. If these deficit increases there is a possibility that the rupee may be depreciated in values.

An exchange rate is the ratio of how many units of one currency you can buy per unit of another currency. Unanticipated currency movements results in risk of changes in the value of assets and liabilities of a firm. It impacts on sales, prices and profits of importers and exporters. It also reduces competitiveness. Purely domestic firms which are not involved in imports and exports may also suffer from exchange rate risk when they compete with foreign companies in their home markets. Thus there is significant impact of exchange rate on NSE returns. Wholesale Price Index - Inflation is measured both in terms of whole sale prices through the Whole Sale Price Index (WPI) and in terms of retail prices through the consumer price index (CPI). The Wholesale Price Index or WPI is "the price of a representative basket of wholesale goods. Some countries use the changes in this index to measure inflation in their economies. Inflation prevailing in the economy has considerable impact on the performance of the companies. Higher rate of inflation upset business plan, lead to cost escalation and results in a squeeze on profit margin. On the other hand inflation leads to erosion of purchasing power in the hand of consumers. This will result in lower demands of products. Thus high rate of inflation in an economy are likely to affect the performance of companies adversely. Industries and companies prosper during time of low inflation. Thus we can say that WPI has significant impact on the returns of NSE. FOREX - Foreign-exchange reserves (also called FOREX reserves) in a strict sense are 'only' the foreign currency deposits and bonds held by central banks and monetary authorities. However, the term in popular usage commonly includes foreign exchange and gold, special drawing rights (SDRs), and International Monetary Fund (IMF) reserve positions. This broader figure is more readily available, but it is more accurately termed



official international reserves or international reserves. These are assets of the central bank held in different reserve currencies, mostly the United States dollar, and to a lesser extent the euro, the pound sterling, and the Japanese yen, and used to back its liabilities. Foreign reserves can be enhanced by storing more and more international currency and this can be done through three ways, by increasing exports, by foreign remittance and by taking official grants or loans. If foreign reserves are increasing due to exports and remittances then the growth of reserves is positive but if it is increasing with the help of loans then growth will be negative. Thus we can say that foreign exchange reserves of India have positive impact on NSE Stock Returns. Market Capitalisation of NSE - Market capitalization (or market cap) is the total value of the issued shares of a publicly traded company; it is equal to the share price times the number of shares outstanding. As outstanding stock is bought and sold in public markets, capitalization could be used as a proxy for the public opinion of a company's net worth and is a determining factor in some forms of stock valuation. To find the market capitalization of a company, we need to multiply the market price of the stock by the number of shares outstanding. LITERATURE REVIEW Mishra (2004) examined the relationship between stock market and foreign exchange markets by applying Granger causality test and Vector Auto Regression technique study which suggested that there is no Granger causality between the exchange rate Return and stock return. Bahmani and Sohrabian (1992) found a bidirectional causality between stock prices rated by the Standard & Poor's 500 index and the effective exchange rate of the dollar, particularly in the short run. The cointegration analysis revealed that there is no long run relationship between the two

variables i.e stock prices and exchange rate. Abdalla and Murinde (1996) examined in his study, interactions between exchange rates and stock prices in the emerging financial markets of India, Korea, Pakistan and the Philippines. The granger causality test was applied and the results show unidirectional causality from exchange rates to stock prices in all the sample countries, except the Philippines. Bhattacharya et. al.(2001) conducted a case study to analyze “Causal Relationship between Stock Market and Exchange Rate, Foreign Exchange Reserves and Value of Trade Balance”. They used methodology of Granger noncausality recently proposed by Toda and Yamamoto for the sample period April 1990 to March 2001.The analysis reveals interesting results in the context of the Indian stock market, particularly with respect to exchange rate, foreign exchange reserves and trade balance. The results suggest that there is no causal relationship between stock prices and the three variables under consideration. Dimitrova (2005) explored in his paper the relationship between stock prices and exchange rates by using multivariate model. The main focus was on the stock markets of United States and the United Kingdom over the period from January 1990 to August 2004. This study developed the hypothesis that there is a link between the foreign exchange and stock markets. The researcher found that relationship is positive when stock prices are the lead variable and likely to negative when exchange rates are the lead variable. Doong et al (2005) investigated the dynamic relationship between stocks and exchange rates for six Asian countries viz. Indonesia, Malaysia, Philippines, South Korea, Thailand, and Taiwan. For the period of 1989 to 2003. According to their study, the financial variables taken are not co-integrated. The Granger causality test result shows that bidirectional causality can be detected in Indonesia, Korea,



Malaysia, and Thailand. Also, there is a significantly negative relation between the stock returns and the contemporaneous change in the exchange rates for all countries except Thailand. Robert Gay (2008) held study to investigate the time-series relationship between stock market index prices and the macroeconomic variables of exchange rate and oil price for Brazil, Russia, India, and China (BRIC) using the Box-Jenkins ARIMA model. He found that there is no significant relationship between respective exchange rate and oil price on the stock market index prices of either BRIC country and also there was no significant relationship found between present and past stock market returns. Hussain et al. (2009) examined the “Impact of Macroeconomics Variables on Stock Prices: Empirical Evidence in Case of KSE” they took the quarterly data of various macro economic variables such as foreign exchange rate, foreign exchange reserve, industrial production index, whole sale price index, gross fixed capital formation, and broad money M2 , of the period from 1986 to 2008 period. The results shows that after the reforms in 1991 the influence of foreign exchange rate and reserve effects significantly to stock market whiles other variables like IIP and GFCF have no impact on stock prices. The result also stated that internal factors of firms like increase production and capital formation does not effects significantly while external factors like exchange rate and reserve effects significantly the stock prices. Abdalla (1996) studied interactions between exchange rates and stock prices in the emerging financial markets of India, Korea, Pakistan and the Philippines. granger causality tests was applied and the results shown uni-directional causality from exchange rates to stock prices in all the sample countries, except the Philippines. Ologunde et al (2006) examined in his paper the relationship between stock market capitalization rate and interest rate

in Nigeria. The least square regression method was used which found that the prevailing interest rate have positive impact on stock market capitalization rate. It was also found that the govt. development stock rate causes negative impact on stock market capitalization rate. RESEARCH METHODOLOGY Study The study was casual in nature. Sample Design Population: The population for study includes NSE stocks and macroeconomic variables. Sample size: Sample size for the study was return of NSE Nifty 50 during 1999 to 2011. Sample element: NSE Nifty 50, Wholesale Price Index, Foreign Exchange Reserve, Market Capitalization of NSE, Exchange Rate, Gold Rate. Sampling Technique: Non probability purposive sampling technique was used. Tools Used for Data Collection: Secondary data of stock indices was collected from the official website of NSE and Macroeconomic variables data was collected from the official website of RBI. Tools used for data analysis: Equation (1) is estimated by using monthly data for NSE returns and other macro economic variables in the model, although Indian NSE returns has economic interactions with many other variables, it conducts a large amount of economic Tran's actions. Data for relevant variables were obtained from official website of NSE and Macroeconomic variables data was collected from the official website of RBI, the data covers the period from April 1999 to March 2012, with total (156) observations, WPI data was deflated by the indices of 1993-94 as a base year prices. The following are an over view of the steps involved in implementing the Engle – Granger (E.G) test , details descriptions



of each steps will follow in the sunsequent sections : 1. First step is to follow the presence of a unit root in each variable in data under investigation, using Augmented Dickey – Fuller (ADF) and test the variables by Phillips- Perrion (PP). 2. Differencing the data in the presence of unit root and conduct the (ADF) test again on the differenced data. 3. Exclude the variables where one of the variables is non stationary and other is stationary. 4. Estimate Co integration using the same order of integrated variables. 5. Granger – Causality test, Breausch – Godefry serial correlation LM test is preformed in order to insure the ansence of the autocorrelation, thus the first step is to apply unit root test to the preformed data, a stationary series is generally characterized by a time invariant mean and a time invariant variance. The stationary of each variable can be tested by the following unit root tests: a. Augmented Dickey – Fuller (ADF). b. Phillips – Perron (PP). An elaborate discussion of these tests can be found in (ADF) and (PP) when applied, if the variables are found to be stationary,

then the standard regression method can be applied to estimate the relationships, the variables are found to be nonstationary in their levels, then has to apply the co integration test. if the tests of unit roots reveal stationary data then standard regression should be done, the residuals of standard regression are picked, then applied the tests of unit root test, if they are reveal stationary in there levels, this indicates that variables in the long run model, also that are co integrated, this means that they are share in a common trend .The second method of co integration tests is the granger causality test. This method explore that linear combinations of the variables in the system that are stationary and that all other linear combinations are nonstationary .Also method boils down to testing for the value of (λ) on the basis of the number of significant Eigen value of vectors , for this purpose the maximum EIGEN value test (trace) and the test (maxim) are applied .then the variables coefficients which inter in the VAR system , the sign and magnitude of the will give important information about short-run dynamics of the system , that is its stability as well as the direction and speed of adjustment towards the long –run equilibrium path.

Table 1. Description of Variables Acronym WPI GR ER FOREX MKTNSE RNSE

Construction of Variables Change In Whole Sale Price Change In Gold Price. Change In Exchange Rate Change In Foreign Exchange Change In Market Capitalization Of NSE NSE return

Data Source RBI RBI RBI RBI RBI NSE

Table 2. Unit Root Test for Stationary Variables Exogenous RNSE

ADF Test H0: Variable is nonstationary Constant, Constant Linear None Trend 11.85978 11.82082 11.58061

PP Test H0: Variable is nonstationary Constant, Constant Linear None Trend 11.89158 11.85415 11.66530

Order of Integr ation I(0)



Table 2. Unit Root Test for Stationary (Contd….) ADF Test

PP Test

WPI

H0: Variable is nonstationary Constant, Constant Linear None Trend -5.33151 -6.34323 -3.18715

H0: Variable is nonstationary Constant, Constant Linear None Trend -8.14645 -8.68956 -6.28895

GR

-5.72464

-6.56982

-4.96523

-11.6459

-12.5055

-11.1177

ER

-9.69035

-9.6617

-9.69478

-9.63963

-9.60829

-9.65038

FOREX

-11.4836

-11.4455

-18.2017

-18.4473

-18.387

-18.6561

MKTNSE

-11.9686

-11.9444

-11.8281

-12.0496

-12.0257

-11.9518

Variables

Exogenous

Order of Integr ation

I(0) I(0)

Asymptotic critical values -2.88

-3.44

-1.94

-2.88

-3.44

-1.94

-2.58

-3.14

-1.61

-2.57

-3.14

-1.61

Note: *** implies significant at I O% level, ** implies significant at 5% level and * implies significant at 10% level. The ADF and PP unit root test are applied to data both unit root test indicates that null hypothesis is rejected at level, thus we

conclude that data is stationary of order I (0).

Table 3. Descriptive Statistics Mean Std.Dev. Skewness Kurtosis JarqueBera Prob.

RNSE 1.325472 7.730283 -0.253854 3.998117

MKTNSE 359.3169 2901.640 -0.677957 8.518992

ER 0.043333 0.748247 0.424996 6.740043

FOREX 1678.897 16507.63 0.283122 62.46326

GR 150.7946 495.8054 1.967797 13.15273

WPI 1.140487 1.553240 0.096766 5.548284

8.151033

209.9355

95.61765

22985.30

770.6841

42.45284

0.016983

0.000000

0.000000

0.000000

0.000000

0.000000

The descriptive statistics for all 5 independent variables under study namely, MKTNSE, ER, FOREX, GR; and WPI Shows that MKTNSE, FOREX and WPI, STANDARD DEVIATION is very high. MKTNSE mean is 359.3169 and S.D is 2901.640. It reflects significant variability in these variables. FOREX mean is 1678.89 and STANDARD DEVIATION is 16507.63. It shows significant variability between these variables. RNSE mean is 1.3254 and STANDARD DEVIATION of RNSE 7.73, implying that there is moderate variability in these variables. ER mean is .043 and STANDARD DEVIATION is 0.748 which shows that there is no variability in these variables. WPI mean is 1.14 and

STANDARD DEVIATION is 1.55 implying that there is no variability in these variables. The value of skweness and qurtosis indicates the lack of symmetric in the distribution. Generally if the value of skweness and quortisis are 0 and 3 respectively, the observed distribution is said to be normally distributed. Furthermore, if the skewness coefficient is in excess of unity it is considered fairly extreme and the low quortisis value indicates extreme leptoqurtic. From the table it is observed that the frequency distribution of underlying variables is not normal. The significant coefficient of Jarquebera statistics also indicates that other than RNSE the frequency



distribution

of

considered

series

are

normal.

Table 4. The Least Square Regression Variable C ER FOREX GR MKTNSE WPI

Coefficient 0.456704 0.286113 8.69E-06 0.000250 0.002165 0.022840

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Durbin-Watson stat

Std. Error 0.488680 0.563728 2.37E-05 0.000789 0.000146 0.243740

0.641131 0.629169 4.707425 3323.978 -459.9612 1.895459

Long term co-integration relationship given by the basic model of OLS method and it is presenting in table 4. In long run equilibrium the coefficient of MKTNSE and WPI. Coefficients are found to be positive and MKTNSE coefficient is found to be positive and MKTNSE coefficient is statically significant at 5% level. But coefficient is too small as 0.002165, where the coefficient of exchange rate, FOREX, gold rate are not statically significant at 5

T-Statistic 0.934566 0.507538 0.366917 0.316967 14.83595 0.093705

Prob. 0.3515 0.6125 0.7142 0.7517 0.0000 0.9255

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic)

1.325472 7.730283 5.973862 6.091164 53.59602 0.000000

% level of significant, these independent variable explain about 64% of the variation of the dependent variable (Return of NSE) as shown in R-Square value, however the MKTNSE has a smaller effect but the exchange rate, FOREX, gold rate, WPI have no significant impact on RNSE. The Log likelihood is -459.9612 and DurbinWatson value is 1.89 and F statistic 53.59 with a probability statically significant at 0% level.

Table 5. Results of ADF residuals Variable RES RNSE

ADF Level -12.02008

The unit root test result suggested that the residuals are stationary. This is due to null hypothesis of the unit root test can be rejected at 1 % level of significant based on these results. We can conclude that

Critical Value -3.472813

independent variable(ER, GR, FOREX, WPI and MKTNSE) and return of NSE are strongly co-integrated that is equilibrium relationship exist between variables.

Table 6. Statistic of i.i.d of assumption of residual



LM test on residual obtained from equation of the VAR system using Autoregressive conditional heretroskedasticity process of appropriated lag order to test hypothesis

the residual are uncorrelated and heretroskedasticity the normality of residual check by using Jarque bera test and result are shown in table 7 Residual correlogram of standard regression (RES)

Table 7. Residual correlogram of standard regression (RES) Autocorrelation .|. | .|. | .|. | *|. | *|. | .|* | .|. | *|. | .|. | .|. | .|* | .|* | .|. | .|. |

Partial Correlation .|. | .|. | .|. | *|. | *|. | .|** | .|. | *|. | .|. | .|* | .|* | .|. | .|. | .|. |

1 2 3 4 5 6 7 8 9 10 11 12 13 14

This table shows that the degree of skewness seems to be too small the correelogram shows that the ideals autocorrelation falls to zero quickly. The

AC 0.041 0.029 0.040 -0.065 -0.062 0.188 -0.044 -0.160 0.019 0.025 0.086 0.125 0.025 -0.041

PAC 0.041 0.028 0.038 -0.069 -0.059 0.197 -0.055 -0.178 0.020 0.075 0.116 0.042 -0.002 0.015

Q-Stat 0.2701 0.4070 0.6689 1.3516 1.9804 7.7685 8.0938 12.364 12.424 12.533 13.796 16.468 16.580 16.867

Prob. 0.603 0.816 0.880 0.853 0.852 0.256 0.324 0.136 0.190 0.251 0.244 0.171 0.219 0.263

residual indicates that residual may not follow normal distributed normally, the Jeraque –Berra test is not significant statistically significant.

Table 8. Arch and White Heteroskedasticity test ARCH Test RNSE

Obs*Rsquared 0.166249

F-statistic 0.164280

White Heteroskedasticity Test RNSE

Result of ARCH for residual and variables under investigation suggest that the residuals are uncorrelated as well as

Obs*Rsquared 13.21804

F-statistic 0.624881

heretroskedasticity in all variables of the VAR model analysis.

Table 9. VAR Lag Order Selection Criteria Endogenous variables: RNSE ER FOREX GR MKTNSE Lag 0 1 2 3 4 5 6 7

Log L -5029.021 -4964.485 -4937.667 -4907.822 -4878.929 -4836.375 -4810.233 -4775.652

LR NA 122.9671 48.92396 52.02751 48.02452 67.28185 39.21279 49.06732

FPE 1.43e+22 9.72e+21* 1.10e+22 1.21e+22 1.34e+22 1.25e+22 1.47e+22 1.56e+22

AIC 68.04082 67.65520* 67.77929 67.86246 67.95850 67.86993 68.00315 68.02233

SC 68.16233* 68.50576 69.35890 70.17112 70.99622 71.63670 72.49897 73.24720

HQ 68.09019 68.00078* 68.42108 68.80046 69.19272 69.40036 69.82979 70.14518



Note: * indicates lag order selected by the criterion. Through VAR Lag Order Selection Criteria as shown in table the FPE, AIC, HQ value are statically stationary at 5 % level of significance. LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion Table 10. Pairwise Granger Causality Tests Null Hypothesis ER does not Granger Cause RNSE RNSE does not Granger Cause ER FOREX does not Granger Cause RNSE RNSE does not Granger Cause FOREX GR does not Granger Cause RNSE RNSE does not Granger Cause GR MKTNSE does not Granger Cause RNSE RNSE does not Granger Cause MKTNSE RNSE does not Granger Cause WPI WPI does not Granger Cause RNSE Pairwise granger causality test results have shown that only ER has impact on RNSE because the probability is less than 5% for it. But the RNSE has no impact on ER.

F-Statistic 1.88797 5.40968 0.83173 2.12263 0.68576 0.09033 0.75404 0.62513 1.59382 1.82843

Probability 0.15498 0.00539 0.43731 0.12332 0.50529 0.91368 0.47225 0.53659 0.20659 0.16425

From the table it is identified that all other variables viz. FOREX, GR, MKTNSE and WPI does not have any impact on the returns of NSE i.e. RNSE.

Table 11. The Max and Trace Test RNSE ER FOREX GR MKTNSE WPI

Eigen value 0.330093 0.264365 0.203486

Likelihood Maximum statistic Ratio 60.49301 46.36020 34.35414

5 Percent Critical Value

Prob.

95.75366 69.81889 47.85613

0.0001 0.0010 0.0058

In the above table present the max and trace statistic test. Null hypothesis vector = 0 is clearly rejected by the both max and trace test statistics at 5 % level of significant. Thus we conclude that one cointegration vector is constituted. CONCLUSION The stock market is a mirror index of an economy. This study investigated long run relationships between stock prices and five macroeconomic variables in India.

Likelihood Trace statistic Ratio 202.7440 142.2510 95.89079

5 Percent Critical Value 40.07757 33.87687 27.58434

Prob. 0.0000 0.0000 0.0000

Hypothesized No. of CE(s) None At most 1 At most 2

Study results indicate that market capitalization have long term relationship with NSE returns but exchange Rate, FOREX, WPI and GR results are not significant. The same results are also shown by A.K Mishra (2004), Bahmani-Oskooee, M. and A. Sohrabian (1992), Bhattacharya B, Mookherjee J (2001) and Gay, R. D. (2008).



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