The impact of domestic gold price on stock price indices-An empirical study of Indian stock exchanges

Universal Journal of Marketing and Business Research (ISSN: 2315-5000) Vol. 2(2) pp. 035-043, May, 2013 Available online http://www.universalresearchjournals.org/ujmbr Copyright © 2013 Transnational Research Journals Full Length Research Paper The impact of domestic gold price on stock price indices-An empirical study of Indian stock exchanges Amalendu Bhunia1 and Somnath Mukhuti2 1 Associate Professor, Department of Commerce, University of Kalyani, West Bengal, India 2 Research Scholar, Department. of Commerce, CMJ University Meghalaya Accepted 29 April, 2013 The present research paper examines the impact of domestic gold price on stock price indices in India for the period for the period from 2 nd January, 1991 to 10 th August, 2012 using appropriate statistics, unit root test and Granger causality test.

The domestic gold price in India is eternally escalating in consequence of its intense domestic demand on account of protection, liquidity along with spreader portfolio. It give the impression of being at the remarkable data brings to the plane that when the stock market crumples or when the dollar worsens, gold prolongs to be a safe haven investment because gold prices increase in such situations. The study is based on secondary data obtained from World Gold Council database and BSE and NSE database. Unit root test indicates that time series are not stationary at levels and the selected time series are stationary at 1st difference.

Granger causality test illustrate that no causality exists between nifty and gold price, gold price and sensex and nifty and sensex and bidirectional causality exists between gold price and nifty, sensex and gold price and sensex and nifty.

Keywords: Gold Price, Sensex, Nifty, India, Correlation, Multiple regression, ADF and PP unit root test, Granger causality test INTRODUCTION The study of the capital market of a country in terms of a wide range of macro-economic and financial variables has been the area under discussion of many researches during the last two decades. Empirical studies make known that when financial deregulation comes to pass, the stock markets of a country become more sensitive to both domestic and peripheral factors and one of these factors is the price of gold. Historical practices give an idea about that in countries in period of stock market slump, the gold for perpetuity trends higher (Neda Bashiri, 2011).

The domestic gold price in India is continually ever-increasing on account of its heavy domestic demand as a consequence of security, liquidity and diversified portfolio. A look at the historic data brings to the surface that when the stock market collapses or when the dollar deteriorates, gold continues to be a safe haven investment because gold prices rise in such circumstances (Gaur and Bansal, 2010). This paper *Corresponding author Email: bhunia.amalendu@gmail.com explores the impact of domestic gold price on stock price indices in India. In other words, the plan of this paper is to observe the causal relationships between the gold price and stock market in India.

Problem statement The global economic disorder is expected to goad improbability in gold prices that has already made it a dodgy asset for investors. Investment demand will return no more than when there are a few transparencies. Gold prices have been on the mount for the past several months and the hot-blooded state of affairs in global markets had helped the precious metal to gain handsomely. Conversely, the coming days will see huge funds moving from gold to sensex and nifty. The domestic gold prices have crowned in India for the first time, breaks all time record. In view of that most stockists are looking to smash their share of the precious metal, in consequence pushing the prices skywards and no

036 Univers. J. Mark. Bus. Res. immediate reinforcement seems to be in sight for the gold buyer. Gold prices usually rise when outlooks on the economy and the financial markets are bearish or there is uncertainty over future trends. Gold is a precious, highly liquid, financial instrument and an important asset class that possesses the characteristics of both commodity and currency, but its tangibility makes it relatively different from paper assets such as stocks (Steven W. Sumner et al, 2012). Many researchers have been done the causal relationships among stock price index and gold price in developed and developing countries.

Empirical results give an idea about that gold price can deeply concern the stock market (Mahmood Yahyazadehfar and Ahmad Babaie, 2012, taken from Bhunia, A, 2013). The objective of this study The plan of the paper was to establish, investigate and assess the impact of domestic gold price on stock price indices of BSE (SENSEX) and NSE (NIFTY). In this way, this paper would attempt to attain the only objective of: Assess the causal relationship between domestic gold price & sensex and gold price & nifty. Importance of the study Stock market is distinguished as an extremely momentous factor of the financial sector of any economy.

Besides, it plays an imperative role in the mobilization of capital in India.

The importance of this paper curtails from the critical position of the Indian financial market for the following grounds: (i) Indian financial market plays an important role in collecting money and encouraging investments, accordingly this paper was devised to search the impact of gold price in India on stock market prices in BSE and NSE. (ii) The importance of the paper gives a belief to domestic as well as foreign investors. (iii) The results of this paper will provide investors helps to compose their individual proper investment decisions. Hypotheses of the Study This paper aspires to study the change in daily gold price and its impact on stock price indices based on the following hypotheses: Hypothesis 1 H0: There is no relationship between gold prices and Indian stock price indices; H1: There is a significant relationship between gold prices and Indian stock price indices.

Hypothesis 2 H0: The selected variables are not non-stationary variables (there is unit root); H1: The selected variables are non-stationary variables (there is unit root). Hypothesis 3 H0: There is no causal relationship between the selected variables; H1: There is a significant causal relationship between the selected variables. Review of Literatures There are diverse studies, technical papers and articles covenanting in aspects that influence stock market prices at the global level such as: Rabi N. Mishra and G. Jagan Mohan, 2012, in their study entitled “Gold Prices and Financial Stability in India” proved that domestic and international gold prices are closely interlinked.

The paper also concludes that implications of correction in gold prices on the Indian financial markets are likely to be muted. According to Mahmood Yahyazadehfar and Ahmad Babaie (2012), the relationship between nominal interest rate and gold price with stock price are negative. Also, the results of Impulse-Response Functions shocks show that stock price reaction to the shocks is very fast. Thai-Ha Le and Youngho Chang (2011) made a study on “Dynamic Relationships between the Price of Oil, Gold and Financial Variables in Japan: A Bounds Testing Approach” and they confirmed that the price of gold and stock, among others, can help form expectations of higher inflation over time.

In the short run, only gold price impacts the interest rate in Japan. Overall the findings of this study could benefit both the Japanese monetary authority and investors who hold the Japanese yen in their portfolios.

Yen-Hsien Lee, Ya-Ling Huang & Hao-Jang Yang (2012) examined the asymmetric long-run relationship between crude oil and gold futures. This study employs the momentum threshold error-correction model with generalized autoregressive conditional heteroskedasticity to investigate asymmetric cointegration and causal relationships between West Texas Intermediate Crude Oil and gold prices in the futures market. From the study it is clear that an asymmetric long-run adjustment exists between gold and oil. Furthermore, the causality

relationship shows that West Texas Intermediate Crude Oil plays a dominant role.

Graham Smith (2001) empirically investigated the relationship between gold prices and stock price indices on US market using Unit Root Test, Johansen’s Co Integration Test, Vector auto regression and VECM. He confirmed that The short-run correlation between returns on gold and returns on US stock price indices is small and negative and for some series and time periods insignificantly different from zero. All of the gold prices and US stock price indices are I(1). Over the period examined, gold prices and US stock price indices are not cointegrated. Granger causality tests find evidence of unidirectional causality from US stock returns to returns on the gold price set in the London morning fixing and the closing price.

MATERIALS AND METHODS Sources of data The study is based on secondary data obtained from various appropriate data sources including BSE and NSE database, World gold council database etc. Besides, the facts, figures and findings advanced in similar earlier studies and the government publications are also used to supplement the secondary data. Research design We have measured daily data encompassing the closing indexes of both Bombay Stock Exchange (SENSEX) and National Stock Exchange (NIFTY) and the closing domestic gold price index using the sample period extents from January 2, 1991 to August 10, 2012; however, there are 5199 observations for Sensex & Nifty and 5639 for gold price.

Eviews 7.0 package program have been utilized for coordinating the data and carrying out of econometric analyses.

Tools used In the course of analysis in the present study, descriptive statistics, correlation statistics, multiple regression statistics, ADF and PP unit root test and Granger causality test have been used. The uses of all these tools at different places have been made in the light of requirement of analysis. Model specification Unit root test A time series is stationary or not or include unit root for which Augmented Dickey-Fuller (ADF-1979) and PhillipsBhunia and Mukhuti 037 Perron (PP-1988) test methods have been used in the study. The series is not stationary if the calculated value is bigger than the absolute critical value, then null hypothesis is rejected and series is decided to be stationary [Claire G.

Gilmore et al, (2009)]. H0: Series is stationary H1: Series is non-stationary If both sets of data are found I (1) (non-stationary), and if the regression produces a I (0) error term, the equation is said to be co-integrated. On the other, if there are two variables, xt and yt, which are both non-stationary in levels but stationary in first differences, then xt and yt would become integrated of order one, I(1), and their linear combination should have the form: zt = xt - ayt However, if there is a I (0) such that zt is also integrated of order zero, I (0), the linear combination of xt and yt is said to be stationary and the two variables are also to be co-integrated (Engle & Granger, 1987 and Claire G.

Gilmore, Brian Lucey Ginette M. McManus, 2005). If two variables are co-integrated, there will be an underlying long-run relationship between them. The first step in our analysis is to test each series for determining the presence of unit roots. This can be done by means of the Augmented Dickey Fuller (ADF) test, an extension of the Dickey and Fuller (1981) method. The ADF test uses a regression of the first differences of the series against the series lagged once, and lagged difference terms, with optional constant and time trend terms: ∆yt = a0 + a1t + γyt-1 + Σbiyt-1 + et (2) In the equation ∆ is the first-difference operator, a0 is an intercept, a1t is a linear time trend, et is an error term, and i is the number of lagged first-differenced terms such that et is the white noise.

The test for a unit root has the null hypothesis that signifies γ = 0. If the coefficient is significantly different from zero, the hypothesis that yt contains a unit root is considered as rejected. If the test on the level series fails to reject, the ADF procedure is then applied to the first-differences of the series. Rejection leads to the conclusion that the series is integrated of order one, I (1).

A limitation of the Dickey-Fuller test is its assumption that the errors are statistically independent and have constant variances. In 1988, Phillips and Perron (PP) 14 generalized the ADF test: ∆yt = b0 + b1(t - T/2) + b1yt-1Σ∆yt-1 +µt (3) Where, among the variables in the equations ∆Yt=Yt-Y (t-1); T is the coefficient of total number of observations, t is the trend variable, stochastic error terms and the disturbance term µt is such that E(µt) = 0, but there is no requirement that the disturbance term is serially uncorrelated or homogeneous. The equation is estimated by OLS and the t-statistic of the b1 coefficient is corrected for serial correlation in µt using the Newey-

038 Univers. J. Mark. Bus. Res. Table 1. Descriptive Statistics GOLD_PRICE NIFTY SENSEX Mean 8.806313 7.441530 8.648325 Median 8.492613 7.171926 8.365752 Maximum 10.37824 8.750279 9.952514 Minimum 7.768380 5.724304 6.862873 Std. Dev. 0.646449 0.728326 0.735241 Skewness 0.929154 0.333418 0.330504 Kurtosis 2.733134 1.988496 1.984920 Jarque-Bera 828.1164 317.9648 317.8578 Probability 0.000000 0.000000 0.000000 Observations 5639 5199 5199 200 400 600 800 1,000 1,200 1,400 1,600 8.0 8.5 9.0 9.5 10.0 Series: GOLD_PRICE Sample 1 5639 Observations 5639 Mean 8.806313 Median 8.492613 Maximum 10.37824 Minimum 7.768380 Std.

Dev. 0.646459 Skewness 0.929154 Kurtosis 2.733134 Jarque-Bera 828.1164 Probability 0.000000 100 200 300 400 500 600 700 800 5.8 6.0 6.2 6.4 6.6 6.8 7.0 7.2 7.4 7.6 7.8 8.0 8.2 8.4 8.6 8.8 Series: NIFTY Sample 1 5639 Observations 5199 Mean 7.441530 Median 7.171926 Maximum 8.750279 Minimum 5.724304 Std. Dev. 0.728326 Skewness 0.333418 Kurtosis 1.988496 Jarque-Bera 317.9648 Probability 0.000000 West (1987) procedure for adjusting the standard errors. Pairwise Granger causality Tests We test for the deficiency of Granger causality by estimating the following VAR model (Olushina Olawale Awe, 2012): Yt = a0 + a1Yt-1+...+ apYt-p+ b1Xt-1+...+ bpXt-p+Ut (4) Xt = c0 + c1Xt-1+...+ cpXt-p+ d1Yt-1+...+ dpYt-p+Vt (5) Testing H0:b1=b2=...=bp=0 against H1: Not H0 is a test that Xt does not Granger-cause Yt.

Similarly, testing H0: d1= d2=...= dp=0 against H1: Not H0 is a test that Yt does not Granger cause Xt. In case of Granger causality between the two variables, null hypothesis is rejected if alpha is more than the probability value (0.05). Empirical Results and Analysis Descriptive Statistics Result Descriptive statistics contain the portrait of mean, median, standard deviation; kurtosis, skewness and J-B statistics with probability for the daily stock price (sensex and nifty) indices of two stock exchanges and daily gold price are exposed in Table 1. It is viewed that mean and standard deviation of the particular series have highest mean.

Positive skewness and kurtosis designates that all the selected series are less peaked than normal distribution. The Jarque-Bera statistic with probability

Bhunia and Mukhuti 039 validates that none of the series are normally distributed. Graphical representations of descriptive statistics are given below: Correlation Statistics Result Correlation statistics in table-2 point out that sensex and nifty are positively correlated with gold prices in the period under study. Correlation test result is incredibly sturdy however it does not talk about the grounds and shock. In order to make out an unequivocal delineation of the shock, it is obligatory to execute multiple regression test between the selected variables.

Multiple Regession Test Results Table-3 gives an idea about multiple regression test results.

Multiple regression test has been assessed with non-stationary data and residuals, at that moment the regression result turns into forged. Since VIF value substantiates that there is an existence of serial correlation or multi-collinearity between the independent variables. At the same time, Durbin-watson statistics authenticates that the residuals are independent. Unit Root Test Results However, Granger causal test is indispensable where there is any underlying impact of gold price on stock price indices of BSE and NSE. Granger causal test is achievable if the series are stationary. In order to stationarity analysis, unit root tests of Augmented DickeyFuller (ADF) and the Phillips-Perron (PP) tests are conducted with the levels and first differences of each series on the condition that the null hypothesis is nonstationary, subsequently rejection of the unit root hypothesis prop up stationarity.

Table-4 illustrates the results of unit root test. It divulges that time series are not stationary at levels. Nevertheless, table illustrates that the gold price and BSE

040 Univers. J. Mark. Bus. Res. Table 2. Correlation Statistics GOLD_PRICE NIFTY SENSEX GOLD_PRICE 1.000000 NIFTY 0.932312 1.000000 SENSEX 0.928865 0.992889 1.000000 Table 3. Multiple Regression Test Dependent Variable: GOLD_PRICE Sample (adjusted): 1 5199 Method: Least Squares Variable Coefficient Std. Error t-Statistic Prob. VIF NIFTY 0.506820 0.030034 16.87511 0.0000 17.851 SENSEX 0.159020 0.029751 5.344999 0.0000 17.851 C 3.540772 0.044353 79.83175 0.0000 R-squared 0.869920 Mean dependent var 0.869920 Adjusted R-squared 0.869870 S.D.

dependent var 0.869870 S.E. of regression 0.187743 Akaike info criterion 0.187743 Sum squared resid 183.1451 Schwarz criterion 183.1451 Log likelihood 1320.717 Hannan-Quinn criter. 1320.717 F-statistic 17374.35 Durbin-Watson stat 17374.35 Prob(F-statistic) 0.000000 R 0.876287 *Included observations: 5199 after adjustments Table 4. Unit Root Test Result ADF at level at 1 st difference Gold price Nifty Sensex 0.784469 -1.6699151 -1.8443263 -77.16061 -50.62846 -65.98076 Critical values 1% 5% 10% -3.431425 -2.861900 -2.567004 -3.431330 -2.861858 -2.566982 PP at level at 1st difference Gold price Nifty Sensex 0.830414 -1.702241 -1.810382 -77.14896 -65.42885 -65.95544 Graphical representations of unit root test are given below: and NSE stock price indices are stationary at 1st difference [1(1)].

Augmented Dickey Fuller unit root analysis test discloses that errors have constant variance and are statistically independent. At the same time Phillip-Perron unit root test is used to ensure the stationarity of the data series. This test tolerates the error variance to be heterogeneously distributed and less dependent. It proves that the selected series are stationary at 1st difference [1(1)]. Therefore, Granger causal test can be applied on these variables, as supported in (Hina Shahzadi and M.N. Chohan, 2012) and Kaliyamoorthy, S and Parithi, S

Bhunia and Mukhuti 041 Table-5. Pairwise Granger Causality Test Results Null Hypothesis Obs F-Statistic Prob. Decision Type of Causality NIFTY ↑ GOLD_PRICE 5197 0.67598 0.5087 DNR H0 No causality GOLD_PRICE ↑ NIFTY 3.87787 0.0208 Reject H0 Bi-directional causality SENSEX ↑ GOLD_PRICE 5197 4.14253 0.0159 Reject H0 Bi-directional causality GOLD_PRICE ↑ SENSEX 2.30010 0.1004 DNR H0 No causality SENSEX ↑ NIFTY 5197 123.853 3.E-53 Reject H0 Bi-directional causality NIFTY ↑ SENSEX 1.61115 0.1998 DNR H0 No causality Note: Decision rule: reject H0 if P-value < 0.05, DNR = Do not reject = does not Granger cause.

(2012).

Pairwise Granger causality Tests Results The Granger causality test (Awe, O. O, 2012 and Hakan Güneş, 2005) is a statistical proposition test for determining whether one time series is helpful in forecasting another. The pairwise Granger causality test has been prepared in the present chapter in hunt for the trend of causation between gold prices and stock price indices. Table-5 exposes that no causality and bi-directional causality subsists between gold price and stock price indices under the study. No causality exists between (i) Nifty and Gold price, (ii) Gold price and Sensex and (iii) Nifty and Sensex.

Bidirectional causality exists between (i) Gold_Price and Nifty, (ii) Sensex and Gold Price and

042 Univers. J. Mark. Bus. Res. (iii) Sensex and Nifty. It is crucial that the outcome of causality between the particular indicators does not mean that movement in one indicator essentially causes movements in another indicator21 . To a great coverage, causality essentially leads to the movements of the time series (Olushina Olawale Awe, 2012). CONCLUSION The present research paper examines the impact of domestic gold price on stock price indices in India. The principal finale of the empirical results is that the preferred time series demonstrate non-stationary and that's why afford signal of Granger causality test.

Descriptive statistics illustrate that all the particular series are more peaked than normal distribution. Correlation statistics indicates that BSE and NSE are positively associated with domestic gold prices in the period of study. Multiple regression test results are spurious and there is an existence of serial correlation as well as multicollinearity. Unit root test result reveals that the gold price and BSE and NSE stock price indices are stationary at 1st difference [1(1)].

Granger causality test illustrates that no causality and bi-directional causality subsists between gold price and stock price indices under the study. No causality exists between (i) Nifty and Gold price, (ii) Gold price and Sensex and (iii) Nifty and Sensex. Bidirectional causality exists between (i) Gold_Price and Nifty, (ii) Sensex and Gold Price and (iii) Sensex and Nifty, as supported in, (Olushina Olawale Awe, 2012). Gold price persists to increase in India because they are considered gold the safe haven investment as a financial asset as well as jewellery. World Gold Council report says that India stands today as the world’s largest single market for gold consumption.

The assessment of the impact of gold price on Indian stock price indices utilized in this study is based on the financial market indicators. There is a need to widen this definition including macro and other market indicators (such as crude price, exchange rate, interest rate, inflation) relevant to the impact to facilitate reach our destination at more robust empirical analysis. This could be a possible area for future research in India (Mishra and Mohan, 2012). REFERENCES Agarwalla RK (2006). Share Prices and Macroeconomic Variables in India: An Approach to Investigate the Relationship between Stock Markets and Economic Growth, project report, working paper, Business Systems and Cybernetics Centre, Tata Consultancy Services Limited.

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