Review of Economics and Development Studies Vol. I, No 2, December 2015 119 Volume and Issues Obtainable at Center for Sustainability Research and Consultancy Review of Economics and Development Studies ISSN:2519-9692 ISSN (E): 2519-9706 Volume 1: Issue 2 December 2015 Journal homepage: www.publishing.globalcsrc.org/reads Stock prices and Macroeconomic Performance in Pakistan: An Analysis 1 Saima Mukhtar, 2 Imran Sharif Chaudhary, 3 Furrukh Bashir 1 M. Phil. Scholar, School of Economics, Bahauddin Zakariya University Multan 2 Professor and Director, School of Economics, Bahauddin Zakariya University Multan, imran@bzu.edu.pk 3 Lecturer, School of Economics, Bahauddin Zakariya University Multan ARTICLE DETAILS ABSTRACT History Revised format: Nov 2015 Available online: Dec 2015 This paper analyzes long-term equilibrium relationships between the Karachi stock exchange index and a group of macroeconomic variables. The macroeconomic variables are represented by the gross domestic product, the consumer price index, M2 and the exchange rate. We employ a multiple regression model to explore such relationships during 1991 to 2012. Our results indicated a "causal" relationship between the stock market and the economy analysis of our results indicates that KSE 100 index has a strong positive impact on GDP and M2 in Pakistan. Whereas it has a negative and significant impact on CPI and exchange rate in Pakistan. Granger causality test shows that KSE 100 index Granger causes GDP, CPI, M2, EXRT, AGRI, FDI and BOT and the direction of causality runs from KSE 100 index to these variables. © 2015 The authors, under a Creative Commons Attribution- NonCommercial 4.0 Keywords, Long run equilibrium Macroeconomic, time series models, Karachi stock exchange ,foreign exchange, asset pricing JEL Classification B22,C22,C32,F31 G12 Corresponding author’s email address: imran@bzu.edu.pk Recommended citation: Mukhtar, S., Chaudhry, I. S. and Bashir, F. (2015). Stock prices and Macroeconomic Performance in Pakistan: An Analysis. Review of Economics and Development Studies, 1 (2) 119-128 DOI: https://doi.org/10.26710/reads.v1i2.118 1: Introduction - Pakistani stock market and macroeconomic variables Over the past few decades, the interaction of share returns and the macroeconomic variables has been a subject of interest among academics and practitioners. It is often argued that stock prices are determined by some fundamental macroeconomic variables such as the interest rate, the exchange rate and the inflation. Anecdotal evidence from the financial press indicates that investors generally believe that monetary policy and macroeconomic events have a large impact on the volatility of stock price. This implies that macroeconomic variables can influence investors’ investment decision and motivates many researchers to investigate the relationships between share returns and macroeconomic variables. But it does not mean that stock prices cannot affect macroeconomic variables. To prove this phenomenon we have conducted this research. http://www.publishing.globalcsrc.org/reads mailto:imran@bzu.edu.pk mailto:imran@bzu.edu.pk Review of Economics and Development Studies Vol. I, No 2, December 2015 120 Stock exchange performance has attained major role in global economics and financial markets, due to their impact on corporate finance and economic activity. For instance Adjasi and Biekpe (2006) stated that stock exchanges allow firms to attain capital quickly, due to the ease with which securities are traded. Stock exchange activity, thus, plays an important role in helping to determine the effects of macroeconomic activities. 2. Pakistan’s Equity Market . Karachi Stock Exchange (KSE) is the biggest and most liquid exchange of Pakistan. For the year 2002, it was declared the best performing stock market of the world. A total of 654 companies were listed on December 8, 2009 with a market capitalization of Rs. 8.561 trillion (US$ 120.5 billion). Pakistan’s industrial export and foreign investment has grown rapidly. Pakistan’s foreign exchange reserves reached 12,425.2 million US$ in the year 2008-2009. Now days our all stock markets traded on international markets. The KSE 100 index reached at 7760.69 in 2009. An international magazine ‘Business Week’ ranked KSE as one of the best performing markets of the world for three years. .Many studies have been conducted on the impact of macroeconomic variables on stock prices such as: Wongbangpo and Sharma (2002), Dickinson (2010), Mishra (2005), Naik and Padhi (2012), Akmal (2007), Aurangzeb (2012), Menike (2006), Nishat and Shaheen (2004), Hasan and Nasir (2008), Kwon and Shin (1999) and many. But no study was conducted to know the impact of stock prices on macroeconomic variables. That is why, our study seeks to examine the impact of stock prices on macroeconomic variables and for this purpose we have selected KSE 100 index and some macroeconomic variables (GDP, CPI, M2, and EXRT). Karachi Stock Exchange 100 index is used to represent the Pakistani stock market index, because it provides an easy way to inspect the performance of capital market and the economy as a whole. 2.1 The Asset Valuation Model and Pricing of Macroeconomic Factors The capital assets pricing model was introduced as a model of risk and return by Sharpe (1964), Linter (1965), Treynor (1962) and Mossin (1966). It has become the most significant theory of the link between risk and return in asset pricing. This was renowned by the works of Black, etc. al., (1972) and Fama and Macbeth (1973). The basis of capital asset pricing model is the making of an efficient market portfolio that maximizes return, at a certain level of risk. The expected return of an individual security is based on its risk covariance with the market. 2.2 Stock prices and Macroeconomic Variables We will take in our study KSE 100 index as a measure for stock prices and five macroeconomic variables, namely Gross Domestic Product(GDP), Agriculture production(value added), CPI(as a proxy for inflation rate), exchange rate and M2(as a measure of money supply) and balance of trade(BOT). We will suppose following relationships between stock prices and macroeconomic variables: Stock prices and GDP We will suppose here a positive relationship between stock prices and GDP. Stock prices and CPI We are taking here CPI as a proxy for inflation and we are going to propose a negative relationship between stock prices and CPI here. Stock prices and Exchange Rate Review of Economics and Development Studies Vol. I, No 2, December 2015 121 We are going to propose a negative relationship between stock prices and exchange rate in our study. Stock prices and Money Supply We are going to propose a positive relationship between stock prices and money supply. 3. Methodology and Data Data The variables which we use to represent Pakistan’s stock market and its output, inflation, money stock and exchange rate are respectively the KSE 100 Index, the gross domestic product (GDP), the Consumer Price Index (CPI) , a broad money supply (M2), and the exchange rate (EXRT). In our study we sourced data from the World Bank and from the Financial Database website. Data on GDP, Agriculture value added, CPI, Broad money (M2), and Exchange rate were taken from the World Bank’s World Development Indicators while the data for KSE 100 Index was gotten from the Financial Database website. Therefore all the data used is secondary in nature. Empirical Methodology Because in our research we have used time series data, so regression analysis was employed to be able to examine if any significant relationship exists between KSE 100 Index and macroeconomic variables(GDP, CPI, M2, EXRT, BOT and FDI). The models in our study are estimated using the co- efficient of independent variables and their level of significance. These tests present an empirical podium for simplification in this study. In our model we have used four models of the form: Y = αo + α1X1 + α2X2 + α3X3 + α4 X4 + et Where: Y = dependent or unexplained variable α0 = constant of the model α1, α2, α3, α4 = coefficients of the model X1, X2, X3, X4, = Independent or explanatory variables. et = error term. We will use following four models Model 1: GDP = α0 + α1 KSE + α2 CPI + α3 AGRI + et Where: GDP = Gross Domestic Product KSE = Karachi Stock Exchange 100 index CPI = Consumer Price Index (a proxy for inflation) AGRI = Agriculture value added Model 2: CPI = α0 + α1 KSE + α2 M2 + α3 GDP +α4AGRI+ et Where: CPI = Consumer price index KSE = Karachi Stock Exchange 100 index M2 = broad money GDP = Gross Domestic Product AGRI= Agriculture Value Added Model 3: M2 = α0 + α1 KSE + α2 CPI + α3 EXRT + α4 GDP + et Where: Review of Economics and Development Studies Vol. I, No 2, December 2015 122 M2 = Broad Money KSE= Karachi Stock Exchange CPI= Consumer Price index EXRT = Exchange rate GDP = Gross Domestic Product Model 4: EXRT = α0+ α1KSE + α2M2 + α3FDI +α4BOT+ et Where: EXRT = Exchange rate KSE = Karachi Stock Exchange 100 Index M2 = Broad Money FDI = Foreign Direct Investment BOT = Balance of Trade 4. Analysis of Results Augmented Dickey Fuller test ADF with Intercept: Table 1 presents the results of ADF test with intercept. The critical t-value at 10% level is -2.65. The results show that KSE series is not stationary at levels, because ADF < t-value here. So we have taken its first difference and after differencing once, it became stationary. So its order of integration is I (1). Series of GDP is stationary at levels. So its order of integration is I (0). Series of AGRI is also stationary at levels. So its order of integration is I (0). Series of CPI became stationary after differencing two times and its order of integration is I (2). Series of M2 and FDI are also stationary at levels and their order of integration is I (0). Series of EXRT became stationary after differencing once and its order of integration is I (1). Series of BOT became stationary after differencing two times and its order of integration is I (2). ADF with Intercept and Trend Table 2 presents the results of ADF test with trend and intercept. The t critical value at 10% level is - 3.2856. The results indicate that KSE is stationary at first difference and its order of integration is I (1). GDP, CPI, M2 and EXRT are stationary at second difference and their order of integration is I (2). Only two series, AGRI and BOT are not stationary. Multiple Regression Analysis We have four models to analyze in our study. The results of each model are presented below Regression Results of Model 1 Our first model is GDP = α0 + α1 KSE + α2 CPI + α3 AGRI + et In first model, we have focused on the impact of KSE 100 index on GDP. In this model GDP is the dependent variable and KSE 100 index along with CPI and Agriculture Value Added are the independent variables. The Regression estimates show that, the coefficient of KSE 100 index is positive and significant. It means when KSE 100 index price increases GDP also increases. This is so because, when KSE 100 index price increases, wealth of investors increases. So, investment and consumption also increases and in this way GDP also increases. The results also indicate that CPI has a negative but less significant impact on GDP and AGRI (Agriculture value added) has a positive and significant impact on GDP. This is so because Pakistan is basically an agrarian country and agriculture production plays an important role in enhancing its GDP. Review of Economics and Development Studies Vol. I, No 2, December 2015 123 R-squared and Adjusted R-squared are 0.99. Its mean 99% variation in GDP is due to these variables. DW statistics is 1.84 which shows there is no multicollinerity. So, our estimated model becomes GDP = -178756.7 +83.67487 KSE +-9813.813 CPI + 5.379802 AGRI Regression Results of Model 2 Table 5.7 presents the regression results of model 2. The model is CPI = α0 + α1 KSE + α2 M2 + α3 GDP +α4Αgri +et In this model we emphasize on the relationship of KSE 100 index and CPI. CPI is our dependent variable and the explanatory variables are KSE, M2 and Exchange Rate. The results indicate that, the coefficient of KSE is negative and significant at 6%. Its mean KSE 100 index has a less powerful negative impact on CPI in Pakistan. The second explanatory variable is M2. Its coefficient is positive and significant. Money supply has a powerful positive impact on CPI in Pakistan because when money in circulation increases prices of goods will increase. Results indicate that GDP also has a positive and significant impact on CPI in Pakistan. This is so because when production will stronger prices would automatically move downward. The same reason is true for AGRI having a positive significant impact on CPI in Pakistan. R-squared and Adjusted R-squared are 0.99; its means 99% of the total variation in CPI is due to these variables. D.W. statistics is 1.75 which shows there is no multicollenerity in our model. So our model becomes CPI = 30.53 -0.002 KSE + 0.0000195M2 -0.00000704 GDP + 0.0000503Αgri Regression Results of Model 3 Our third model is M2 = α0 + α1 KSE + α2 CPI + α3 EXRT + α4 GDP + et In this model our aim is to identify the impact of KSE 100 index on money supply (M2) in Pakistan. Dependent variable is M2 and the explanatory variables are KSE 100 index, CPI, EXRT and GDP. According to the results, constant is -330064.4. The results indicate that KSE 100 index has a strong positive impact on M2 in Pakistan. CPI has a positive but less significant impact on M2. EXRT has also a positive and significant impact on M2. GDP has also a positive and powerful significant impact on M2. R-squared and Adjusted R-squared are 0.99; it shows that 99% of the total variation in M2 is just because of these variables. D.W. statistics is 2.08 which indicate no multicollinerity in this model. So, after estimation model becomes M2 = -330064.4 +109.7765 KSE +3204.924 CPI + 10842.46 EXRT + 0.252592 GDP Regression Results of Model 4 Our fourth and last model is EXRT = α0 + α1 KSE + α2 M2 + α3 FDI + α4BOT + et In this model, our emphasis is on the impact of KSE 100 index on Exchange Rate in Pakistan. In this model our dependent variable is Exchange Rate and the explanatory variables are KSE 100 index, M2, FDI and BOT. The results indicate that KSE 100 index has a strong negative impact on Exchange Rate in Pakistan. M2, FDI and BOT have a strong positive impact on Exchange Rate in Pakistan. Adjusted R- squared is 0.94, which indicates that 94% of the variation in Exchange Rate is due to these variables. D.W. statistics is 1.787 which shows there is no problem of multicollinerity in this model. So, our estimated model becomes EXRT =26.57696- 0.003441 KSE + 1.90E-05M2 + 0.006712FDI +0.002381BOT Granger Causality Test Granger Causality test shows the strength and direction of the relationship between variables. Granger causality test shows that KSE 100 index Granger causes GDP, CPI, M2, EXRT and FDI and the direction of causality runs from KSE 100 index to these variables. CPI Granger causes GDP as well as M2, EXRT, FDI and Bot. M2, EXRT and BOT granger cause AGRI. M2, FDI and BOT granger cause CPI. M2 granger causes EXRT. A two way causality runs from FDI to M2 and M2 to FDI. M2 granger Review of Economics and Development Studies Vol. I, No 2, December 2015 124 causes BOT. FDI granger causes EXRT. A two way causality runs from BOT to EXRT and from EXRT to BOT. FDI granger causes BOT. Table 1 Correlation Matrix Column1 KSE GDP AGRI CPI M2 EXRT FDI BOT KSE 1 0.859 0.842 0.851 0.909 0.785 0.747 -0.914 GDP 0.859 1 0.998 0.994 0.992 0.923 0.432 -0.888 AGRI 0.842 0.998 1 0.996 0.987 0.933 0.419 -0.874 CPI 0.851 0.994 0.996 1 0.989 0.950 0.450 -0.881 M2 0.909 0.992 0.987 0.989 1 0.931 0.523 -0.912 EXRT 0.785 0.923 0.933 0.950 0.931 1 0.454 -0.778 FDI 0.747 0.432 0.419 0.450 0.523 0.454 1 -0.728 BOT -0.914 -0.888 -0.874 -0.881 -0.912 -0.778 -0.728 1 Table 2 ADF with Intercept Column1 Level 1st.Diff. 2nd.Diff Order KSE -0.596719 -3.409493 I(1) GDP 4.241393 I(0) AGRI 3.014931 I(0) CPI 1.482619 -0.294403 -3.14724 I(2) M2 5.02223 I(0) EXRT 0.099095 -3.136907 I(1) FDI -2.828408 I(0) BOT 0.111821 -2.263768 - 3.057492 I(2) Table 3 ADF with Intercept and Trend Column1 Level 1st.Diff. 2nd.Diff. Order KSE -2.423479 -3.370494 I(1) GDP 3.05793 -0.440299 -3.935145 I(2) AGRI 1.323431 -1.117215 -2.655097 CPI -0.133954 -1.387748 -3.307875 I(2) M2 2.927076 -1.378478 -5.626014 I(2) EXRT -2.118426 -3.213241 -4.839824 I(2) FDI -3.699406 I(0) BOT -1.49575 -2.566863 -2.930172 Regression Results of Model 1 Dependent Variable: GDP Review of Economics and Development Studies Vol. I, No 2, December 2015 125 Method: Least Squares Sample: 1991 2012 Included observations: 22 Variable Coefficien t Std. Error t-Statistic Prob. C -178756.7 555157.2 -0.321993 0.7512 KSE 83.67487 30.98167 2.700787 0.0146 CPI -9813.813 15993.60 -0.613609 0.5472 AGRI 5.379802 0.753326 7.141399 0.0000 R-squared 0.997003 Mean dependent var 6336571. Adjusted R-squared 0.996503 S.D. dependent var 5665763. S.E. of regression 335039.9 Akaike info criterion 28.44485 Sum squared resid 2.02E+12 Schwarz criterion 28.64322 Log likelihood -308.8934 F-statistic 1995.806 Durbin-Watson stat 1.841323 Prob(F-statistic) 0.000000 Regression Results of Model 2 Dependent Variable: CPI Method: Least Squares Sample: 1991 2012 Included observations: 22 Variable Coefficien t Std. Error t-Statistic Prob. C 30.52593 2.068534 14.75727 0.0000 KSE -0.001525 0.000784 -1.946426 0.0683 M2 1.95E-05 5.93E-06 3.290343 0.0043 GDP -7.04E-06 3.12E-06 -2.254403 0.0377 AGRI 5.03E-05 1.37E-05 3.673634 0.0019 R-squared 0.995487 Mean dependent var 97.37651 Adjusted R-squared 0.994425 S.D. dependent var 52.63995 S.E. of regression 3.930308 Akaike info criterion 5.772029 Sum squared resid 262.6045 Schwarz criterion 6.019993 Log likelihood -58.49232 F-statistic 937.5028 Durbin-Watson stat 1.752945 Prob(F-statistic) 0.000000 Regression Results of Model 3 Dependent Variable: M2 Method: Least Squares Sample: 1991 2012 Included observations: 22 Variable Coefficien t Std. Error t-Statistic Prob. C -330064.4 181099.5 -1.822559 0.0860 KSE 109.7765 11.07935 9.908209 0.0000 Review of Economics and Development Studies Vol. I, No 2, December 2015 126 CPI 3204.924 6843.699 0.468303 0.6455 EXRT 10842.46 5001.870 2.167680 0.0447 GDP 0.252592 0.052517 4.809679 0.0002 R-squared 0.997782 Mean dependent var 2727026. Adjusted R-squared 0.997260 S.D. dependent var 2250941. S.E. of regression 117827.8 Akaike info criterion 26.38855 Sum squared resid 2.36E+11 Schwarz criterion 26.63652 Log likelihood -285.2741 F-statistic 1911.732 Durbin-Watson stat 2.083293 Prob(F-statistic) 0.000000 Regression Results of Model 4 Dependent Variable: EXRT Method: Least Squares Date: 11/10/13 Time: 11:10 Sample: 1991 2012 Included observations: 22 Variable Coefficien t Std. Error t-Statistic Prob. C 26.57696 1.963845 13.53313 0.0000 KSE -0.003441 0.000893 -3.854817 0.0013 M2 1.90E-05 1.95E-06 9.735691 0.0000 FDI 0.006712 0.001557 4.309855 0.0005 BOT 0.002381 0.000551 4.322954 0.0005 R-squared 0.952689 Mean dependent var 54.53746 Adjusted R-squared 0.941557 S.D. dependent var 20.58708 S.E. of regression 4.976909 Akaike info criterion 6.244211 Sum squared resid 421.0836 Schwarz criterion 6.492176 Log likelihood -63.68633 F-statistic 85.58165 Durbin-Watson stat 1.787284 Prob(F-statistic) 0.000000 Granger Causality Test Pair wise Granger Causality Tests Sample: 1991 2012 Lags: 2 Null Hypothesis: Obs F-Statistic Probability GDP does not Granger Cause KSE 20 1.31898 0.29670 KSE does not Granger Cause GDP 1.34184 0.29100 AGRI does not Granger Cause KSE 20 1.35294 0.28827 KSE does not Granger Cause AGRI 4.76361 0.02502 CPI does not Granger Cause KSE 20 2.37887 0.12666 KSE does not Granger Cause CPI 5.80262 0.01360 M2 does not Granger Cause KSE 20 6.04567 0.01187 KSE does not Granger Cause M2 0.74476 0.49162 EXRT does not Granger Cause KSE 20 2.50948 0.11478 KSE does not Granger Cause EXRT 3.82514 0.04546 FDI does not Granger Cause KSE 20 1.04725 0.37520 Review of Economics and Development Studies Vol. I, No 2, December 2015 127 KSE does not Granger Cause FDI 2.74552 0.09637 BOT does not Granger Cause KSE 20 1.15348 0.34200 KSE does not Granger Cause BOT 32.6135 3.5E-06 AGRI does not Granger Cause GDP 20 0.74977 0.48938 GDP does not Granger Cause AGRI 2.23225 0.14170 CPI does not Granger Cause GDP 20 10.4821 0.00142 GDP does not Granger Cause CPI 2.03060 0.16579 M2 does not Granger Cause GDP 20 2.71481 0.09857 GDP does not Granger Cause M2 1.04648 0.37545 EXRT does not Granger Cause GDP 20 3.73329 0.04832 GDP does not Granger Cause EXRT 1.98323 0.17210 FDI does not Granger Cause GDP 20 4.89595 0.02309 GDP does not Granger Cause FDI 1.22884 0.32047 BOT does not Granger Cause GDP 20 4.00733 0.04033 GDP does not Granger Cause BOT 2.76136 0.09526 CPI does not Granger Cause AGRI 20 18.7802 8.2E-05 AGRI does not Granger Cause CPI 0.09113 0.91340 M2 does not Granger Cause AGRI 20 4.52090 0.02907 AGRI does not Granger Cause M2 0.56306 0.58105 4. Conclusion Our study focused on the impact of KSE 100 index on four macroeconomic variables; GDP, CPI, M2 and EXRT in Pakistan. The study can be concluded in few lines as follows. In model one we have concluded that KSE 100 index has a positive and significant impact on GDP in Pakistan. In model two, we have concluded that KSE 100 index has a negative impact on CPI in Pakistan. It means that stock prices should remain high in order to cut down inflation. In model three, we have concluded that KSE 100 index has a positive significant impact on supply of money. Finally, in model four, we have concluded that KSE 100 index has a negative significant impact on Exchange Rate in Pakistan. 5. Policy Implications and Recommendations The policy implication state that the Macroeconomic factors are not responsive to changes in Pakistani stock exchange prices in spite of the sizable proportion of stock market capitalization as a share of the country’s GDP. Hence, predicting stock prices and returns via Changes in stock prices becomes precarious and this affects economic forecast, planning and growth. It thus becomes obvious that the macroeconomic factors might be very sensitive to global stock markets or other salient issues in the Pakistani environment which of course warrants further investigation. Under the light of above results it is highlighted that there is a need of well managed macroeconomic policies in order to obtain the benefits from the capital market. In order to take the full advantage of stock market and carry on with the international markets well managed macroeconomic policies are necessary in which interest rates and inflation rate are thoroughly monitor and try to reduce the value as much possible. Review of Economics and Development Studies Vol. I, No 2, December 2015 128 References Wongbangpo, P., & Sharma, S. C. (2002). Stock market and macroeconomic fundamental dynamic interactions: ASEAN -5 countries. 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