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1. Introduction

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1. Introduction

  1. 1. Impact of four major macroeconomic variables on the stock market indices in Malaysia, China and U.S. Seyed Mehdi Hosseini Graduate Student, Graduate School of Business, USM, Malaysia Seyed_Mehdi_Hosseini80@yahoo.com Prof Zamri Ahmad School of Management, USM, Malaysia zahmad@usm.my Prof Lai Yew Wah. Graduate School of Business, USM, Malaysia ywlai@usm.my Abstract The aim of this paper is to test long and short run relationships between crude oil price (COP), money supply (M2), industrial production (IP) and inflation rate (IR) and stock market indices in three countries comprising Malaysia, China, and United States. This study covers the period 1999.I to 2009.I. Using the Augmented Dickey Fuller unit root test, the underlying series are tested as non-stationary in levels but stationary in first difference. Using Johansen-Juselius (1990) Multivariate Cointegration procedure and the Vector Error Correction Model, reveal that there is both long and short run relationship between macroeconomic variables and stock market index in each of these three countries. Keywords: Crude oil price, money supply, industrial production, inflation rate, and stock market. 1. Introduction There are many studies about the relationship between macroeconomic variables and stock markets especially for developed countries. However, the available research into this 1
  2. 2. phenomenon is limited with respect to Malaysia, China and the United States. This study will examine the impact of four macroeconomic variables namely crude oil price, money supply, industrial production and inflation rate on the stock market of these countries. 2. Literature review There are many researches examining the impact of macroeconomic variables on the stock market. Sadorsky (2003) finds that conditional volatilities of oil prices and the customer price index each have significant effects on the conditional volatility of technology stock prices. Papapetrou (2001) identified that oil price changes affect real economic activity. According to Panopoulou (2007), one of the most powerful indicators of a country’s performance is the stock market returns. Wongbangpo and Sharma (2002) indicate that in the ASEAN-5 countries, high inflation in Indonesia and Philippine has an impact on the negative long run relationship between stock prices and the money supply, while the money growth in Malaysia, Singapore and Thailand causes a positive impact on their stock markets. According to Binswanger (2001) most of the previous researches focus on industrial production as a proxy for real economic activity. Fama (1981) suggests that measures of economic activity such as industrial production and inflation are important in the analysis of stock market activity. Kim (2003) finds the S&P 500 stock price has positive relationship with industrial production. Apergis and Eleftheriou (2002) study the relationship between stock prices and inflation in an economy with high inflationary pressures, such as the Greek economy. His findings show that stock prices in Greece have relationship with inflation movement, and if inflation in Greece decreases, the stock prices will go up substantially. In contrast, the study by Hondroyiannis and Papapetrou (2006) reveals that actual inflation has no significantly effect on real stock market in Greece. Boyd, Levine, & Smith (2001) indicates that there is a 2
  3. 3. significant, and economically important, negative correlation between inflation rate and growth in banking sector and equity market activity. 3. Problem statement Although there is much research about short and long run relationships between macroeconomic variables and stock markets especially in developed countries, there is a gap in literature on this area in developing and fast developing economies. This study will help to understand the levels of cointegration between stock markets and four macroeconomic variables in the selected countries. This understanding, along with the short run relationships between the variables, will help investors across the world observe which of the three countries have the greatest potential and opportunity for portfolio diversification. To address the objectives, the present study focuses on the following question: what is the long and short run equilibrium between the stock exchange and four macroeconomic variables? 4. Methodology 4.1 Unit Root Test In order to establish the order of integration of the variables in our data set, we employ the standard ADF unit root test. Table 1 reveals that at the level, all the five variables are non- stationary since the unit root tests are not rejected, except for industrial production in China and crude oil price. The variables used are Kuala Lumpur Composite Index (KLCI), crude oil price (COP), money supply (M2), industrial production (IP), industrial production growth rate (IP_GR), inflation rate (IR), Shanghai Stock Exchange (SSE) and New York Stock Exchange (NYSE). The results indicate that at the first difference, all five series in these countries are stationary. 3
  4. 4. Table 1: The ADF Unit Root Tests Results Level 1st Difference Countries C C&T C C&T KLCI -1.61 -1.53 -9.66** -9.68** COP -2.12 -3.90* -7.14** -7.15** Malaysia M2 3.13 -1.06 -5.43** -9.51** IP_GR -2.52 -2.69 -6.55** -6.50** IR -2.52 -3.30 -8.56** -8.53** SSE -2.10 -2.53 -5.08** -5.07** COP -2.12 -3.90* -7.14** -7.15** China M2 7.35 0.51 -0.085 -10.16** IP -1.64 -7.45** -9.68** -10.01** IR -1.64 -3.18 -8.81** -8.83** NYSE -1.32 -0.82 -11.11** -11.18** COP -2.12 -3.90* -7.14** -7.15** U.S M2 1.95 0.208 -4.29** -4.65** IP -1.59 -0.13 -3.76** -3.93* IR -2.39 -2.29 -3.91** -3.78* Notes: Asterisk * and ** denote significance at 5% and 1% value, respectively. C stands for “Intercept” and C&T represents “Trend and Intercept”. 4.2 Multivariate Cointegration Test Most macroeconomic variables are non-stationary, with time-dependent means and variances. However, a linear combination of non-stationary variables may be stationary. If there is such a stationary linear combination, then variables are cointegrated. Table 2: Cointegration test results for stock market and its variables λ trace λ max Countries H0 95% 95% r=0 215.54** 69.81 118.44** 33.87 r≤1 97.10 ** 47.85 48.21** 27.58 Malaysia r≤2 48.88** 29.79 33.92** 21.13 r≤3 14.95 15.49 9.32 14.26 r≤4 5.63 3.84 5.63 3.84 r=0 110.84** 76.97 48.90** 34.80 r≤1 61.93** 54.07 31.56* 28.58 China r≤2 30.36 35.19 20.59 22.29 r≤3 9.77 20.26 7.67 15.89 r≤4 2.10 9.16 2.10 9.16 r=0 120.48** 76.97 59.30** 34.80 r≤1 61.18* 54.07 30.51* 28.58 U.S. r≤2 30.66 35.19 18.22 22.29 r≤3 12.43 20.26 8.94 15.89 r≤4 3.49 9.16 3.49 9.16 Note: Asterisk * and ** denote significance at 5% and 1% value, respectively Table 2 shows the Johansen-Juselius cointegration test results based on the trace statistics (λ trace) maximum eigenvalues (λ max) in each of these three countries. In all these countries 4
  5. 5. both the maximum eigenvalue test and trace test indicate that stock exchange and their determinants have long-run relationship and are moving together in the long-run (equations 1 to 3). Based on the maximum eigenvalues and trace tests, one can reject the null hypothesis of no cointegration against the alternative of existence of cointegration. KLCI = +83.8912+0.7389 COP+ 0.001656** M2+26,683.72*** IP_GR – 92.4762** IR (1) SSE = -36,270.658+619.715 COP-1.929 M2+399.687 IP +1,497.071 IR (2) NYSE = -4276.629+47.841 COP-1.463 M2+16.065 IP+847.346 IR (3) Based on cointegration results (equations 1 to 3) the long-term impact of crude oil price on stock market for all three chosen countries is positive. Money supply has positive impact on Malaysia but in China and the United States this effect is negative. The effect of increases in industrial production on all current countries indices is positive. Moreover, increases in consumer price index (CPI) have negative effect on stock market in Malaysia but for China and the United States, this effect is positive. 4.3 Vector Error Correction Models (VECM) To find the short run relationship between macroeconomic variables and stock market indices in these three countries we employ the VECM test. Tables 3 to 5 reveal the results of this test. The results of the Vector Error Correction are as follows: Malaysia: 1 month lag (Significant level: *1%, **5%, ***10%) ∆(KLCI) = -2.8835+0.0841 ∆KLCIt-1 -0.1137 ∆COPt-1+ 0.0013**∆M2t-1 -228.0732**∆IP_GRt-1 -19.8665**∆IRt-1 -0.019655**ECT (4) Table 3: Summary of VECM Results for Malaysia Optimal lag ∆KLCI ∆COP ∆M2 ∆IP_GR ∆IR -1 + − +** −** −** Note: Asterisk *, ** and *** denote significance at 10%, 5% and 1% value, respectively. 5
  6. 6. The impact of crude oil price in the previous month on current Malaysian stock market (KLCI) is negative and insignificant. Increases in money supply lagged one month have a positive and significant impact on the current KLCI. Industrial production increases in the previous month has a negative and significant impact on the current KLCI and increases in the CPI in the previous month have a significant negative impact on the current Malaysian stock market KLCI. China: 3 months lag (Significant level: *1%, **5%, ***10%) ∆(SSE)= -0.0317 ∆SSE t-1+ 0.0668 ∆SSE t-2 + 0.131 ∆SSE t-3 -2.919 ∆COPt-1 -5.1265 ∆COPt-2 +1.0505 ∆COPt-3 + 0.1588* ∆M2t-1+0.0405 ∆M2t-2+0.1215 ∆M2t-3+17.2809 **∆IPt-1 +9.6073 ∆IPt-2+3.0196 ∆IPt-3+ 91.6307** ∆IRt-1 +75.7163 ∆IRt-2 -25.7249 ∆IRt-3 –0.015407 ECT (5) Table 4: Summary of VECM Results for China Optimal lag ∆SSE ∆COP ∆M2 ∆IP ∆IR -1 − − +* +** +** -2 + − + + +* -3 + + + + − Note: Asterisk *, ** and *** denote significance at 10%, 5% and 1% value, respectively. The impact of crude oil price lagged one and two months on the current Chinese stock market (SSE) is negative and insignificant. However, the effect is positive and insignificant for crude oil price lagged 3 months. Money supply in the last 3 months has a positive impact on the current SSE, but only money supply in the previous month has a significant impact. Industrial production in the last 3 months has positive impact on the current SSE, but the effect is significant only for industrial production lagged one month. CPI in the last two months has positive and significant impact on the current SSE. In contrast, the effect of CPI in 3 months ago on current SSE is negative and insignificant. U.S: 2 months lag (Significant level: *1%, **5%, ***10%) 6
  7. 7. ∆(NYSE)= -0.1054 ∆NYSE t-1 +0.0041 ∆NYSE t-2 + 4.0002 ∆COPt-1+8.9777 ∆COPt-2 + 0.583 ∆M2t-1 + 2.9465** ∆M2t-2+149.9026*** ∆IPt-1 +203.6835*** ∆IPt-2 -81.5048 ∆IRt-1 –103.5628 ∆IRt-2 -0.010269**ECT (6) Table 5: Summary of VECM Results for United States Optimal lag ∆NYSE ∆COP ∆M2 ∆IP ∆IR -1 − + + +*** − ** -2 + + + +*** − Note: Asterisk *, ** and *** denote significance at 10%, 5% and 1% value, respectively. The crude oil price effect over the last two months is positive and insignificant on the current stock market in the United States (NYSE). Money supply lagged one and two months has positive effect on the current NYSE. But it is only significant if it is lagged two months. Industrial production with a one and two months lag has positive and significant effect on the current NYSE. Increases in CPI lagged one and two months have negative and insignificant effect on the NYSE. 5. Findings of the study In Malaysia the question why there is positive relationship between crude oil price and its stock price may be explained by the fact it is a net exporter of oil. The positive long run relationship between crude oil price and SSE conforms to the findings by Cong, Wei, Jiao, & Fan (2008). They show that increase in oil price changes may increase the speculation in mining and petrochemicals index. This may enhance the stock market. Davis and Aliaga-Diaz (2008) indicate that companies in energy, industrial and material sectors depend on the world business cycle and they react to rise in crude oil price attributed to global demand. Although cost pressure goes up due to higher oil price, profit margins may also increase. It could be the reason why the effect of oil price on the stock index in U.S. is positive in both the short and long term. Positive and significant long run relationship between increases in money supply 7
  8. 8. in Malaysia may be explained by the findings of Maysami and Koh (2000) who find money supply has a direct positive liquidity impact on the stock market. Moreover, Mukherjee and Naka (1995) pointed out that injection of money supply leads to boost in corporate earnings. This positive long-term relationship between money supply and the stock market may also be explained by a strong pro-cyclical monetary policy, which is consistent with Du’s (2006) findings. When pro-cyclical, neutral or counter-cyclical monetary policies are weak the long run impact would be negative. Another reason for this negative effect could be explained by the result of Abugri (2008), which stated that when money supply rises, it causes higher inflation and lower returns. There is positive and significant long-term relationship between industrial production and the stock market indices in selected countries except the U.S., which is positive but not significant. This positive relationship is possible since when there is expected growth rates of real activity on future cash flow, stock prices rise. The positive long-term relationships between inflation and stock market indices in China and the United States may be due to pro-cyclical monetary policies in these countries. According to Boucher (2006), when inflation rises, the price-earnings ratio declines and expected market returns increase. This increase in expected return leads to higher share prices. Moreover, positive relationships between inflation and stock market returns lagged one period in China supports Nelson’s (1976) claim that correlation between current nominal returns and lagged inflation should be direct because of the positive relationship between past and expected inflation rates. Khil and Lee (2000) indicate that a negative relationship between market returns and inflation may be due to the dominance of real changes relative to monetary shocks. This may be the reason why inflation in the long run has a negative impact on stock market indices in Malaysia. Another reason for this negative relationship may be due to the fact that high inflation makes liquid and illiquid assets less valuable. 8
  9. 9. 6. Conclusion The findings show that in both the long and short run, there is a relationship between the four macroeconomic variables, namely crude oil price, money supply, industrial production and inflation rate and the stock market indices in Malaysia, China and the U.S. The long run impact of crude oil price on stock market for all three chosen countries is positive. Money supply has positive impact on Malaysia but in China and United States, this effect is negative. The effect of increases in industrial production on the indices is positive in all three countries. Moreover, increases in consumer price index (CPI) have negative effect on the stock market in Malaysia but for China and U.S., this effect is positive. In the short run, the effect of one- month lagged crude oil is positive and insignificant in U.S. but for Malaysia and China, this effect is negative and insignificant. The effect of one-month lagged money supply is positive in all the countries, but only in the U.S., it is insignificant. . Industrial production impact in previous month is negative in Malaysia but for China and U.S, this impact is positive and significant. The effect of increases in CPI lagged one month in Malaysia and U.S. is negative, but only in Malaysia it is statistically significant. For China, this effect is positive and significant. This implies higher inflation leads to less investment and lower levels of consumption. As a result, demand for goods decline and this leads to lower sales, earnings and expected returns, which in turn, lower share prices. 7. References Abugri, B. A. (2008). Empirical relationship between macroeconomic volatility and stock returns: Evidence from Latin American markets. International Review of Financial Analysis, 17(2), 396-410. Apergis, N., & Eleftheriou, S. (2002). Interest rates, inflation, and stock prices: the case of the Athens Stock Exchange. Journal of Policy Modeling, 24(3), 231-236. Binswanger, M. (2001). Does the stock market still lead real activity?—An investigation for the G-7 countries. Financial Markets and Portfolio Management, 15(1), 15-29. 9
  10. 10. Boucher, C. (2006). Stock prices-inflation puzzle and the predictability of stock market returns. Economics Letters, 90(2), 205-212. Boyd, J. H., Levine, R., & Smith, B. D. (2001). The impact of inflation on financial sector performance. Journal of Monetary Economics, 47(2), 221-248. Cong, R.-G., Wei, Y.-M., Jiao, J.-L., & Fan, Y. (2008). Relationships between oil price shocks and stock market: An empirical analysis from China. Energy Policy, 36(9), 3544-3553. Davis, J. H., & Aliaga-Diaz, R. (2008). Oil, the Economy, and the Stock Market: SSRN. Du, D. (2006). Monetary policy, stock returns and inflation. Journal of Economics and Business, 58(1), 36-54. Fama, E. F. ( 1981). Stock Returns, Real Activity, Inflation, and Money. American Economic Review, 71(4). 545–565 Hondroyiannis, G., & Papapetrou, E. (2006). Stock returns and inflation in Greece: A Markov switching approach. Review of Financial Economics, 15(1), 76-94. Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration with applications to the demand for money. Oxford Bulletin of Economics and statistics, 52(2), 169-210. Khil, J., & Lee, B.-S. (2000). Are common stocks a good hedge against inflation? Evidence from the Pacific-rim countries. Pacific-Basin Finance Journal, 8(3-4), 457-482. Kim, K.-h. (2003). Dollar exchange rate and stock price: evidence from multivariate cointegration and error correction model. Review of Financial Economics, 12(3), 301-313. Maysami, R. C., & Koh, T. S. (2000). A vector error correction model of the Singapore stock market. International Review of Economics & Finance, 9(1), 79-96. Mukherjee, T. K., & Naka, A. (1995). Dynamic relations between macroeconomic variables and the Japanese stock market: an application of a vector error correction model. Journal of Financial Research, 18, 223-223. Nelson, C. R. (1976). Inflation and rates of return on common stocks. Journal of Finance, 31(2), 471-483. Papapetrou, E. (2001). Oil price shocks, stock market, economic activity and employment in Greece. Energy Economics, 23(5), 511-532. Panopoulou, E. (2007). Predictive financial models of the euro area: A new evaluation test. International Journal of Forecasting, 23(4), 695-705. Sadorsky, P. (2003). The macroeconomic determinants of technology stock price volatility. Review of Financial Economics, 12(2), 191-205. Wongbangpo, P., & Sharma, S. C. (2002). Stock market and macroeconomic fundamental dynamic interactions: ASEAN-5 countries. Journal of Asian Economics, 13(1), 27-51. 10

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