Is there any relationship between crude oil price, money supply, industrial production and

                inflation with...
correlation between major macroeconomic variables and stock markets for two Asian

countries plus Russia. This research al...
rate as a measure of business-cycle fluctuations. Ewing and Thompson (2007) also explore

the cyclical correlation between...
industrial production in China and money supply growth rate in India. The variables used in

this study are crude oil pric...
Table 2: Cointegration test results for stock market and its variables
                                λ trace            ...
-1                 −                −                 +*                +**         +**
      -2                 +        ...
-2                 +                  −                   +**                +*          +**
      -3                 −   ...
months has positive impact on current RTS. This impact in previous month is significant.

CPI in previous month has positi...
consistent with Du’s (2006) findings. Otherwise when there is a weak pro-cyclical, neutral or

counter-cyclical monetary p...
monetary shocks. This may be the reason why inflation in the long run has a negative impact

on stock market index in Russ...
Cong, R.-G., Wei, Y.-M., Jiao, J.-L., & Fan, Y. (2008). Relationships between oil price
        shocks and stock market: A...
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1. Introduction

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1. Introduction

  1. 1. Is there any relationship between crude oil price, money supply, industrial production and inflation with the stock market index in China, India and Russia? 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 Previous studies have investigated the linkages between some macroeconomic variables and stock market returns. This paper investigates long and short run relationships between four macroeconomics variables comprising crude oil price (COP), money supply (M2), industrial production (IP) and inflation rate (IR) and stock market indices in China, India, and Russia. The period covered in this study is between January 1999 to January 2009. Using the Augmented Dickey Fuller unit root test, the underlying series are tested as non-stationary at the level but stationary in first difference. The use of Johansen-Juselius (1990) Multivariate Cointegration and Vector Error Correction Model technique, indicate that there are both long and short run linkages between macroeconomic variable and stock market index in each of these two countries. Keywords: Crude oil price, money supply, industrial production, inflation rate, and stock market index. 1. Introduction There are many studies about correlation between macroeconomic variables and stock markets especially for developed countries. This research is intended to examine time series 1
  2. 2. correlation between major macroeconomic variables and stock markets for two Asian countries plus Russia. This research also aims to enhance investor portfolio understanding and evaluation in terms of sensitivity of respective stock market index to the systematic impact of macroeconomic factors of crude oil prices, inflation rate, money supply growth rate, industrial production growth rate on stock market index. 2. Literature review Cong, Wei, Jiao, & Fan (2008) show that oil price shocks or volatility has no statistically significant effect on the real stock returns of most Chinese stock market indices, except some manufacturing index and some oil companies such as stock returns in mining and petrochemicals index. Nandha and Faff (2008) indicate that increase in oil price has a negative effect on stock returns for most sectors except mining and some related industries such as oil and gas industries. Sadorsky (2008) shows that increases in firm size or oil prices reduce stock market price returns, and increases in oil prices have more impact on stock market returns than decreases in oil prices do. Panopoulou (2007) indicates that one of the most powerful indicators on a country’s economic performance is the stock market returns. Wongbangpo and Sharma (2002) show that in ASEAN-5 countries, high inflation in Indonesia and Philippine has an effect on negative long run relationships between stock prices and the money supply, while the money growth in Malaysia, Singapore and Thailand causes a positive effect on their stock market indices. Bulmash and Trivoli (1991) reveal the impacts of business cycle movements on the linkage between stock returns and money growth. Fama (1981) suggests that measures of economic activity such as industrial production and inflation have important roles in the analysis of stock market activity. Campbell, Lettau, Malkiel, & Xu (2001) in their empirical studies of the macroeconomic determinants of stock market change have concentrated on the industrial production growth 2
  3. 3. rate as a measure of business-cycle fluctuations. Ewing and Thompson (2007) also explore the cyclical correlation between industrial production, consumer prices, unemployment, and stock prices using time series filtering methods. Nasseh and Strauss (2000) find the existence of a strong, long-run relationship between stock prices and domestic and international economic activity in six European economies. Kearney (2000) finds that inflation innovations have negative and significant association with changes in stock market in all markets. The findings by Du (2006) show that the positive correlation between returns in stock market and inflation in the 1930s is mainly due to strongly pro-cyclical monetary policy. However, the strong negative relationship of stock returns and inflation over the period 1952-1974 is because of supply shocks during this period. 3. Problem statement Many studies explore short and long run linkage between crude oil price, money supply, industrial production, and consumer price index and stock market index, but there is a gap in literature review about developing economies in this area. There is also a lack of work in the comparison between countries in these linkages. Therefore, the central issue for this research can be recognized as: to what extent these stock markets cointegrate with the selected macroeconomic variables? What is the short term relationship between chosen stock markets and these macroeconomics variables? 4. Methodology 4.1 Unit Root Test To see 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 inflation rate in Russia and for crude oil price, 3
  4. 4. industrial production in China and money supply growth rate in India. The variables used in this study are crude oil price (COP), money supply (M2), money supply growth rate (M2_GR), industrial production (IP), industrial production growth rate (IP_GR), inflation rate (IR), Shanghai Stock Exchange (SSE), Bombay Stock Exchange (BSE) and Russian stock index (RTS).The results indicate that at the first difference, all five series in the two countries are stationary. Table 1: The ADF Unit Root Tests Results Level 1st Difference Countries C C&T C C&T 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** BSE -1.13 -1.27 -9.40** -9.38** COP -2.12 -3.90* -7.14** -7.15** India M2_GR -2.08 -7.40** -9.14** -9.10** IP_GR -1.71 -1.53 -14.58** -14.56** IR -1.96 -4.02* -8.30** -8.43** RTS -1.40 -1.22 -7.43** -7.49** COP -2.12 -3.90* -7.14** -7.15** Russia M2_GR -0.42 -0.83 -3.60** -3.73* IP 0.51 -2.32 -3.46* -3.50* IR -3.33* -2.85 -8.21** -8.44** 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 There are various approaches to test for cointegration in multivariate models. The Engle and Granger (1987) and the Johansen-Juselius method (Johansen, 1988; Johansen-Juselius, 1990) are two broad ways to estimate cointegration equations. This study is based on the full information Johansen Maximum Likelihood (JML) procedure. Tables 2 shows the Johansen- Juselius cointegration test findings based on the trace statistics (λ trace) maximum eigenvalues (λ max) in each of these three countries. In all these countries 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 (equation 1 to 3). 4
  5. 5. Table 2: Cointegration test results for stock market and its variables λ trace λ max Countries H0 95% 95% 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=0 161.55** 76.97 77.24** 34.80 r≤1 84.31** 54.07 46.56** 28.58 India r≤2 37.75* 35.19 24.51* 22.29 r≤3 13.23 20.26 11.96 15.89 r≤4 1.27 9.16 1.27 9.16 r=0 103.56** 76.97 37.96* 34.80 r≤1 65.59 ** 54.07 32.14* 28.58 Russia r≤2 33.45 35.19 20.36 22.29 r≤3 13.09 20.26 8.64 15.89 r≤4 4.45 9.16 4.45 9.16 Note: Asterisk * and ** denote significance at 5% and 1% value, respectively SSE = -36,270.658+619.715 COP-1.929 M2+399.687 IP +1,497.071 IR (1) BSE = -4,019.585+25.771 COP+29,871.607 M2_GR+2,330.263 IP_GR +1,272.749 IR (2) RTS = -5379.529***+8.827 COP+74,038.06*** M2_GR+33.419* IP-0.841 IR (3) 4.3 Vector Error Correction Models (VECM) To find short run correlation between macroeconomic variables and stock market indices in these three countries, this paper employed VECM test. Tables 3 to 5 reveal the results of this test. Based on the Vector Error Correction Models results findings are as follows: 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+1.051 ∆COPt-3+0.1588 ∆M2t-1+0.041 ∆M2t-2+0.122 ∆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 Table 3: Summary of VECM Results for China Optimal lag ∆SSE ∆COP ∆M2 ∆IP ∆IR 5
  6. 6. -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. India: 6 months lag (Significant level: *1%, **5%, ***10%) ∆(BSE)= +0.2156**∆BSE t-1+ 0.0325 ∆BSE t-2-0.0755 ∆BSE t-3 + 0.4054***∆BSE t-4 + 0.1551 ∆BSE t-5 -0.0368 ∆BSE t-6+15.2729*∆COPt-1 -4.9538 ∆COPt-2 -10.9949 ∆COPt-3 -37.365***∆COPt-4+ 7.5043 ∆COPt-5 -10.9902 ∆COPt-6+21813.79 ***∆M2_GRt-1 + 17396 ** ∆M2_GRt-2+14159.23** ∆M2_GRt-3+10755.82** ∆M2_GRt-4+ 5054* ∆M2_GRt-5 + 3100.493 ∆M2_GRt-6+4187.526** ∆IP_GRt-1+ 3081.651* ∆IP_GRt-2 -569.9792 ∆IP_GRt-3 -3167.201*∆IP_GRt-4 -2972.792* ∆IP_GRt-5 -900.8111 ∆IP_GRt-6 -9.4663 ∆IRt-1 + 134** ∆IRt-2 -149.5096** ∆IRt-3 +103* ∆IRt-4+ 24.2995 ∆IRt-5 -99.01676*∆IRt-6 -0.0548**ECT The effect of crude oil price lagged one and five months on the current Indian stock market (BSE) is positive, but only in the previous month this impact is significant. For lags of two, three, four, and six months, this effect is negative, but only when the lag is four months, this effect is significant. Table 4: Summary of VECM Results for India Optimal lag ∆BSE ∆COP ∆M2_GR ∆IP_GR ∆IR -1 +** +* +*** +** − 6
  7. 7. -2 + − +** +* +** -3 − − +** − −** -4 +*** −*** +** −* +* -5 + + +* −* + -6 − − + − −* Note: Asterisk *, ** and *** denote significance at 10%, 5% and 1% value, respectively. The effect of money supply lagged one to six months on the current BSE is positive. Except for the lag of six months, this effect is significant. The effect of lagged industrial production on the current BSE is negative except for lags one and two months. Moreover, in each of the lags up to six months, except for three and six-month lags, this effect is significant. The effect of CPI lagged one, three and six months on the current BSE is negative. For lags of two, four and five months, this impact is positive. All these impacts are significant except for lags of one month and five months. Russia: 3 months lag (Significant level: *1%, **5%, ***10%) ∆(RTS)= -0.0534 ∆RTS t-1+ 0.1398* ∆RTS t-2+ 0.1429* ∆RTS t-3 +5.7494*** ∆COPt-1 + 0.9667 ∆COPt-2 -5.9711*** ∆COPt-3 -493.6155 ∆M2_GRt-1 -261.1981 ∆M2_GRt-2 + 63.6634 ∆M2_GRt-3+3.4326** ∆IPt-1+ 1.9686 ∆IPt-2+ 0.3758 ∆IPt-3+ 0.1328 ∆IRt-1 -0.6822 ∆IRt-2 -0.2166 ∆IRt-3 -0.007025 ECT Table 5: Summary of VECM Results for Russia Optimal lag ∆RTS ∆COP ∆M2_GR ∆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 Russian stock market (RTS) is positive, but the impact is significant only for COP lagged one month. This effect of lags up to three is negative and significant. Increases in money supply lagged one and two months has negative and insignificant impact on the current RTS. For lags up to three months this effect is positive and insignificant. Industrial production lagged one to three 7
  8. 8. months has positive impact on current RTS. This impact in previous month is significant. CPI in previous month has positive and insignificant impact on the current RTS. For lags of two and three months, the effect of CPI on current RTS is negative and insignificant. 5. Findings of the study Cong and et al. (2008) find a positive long run relationship between crude oil price and SSE which support our findings. They show increase in oil price changes may increase the speculation in mining and petrochemicals index, leading to an enhancement in their stock. Gogineni (2008) say expectations on future economic activity result in a positive correlation between changes in oil and stock price. In Russia the question is why there is a positive relationship between crude oil price and its stock price may be explained by the fact that it is a net exporter of oil. In the short term for Russia, there is positive and significant stock market reaction to increases of crude oil prices lagged one month. This is consistent with a study by Park and Ratti (2008) who investigate the stock market return in Norway. However, when the US dollar appreciated 3 months ago, there was a negative and significant relationship between oil price and Russian stock exchange. This result is similar to study by Lanza, Manera, Grasso & Giovannini (2005) who indicate that since the transaction currency in oil markets is in USD, as a result, the stock price of a non-US company decline when the dollar appreciates relative to the local currency. The positive long-term relationships between increases in money supply in Russia and India may be explained by the fact that money supply has a direct positive liquidity impact on the stock market this is consistent with finding of Maysami and Koh (2000). Moreover, Mukherjee and Naka (1995) pointed out that the injection of money supply leads to boost corporate earnings. This positive long-term relationship between money supply and the stock market may be explained by strong pro-cyclical monetary policies. This is 8
  9. 9. consistent with Du’s (2006) findings. Otherwise when there is a weak pro-cyclical, neutral or counter-cyclical monetary policy, it exerts a negative long run impact. There is positive long- term relationship between industrial production and stock market indices in the selected countries. Moreover, there is positive and significant effect of increases in industrial production, lagged one month on the stock market indices in these countries. This positive relationship results from the fact that when real activities are expected to grow, this will be improved cash flows. This, in turn, influences stock prices positively. In contrast, there are negative effects of industrial production in lagged three, four, five and six months on current Indian stock market index. This may be due to the correlation between interest innovations in real activities rather than stock market returns. Investors overact to interest news. As a result, an increase (decrease) in real interest rates shows a rise (fall) in industrial production and exaggerated fall (rise) in share prices. This is consistent with a study compiled by Gjerde and Sættem (1999). Positive long-term relationships between inflation and stock market indices in China and India may be due to the monetary policy in these countries that may prove pro- cyclical. Another reason could be investors in these countries have an inflation expectation, and thereby want more return for their investment in the market to compensate for an increase in expected risk. 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 in Russia and China supports Nelson’s (1976) claim that correlation between current nominal returns and one-period lagged inflation should be direct because of the positive relationship between past and expected inflation rates. Based on equilibrium models, correlation between price volatility and equity returns depend on the source of change in inflation (monetary or real). 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 9
  10. 10. monetary shocks. This may be the reason why inflation in the long run has a negative impact on stock market index in Russia. Moreover, this relationship can perhaps be explained by a type of monetary policy in this country is counter-cyclical. 6. Conclusion The findings show that in both long and short run, there is a linkage between four macroeconomics variables and stock market indices in China, India and Russia. . In the long run, the effect of crude oil in all the countries is positive. In terms of money supply, the impact on Indian and Russian stock market is positive, but only in Russia it is significant. This impact in China is negative and insignificant. The effect of industrial production is positive for all these three countries but only for Russia, it is significant. The effect of increases in CPI on these stock indices is positive in China and India. This effect is negative in Russia. This effect is insignificant for all the countries. In the short run, the effect of one- month lagged crude oil price is negative and insignificant in China but it is positive and significant in India and Russia. The impact of previous month’s money supply on current Russian stock market is negative and insignificant but for the rest of countries it is positive and significant. Moreover, the effect of one-month lagged increases in industrial production on these indices is positive and significant. The impact of inflation rate in the previous month on all these countries is positive except in India it is negative. 7. References Bulmash, S. B., & Trivoli, G. W. (1991). Time-lagged interactions between stock prices and selected economic variables. Journal of Portfolio Management(SUMMER 1991). Boucher, C. (2006). Stock prices-inflation puzzle and the predictability of stock market returns. Economics Letters, 90(2), 205-212. Campbell, J. Y., Lettau, M., Malkiel, B. G., & Xu, Y. (2001). Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk. Journal of Finance, 56(1), 1-43. 10
  11. 11. 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. Du, D. (2006). Monetary policy, stock returns and inflation. Journal of Economics and Business, 58(1), 36-54. Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica: Journal of the Econometric Society, 55(2), 251-276. Ewing, B. T., & Thompson, M. A. (2007). Dynamic cyclical comovements of oil prices with industrial production, consumer prices, unemployment, and stock prices. Energy Policy, 35(11), 5535-5540. Fama, E. F. ( 1981). Stock Returns, Real Activity, Inflation, and Money. American Economic Review, 71(4). 545–565 Gogineni, S. (2008). The Stock Market Reaction to Oil Price Changes: SSRN. Gjerde, Ø., & Sættem, F. (1999). Causal relations among stock returns and macroeconomic variables in a small, open economy. Journal of International Financial Markets, Institutions and Money, 9(1), 61-74. Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of economic dynamics and control, 12(2/3), 231-254. 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. Kearney, C. (2000). The determination and international transmission of stock market volatility. Global Finance Journal, 11(1-2), 31-52. 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. Lanza, A., Manera, M., Grasso, M., & Giovannini, M. (2005). Long-run models of oil stock prices. Environmental Modelling & Software, 20(11), 1423-1430. 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. Nandha, M., & Faff, R. (2008). Does oil move equity prices? A global view. Energy Economics, 30(3), 986-997. Nasseh, A., & Strauss, J. (2000). Stock prices and domestic and international macroeconomic activity: a cointegration approach. The Quarterly Review of Economics and Finance, 40(2), 229-245. Nelson, C. R. (1976). Inflation and rates of return on common stocks. Journal of Finance, 31(2), 471-483. Panopoulou, E. (2007). Predictive financial models of the euro area: A new evaluation test. International Journal of Forecasting, 23(4), 695-705. Park, J., & Ratti, R. A. (2008). Oil price shocks and stock markets in the U.S. and 13 European countries. Energy Economics, 30(5), 2587-2608. Sadorsky, P. (2008). Assessing the impact of oil prices on firms of different sizes: Its tough being in the middle. Energy Policy, 36(10), 3854-3861. Wongbangpo, P., & Sharma, S. C. (2002). Stock market and macroeconomic fundamental dynamic interactions: ASEAN-5 countries. Journal of Asian Economics, 13, 27-51. 11

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