Background of the Research
Stock market has long been the fascinating place to the financial fraternity as there is a lot of money to be made if someone able to predict the movement of the market. The existence of high performing investors such as Warren Buffett, George Soros, Peter lynch, John Templeton, Kenneth Fisher and many others indicates that there are approaches to yield better than averages performance than the return of the broad stock market index. As such, to find out a useful ways to predict the stock market or to understand how the market works is highly useful.
However, to predict the future movement of stock return is never an easy task. The work to predict stock market return is highly challenging, as there are many factors that can affect the stock market movement in the future. Various approaches to predict stock market returns are available, where the two famous techniques include fundamental and technical analysis. Apparently, fundamental analysis is perceived as a better way to deal with the task of stock market prediction, as such approaches is less subjective in nature. Under the fundamental approaches, the many approaches can be categorized into four main categories, as follow: statistical methods, risk premium approach, discounted cash flow methods, and financial market equilibrium methods (Maginn, Tuttle, Pinto & McLeavy, 2007).
In this dissertation, the statistical methods will be used. In other words, the use of statistical tools, techniques and software to analyze the relationship of stock market return to other predicting variables will be investigated under this dissertation. The predicting variables to be used will be the macroeconomic variables widely used by economists in gauging the strengths or weaknesses of the economic activity for a country. There are many macroeconomics variables available, namely interest rates, consumer price index, industrial production index, gross domestic products, money supply, oil prices and many others. However, in this research, interest rates will be used to predict the stock market return. The research will be conducted on Malaysia market, as many such kind of research had already been performed in the more advanced and popular country.
Rationale of the Research
The reasons of conducting such research are not hard to understand. Firstly, if there are obvious statistical relationships that can be found between interest rates and stock market return, then we can utilize such information to yield better than average performance from the stock market. Secondly, by performing such research, we can understand if interest rates are also useful to predict stock market return from country such as Malaysia. In the past, such relationships are found to be true in the developed countries (as shall be discussed in Literature Review section in next Chapter). Thirdly, as the study will employ the lastest 10 years data for analysis purposes, such a research can inform readers if the relationships between interest rates and stock market returns remain valid despite many people already understand that certain macroeconomic variables may have predicting power to time the market.
Following the arguments presented above, the following research objective can be formed: to understand the relationships of interest rates to stock market return in Malaysia. Specifically, this study is designed to understand the statistical relationships, namely the Pearson correlation & Granger Causality between stock market returns to the changes of interest rates in Kuala Lumpur Stock Exchange.
The use of economic indicators to predict stock market movement for superior profits is nothing new. Many people are attracted to do so in order to yield above average performance in the financial market. For example, stock market related books on how to utilize macroeconomics information to time or predict the market is widely available. Among those famous books include: The Secret of Economic Indicators: Hidden Clues to Future Economic Trends and Investment Opportunities (Baumohl, 2005), The Trader’s Guide to Key Economic Indicators (Yamarone, 2007), and Using Economic Indicators to Improve Investment Analysis (Tainer, 2006).
Besides, the study of macroeconomics has been perceived as important by academicians. Many research and literature related to macroeconomics variables and stock market returns or characteristics can be found. Most of the researches of this kind are found to concentrate on the developed countries.
Among the many macroeconomic variables, interest rates are found to be highly impactful to stock market returns. This is economically reasonable. As commented by Siegal (2002), interest rates can be influential towards stock market because: (a) interest rates is often used as the discount rates to estimates the fair price of securities, (b) long term interest rates will often compete for investors’ dollar (i.e., when interest rates increase, investors pull out from the stock market and then to park their money in the bank), (c) rising interest rates often may hurt the profitability of the various businesses, as the financing costs in running businesses have increase. Not only that, according to Abdullah & Hayworth (1993), rising interest rates will adversely affect the stock market sentiment, as well as limiting the possibilities of merger and acquisition activities (when companies find it more costly to finance these corporate acquisition activities). Zweig (1997) however suggested that the increase of interest rates is often indication of restrictionary monetary policies implemented by government. As the government reduce money supply, most of the than not, stock market will suffer.
As mentioned before, there are many literatures available in investigating the relationship between interest rates and stock market returns. For example, it is found that interest rates can be one influential risk factors being priced in explaining stock market returns in the United States (Kim & Wu, 2987). Such a finding is also supported by the finding from Chen (1991). Chen (1991) found out that the 1-month T-bill rate (i.e., short term interest rates) is one of the impactful variables that can be used to predict stock returns in the future. Then, Abdullah et. al. (1993) found and documented that the stock market returns are actually negatively correlated to changes in interest rates (i.e., an inverse relationship – when interest rates increase, stock market returns dropped). Although all of the researches mentioned above are relevant only to findings in the United States, there are also many empirical evidences that stock market returns had strong association to the changes in interest rates. For example, in a cross sectional study by Bodurtha et. al (1989), it is found that the international interest rates is significantly priced in explaining the average stock returns in countries as follow: Canada, United States, United Kingdom, France, Germany, Australia, and Japan. While all of the countries researched by Bodurtha et. al (1989) are developed countries, Fifield et. al. (2002) performs a study on the emerging countries. These authors found that interest rates changes are indeed priced into stock market returns in thirteen emerging countries around the world.
From another perspective, there are also many researches in investigating the causality relationships between stock market returns and interest rates. For example, Kraft and Kraft (2003) found that there is unidirectional causality from stock prices to interest rates, using data from the stock exchange in the United States. Then, Abdullah and Hayworth (1993) also discovered that both the long-term as well as the short-term interest rates indeed Granger cause stock returns. However, the evidences on Granger causality are not uniform. In some studies, Granger causality is not found. For example, a study by Muradoglu et. al (2000) found that Granger causality between stock market returns and interest rates can only be observed in certain countries, for example, in Brazil, Pakistan, Zimbabwe, Korea, Argentina and Mexico. Under their study, Granger causality between stock market returns and interest rates cannot be observed in another seven countries (i.e., a total of 13 countries are investigated).
In short, it seems that there are evidences that changes in interest rates are related to the stock market returns. Practitioners and academicians apparently acknowledge such idea, or at least found that to be economically reasonable. Evidences that there are statistically significant correlations between changes in interest rates to the stock market returns are also available. Apart from that, although not an exact science, there are also evidences of Granger causality between these two variables. Nonetheless, as we had discussed before, there is no research investigating such kind of research in Malaysia. Secondly, as far as the author knows, there is no recent research of such kind in these few years (as most financial and economic related journals are concentrating on discussions of subprime or the sovereign crises happen recently) – thus, a research to understand if such a relationships still stay valid in the recent years is necessary.
Data to be used in Research
In this research, two series of data will be used. The first variable is the conventional interbank rates disclosed by the Central Bank of Malaysia, namely Bank Negara Malaysia (BNM). The conventional interbank rates are defined by Bank Negara Malaysia (BNM) as the “daily average of interbank deposit rates at the Interbank Money Market in Kuala Lumpur, with individual rates being weighted accordingly by the volume of transactions at those rates”. One the data is obtained from the official government website, the monthly figure of these interest rates will be extracted to be used for data analysis in this study. The official website of Bank Negara Malaysia (BNM) is at http://www.bnm.gov.my/. Then, the stock market index figure will be obtained from Yahoo Finance, due to the convenience of the services provided by Yahoo Finance. Data of the stock market index of Malaysia, namely Kuala Lumpur Composite Index (KLCI) can be obtained. The data obtained from Yahoo Finance will be cross-checked for its accuracy from the data display in Malaysian Stock Exchange Website (or also known as Bursa Saham Malaysia). The official website of Malaysian Stock Exchange is at http://www.klse.com.my.
Statistical Tools and Techniques Used
In this section, the several steps as well as the relevant statistical models to be used in investigating the relationships between changes in interest rates to stock market returns will be performed. In order to understand if there are any statistical relationships between stock market returns to the changes of interest rates, Pearson correlation between these two variables will be computed. With this, the degree of association between the two variables can be investigated. However, it is acknowledged that correlation does not mean causation. It is unsure if the changes of interest rates lead or lag the stock market returns. This, more statistical tools will be used. The Granger causality methods will be employed in the second part. Granger causality will be useful to identify any possible causation between the two variables under researched.
In order to analyze association relationships between the data, SPSS can be used (to generate Pearson correlation coefficients). Besides, in order to analyze the causation relationships between the data, Eviews will be used (to perform Granger causality test).
References & Bibliography
Abdullah, D. A., & Hayworth, S. C. (1993). Macroeconometrics of stock price fluctuations. Quarterly Journal of Business and Economics, 32(1), 50-67.
Baumohl, B. (2005). The secrets of economic indicators: hidden clues to future economic trends and investment opportunities. New Jersey: Wharton School Publishing.
Bernstein, P. L. (2007). Capital ideas evolving. New Jersey: John Wiley & Sons, Inc.
Binder, J. J., & Merges, M. J. (2001). Stock market volatility and economic factors. Review of Quantitative Finance and Accounting, 17(1), 5-26.
Black, A., Fraser, P., & Groenewold, N. (2003). Fundamental UK stock prices as determined by the macroeconomy. Journal of Asset Management, 4(1), 5-9.
Bodie, Z., Kane, A., & Marcus, A. J. (2007). Essentials of investments. New York: McGraw Hill.
Bodurtha, J. N., Cho, D. C., & Senbet, L. W. (1989). Economic forces and the stock market: an international perspective. The Global Finance Journal, 1(1), 21-46.
Chen, N. F. (1991). Financial investment opportunities and the macroeconomy. The Journal of Finance, XLVI(2), 529-554.
Chen, N. F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. Journal of Business, 59(3), 383-403.
Connor, G. (1995). The three types of factor models: a comparison of their explanatory power. Financial Analysts Journal, 42-46.
Conover, C. M., Jensen, G. R., Johnson, R. R., & Mercer, J. M. (2005). Is Fed policy still relevant for investors? Financial Analysts Journal, 61(1), 70-79.
DeLurgio, S. A. (1998). Forecasting principles and applications. New York: McGraw Hill/ Irwin.
DeStefano, M. (2004). Stock returns and the business cycle. The Financial review, 39, 527-547.
Dupor, B., & Conley, T. (2004). The Fed responses to equity prices and inflation. The American Economic Review, 94(2), 24-28.
El-Wassal, K. A. (2005). Stock market growth: an analysis of cointegration and causality. Economic Issues, 10(1), 37-58.
Enders, W. (2004). Applied econometric time series (2nd ed.). New Jersey: John Wiley & Sons, Inc.
Eviews 5 Users Guide. (2004). Irvine CA: Quantitative Micro Software, LLC.
Ewing, B. T., Forbes, S. M., & Paynes, J. E. (2003).The effects of macroeconomic shocks on sector-specific returns. Applied Economics, 35, 201-207.
Fifield, S. G. M., Power, D. M., and Sinclair, C. D. (2002). Macroeconomic factors and share returns: an analysis using emerging market data. International Journal of Finance & Economics, 7(1), 51- 62.
Flannery, M. J., & Protopapadakis, A. A. (2002). Macroeconomic factors do influence aggregate stock returns. The Review of Financial Studies, 15(3), 751-782.
Goedhart, M. H., Koller, T. M., & Wessels, D. (2005). What really drives the market? MIT Sloan Management Review, 47(1), 20-24.
Greene, W. H. (2008). Econometric analysis (6th ed.). New Jersey: Pearson Prentice Hall.
Gujarati, D. N. (2003). Basic econometrics (4th ed.). New York: McGraw Hill/ Irwin.
Hondroyiannis, G., & Papapetrou, E. (2001). Macroeconomic influences on the stock market. Journal of Economics and Finance, 25(1), 33-49.
Ilmanen, A. (2003). Stock-bond correlations. The Journal of Fixed Income, 13(2), 55-70.
Kavussanos, M. G., Marcoulis, S. N., & Arkoulis, A. G. (2002). Macroeconomic factors and international industry returns. Applied Financial Economics, 12, 923-931.
Kim, M. K., & Wu, C. (1987). Macro-economic factors and stock returns. The Journal of Financial Research, X(2), 87-98.
Kraft, J. & Kraft, A. (1977). Determinants of common stock prices: a time series analysis. The Journal of Finance, XXXII(2), 417-425.
Kwon, C. S., & Shin, T. S. (1999). Cointegration and causality between macroeconomic variables and stock market returns. Global Finance Journal, 10(1), 71-81.
Kwon, C. S., Shin, T. S., & Bacon, F. W. (1997). The effect of macroeconomic variables on stock market returns in developing markets. Multinational Business Review, 5(2), 63-70.
Lee, B. S. (2003). Asset returns and inflation in responses to supply, monetary, and fiscal disturbances. Review of Quantitative Finance and Accounting, 21(3), 207-231.
Maginn, J. L., Tuttle, D. L., Pinto, J. E., & McLeavy, D. W. (Eds). (2007). Managing investment portfolios: a dynamic process (CFA Institute investment series). New Jersey: John Wiley & Sons, Inc.
Moss, D. A. (2007). A concise guide to macroeconomics: what managers, executives, and students need to know. Boston: Harvard Business School Press.
Muradoglu, G., Taskin, F., & Bigan, I. (2000). Causality between stock returns and macroeconomic variables in emerging markets. Russian and East European Finance and Trade, 36(6), 33-53.
Patra, T., & Poshakwale, S. (2006). Economic variables and stock market returns: evidences from the Athens stock exchange. Applied Financial Economics, 16, 993-1005.
Reilly, F. K., & Brown, K. C. (2003). Investment analysis & portfolio management (7th ed.). Ohio: Thomson, South-Western.
Resnick, B. G., & Shoesmith, G. L. (2002). Using the yield curve to time the stock market. Financial Analysts Journal, 58(3), 82-90.
Siegel, J. J. (2002). Stock for the long run (3rd ed.). New York: McGraw Hill.
Tainer, E. M. (2006). Using economic indicators to improve investment analysis (3rd ed.). New Jersey: John Wiley & Sons, Inc.
Tsoukalas, D., & Sil, S. (1999). The determinants of stock prices: evidences from the United Kingdom stock market. Management Research News, 22(5), 1-14.
Yamarone, R. (2007). The trader’s guide to key economic indicators (2nd ed.). New York: Bloomberg Press.
Zweig, M. E. (1997). Winning on Wall Street: how to spot market trends early, which stocks to pick, and when to buy and sell for peak profits and minimum risk. New York: Warner Books, Inc.