Stock market index prediction using machine learning: evidence from leading Southeast Asian countries
Keywords:
Stock market, Support Vector Machine, SVM, Toda-Yamamoto causalityAbstract
Financial investment in stock markets in emerging economies has played an important role in wealth management. Thus, this study aimed to present the application of the Support Vector Machine (SVM) with using the Toda-Yamamoto causality test to select economic indicators for predicting the movement of one month ahead of the stock market index in four leading Southeast Asian (ASEAN) nations: Indonesia, Malaysia, Singapore, and Thailand. Monthly data were sampled, ranging from January 2002 to December 2019. The linear kernel SVM provided useable results with accuracy ranging from 58.14 % to 65.12 %, which performed better than the sigmoid kernel SVM. According to the efficient-market hypothesis (EMH), the stock markets of Singapore and Malaysia were the most efficient among the four stock markets, whereas investors could strategically utilise this SVM algorithm to gain more returns from stock markets in Thailand and Indonesia.
References
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Vapnik, V., & Chervonekis, A., 1964. A note on one class of perceptrons. Automation and Remote Control, 25, 112-120.
Wang, Y. (2014). Stock price direction prediction by directly using prices data: An empirical study on the KOSPI and HIS. International Journal of Business Intelligence and Data Mining, 9(2), 145-160.
Chaengkham, S., & Wianwiwat, S., 2021. The impacts of macroeconomic and financial indicators on stock market index: Evidence from Thailand. International Journal of Trade and Global Markets, 14(2), 197-205.
Anwar, S., & Ismal, R. (2011). Robustness analysis of artificial neural networks and support vector machine in making prediction. In Proceedings of the 2011 IEEE Ninth International Symposium on Parallel and Distributed Processing with Applications. (pp. 256–261). Washington, DC: IEEE Computer Society.
Aras, G., & Yilmaz, M. K. (2008). Price-earnings ratio, dividend yield, and market-to-book ratio to predict return on stock market: Evidence from the emerging markets. Journal of Global Business and Technology, 4(1), 18–30.
Arfaoui, M., & Ben Rejeb, A. (2017). Oil, gold, US dollar and stock market interdependencies: A global analytical insight. European Journal of Management and Business Economics, 26(3), 278–293.
Arora, S., Bhattacharjee, D., Nasipuri, M., Malik, L., Kundu, M., & Basu, D.K. (2010). Performance comparison of SVM and ANN for handwritten Devnagari character recognition. International Journal of Computer Science Issues, 7(3), 1-10.
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory. (pp. 144–152). New York: Association for Computing Machinery.
Chen, M.-H., Kim, W. G., & Kim, H. J. (2005). The impact of macroeconomic and non-macroeconomic forces on hotel stock returns. International Journal of Hospitality Management, 24(2), 243–258.
Chiu, D. Y., & Chen, P. J. (2009). Dynamically exploring internal mechanism of stock market by fuzzy-based support vector machines with high dimension input space and genetic algorithm. Expert Systems with Applications, 36(2), 1240-1248.
Dagher, L., & Yacoubian, T. (2012). The causal relationship between energy consumption and economic growth in Lebanon. Energy Policy, 50, 795–801.
Dickey, D., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057–1072.
Dutta, A. (2018). Implied volatility linkages between the U.S. and emerging equity markets: A note. Global Finance Journal, 35, 138–146.
Emir, S., Dincer, H., & Timor, M. (2012). A stock selection model based on fundamental and technical analysis variables by using artificial neural networks and support vector machines. Review of Economics and Finance, 2, 106-122.
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.
Forson, J. A., & Janrattanagul, J. (2014). Selected macroeconomic variables and stock market movements: Empirical evidence from Thailand. Contemporary Economics, 8(2), 154–174.
Gokmenoglu, K. K., & Fazlollahi, N. (2015). The interactions among gold, oil, and stock market: Evidence from S&P500. Procedia Economics and Finance, 25, 478–488.
Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438.
Grigoryan, H. (2016). A stock market prediction method based on Support Vector Machines (SVM) and Independent Component Analysis (ICA). Database Systems Journal, 7(1), 12–21.
Hsing, Y. (2011) The stock market and macroeconomic variables in a BRICS country and policy implications. International Journal of Economics and Financial Issues, 1(1), 12-18.
Hussainey, K., & Ngoc, L. K. (2009) The impact of macroeconomic indicators on Vietnamese stock prices. The Journal of Risk Finance, 10(4), 321-332.
Kara, Y., Boyacioglu, M. A., & Baykan, O. K. (2011) Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert Systems with Applications, 38(5), 5311-5319.
Khan, M. K., Teng, J. Z., Pervaiz, J., & Chaudhary, S. K. (2017). Nexuses between economic factors and stock returns in China. International Journal of Economics and Finance, 9(9), 182–191.
Lahmiri, S. (2011). A comparison of PNN and SVM for stock market trend prediction using economic and technical information. International Journal of Computer Applications, 29(3), 24–30.
Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch,F., & Chang, C. (2019). Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. Retrieved from https://CRAN.R-project.org/package=e1071.
Rasiah, V., & Ratneswary, R. (2010). Macroeconomic activity and the Malaysian stock market: Empirical evidence of dynamic relations. The International Journal of Business and Finance Research, 4(2), 59–69.
Ren, C. (2012). ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging. Knowledge-Based Systems, 26, 144-153.
Sadeghzadeh, K. (2018). The effects of microeconomic factors on the stock market: A panel for the stock exchange in Istanbul ARDL analysis. Theoretical and Applied Economics, 25(3), 113–134.
Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464.
Shrimalve, H. H., & Talekar, S.A. (2018). Comparative analysis of stock market prediction system using SVM and ANN. International Journal of Computer Applications, 6(2), 59–64.
Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1), 225–250.
Usmani, M., Adil, S. H., Raza, K., & Ali, S. S. A. (2016). Stock market prediction using machine learning techniques. 2016 3rd International Conference on Computer and Information Sciences (ICCOINS). (pp. 322–327). Washington, DC: IEEE Computer Society.
Vapnik, V., & Chervonekis, A., 1964. A note on one class of perceptrons. Automation and Remote Control, 25, 112-120.
Wang, Y. (2014). Stock price direction prediction by directly using prices data: An empirical study on the KOSPI and HIS. International Journal of Business Intelligence and Data Mining, 9(2), 145-160.
Chaengkham, S., & Wianwiwat, S., 2021. The impacts of macroeconomic and financial indicators on stock market index: Evidence from Thailand. International Journal of Trade and Global Markets, 14(2), 197-205.
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Published
2021-06-07
How to Cite
Chaengkham, S., & Wianwiwat, S. (2021). Stock market index prediction using machine learning: evidence from leading Southeast Asian countries. Thailand and The World Economy, 39(2), 56–64. Retrieved from https://so05.tci-thaijo.org/index.php/TER/article/view/251892
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