Lam Weng Siew, Department of Physical and Mathematical Science, Faculty of Science, Universiti Tunku Abdul Rahman, Kampar, Perak, Malaysia; Centre for Mathematical Sciences, Centre for Business and Management, Universiti Tunku Abdul Rahman, Kampar, Perak, Malaysia.

Lam Weng Hoe, Department of Physical and Mathematical Science, Faculty of Science, Universiti Tunku Abdul Rahman, Kampar, Perak, Malaysia; Centre for Mathematical Sciences, Centre for Business and Management, Universiti Tunku Abdul Rahman, Kampar, Perak, Malaysia.

Abstract
Stock market index measures the general behavior and performance of stock market overtime. Index tracking is a popular investment strategy in the components of stock market index. Index tracking aims to construct a tracking portfolio to achieve similar mean return with the benchmark stock market index mean return without investing in all stocks that make up the index. The objective of this paper is to determine the stock selection in constructing the portfolio for tracking Malaysia stock market index by using regression model. In this study, the data consists of weekly stock prices from Malaysia stock market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index (FBMKLCI). The results of this study indicate that the portfolio consists of 12 stocks with different weights to track FBMKLCI Index which comprises 30 stocks. The portfolio of the regression model is able to track FBMKLCI Index effectively at minimum tracking error 0.4531% which approaches zero tracking error. Therefore, the regression model is appropriate for the investors to track the stock market index in Malaysia. The significance of this study is to determine the portfolio composition in tracking Malaysia stock market index which generates weekly excess return 0.0019% at minimum tracking error 0.4531% without purchasing all the index components.

Keywords
Index Tracking, Regression Model, Mean Return, Tracking Error, Portfolio Composition

Cite this paper
Lam Weng Siew, Lam Weng Hoe, Stock Selection With Regression Model In Tracking Malaysia Stock Market Index , SCIREA Journal of Management. Volume 1, Issue 1, October 2016 | PP. 22-30.

References

[ 1 ]	Gitman, L. J., Joehnk, M. D. and Smart, L. J., 2011. Fundamentals of Investing. 11th Edition, Pearson.
[ 2 ]	Roll, R., 1992. A Mean Variance Analysis of Tracking Error. The Journal of Portfolio Management, 18(1): 13-22.
[ 3 ]	Beasley, J. E., Meade, N. and Chang, T. J., 2003. An evolutionary heuristics for the index tracking problem. European Journal of Operational Research, 148: 621-643.
[ 4 ]	Alexander, C. and Dimitriu, A., 2005. Indexing and statistical arbitrage. The Journal of Portfolio Management, 50-63.
[ 5 ]	Canakgoz, N. A. and Beasley, J. E., 2008. Mixed integer programming approaches for index tracking and enhanced indexation. European Journal of Operational Research, 196: 384-399.
[ 6 ]	Guastaroba, G. and Speranza, M. G., 2012. Kernel Search: An application to index tracking problem. European Journal of Operational Research: 217, 54-68.
[ 7 ]	Lam, W. S. and Lam, W. H., 2015. Portfolio Selection for Index Tracking Problem in Malaysian Stock Market. International Journal of Administration and Governance, 1(3): 15-17.
[ 8 ]	Lam, W. S., Saiful, J. and Hamizun, I., 2014. Comparison between Two Stage Regression Model and Variance Model in Portfolio Optimization. Journal of Applied Science and Agriculture, 9(18): 36-40.
[ 9 ]	Lam, W. S., Saiful, J. and Hamizun, I., 2014. Index Tracking Modelling in Portfolio Optimization with Mixed Integer Linear Programming. Journal of Applied Science and Agriculture, 9(18): 47-50.
[ 10 ]	Lam, W. S., Saiful, J. and Hamizun, I., 2015. The impact of human behavior towards portfolio selection in Malaysia. Procedia of Social and Behavioral Sciences, 172: 674-678.
[ 11 ]	Lam, W. S., Saiful, J. and Hamizun, I., 2015. Investigation on relationship between human behavior and portfolio selection problem in Malaysia, Advances in Environmental Biology, 9(7): 6-10.
[ 12 ]	Lam, W. S., Saiful, J. and Hamizun, I., 2015. An empirical study on the characteristics on high risk aversion behavior in portfolio decision making, Advances in Environmental Biology, 9(7): 17-20.
[ 13 ]	Lam, W. S. and Lam, W. H., 2016. Mathematical modeling in enhanced index tracking with optimization model. Journal of Numerical Analysis and Applied Mathematics, 1(1): 1-5.
[ 14 ]	Wu, L. C., Chou, S. C., Yang, C. C. and Ong, C. S., 2007. Enhanced Index Investing Based on Goal Programming. The Journal of Portfolio Management, 33: 49-56.
[ 15 ]	Fry, G. S., 2014. Business Statistics: A Decision-Making Approach. 9th Edition. London: Pearson.
[ 16 ]	Meade, N. and Salkin, G. R., 1990. Developing and Maintaining an Equity Index Fund. Journal of Operation Research Society, 41(7): 599-607.
[ 17 ]	Bodie, Z., Kane, A. and Marcus, A. J., 2008. Investments. 7th Edition. New York: McGraw-Hill.