A predictive model construction applying rough set methodology for Malaysian stock market returns

Abstract

This paper describes the invention about the stock market prediction for use of investors. More specifically, the stock market’s movements are analyzed and predicted in order to retrieve knowledge that could guide investors on when to buy and sell. Through a case study on trading Kuala Lumpur Composite Index and individual firms listed in Bursa Malaysia, rough sets is shown to be an applicable and effective tool for stock market analysis. The ability of rough set approach to discover dependencies in data while eliminating superfluous factors in noisy stock market data deems very useful to extract trading rules. This is very crucial to detect market timing for market timing is detected by capturing the major turning points in data. Nevertheless, one failure of the predictive system developed in this research is its inability to detect numerous minor trends displayed by volatile individual firms, thus the failure to produce effective trading signals to generate profits above the naive strategy for these firms.