A methodology for stock market analysis utilizing rough set theory

Abstract

Quants are aiding brokers and investment managers for stock market analysis and prediction. The Quant's black magic stems from many of the evolving artificial intelligence (AI) techniques. Extensive literature exists describing attempts to use AI techniques, and in particular neural networks, for analyzing stock market variations. The main problem with neural networks, however is the tremendous difficulty in interpreting the results. The neural nets approach is a black box approach in which no new knowledge regarding the nature of the interactions between the market indicators and the stock market fluctuations is extracted from the market data. Consequently, there is a need to develop methodologies and tools which would help in increasing the degree of understanding of market processes and, at the same time, would allow for relatively accurate predictions. The methods stemming from the research on knowledge discovery in databases (KDD) seem to provide a good mix of predictive and knowledge acquisition capabilities for the purpose of market prediction and market data analysis. This paper describes the methodology of rough sets while citing two applications which apply rough set theory (BST) for stock market analysis using Datalogic/R+. This is based on the variable precision model of rough sets (VPRS) to acquire new knowledge from market data.