The extraction of trading rules from stock market data using rough sets

Abstract

We propose the rough set approach to the extraction of trading rules for discriminating between bullish and bearish patterns in the stock market. Rough set theory is quite valuable for extracting trading rules because it can be used to discover dependences in data while reducing the effect of superfluous factors in noisy data. In addition, it does not generate a signal to trade when the pattern of the market is uncertain because the selection of reducts and the extraction of rules are controlled by the strength of each reduct and rule. The experimental results are encouraging and show the usefulness of the rough set approach for stock market analysis with respect to profitability.