AbstractThis study describes a technique originated from the emerging field of machine learning and demonstrates its effectiveness in stock screening. We have derived screening rules by applying a rule induction method, constructed portfolios using the rules, and evaluated the portfolios' performance using the Sharpe, Treynor and Jensen indexes. Results indicate that regularities among stocks can be identified, and portfolios so constructed outperformed the NYSE Composite index and the S&P 500 over the same period.