Our experiments with capital markets data suggest that the domain can be effectively modeled by classification rules induced from available historical data for the purpose of making gainful predictions for equityinvestments. New classification techniques developed at IBM Research, including minimal rule generation (R-MINI) and contextual feature analysis, seem robust enough for consistently extracting useful information from noisy domains such as financial markets. We will briefly introduce the rationale for our minimal rule generation technique, and the motivation for the use of contextual information in analyzing features. We will then describe our experience from several experiments with the S&P 500 data, illustrating the general methodology, and the results of correlations and simulated managed investment based on classification rules generated by R-MINI. Wewillsketchhow the rules for classifications can be effectively used for numerical prediction, and eventually to an investment ...