Predicting Equity Returns from Securities Data with Minimal Rule Generation

Applications

Based on our experiments with financial market data, we have demonstrated that the domain can be effectively modeled by classification rules induced from available historical data for the purpose of making gainfu.l predictlons for equity investments, and thatnew techniques developed atiBM Research, including minimal rule generation (I~MINI} and contextual feature analysis, are robust enough to consistently extract useful information from noisy domains such as financial markets. We will briefly introduce the rationale for our rule minimisation technique, and the motivation for the use of contextual information in analysing features. We will then describe our experience from several experiments with the S&P 500data, Showing the general methodology, and the restdts of Correlations and managed investment based on classification rules generated by R-MINLWe will sketch how the rules for clauificationscan be effectively used for numerical prediction, and eventually to an investment policy. Both the development of robust minimal classification rule generation, ms well as its application to the financial markets, are part of a continuing study.