An Empirical Study of Financial Factor Mining Based on Gene Expression Programming

摘要

Financial factor mining has important theoretical significance and practical value for further improving the investor's rate of return, but it has always been manually mining. Automatic mining of investment factors by genetic algorithm can better realize the preliminary work of finding factors, understanding factors, and applying factors. This paper proposes a method based on gene expression programming (GEP) to mine the relationship between data and establish the predictive model, and the optimization goal is the return rate after the next 5 trading days. The empirical research on the financial factor mining through genetic optimization method has achieved certain results and the experimental results have been analyzed.