New evidence about the profitability of small and large stocks and the role of volume obtained using Strongly Typed Genetic Programming

Abstract

We employ a special adaptive form of the Strongly Typed Genetic Programming (STGP)-based learning algorithm to develop trading rules based on a survival of the fittest principle. Employing returns data for the Russell 1000, Russell 2000 and Russell 3000 indices the STGP method produces greater returns compared to random walk benchmark forecasts, and the forecasting models are statistically significant in respect of their predictive effectiveness for all three indices both in- and out-of-sample. Using one-step-ahead STGP models to investigate the differences in return patterns between small and large stocks we demonstrate the superiority of models developed for small-cap stocks over those developed for large-cap stocks, indicating that small stocks are more predictable. We also investigate the relationship between trading volume and returns, and find that trading volume has negligible predictive strength, implying it is not advantageous to develop volume-based trading strategies.