A novel hybrid stock selection method with stock prediction

Abstract

The success of stock selection is contingent upon the future performance of stock markets. We incorporate stock prediction into stock selection to specifically capture the future features of stock markets, thereby forming a novel hybrid (two-step) stock selection method (involving stock prediction and stock scoring). (1) Stock returns for the next period are predicted using emerging computational intelligence (CI), i.e., extreme learning machine with a powerful learning capacity and a fast computing speed. (2) A stock scoring mechanism is developed as a linear combination of the predicted factor (generated in the first step) and the fundamental factors (popular in existing literature) based on CI-based optimization for weights, and top-ranked stocks are selected for an equally weighted portfolio. Using the A-share market of China as the study sample, the empirical results show that the novel hybrid approach, using highly weighted predicted factors, statistically outperforms both traditional methods (without stock prediction) and similar counterparts (with other model designs) in terms of market returns, which suggests the great contribution of stock prediction for improving stock selection.