A causal feature selection algorithm for stock prediction modeling

Abstract

A key issue of quantitative investment (QI) product design is how to select representative features for stock prediction. However, existing stock prediction models adopt feature selection algorithms that rely on correlation analysis. This paper is the first to apply observational data-based causal analysis to stock prediction. Causalities represent direct influences between various stock features (important for stock analysis), while correlations cannot distinguish direct influences from indirect ones. This study proposes the causal feature selection (CFS) algorithm to select more representative features for better stock prediction modeling. CFS first identifies causalities between variables and then, based on the results, generates a feature subset. Based on 13-year data from the Shanghai Stock Exchanges, comparative experiments were conducted between CFS and three well-known feature selection algorithms, namely, principal component analysis (PCA), decision trees (DT; CART), and the least absolute shrinkage and selection operator (LASSO). CFS performs best in terms of accuracy and precision in most cases when combined with each of the seven baseline models, and identifies 18 important consistent features. In conclusion, CFS has considerable potential to improve the development of QI product.