A Comparative Study of Regression and Evolution-Based Stock Selection Models for Investor Sentiment

Stock selection has long been recognized as an important task in finance. Researchers and practitioners in this area often use regression models to tackle this problem due to their simplicity and effectiveness. Recent advances in machine learning (ML) are leading to significant opportunities to solve these problems more effectively. In this paper, we present a comparative study between the traditional regression-based and evolution-based models using investor sentiment indicators for stock selection. In the evolution-based models, Genetic Algorithms (GA) are used for optimization of model parameters and feature selection of input variables simultaneously. We will show that our proposed GA-based method significantly outperforms the traditional regression-based method as well as the benchmark. We thus expect this evolution-based methodology to advance the research in machine learning for behavioral finance.