A comparative study of stock scoring using regression and genetic-based linear models

Stock selection has long been a challenging and important task in investment and 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 ML-based linear models for stock scoring, which is crucial to the success of stock selection. In ML-based models, Genetic Algorithms (GA), a class of well-known search algorithms in the area of ML, is used for optimization of model parameters and selection of input variables to the stock scoring model. We will show that our proposed genetic-based method significantly outperforms the traditional regression-based method as well as the benchmark. As a result, we expect this genetic-based methodology to advance the research in machine learning for finance and provide an attractive alternative to stock selection over the regression-based approach.