Theoretical Evolution, Practical Application, and Cutting-edge Challenges of Multi-factor Stock Selection Models

Abstract

With the development of financial computing, the importance of quantitative investing has steadily increased. Multi-factor stock selection models, as a crucial component of quantitative investing, play an irreplaceable and crucial role in investment practice. This article first reviews the theoretical evolution of multi-factor models, from the Capital Asset Pricing Model (CAPM) to the Fama-French Five Factors Model (FF5). Secondly, the theoretical models such as CAPM pointed out in this article need to screen out investable factors from academic factors and then be applied in the market to improve the fit between model theory and reality. Next, he demonstrated how multi-factor models can be localized in the Chinese market by introducing factors with high adaptability to the Chinese market, such as pricing factors. Finally, he discussed how, given the current pace of technological development, emerging machine learning, through its superior computational capabilities for high-dimensional, large-scale data, can help multi-factor models improve their predictive effectiveness for future stock market trends and their ability to interpret past data.