Exploring Low-Risk Anomalies: A Dynamic CAPM Utilizing a Machine Learning Approach

Abstract

Low-risk pricing anomalies, characterized by lower returns in higher-risk stocks, are prevalent in equity markets and challenge traditional asset pricing theory. Previous studies primarily relied on linear regression methods, which analyze a limited number of factors and overlook the advantages of machine learning in handling high-dimensional data. This study aims to address these anomalies in the Chinese market by employing machine learning techniques to measure systematic risk. A large dataset consisting of 770 variables, encompassing macroeconomic, micro-firm, and cross-effect factors, was constructed to develop a machine learning-based dynamic capital asset pricing model. Additionally, we investigated the differences in factors influencing time-varying beta between state-owned enterprises (SOEs) and non-SOEs, providing economic explanations for the black-box issues. Our findings demonstrated the effectiveness of random forest and neural networks, with the four-layer neural network performing best and leading to a substantial rise in the excess return of the long–short portfolio, up to 0.36%. Notably, liquidity indicators emerged as the primary drivers influencing beta, followed by momentum. Moreover, our analysis revealed a shift in variable importance during the transition from SOEs to non-SOEs, as liquidity and momentum gradually replaced fundamentals and valuation as key determinants. This research contributes to both theoretical and practical domains by bridging the research gap in incorporating machine learning methods into asset pricing research.