Reflections on Asset Pricing Factors: A Machine Learning-Based Perspective

Abstract

In recent years, scholars have explored hundreds of asset pricing factors built upon the foundation of the Fama five-factor model, sparking widespread discussion in the academic community. Simultaneously, the advancement of machine learning techniques has brought innovation to asset pricing factors. This article provides an overview of typical asset pricing factors and the application of machine learning in pricing models. It begins by discussing the construction of new asset pricing factors and then delves into the innovations brought about by machine learning in asset pricing models. The diversity of factors increases the fit of asset pricing models but also presents corresponding challenges. The application of machine learning techniques addresses issues such as overfitting in pricing models, further enhancing model effectiveness. The primary value of this article lies in summarizing various perspectives on asset pricing factors in recent years, exploring the significant applications of machine learning in asset pricing models, and providing a forward-looking view on the development of asset pricing models.