From Econometrics to Machine Learning: Transforming Empirical Asset Pricing

ABSTRACT Empirical asset pricing is undergoing a transformation with the advent of big data and machine learning. Traditional multifactor models offer simplicity and interpretability but struggle with high‐dimensional covariates and nonlinear relationships. Machine learning, with its predictive power and flexibility, provides a promising alternative. This paper surveys the transition from econometrics to machine learning, tracing the evolution of asset pricing models, addressing empirical challenges, and comparing the strengths and challenges of both approaches. A unified framework based on the stochastic discount factor is proposed, integrating machine learning while preserving economic interpretability. By emphasizing predictive accuracy and theoretical rigor, this paper highlights how machine learning can reshape empirical asset pricing, offering deeper insights into financial markets and new directions for robust empirical research.