From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing

This paper comprehensively reviews the application of machine learning (ML) and AI in finance, specifically in the context of asset pricing. It starts by summarizing traditional asset pricing models and examining their limitations in capturing the complexities of financial markets. It explores how (1) ML models — including supervised, unsupervised, semi-supervised, and reinforcement learning — provide versatile frameworks to address these complexities, and (2) incorporating advanced ML algorithms into traditional financial models enhances return prediction and portfolio optimization. These methods can adapt to changing market dynamics by modeling structural changes and incorporating heterogeneous data sources, such as text and images. The paper also explores challenges in applying ML to asset pricing, including the growing demand for explainability and mitigating overfitting in complex models, aiming to provide insights into methodologies that showcase ML’s potential to reshape the future of quantitative finance.

Recommended citation: Ye*, J., Goswami*, B., Gu*, J., Uddin, A., & Wang, G. (2024). "From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing." arXiv preprint arXiv:2403.06779.
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