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

TL;DR

This paper reviews how machine learning and AI techniques are transforming asset pricing by overcoming traditional model limitations, enhancing predictive accuracy, and addressing challenges like explainability and overfitting in financial markets.

Contribution

It provides a comprehensive overview of ML applications in asset pricing, highlighting novel methodologies and their potential to reshape quantitative finance.

Findings

ML models improve return prediction and portfolio optimization

Advanced ML algorithms adapt to changing market dynamics

Addressing explainability and overfitting challenges in ML applications

Abstract

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 the 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) the incorporation of 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. In addition, this paper explores challenges in applying ML in asset pricing, addressing the growing demand for explainability in decision-making and mitigating overfitting in complex models. This paper aims to provide insights into novel methodologies showcasing the potential of ML to reshape the future of quantitative finance.