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

ArXiv ID: 2403.06779 “View on arXiv”

Authors: Unknown

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.

Keywords: Machine Learning, Asset Pricing, Portfolio Optimization, Quantitative Finance, Explainability, Equities

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Lab Rats
  • Why: The paper presents substantial mathematical density with multiple advanced equations, derivatives, and formal proofs regarding latent factor estimation and non-linear modeling. However, it is a comprehensive review article with no empirical backtests, code, or implementation details, relying instead on theoretical discussions of ML applications.
  flowchart TD
    A["Research Goal: Review ML<br>in Asset Pricing"] --> B["Data: Heterogeneous<br>Market Data"]
    B --> C["Methodology: ML &<br>Traditional Models"]
    C --> D["Computational Process:<br>Supervised/Unsupervised Learning"]
    D --> E["Outcome: Enhanced<br>Return Prediction"]
    D --> F["Outcome: Improved<br>Portfolio Optimization"]
    E & F --> G["Key Challenge:<br>Explainability & Overfitting"]