Asset Pricing and Machine Learning: A critical review

2022/09/04 by Matteo Bagnara · 3 citations
Decision Sciences · Economics, Econometrics and Finance · #Financial Markets and Investment Strategies #Housing Market and Economics #Stock Market Forecasting Methods

paper · pdf · doi:10.1111/joes.12532

openalex publication_date 2022/09/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03

Abstract

Abstract The latest development in empirical Asset Pricing is the use of Machine Learning methods to address the problem of the factor zoo. These techniques offer great flexibility and prediction accuracy but require special care as they strongly depart from traditional Econometrics. We review and critically assess the most recent and relevant contributions in the literature grouping them into five categories defined by the Machine Learning (ML) approach they employ: regularization, dimension reduction, regression trees/random forest (RF), neural networks (NNs), and comparative analyses. We summarize the empirical findings with particular attention to their economic interpretation providing hints for future developments.

Citations

• Common risk factors in the returns on stocks and bonds
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Cited by

• When Alpha Disappears: A One-Switch Benchmark for Decision-Time Leakage in Financial Backtests
• Multilayer Perceptron Neural Network Models in Asset Pricing: An Empirical Study on Large-Cap US Stocks
• Revisiting the spanning hypothesis with machine learning methods: a systematic literature review

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