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Factor Models, Machine Learning, and Asset Pricing
Abstract
We survey recent methodological contributions in asset pricing using factor models and machine learning. We organize these results based on their primary objectives: estimating expected returns, factors, risk exposures, risk premia, and the stochastic discount factor as well as model comparison and alpha testing. We also discuss a variety of asymptotic schemes for inference. Our survey is a guide for financial economists interested in harnessing modern tools with rigor, robustness, and power to make new asset pricing discoveries, and it highlights directions for future research and methodological advances.
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Metadata
- Authors
- Giglio, S., Kelly, B., & Xiu, D.
- Affiliations
- Yale University (US)
- University of Chicago (US)
- Venue
- Annual Review of Financial Economics
- Publisher
- Annual Reviews
- Published
- 2022-11-01
- Volume
- 14
- Issue
- 1
- Pages
- 337-368
- ISSN
- 1941-1367 (ISSN-L)
- 1941-1375
- OpenAlex
- W4290725190
- Cited by
- 187
- References
- 112
- Citations by year
- 2026: 32 · 2025: 61 · 2024: 43 · 2023: 28 · 2022: 9
- FWCI
- 7.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annual Review of Financial Economics (publishedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Risk and Volatility Modeling
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Keywords
- asset pricing, factor models, machine learning, expected returns, risk exposure, risk premium, stochastic discount factor, model comparison, alpha testing, asymptotic inference, factor estimation
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