The DOI matches its Crossref record; 112 of the 112 references it cites were found in a registry or an index.
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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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Common risk factors in the returns on stocks and bonds
Fama, E. F., & French, K. R. (1993) · Journal of Financial Economics · doi:10.1016/0304-405x(93)90023-5 · Cited by 25,754
Risk, Return, and Equilibrium: Empirical Tests
Fama, E. F., & MacBeth, J. D. (1973) · Journal of Political Economy · doi:10.1086/260061 · Cited by 14,258
Large Sample Properties of Generalized Method of Moments Estimators
Hansen, L. P. (1982) · Econometrica · doi:10.2307/1912775 · Cited by 13,467
Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558
A five-factor asset pricing model
Fama, E. F., & French, K. R. (2015) · Journal of Financial Economics · doi:10.1016/j.jfineco.2014.10.010 · Cited by 7,852
The arbitrage theory of capital asset pricing
Ross, S. A. (1976) · Journal of Economic Theory · doi:10.1016/0022-0531(76)90046-6 · Cited by 7,081
An Intertemporal Capital Asset Pricing Model
Merton, R. C. (1973) · Econometrica · doi:10.2307/1913811 · Cited by 6,559
Economic Forces and the Stock Market
Chen, N., Roll, R. W., & Ross, S. A. (1986) · The Journal of Business · doi:10.1086/296344 · Cited by 5,408
By Force of Habit: A Consumption‐Based Explanation of Aggregate Stock Market Behavior
Campbell, J. Y., & Cochrane, J. H. (1999) · Journal of Political Economy · doi:10.1086/250059 · Cited by 5,210
Flagged first, then by citations
Research works printed but not linked: 8
- Benjamini Y, 2001, Ann. Stat., 28, 1165
- Bryzgalova S., 2015, Spurious factors in linear asset pricing models
- Bryzgalova S, 2020, Forest through the trees: building cross-sections of asset returns
- Cong LW, 2021, Alphaportfolio: direct construction through deep reinforcement learning and interpretable AI
- Jensen TI, 2021, J. Finance.
- Kelly B, 2021, J. Finance.
- Kingma D, Ba J. 2014. Adam: a method for stochastic optimization. arXiv:1412.6980 [cs.LG] · arXiv
- Sharpe WF., 1964, J. Finance, 19, 425
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Nucera, F., Sarno, L., & Zinna, G. (2023) · Review of Financial Studies · doi:10.1093/rfs/hhad049 · Cited by 48
Giglio, S., Xiu, D., & Zhang, D. (2024) · The Journal of Finance · doi:10.1111/jofi.13415 · Cited by 54
Self-citationLearning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models
Koa, K. J. L., Ma, Y., Ng, R., & Chua, T.-S. (2024) · Proceedings of the ACM Web Conference 2024 · doi:10.1145/3589334.3645611 · Cited by 47
Asset Pricing and Machine Learning: A Critical Review of Empirical Findings
Bagnara, M. (2021) · Journal of Economic Surveys · doi:10.1111/joes.12532 · Cited by 66
Attention is all you need: An interpretable transformer-based asset allocation approach
Ma, T., Wang, W., & Chen, Y. (2023) · International Review of Financial Analysis · doi:10.1016/j.irfa.2023.102876 · Cited by 42
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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