Cited by 188Financial Markets and Investment StrategiesFinancial Risk and Volatility ModelingStock Market Forecasting Methods

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Factor Models, Machine Learning, and Asset Pricing

Giglio, Stefano, Kelly, Bryan, Xiu, Dacheng·2022·Annual Review of Financial Economics

DOI 10.1146/annurev-financial-101521-104735

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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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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
Bayes factors
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

Show all 112 references

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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Authors: Giglio, S., Kelly, B., & Xiu, D.
Affiliations: Yale University (US)
University of Chicago (US)
ORCID: Giglio, Stefano
Kelly, Bryan
Xiu, Dacheng
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
DOI: 10.1146/annurev-financial-101521-104735
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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Factor Models, Machine Learning, and Asset Pricing

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