Financial Machine Learning
Open accessAbstract
We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping machine learning tools, as well as for statisticians and machine learners seeking interesting financial contexts where advanced methods may be deployed.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Macro assets, such as commodities, fixed income, currencies, and equity indices, have a smaller cross section than equity markets, creating a 'small data' challenge for training models.
“Macro assets, such as commodities, fixed income, currencies, and equity indices, have a smaller cross section than equity markets, creating a 'small data' challenge for training models (Kelly and Xiu, 2023).”
From The Growth and Performance of Artificial Intelligence in Asset Management (2026) · cites this paper as Kelly and Xiu, 2023
Cited by
Sorted by citations
The Growth and Performance of Artificial Intelligence in Asset Management
Chen, S., Sialm, C., & Xu, D. (2026) · National Bureau of Economic Research · doi:10.3386/w35273
- Authors
- Kelly, B. T., & Xiu, D.
- Affiliations
- National Bureau of Economic Research (US)
- Corresponding author
- Kelly, Bryan T.
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2023
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W4384210651
- Cited by
- 9
- References
- 0
- Citations by year
- 2025: 3 · 2024: 1 · 2023: 3 · 2020: 1 · 2019: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (acceptedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Social Sciences › Business, Management and Accounting › Management Information Systems › FinTech, Crowdfunding, Digital Finance
- Keywords
- machine learning, financial markets, financial econometrics, high-dimensional data, time series forecasting, asset pricing, trading strategies
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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