Machine Learning for Stock Selection
Abstract
Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning techniques can be practical investment tools. Although machine learning algorithms can uncover subtle, contextual, and nonlinear relationships, overfitting poses a major challenge when one is trying to extract signals from noisy historical data. We describe some of the basic concepts of machine learning and provide a simple example of how investors can use machine learning techniques to forecast the cross-section of stock returns while limiting the risk of overfitting.
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- Authors
- Rasekhschaffe, K. C., & Jones, R. C.
- Affiliations
- Keywan Christian Rasekhschaffe is a senior quantitative strategist at Gresham Investment Management, LLC, in New York City.
- Robert C. Jones, CFA, is chair and chief investment officer of System Two Advisors, LP, in Summit, New Jersey.
- Venue
- Financial Analysts Journal
- Publisher
- Informa UK Limited
- Published
- 2019-05-13
- Volume
- 75
- Issue
- 3
- Pages
- 70-88
- ISSN
- 0015-198X (ISSN-L)
- 1938-3312
- OpenAlex
- W2923002376
- Cited by
- 204
- References
- 0
- Citations by year
- 2026: 12 · 2025: 22 · 2024: 59 · 2023: 29 · 2022: 30 · 2021: 24 · 2020: 20 · 2019: 7 · 2017: 1
- FWCI
- 14.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Financial Analysts Journal (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- 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 › Accounting › Financial Analysis and Corporate Governance
- Keywords
- machine learning, stock selection, financial mathematics, overfitting, cross-section of stock returns, return forecasting, nonlinear relationship, investment tools
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