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Machine Learning for Stock Selection

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  • Keywan Christian Rasekhschaffe
  • Robert C. Jones

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. Disclosure: The authors report no conflicts of interest. Editor’s Note Submitted 19 July 2018Accepted 30 January 2019 by Stephen J. Brown

Suggested Citation

  • Keywan Christian Rasekhschaffe & Robert C. Jones, 2019. "Machine Learning for Stock Selection," Financial Analysts Journal, Taylor & Francis Journals, vol. 75(3), pages 70-88, July.
    • Handle: RePEc:taf:ufajxx:v:75:y:2019:i:3:p:70-88
    DOI: 10.1080/0015198X.2019.1596678
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    Cited by:

    1. Caparrini, Antonio & Arroyo, Javier & Escayola Mansilla, Jordi, 2024. "S&P 500 stock selection using machine learning classifiers: A look into the changing role of factors," Research in International Business and Finance, Elsevier, vol. 70(PA).
    2. YingShuang Tan & Wanshuo Yang & Sid Suntrayuth & Xin Yu & Stavros Sindakis & Saloome Showkat, 2024. "RETRACTED ARTICLE: Optimizing Stock Portfolio Performance with a Combined RG1-TOPSIS Model: Insights from the Chinese Market," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(2), pages 9029-9052, June.
    3. Felix Divo & Eric Endress & Kevin Endler & Kristian Kersting & Devendra Singh Dhami, 2024. "Forecasting Company Fundamentals," Papers 2411.05791, arXiv.org.
    4. Francisco Peñaranda & Enrique Sentana, 2024. "Portfolio management with big data," Working Papers wp2024_2411, CEMFI.
    5. Haixiang Yao & Shenghao Xia & Hao Liu, 2024. "Return predictability via an long short‐term memory‐based cross‐section factor model: Evidence from Chinese stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1770-1794, September.

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