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How well do machine learning models in finance work?

Author

Listed:
  • Yeonchan Kang

    (Sungkyunkwan University, Department of Economics)

  • Doojin Ryu

    (Sungkyunkwan University, Department of Economics)

  • Robert I. Webb

    (University of Virginia, McIntire School of Commerce)

Abstract

We examine how machine learning models predict stock returns in the Korean market. By analyzing various firm characteristics and macroeconomic variables, we find that tree-based models outperform other machine learning approaches. This finding suggests that, in data-constrained contexts, moderately complex models outperform advanced methods that require extensive datasets. Using PFI, SHAP, and LIME, we consistently identify the 36-month momentum as the key predictor. PDP, ICE, and ALE analyses reveal threshold effects of 36-month momentum that diminish at higher return levels. Our findings underscore the value of ensemble-based methods in settings characterized by short data histories and heightened volatility. This study illustrates how multimethod interpretability can yield deeper economic insights, ultimately guiding more effective investment strategies and policy decisions.

Suggested Citation

  • Yeonchan Kang & Doojin Ryu & Robert I. Webb, 2025. "How well do machine learning models in finance work?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-30, December.
    • Handle: RePEc:spr:fininn:v:11:y:2025:i:1:d:10.1186_s40854-025-00870-0
    DOI: 10.1186/s40854-025-00870-0
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    Keywords

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    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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