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<p>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.</p>

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How well do machine learning models in finance work?

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月27日

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  • Yeonchan Kang,
  • Doojin Ryu,
  • Robert I. Webb

摘要

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.