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Machine Learning Classification and Portfolio Construction: Does the Loss Function Matter?

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  • Yang Bai
  • Kuntara Pukthuanthong

Abstract

Classification outperforms regression across matched machine learning models in portfolio construction. A stacking ensemble of gradient boosted tree, random forest, and neural network yields a value-weighted annualized Sharpe ratio of 1.83 for classification and 1.11 for regression. This outperformance persists in multiclass settings, across subsamples, and after transaction costs. Spanning tests show that classification retains economically large alphas after we control for regression, whereas regression alphas shrink substantially once we control for classification. These results indicate that classification extracts more return information than matched regression. Our diagnostics trace classification's advantage to sharper and more precise separation of return deciles.

Suggested Citation

  • Yang Bai & Kuntara Pukthuanthong, 2021. "Machine Learning Classification and Portfolio Construction: Does the Loss Function Matter?," Papers 2108.02283, arXiv.org, revised Jun 2026.
    • Handle: RePEc:arx:papers:2108.02283
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