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

Yang Bai; Kuntara Pukthuanthong

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arXiv · 2108.02283

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

Yang Bai·Kuntara Pukthuanthong

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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 2.08 for classification and 1.39 for regression. This outperformance strengthens with class granularity and persists 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 more precise separation of return deciles.

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Yang Bai, Kuntara Pukthuanthong. 2026-09-04. Machine Learning Classification and Portfolio Construction: Does the Loss Function Matter?. https://arxiv.org/abs/2108.02283

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