Machine Learning Classification and Portfolio Allocation: with Implications from Machine Uncertainty

Yang Bai and Kuntara Pukthuanthong

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Abstract: We use multi-class machine learning classifiers to identify the stocks that outperform or underperform other stocks. The resulting long-short portfolios achieve annual Sharpe ratios of 1.67 (value-weighted) and 3.35 (equal-weighted), with annual alphas ranging from 29\% to 48\%. These results persist after controlling for machine learning regressions and remain robust among large-cap stocks. Machine uncertainty, as measured by predicted probabilities, impairs the prediction performance. Stocks with higher machine uncertainty experience lower returns, particularly when human proxies of information uncertainty align with machine uncertainty. Consistent with the literature, such an effect is driven by the past underperformers.

Date: 2021-08, Revised 2025-07
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk and nep-isf
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