Machine learning is central to empirical asset pricing, but portfolio construction still relies on point predictions and largely ignores asset-specific estimation uncertainty. We propose a simple change: sort assets using uncertainty-adjusted prediction bounds instead of point predictions alone. Across a broad set of ML models and a U.S. equity panel, this approach improves portfolio performance relative to point-prediction sorting. These gains persist even when bounds are built from partial or misspecified uncertainty information. They arise mainly from reduced volatility and are strongest for flexible machine learning models. Identification and robustness exercises show that these improvements are driven by asset-level rather than time or aggregate predictive uncertainty.

翻译：机器学习已成为实证资产定价的核心工具，但投资组合构建仍主要依赖点预测，基本忽略了资产层面的估计不确定性。我们提出一种简单改进：使用不确定性调整的预测边界而非单纯的点预测对资产进行排序。在广泛的机器学习模型和美国股票面板数据中，该方法相较于点预测排序显著提升了投资组合表现。即使预测边界基于部分或有偏误的不确定性信息构建，这些收益优势依然存在。其收益主要源于波动率的降低，且在灵活的机器学习模型中表现最为突出。识别检验与稳健性分析表明，这些改进主要由资产层面的不确定性驱动，而非时间维度或整体预测不确定性。