Much of the extant literature predicts market returns with “simple” models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to “complex” models in which the number of parameters exceeds the number of observations. We empirically document the virtue of complexity in U.S. equity market return prediction. Our findings establish the rationale for modeling expected returns through machine learning.

中文翻译：

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回报预测中复杂性的优点

现有的许多文献都使用仅使用几个参数的“简单”模型来预测市场回报。与传统观点相反，我们从理论上证明，与参数数量超过观测数量的“复杂”模型相比，简单模型严重低估了回报的可预测性。我们凭经验记录了美国股市回报预测的复杂性的优点。我们的研究结果确立了通过机器学习对预期回报进行建模的基本原理。