The Virtue of Complexity in Return Prediction
理论证明相比参数少的简单模型，参数多于观测数的复杂模型能更准确预测市场收益，并在美国股市数据中验证了这一优势，为使用机器学习建模预期收益提供了依据。
ABSTRACT 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.