The Virtue of Complexity in Return Prediction

Bryan Kelly, Semyon Malamud and Kangying Zhou

No 17194, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: We theoretically characterize the behavior of return prediction models in the high complexity regime, i.e. when the number of parameters exceeds the number of observations. Contrary to conventional wisdom in finance, return prediction R2 and optimal portfolio Sharpe ratio generally increase with model parameterization, even when minimal regularization is used. Empirically, we document this "virtue of complexity" in US equity market prediction. High complexity models deliver economically large and statistically significant out-of-sample portfolio gains relative to simpler models, due in large part to their remarkable ability to predict recessions.

Keywords: Portfolio choice; Machine learning; Random matrix theory; Benign overfit; Overparameterization (search for similar items in EconPapers)
JEL-codes: C3 C58 C61 G11 G12 G14 (search for similar items in EconPapers)
Date: 2022-04
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