Complexity in Factor Pricing Models

Antoine Didisheim, Barry Ke, Bryan Kelly and Semyon Malamud

No 18812, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance---in terms of SDF Sharpe ratio and test asset pricing errors---is improving in model parameterization (or "complexity''). Our empirical findings verify the theoretically predicted "virtue of complexity'' in the cross-section of stock returns. Models with an extremely large number of factors (more than the number of training observations or base assets) outperform simpler alternatives by a large margin

Keywords: Stochastic discount factor; Portfolio choice; Alpha; Pricing errors (search for similar items in EconPapers)
JEL-codes: C3 C58 C61 G11 G12 G14 (search for similar items in EconPapers)
Date: 2024-02
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