Complexity in Factor Pricing Models

Antoine Didisheim, Shikun (Barry) Ke, Bryan T. Kelly and Semyon Malamud

No 31689, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C1 C4 C58 G1 G10 G12 G14 G17 (search for similar items in EconPapers)
Date: 2023-09
Note: AP
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