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Complexity in factor pricing models

Antoine Didisheim, Barry Ke, Bryan Kelly and Semyon Malamud
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
Year of publication:
05 February 2024
Authors: Didisheim, Antoine ; Ke, Barry ; Kelly, Bryan T. ; Malamud, Semyon
Publisher: London : Centre for Economic Policy Research
Subject: Stochastic discount factor | Pricing errors | Alpha | Portfolio choice | CAPM | Portfolio-Management | Portfolio selection | Künstliche Intelligenz | Artificial intelligence | Diskontierung | Discounting | Theorie | Theory
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