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Complexity in Factor Pricing Models

Antoine Didisheim, Shikun (Barry) Ke, Bryan T. Kelly, 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:
September 2023
Authors: Didisheim, Antoine ; Ke, Shikun ; Kelly, Bryan T. ; Malamud, Semyon
Institutions: National Bureau of Economic Research (issuing body)
Publisher: Cambridge, Mass : National Bureau of Economic Research
Subject: CAPM | Künstliche Intelligenz | Artificial intelligence | Modellierung | Scientific modelling | Finanzmarktökonometrie | Financial econometrics
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