Complexity in Factor Pricing Models

Antoine Didisheim, Shikun Ke, Bryan T. Kelly and Semyon Malamud
Additional contact information
Antoine Didisheim: Swiss Finance Institute, UNIL
Shikun Ke: Yale School of Management
Bryan T. Kelly: Yale SOM; AQR Capital Management, LLC; National Bureau of Economic Research (NBER)
Semyon Malamud: Ecole Polytechnique Federale de Lausanne; Centre for Economic Policy Research (CEPR); Swiss Finance Institute

No 23-19, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

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 average pricing errors—is improving in model parameterization (or “complexity”). Our results predict that the best asset pricing models (in terms of expected out-of-sample performance) have an extremely large number of factors (more than the number of training observations or base assets). Our empirical findings verify the theoretically predicted “virtue of complexity” in the cross-section of stock returns and find that the best model combines tens of thousands of factors. We also derive the feasible Hansen- Jagannathan (HJ) bound: The maximal Sharpe ratio achievable by a feasible portfolio strategy. The infeasible HJ bound massively overstates the achievable maximal Sharpe ratio due to a complexity wedge that we characterize.

Keywords: Portfolio choice; asset pricing tests; optimization; expected returns; predictability (search for similar items in EconPapers)
JEL-codes: C3 C58 C61 G11 G12 G14 (search for similar items in EconPapers)
Pages: 148 pages
Date: 2023-03
New Economics Papers: this item is included in nep-ecm and nep-fmk
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2319

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