Didisheim, Antoine
[VerfasserIn];
Ke, Shikun
[VerfasserIn];
Kelly, Bryan T.
[VerfasserIn];
Malamud, Semyon
[VerfasserIn]
;
National Bureau of Economic Research
Reproduktionsnotiz:
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Beschreibung:
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