The Virtue of Complexity in Return Prediction
© 2022 EPFL
Professors Semyon Malamud from SFI@EPFL, Bryan T. Kelly and Kangying Zhou both from Yale School of Management have a new forthcoming paper in the prestigious Journal of Finance.
The Virtue of Complexity in Return Prediction
Abstract
Much of the extant literature predicts market returns with \simple" models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to "complex" models in which the number of parameters exceeds the number of observations. We empirically document the virtue of complexity in US equity market return prediction. Our fundings establish the rationale for modeling expected returns through machine learning.