Prof. Malamud's latest publication seen as potentially transformative
© 2022 EPFL
Prof. Semyon Malamud's lastest paper: "The Virtue of Complexity in Return Prediction" written with Bryan T. Kelly (Yale SOM) and Kangying Zhou (Yale School of Management) is the lead topic of a review written by Jin Won Choi, COE of Enjine. He cites this research as one of the few papers that challenge conventional wisdom about financial machine learning.
Abstract of the paper
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.