The Virtue of Complexity in Return Prediction / Bryan T. Kelly, Semyon Malamud, Kangying Zhou.

Format:

Book

Author/Creator:

Kelly, Bryan T.

Contributor:

National Bureau of Economic Research.

Malamud, Semyon.

Zhou, Kangying.

Series:

Working Paper Series (National Bureau of Economic Research) no. w30217.

NBER working paper series no. w30217

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2022.

Summary:

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 findings establish the rationale for modeling expected returns through machine learning.

Notes:

Print version record

July 2022.