The Virtue of Complexity in Return Prediction

• 成果类型: Article
• 署名作者: Kelly, Bryan; Malamud, Semyon; Zhou, Kangying
• 署名单位: Yale University; National Bureau of Economic Research; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Swiss Finance Institute (SFI); Centre for Economic Policy Research - UK; Yale University
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/jofi.13298
• 发表日期: 2024
• 页码: 459-503
• 关键词: CONDITIONING INFORMATION; premium; sample; predictability; regression; models

摘要

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 U.S. equity market return prediction. Our findings establish the rationale for modeling expected returns through machine learning.