Anomalies and the Expected Market Return

• 成果类型: Article
• 署名作者: Dong, Xi; Li, Yan; Rapach, David E.; Zhou, Guofu
• 署名单位: City University of New York (CUNY) System; Baruch College (CUNY); Southwestern University of Finance & Economics - China; Saint Louis University; Washington University (WUSTL)
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/jofi.13099
• 发表日期: 2022
• 页码: 639-681
• 关键词: cross-section; ASYMPTOTIC INFERENCE; stock returns; RISK; regression; momentum; tests; INFORMATION; volatility; arbitrage

摘要

We provide the first systematic evidence on the link between long-short anomaly portfolio returns-a cornerstone of the cross-sectional literature-and the time-series predictability of the aggregate market excess return. Using 100 representative anomalies from the literature, we employ a variety of shrinkage techniques (including machine learning, forecast combination, and dimension reduction) to efficiently extract predictive signals in a high-dimensional setting. We find that long-short anomaly portfolio returns evince statistically and economically significant out-of-sample predictive ability for the market excess return. The predictive ability of anomaly portfolio returns appears to stem from asymmetric limits of arbitrage and overpricing correction persistence.