Anomalies and the Expected Market Return

60 Pages Posted: 7 Apr 2020 Last revised: 16 Nov 2021

See all articles by Xi Dong

Xi Dong

CUNY Baruch College

Yan Li

Southwestern University of Finance and Economics (SWUFE) - School of Accounting

David Rapach

Research Department, Federal Reserve Bank of Atlanta; Washington University in St. Louis

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School

Date Written: November 15, 2021

Abstract

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.

Keywords: Out-of-sample predictability, Market excess return, Long-short anomaly portfolio return, Machine learning, Limits of arbitrage, Mispricing correction persistence

JEL Classification: G11, G14

Suggested Citation

Dong, Xi and Li, Yan and Rapach, David and Zhou, Guofu, Anomalies and the Expected Market Return (November 15, 2021). Journal of Finance, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3562774 or http://dx.doi.org/10.2139/ssrn.3562774

Xi Dong

CUNY Baruch College ( email )

Yan Li

Southwestern University of Finance and Economics (SWUFE) - School of Accounting ( email )

55 Guanghuacun St
Sichuan, 610072
China

HOME PAGE: http://https://sites.google.com/view/yanli/home

David Rapach (Contact Author)

Research Department, Federal Reserve Bank of Atlanta ( email )

1000 Peachtree Street N.E.
Atlanta, GA 30309-4470
United States

Washington University in St. Louis ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

HOME PAGE: http://https://sites.google.com/slu.edu/daverapach

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School ( email )

Washington University
Campus Box 1133
St. Louis, MO 63130-4899
United States
314-935-6384 (Phone)
314-658-6359 (Fax)

HOME PAGE: http://apps.olin.wustl.edu/faculty/zhou/

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