Predicting Anomalies

64 Pages Posted: 30 Sep 2024

See all articles by Boone Bowles

Boone Bowles

Mays Business School at Texas A&M University

Adam V. Reed

University of North Carolina Kenan-Flagler Business School

Matthew C. Ringgenberg

University of Utah - Department of Finance

Jake Thornock

Brigham Young University

Date Written: August 16, 2024

Abstract

We show that stock returns follow predictable patterns before the publication of anomaly trading signals. Moreover, anomaly trading signals derived from financial data are themselves predictable, making it possible to trade before financial statements are released. A trading strategy based on predicted signals earns an annualized return of 3.65% in the quarter before the signal is released. In recent periods this predictability is concentrated in signals that are harder to forecast and returns are increasingly earned several quarters before signals are released. Our findings suggest anomalies are more anomalous than previously recognized.

Keywords: Anomalies, Asset Pricing, Information Economics

JEL Classification: G12, G14

Suggested Citation

Bowles, Boone and Reed, Adam V. and Ringgenberg, Matthew C. and Thornock, Jacob, Predicting Anomalies (August 16, 2024). Available at SSRN: https://ssrn.com/abstract=4939779 or http://dx.doi.org/10.2139/ssrn.4939779

Boone Bowles (Contact Author)

Mays Business School at Texas A&M University ( email )

360Q Wehner Building
College Station, TX 77843-4218
United States

HOME PAGE: http://boonebowles.com

Adam V. Reed

University of North Carolina Kenan-Flagler Business School ( email )

Kenan-Flagler Business School
Chapel Hill, NC 27599-3490
United States

Matthew C. Ringgenberg

University of Utah - Department of Finance ( email )

David Eccles School of Business
Salt Lake City, UT 84112
United States

Jacob Thornock

Brigham Young University ( email )

Provo, UT 84602
United States
8014220828 (Phone)

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