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Zeroing In on the Expected Returns of Anomalies

Published online by Cambridge University Press:  12 August 2022

Andrew Y. Chen
Affiliation:
Federal Reserve Board Research and Statistics [email protected]
Mihail Velikov*
Affiliation:
Pennsylvania State University Smeal College of Business
*
[email protected] (corresponding author)
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Abstract

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We zero in on the expected returns of long-short portfolios based on 204 stock market anomalies by accounting for i) effective bid–ask spreads, ii) post-publication effects, and iii) the modern era of trading technology that began in the early 2000s. Net of these effects, the average anomaly’s expected return is a measly 4 bps per month. The strongest anomalies net, at best, 10 bps after controlling for data mining. Several methods for combining anomalies net around 20 bps. Expected returns are negligible despite cost mitigations that produce impressive net returns in-sample and the omission of additional trading costs, like price impact.

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

This article originated from a conversation with Svetlana Bryzgalova. We thank Marie Briere (HFPE discussant), Jennifer Conrad (the editor), Victor DeMiguel, Yesol Huh, Markus Ibert, Nina Karnaukh, Alberto Martin-Utrera (FDU discussant), R. David McLean (the referee), Andy Neuhierl, Steve Sharpe, Nitish Sinha, Ingrid Tierens (Jacobs Levy discussant), Tugkan Tuzun, Michael Weber, Haoxiang Zhu, and seminar participants at the Federal Reserve Board, Penn State University, University of Georgia, the 11th Annual Hedge Fund and Private Equity Research Conference, 2019 Finance Down Under Meetings, 2019 Eastern Finance Association Meetings, 2019 Jacobs Levy Frontiers in Quantitative Finance conference, and 2020 INFORMS Annual Meeting for helpful comments. We are grateful to Victor DeMiguel, Alberto Martin-Utrera, Francisco Nogales, and Raman Uppal for making their data available to us and we thank Rebecca John for excellent research assistance. The views expressed herein are those of the authors and do not necessarily reflect the position of the Board of Governors of the Federal Reserve or the Federal Reserve System.

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