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Earnings Autocorrelation and the Post-Earnings-Announcement Drift: Experimental Evidence

Published online by Cambridge University Press:  24 July 2023

Josef Fink ,
Stefan Palan Open the ORCID record for Stefan Palan [Opens in a new window]  and
Erik Theissen Open the ORCID record for Erik Theissen [Opens in a new window]
Josef Fink
Affiliation:
McKinsey and Company Vienna josef_fink@mckinsey.com
Stefan Palan*
Affiliation:
University of Graz Institute of Banking and Finance
Erik Theissen
Affiliation:
University of Mannheim Finance area theissen@uni-mannheim.de
*
stefan.palan@uni-graz.at (corresponding author)
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Abstract

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Post-earnings-announcement drift (PEAD) is one of the most solidly documented asset pricing anomalies. We use the controlled conditions of the experimental lab to investigate whether earnings autocorrelation is the driving cause of this anomaly. We observe PEAD in settings with uncorrelated and correlated earnings surprises, confirming that earnings autocorrelation is not a necessary condition for PEAD. Instead, it acts as an accelerator: PEAD is stronger when earnings surprises are correlated. We further show that market prices underadjust to fundamental value changes, and that trading strategies can profitably exploit the PEAD.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 59 , Issue 6 , September 2024 , pp. 2799 - 2837
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), 2023. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

We thank Jennifer Conrad and Elena Asparouhova—respectively, the editor and referee of the current version—as well as two anonymous reviewers of a previous submission, Ray Ball, Te Bao, Bruno Biais, Peter Bossaerts, Jürgen Brandner, Peiran Jiao, Alexander Kempf, Michael Kirchler, Olaf Korn, Christian Leuz, Roland Mestel, Kerstin Mitterbacher, Stefan Nagel, Thomas Post, Ryan Riordan, Andrea Schertler, Thomas Stöckl, Martin Weber, Yilong Xu, and the participants of the 2019 Austrian Working Group on Banking and Finance workshop, the 2020 virtual finance workshop at Radboud University, the 2021 Experimental Finance conference, and seminar participants at the UC Louvain, the University of Cologne, the University of Graz, the University of Maastricht, and the University of Mannheim for valuable comments. We also thank Adele Theiler for research assistance. This work was supported by the Austrian Science Fund FWF (project no. P32124-G27). The research project was approved by the IRB of the University of Graz (case ID 39/57/63). The authors have no conflicts of interest to disclose.

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