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PEAD.txt: Post-Earnings-Announcement Drift Using Text

Published online by Cambridge University Press:  17 October 2022

Vitaly MeursaultOpen the ORCID record for Vitaly Meursault[Opens in a new window],
Pierre Jinghong LiangOpen the ORCID record for Pierre Jinghong Liang[Opens in a new window],
Bryan R. RoutledgeOpen the ORCID record for Bryan R. Routledge[Opens in a new window] and
Madeline Marco ScanlonOpen the ORCID record for Madeline Marco Scanlon[Opens in a new window]
Vitaly Meursault*
Affiliation:
Federal Reserve Bank of Philadelphia Research Department
Pierre Jinghong Liang
Affiliation:
Carnegie Mellon University Tepper School of Business liangj@andrew.cmu.edu
Bryan R. Routledge
Affiliation:
Carnegie Mellon University Tepper School of Business routledge@cmu.edu
Madeline Marco Scanlon
Affiliation:
University of Pittsburgh Katz School of Business mms238@pitt.edu
*
vitaly.meursault@phil.frb.org (corresponding author)
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Abstract

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We construct a new numerical measure of earnings announcement surprises, standardized unexpected earnings call text (SUE.txt), that does not explicitly incorporate the reported earnings value. SUE.txt generates a text-based post-earnings-announcement drift (PEAD.txt) larger than the classic PEAD. The magnitude of PEAD.txt is considerable even in recent years when the classic PEAD is close to 0. We explore our text-based empirical model to show that the calls’ news content is about details behind the earnings number and the fundamentals of the firm.

Information

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 58 , Issue 6 , September 2023, pp. 2299 - 2326
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 (https://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

We are grateful for the helpful comments of the conference and seminar participants at the 2021 SFS Cavalcade North America, 2021 FARS, ETH Zurich, Bocconi University, Indiana University, and Brown University. We thank Andrew Gross for his research assistance with the annotation task. Meursault is grateful for the 2020 William W. Cooper Doctoral Dissertation Award and PNC Presidential Fellowship for Outstanding Research on the Future of Financial Services that supported this project while he was a Ph.D. student at the Tepper School of Business, Carnegie Mellon University. We also thank an anonymous referee, Sean Cao (a referee), and Jarrad Harford (the editor) for their guidance throughout the revision and publication process. The views expressed in this article are solely those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Philadelphia or the Federal Reserve System. Any errors or omissions are the responsibility of the authors. No statements here should be treated as legal advice.

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