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Double Machine Learning: Explaining the Post-Earnings Announcement Drift

Published online by Cambridge University Press:  20 February 2023

Jacob H. Hansen Open the ORCID record for Jacob H. Hansen [Opens in a new window]  and
Mathias V. Siggaard
Jacob H. Hansen*
Affiliation:
Aarhus University Department of Economics and Business Economics, CREATES
Mathias V. Siggaard
Affiliation:
Aarhus University Department of Economics and Business Economics, CREATES [email protected]
*
[email protected] (corresponding author)
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Abstract

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We demonstrate the benefits of merging traditional hypothesis-driven research with new methods from machine learning that enable high-dimensional inference. Because the literature on post-earnings announcement drift (PEAD) is characterized by a “zoo” of explanations, limited academic consensus on model design, and reliance on massive data, it will serve as a leading example to demonstrate the challenges of high-dimensional analysis. We identify a small set of variables associated with momentum, liquidity, and limited arbitrage that explain PEAD directly and consistently, and the framework can be applied broadly in finance.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 59 , Issue 3 , May 2024 , pp. 1003 - 1030
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 participants of the 2022 Duke Financial Econometrics Seminar, 2022 Aarhus University Econometrics-Finance Seminar Series, and Nordic Finance Network Workshop for their many useful comments and questions. We also thank Tim Bollerslev, Kim Christensen, Tom Engsted, Jonas Nygaard Eriksen, Campbell R. Harvey, Jae Hoon Kim, Stig Vinther Møller, Nicolaj Søndergaard Mühlbach, Thomas Quistgaard Pedersen, Jylhä Petri, and Allan Timmermann for their valuable comments and insightful suggestions. Special thanks are due to Jennifer Conrad (the editor) and Vitaly Meursault (the referee) for detailed comments and helpful suggestions, which helped improve many aspects of this article.

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