Double Machine Learning: Explaining the Post-Earnings Announcement Drift

80 Pages Posted: 30 Jan 2022 Last revised: 18 Oct 2022

See all articles by Jacob Hald Hansen

Jacob Hald Hansen

Aarhus University - Department of Economics and Business Economics; Aarhus University - CREATES

Mathias Siggaard

Aarhus University

Date Written: January 28, 2022

Abstract

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.

Keywords: Post-Earnings Announcement Drift, Double Machine Learning, Inference, Lasso, Variable Se- lection

JEL Classification: C14, G12, G14

Suggested Citation

Hald Hansen, Jacob and Siggaard, Mathias, Double Machine Learning: Explaining the Post-Earnings Announcement Drift (January 28, 2022). Available at SSRN: https://ssrn.com/abstract=4017917 or http://dx.doi.org/10.2139/ssrn.4017917

Jacob Hald Hansen (Contact Author)

Aarhus University - Department of Economics and Business Economics ( email )

Fuglesangs Alle 4
Aarhus, 8210
Denmark

HOME PAGE: http://pure.au.dk/portal/da/persons/jacob-hald-hansen(5b191a3a-0a2c-4b10-b89f-1464f3532b65).html

Aarhus University - CREATES ( email )

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C
Denmark

Mathias Siggaard

Aarhus University ( email )

Nordre Ringgade 1
DK-8000 Aarhus C, 8000
Denmark

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