Journal of Financial and Quantitative Analysis Vol. 59 No. 3 2024
Double Machine Learning: Explaining the Post-Earnings Announcement Drift
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.
- DOI
- 10.1017/s0022109023000133
- Volume
- 59
- Issue
- 3
- Pages
- 1003-1030
- Language
- en
- Sources
- bibtex:phds-export.bib openalex crossref