Double Machine Learning: Explaining the Post-Earnings Announcement Drift

Jacob H. Hansen and Mathias V. Siggaard

Journal of Financial and Quantitative Analysis, 2024, vol. 59, issue 3, 1003-1030

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.

Date: 2024
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