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Double Machine Learning: Explaining the Post-Earnings Announcement Drift

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  • Hansen, Jacob H.
  • Siggaard, Mathias V.

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.

Suggested Citation

  • Hansen, Jacob H. & Siggaard, Mathias V., 2024. "Double Machine Learning: Explaining the Post-Earnings Announcement Drift," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 59(3), pages 1003-1030, May.
    • Handle: RePEc:cup:jfinqa:v:59:y:2024:i:3:p:1003-1030_3
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