Federal ID: 91-6001537
ISSN: 0022-1090 (Print) | 1756-6916 (Online)
Double Machine Learning: Explaining the Post-Earnings Announcement Drift
Jacob H. Hansen and Mathias V. Siggaard
♦ 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.
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