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.
CITATION STYLE
Hansen, J. H., & Siggaard, M. V. (2023). Double Machine Learning: Explaining the Post-Earnings Announcement Drift. Journal of Financial and Quantitative Analysis. https://doi.org/10.1017/S0022109023000133
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