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.

Keywords: Post-Earnings Announcement Drift, Double Machine Learning, Inference, Lasso, Variable Se- lection

JEL Classification: C14, G12, G14