Double Machine Learning: Explaining the Post-Earnings Announcement Drift
展示了将传统假设驱动研究与机器学习高维推断方法相结合的益处，以盈余公告后漂移现象为例，识别出动量、流动性和有限套利等少数变量能直接且一致地解释该现象，框架可广泛应用于金融领域。
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.