收益
推论
动量(技术分析)
套利
市场流动性
盈利后公告漂移
经济
计量经济学
集合(抽象数据类型)
金融经济学
计算机科学
财务
人工智能
收益反应系数
程序设计语言
作者
Jacob H. Hansen,Mathias V. Siggaard
标识
DOI:10.1017/s0022109023000133
摘要
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.
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