Double Machine Learning: Explaining the Post-Earnings Announcement Drift

Jacob Hald Hansen, Mathias Voldum Siggaard

Publikation: Working paper/Preprint Working paperForskningpeer review

Abstract

The last 60 years of research striving to explain the post-earnings announcement drift (PEAD) have resulted in numerous potential explanations. This ”zoo” of explanations, limited academic consensus, and a literature relying on thousands of earnings announcement make researchers able to detect subtle and complex effects with little practical significance. This paper exploits new capabilities of inference via machine learning to systematically examine leading variables explaining the PEAD. First, we identify a small set of variables associated with momentum, liquidity, and limited arbitrage that directly and consistently affect the PEAD. Secondly, we demonstrate the danger of hand-picking a small set of control variables, which can lead to unreliable results, overestimation of coefficients, and underestimation of their standard errors. Finally, we explore multiple variables related to general equity risk premia and find a more prominent role for price trends than otherwise suggested.
OriginalsprogEngelsk
Antal sider80
StatusUdgivet - jan. 2022

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