PEAD.txt: Post-Earnings-Announcement Drift Using Text

October 17, 2022

Vitaly Meursault, Federal Reserve Bank of Philadelphia Research Department
Pierre Jinghong Liang, Carnegie Mellon University
Bryan R. Routledge, Carnegie Mellon University
Madeline Marco Scanlon, University of Pittsburgh

We construct a new numerical measure of earnings announcement surprises, standardized unexpected earnings call text (SUE.txt), that does not explicitly incorporate the reported earnings value. SUE.txt generates a text-based post-earnings-announcement drift (PEAD.txt) larger than the classic PEAD. The magnitude of PEAD.txt is considerable even in recent years when the classic PEAD is close to 0. We explore our text-based empirical model to show that the calls’ news content is about details behind the earnings number and the fundamentals of the firm.