Download This PaperPEAD.txt: Post-Earnings-Announcement Drift Using Text
67 Pages Posted: 22 Apr 2021 Last revised: 13 Jan 2022
There are 2 versions of this paper
Pead.Txt: Post-Earnings-Announcement Drift Using Text
Date Written: April 9, 2021
Abstract
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 zero. 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.
Keywords: PEAD, Machine Learning, NLP, Text Analysis
JEL Classification: G14, G12, C00
Suggested Citation: Suggested Citation
Pierre Jinghong Liang
Tepper School of Business, Carnegie Mellon University ( email )
5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States
412-268-3315 (Phone)
412-268-6837 (Fax)
HOME PAGE: http://www2.tepper.cmu.edu/andrew/liangj
Bryan R. Routledge
Carnegie Mellon University - David A. Tepper School of Business ( email )
5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States
(412) 268-7588 (Phone)
(412) 268-7064 (Fax)