Pead.Txt: Post-Earnings-Announcement Drift Using Text

Posted: 14 May 2021

See all articles by Pierre Jinghong Liang

Pierre Jinghong Liang

Independent

Vitaly Meursault

Federal Reserve Banks - Federal Reserve Bank of Philadelphia

Bryan Routledge

Independent

Multiple version iconThere are 2 versions of this paper

Date Written: February, 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 and can be used to create a profitable trading strategy. Leveraging the prediction model underlying SUE.txt, we propose new tools to study the news content of text: paragraph-level SUE.txt and paragraph classification scheme based on the business curriculum. With these tools, we document many asymmetries in the distribution of news across content types, demonstrating that earnings calls contain a wide range of news about firms and their environment

Keywords: PEAD, Machine Learning, NLP, Text Analysis

JEL Classification: G14, G12, C00

Suggested Citation

Liang, Pierre Jinghong and Meursault, Vitaly and Routledge, Bryan, Pead.Txt: Post-Earnings-Announcement Drift Using Text (February, 2021). FRB of Philadelphia Working Paper No. 21-7, Available at SSRN: https://ssrn.com/abstract=3843818 or http://dx.doi.org/10.21799/frbp.wp.2021.07

Vitaly Meursault

Federal Reserve Banks - Federal Reserve Bank of Philadelphia ( email )

Ten Independence Mall
Philadelphia, PA 19106-1574
United States

Bryan Routledge

Independent ( email )

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