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

Pierre Jinghong Liang, Vitaly Meursault, Bryan Routledge () and Madeline Marco Scanlon
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Vitaly Meursault: https://www.philadelphiafed.org/our-people/meursault-vitaly

No 21-07, Working Papers from Federal Reserve Bank of Philadelphia

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 (search for similar items in EconPapers)
JEL-codes: C00 G12 G14 (search for similar items in EconPapers)
Pages: 90
Date: 2021-02-19
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedpwp:89938

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DOI: 10.21799/frbp.wp.2021.07

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