Working Paper
PEAD.txt: Post-Earnings-Announcement Drift Using Text
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;
https://doi.org/10.21799/frbp.wp.2021.07
Access Documents
Bibliographic Information
Provider: Federal Reserve Bank of Philadelphia
Part of Series: Working Papers
Publication Date: 2021-02-19
Number: 21-07