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

Author

Listed:
  • Pierre Jinghong Liang
  • Vitaly Meursault
  • Bryan B. Routledge
  • Madeline Marco Scanlon

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

Suggested Citation

  • Pierre Jinghong Liang & Vitaly Meursault & Bryan B. Routledge & Madeline Marco Scanlon, 2021. "PEAD.txt: Post-Earnings-Announcement Drift Using Text," Working Papers 21-07, Federal Reserve Bank of Philadelphia.
    • Handle: RePEc:fip:fedpwp:89938
    DOI: 10.21799/frbp.wp.2021.07
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    Cited by:

    1. García, Diego & Hu, Xiaowen & Rohrer, Maximilian, 2023. "The colour of finance words," Journal of Financial Economics, Elsevier, vol. 147(3), pages 525-549.

    More about this item

    Keywords

    PEAD; Machine Learning; NLP; Text Analysis;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C00 - Mathematical and Quantitative Methods - - General - - - General

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