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Corporate Disclosure: Facts or Opinions?

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Abstract

A large body of literature documents the link between textual communication (e.g., news articles, earnings calls) and firm fundamentals, either through pre-defined “sentiment” dictionaries or through machine learning approaches. Surprisingly, little is known about why textual communication matters. In this paper, we take a step in that direction by developing a new methodology to automatically classify statements into objective (“facts”) and subjective (“opinions”) and apply it to transcripts of earnings calls. The large scale estimation suggests several novel results: (1) Facts and opinions are both prominent parts of corporate disclosure, taking up roughly equal parts, (2) higher prevalence of opinions is associated with investor disagreement, (3) anomaly returns are realized around the disclosure of opinions rather than facts, and (4) facts have a much stronger correlation with contemporaneous financial performance but facts and opinions have an equally strong association with financial results for the next quarter.

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  • Shimon Kogan & Vitaly Meursault, 2021. "Corporate Disclosure: Facts or Opinions?," Working Papers 21-40, Federal Reserve Bank of Philadelphia.
    • Handle: RePEc:fip:fedpwp:93414
    DOI: 10.21799/frbp.wp.2021.40
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    References listed on IDEAS

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    1. Paul C. Tetlock & Maytal Saar‐Tsechansky & Sofus Macskassy, 2008. "More Than Words: Quantifying Language to Measure Firms' Fundamentals," Journal of Finance, American Finance Association, vol. 63(3), pages 1437-1467, June.
    2. Matthew Gentzkow & Bryan Kelly & Matt Taddy, 2019. "Text as Data," Journal of Economic Literature, American Economic Association, vol. 57(3), pages 535-574, September.
    3. Tim Loughran & Bill McDonald, 2020. "Textual Analysis in Finance," Annual Review of Financial Economics, Annual Reviews, vol. 12(1), pages 357-375, December.
    4. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    5. John H. Cochrane, 2011. "Presidential Address: Discount Rates," Journal of Finance, American Finance Association, vol. 66(4), pages 1047-1108, August.
    6. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    7. Joseph Engelberg & R. David Mclean & Jeffrey Pontiff, 2018. "Anomalies and News," Journal of Finance, American Finance Association, vol. 73(5), pages 1971-2001, October.
    8. Joshua Livnat & Richard R. Mendenhall, 2006. "Comparing the Post–Earnings Announcement Drift for Surprises Calculated from Analyst and Time Series Forecasts," Journal of Accounting Research, Wiley Blackwell, vol. 44(1), pages 177-205, March.
    9. Volkan Muslu & Suresh Radhakrishnan & K. R. Subramanyam & Dongkuk Lim, 2015. "Forward-Looking MD&A Disclosures and the Information Environment," Management Science, INFORMS, vol. 61(5), pages 931-948, May.
    10. Jeremiah Green & John R. M. Hand & X. Frank Zhang, 2017. "The Characteristics that Provide Independent Information about Average U.S. Monthly Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 30(12), pages 4389-4436.
    11. Harrison Hong & Jeremy C. Stein, 2007. "Disagreement and the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 109-128, Spring.
    12. J. Anthony Cookson & Marina Niessner, 2020. "Why Don't We Agree? Evidence from a Social Network of Investors," Journal of Finance, American Finance Association, vol. 75(1), pages 173-228, February.
    13. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
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    More about this item

    Keywords

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

    JEL classification:

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

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