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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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    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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