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Beyond the numbers: an analysis of optimistic and pessimistic language in earnings press releases

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  • Angela K. Davis
  • Jeremy M. Piger
  • Lisa M. Sedor

Abstract

In this paper, we examine whether managers use optimistic and pessimistic language in earnings press releases to provide information about expected future firm performance to the market, and whether the market responds to optimistic and pessimistic language usage in earnings press releases after controlling for the earnings surprise and other factors likely to influence the market*s response to the earnings announcement. We use textual-analysis software to measure levels of optimistic and pessimistic language for a sample of approximately 24,000 earnings press releases issued between 1998 and 2003. We find a positive (negative) association between optimistic (pessimistic) language usage and future firm performance and a significant incremental market response to optimistic and pessimistic language usage in earnings press releases. Results suggest managers use optimistic and pessimistic language to provide credible information about expected future firm performance to the market, and that the market responds to managers' language usage.

Suggested Citation

  • Angela K. Davis & Jeremy M. Piger & Lisa M. Sedor, 2006. "Beyond the numbers: an analysis of optimistic and pessimistic language in earnings press releases," Working Papers 2006-005, Federal Reserve Bank of St. Louis.
    • Handle: RePEc:fip:fedlwp:2006-005
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    References listed on IDEAS

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    Cited by:

    1. Michelle T. Armesto & Ruben Hernandez-Murillo & Michael T. Owyang & Jeremy M. Piger, 2007. "Identifying asymmetry in the language of the Beige Book: a mixed data sampling approach," Working Papers 2007-010, Federal Reserve Bank of St. Louis.
    2. Jacob Boudoukh & Ronen Feldman & Shimon Kogan & Matthew Richardson, 2013. "Which News Moves Stock Prices? A Textual Analysis," NBER Working Papers 18725, National Bureau of Economic Research, Inc.
    3. Benjamin Born & Michael Ehrmann & Marcel Fratzscher, 2011. "Macroprudential policy and central bank communication," BIS Papers chapters, in: Bank for International Settlements (ed.), Macroprudential regulation and policy, volume 60, pages 107-110, Bank for International Settlements.
    4. Sri Ningsih & Iman Harymawan & Nurul Fitriani & Brian Lam, 2021. "Pessimistic Tone in Earnings Announcement and CSR Disclosure: Exploring the Interacting Role of CEO Busyness," Sustainability, MDPI, vol. 13(24), pages 1-19, December.
    5. Shuyu Zhang & Xuanyu Zhou & Huifeng Pan & Junyi Jia, 2019. "Cryptocurrency, confirmatory bias and news readability – evidence from the largest Chinese cryptocurrency exchange," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(5), pages 1445-1468, March.
    6. Brière, Marie & Huynh, Karen & Laudy, Olav & Pouget, Sébastien, 2023. "What do we Learn from a Machine Understanding: News Content? Stock Market Reaction to News," TSE Working Papers 23-1401, Toulouse School of Economics (TSE).
    7. Balakrishnan, Ramji & Qiu, Xin Ying & Srinivasan, Padmini, 2010. "On the predictive ability of narrative disclosures in annual reports," European Journal of Operational Research, Elsevier, vol. 202(3), pages 789-801, May.
    8. Gehan A. Mousa & Elsayed A. H. Elamir & Khaled Hussainey, 2022. "Using machine learning methods to predict financial performance: Does disclosure tone matter?," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 19(1), pages 93-112, March.

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