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Imprecise and Informative: Lessons from Market Reactions to Imprecise Disclosure

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Listed:
  • Cookson, J. Anthony
  • Moon, S. Katie
  • Noh, Joonki

Abstract

Imprecise language in corporate disclosures can convey valuable information on ?firms' fundamentals during uncertain times. To evaluate this idea, we develop a novel measure of linguistic imprecision based on sentences marked with the \weasel tag" on Wikipedia. For a 10-week window following the 10-K disclosure, we ?nd that the use of imprecise language in 10-Ks predicts 1) positive and non-reverting abnormal returns, 2) improvements to stock liquidity, 3) greater intensities of insider and informed buying, and 4) higher news sentiment. These fi?ndings are the strongest when the fi?rm disclosures are more forward looking, and for fi?rms with greater idiosyncratic volatility. Taken together, our fi?ndings imply that the imprecise language in 10-Ks contains new information on positive but yet immature prospects of future cash flow.

Suggested Citation

  • Cookson, J. Anthony & Moon, S. Katie & Noh, Joonki, 2020. "Imprecise and Informative: Lessons from Market Reactions to Imprecise Disclosure," SocArXiv akt2c, Center for Open Science.
    • Handle: RePEc:osf:socarx:akt2c
    DOI: 10.31219/osf.io/akt2c
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    1. Fama, Eugene F., 1998. "Market efficiency, long-term returns, and behavioral finance," Journal of Financial Economics, Elsevier, vol. 49(3), pages 283-306, September.
    2. Emir Kamenica & Matthew Gentzkow, 2011. "Bayesian Persuasion," American Economic Review, American Economic Association, vol. 101(6), pages 2590-2615, October.
    3. Tim Loughran & Bill Mcdonald, 2014. "Measuring Readability in Financial Disclosures," Journal of Finance, American Finance Association, vol. 69(4), pages 1643-1671, August.
    4. Loughran, Tim & McDonald, Bill, 2013. "IPO first-day returns, offer price revisions, volatility, and form S-1 language," Journal of Financial Economics, Elsevier, vol. 109(2), pages 307-326.
    5. Diego García, 2013. "Sentiment during Recessions," Journal of Finance, American Finance Association, vol. 68(3), pages 1267-1300, June.
    6. Gerard Hoberg & Gordon Phillips, 2016. "Text-Based Network Industries and Endogenous Product Differentiation," Journal of Political Economy, University of Chicago Press, vol. 124(5), pages 1423-1465.
    7. Brian J. Bushee & Ian D. Gow & Daniel J. Taylor, 2018. "Linguistic Complexity in Firm Disclosures: Obfuscation or Information?," Journal of Accounting Research, Wiley Blackwell, vol. 56(1), pages 85-121, March.
    8. Michael J. Brennan & Sahn-Wook Huh & Avanidhar Subrahmanyam, 2018. "High-Frequency Measures of Informed Trading and Corporate Announcements," The Review of Financial Studies, Society for Financial Studies, vol. 31(6), pages 2326-2376.
    9. Li, Feng, 2008. "Annual report readability, current earnings, and earnings persistence," Journal of Accounting and Economics, Elsevier, vol. 45(2-3), pages 221-247, August.
    10. Jegadeesh, Narasimhan & Wu, Di, 2013. "Word power: A new approach for content analysis," Journal of Financial Economics, Elsevier, vol. 110(3), pages 712-729.
    11. 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.
    12. Hoberg, Gerard & Moon, S. Katie, 2017. "Offshore activities and financial vs operational hedging," Journal of Financial Economics, Elsevier, vol. 125(2), pages 217-244.
    13. Amihud, Yakov, 2002. "Illiquidity and stock returns: cross-section and time-series effects," Journal of Financial Markets, Elsevier, vol. 5(1), pages 31-56, January.
    14. Barber, Brad M. & Lyon, John D., 1997. "Detecting long-run abnormal stock returns: The empirical power and specification of test statistics," Journal of Financial Economics, Elsevier, vol. 43(3), pages 341-372, March.
    15. Hoberg, Gerard & Lewis, Craig, 2017. "Do fraudulent firms produce abnormal disclosure?," Journal of Corporate Finance, Elsevier, vol. 43(C), pages 58-85.
    16. Gerard Hoberg & Vojislav Maksimovic, 2015. "Redefining Financial Constraints: A Text-Based Analysis," The Review of Financial Studies, Society for Financial Studies, vol. 28(5), pages 1312-1352.
    17. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    18. Hwang, Byoung-Hyoun & Kim, Hugh Hoikwang, 2017. "It pays to write well," Journal of Financial Economics, Elsevier, vol. 124(2), pages 373-394.
    19. Paul Goldsmith-Pinkham & Beverly Hirtle & David O. Lucca, 2016. "Parsing the content of bank supervision," Staff Reports 770, Federal Reserve Bank of New York.
    20. Gerard Hoberg & Gordon Phillips & Nagpurnanand Prabhala, 2014. "Product Market Threats, Payouts, and Financial Flexibility," Journal of Finance, American Finance Association, vol. 69(1), pages 293-324, February.
    21. Casey Dougal & Joseph Engelberg & Diego García & Christopher A. Parsons, 2012. "Journalists and the Stock Market," The Review of Financial Studies, Society for Financial Studies, vol. 25(3), pages 639-679.
    22. 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.
    23. Todd A. Gormley & Bong Hwan Kim & Xiumin Martin, 2012. "Do Firms Adjust Their Timely Loss Recognition in Response to Changes in the Banking Industry?," Journal of Accounting Research, Wiley Blackwell, vol. 50(1), pages 159-196, March.
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