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The Information Content of Forward-Looking Statements in Corporate Filings-A Naïve Bayesian Machine Learning Approach

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  • FENG LI
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    Abstract

    ABSTRACT This paper examines the information content of the forward-looking statements (FLS) in the Management Discussion and Analysis section (MD&A) of 10-K and 10-Q filings using a Naïve Bayesian machine learning algorithm. I find that firms with better current performance, lower accruals, smaller size, lower market-to-book ratio, less return volatility, lower MD&A Fog index, and longer history tend to have more positive FLSs. The average tone of the FLS is positively associated with future earnings even after controlling for other determinants of future performance. The results also show that, despite increased regulations aimed at strengthening MD&A disclosures, there is no systematic change in the information content of MD&As over time. In addition, the tone in MD&As seems to mitigate the mispricing of accruals. When managers "warn" about the future performance implications of accruals (i.e., the MD&A tone is positive (negative) when accruals are negative (positive)), accruals are not associated with future returns. The tone measures based on three commonly used dictionaries (Diction, General Inquirer, and the Linguistic Inquiry and Word Count) do not positively predict future performance. This result suggests that these dictionaries might not work well for analyzing corporate filings. Copyright (c), University of Chicago on behalf of the Accounting Research Center, 2010.

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    Article provided by Wiley Blackwell in its journal Journal of Accounting Research.

    Volume (Year): 48 (2010)
    Issue (Month): 5 (December)
    Pages: 1049-1102

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    Handle: RePEc:bla:joares:v:48:y:2010:i:5:p:1049-1102

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    Cited by:
    1. Elshandidy, Tamer & Fraser, Ian & Hussainey, Khaled, 2013. "Aggregated, voluntary, and mandatory risk disclosure incentives: Evidence from UK FTSE all-share companies," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 320-333.
    2. Stolowy, Hervé & Jeanjean, Thomas & Erkens, Michael, 2011. "The economic consequences of increasing the international visibility of financial reports," Les Cahiers de Recherche 957, HEC Paris.
    3. 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.
    4. Badertscher, Brad & Shroff, Nemit & White, Hal D., 2013. "Externalities of public firm presence: Evidence from private firms' investment decisions," Journal of Financial Economics, Elsevier, vol. 109(3), pages 682-706.
    5. Tim Loughran & Bill McDonald, 2014. "Regulation and financial disclosure: The impact of plain English," Journal of Regulatory Economics, Springer, vol. 45(1), pages 94-113, February.
    6. Jegadeesh, Narasimhan & Wu, Di, 2013. "Word power: A new approach for content analysis," Journal of Financial Economics, Elsevier, vol. 110(3), pages 712-729.
    7. Bozanic, Zahn & Dirsmith, Mark W. & Huddart, Steven, 2012. "The social constitution of regulation: The endogenization of insider trading laws," Accounting, Organizations and Society, Elsevier, vol. 37(7), pages 461-481.

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