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

机译：公司备案中前瞻性陈述的信息内容-一种朴素的贝叶斯机器学习方法

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摘要

This paper examines the information content of the forward-looking state ments (FLS) in the Management Discussion and Analysis section (MD&A) of 10-K and 10-Q filings using a Naive 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 in dex, 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, de spite 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 accru als (i.e., the MD&A tone is positive (negative) when accruals are negative (positive)), accruals are not associated with future returns. The tone mea sures 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.
机译：本文使用朴素贝叶斯机器学习算法在10-K和10-Q申请的管理讨论和分析部分（MD＆A）中检查了前瞻性陈述（FLS）的信息内容，我发现当前公司状况更好性能，较低的应计费用，较小的规模，较低的市帐率，较低的收益波动率，较低的MD＆A dex雾霾以及较长的历史记录往往会带来更积极的FLS。即使在控制了未来表现的其他决定因素之后，FLS的平均基调与未来收益也呈正相关。结果还表明，尽管制定了旨在加强MD＆A披露的法规，但MD＆A的信息内容并未随时间发生系统变化。此外，MD＆A中的基调似乎减轻了应计项目的定价错误。当管理者“警告”权责发生制对未来绩效的影响时（即，当权责发生制为负（正）时，MD＆A基调为正（负）），则权责发生制与未来收益无关。基于三种常用词典（词典，一般询问者以及语言询问和字数统计）的语气测量结果不能肯定地预测未来的表现。该结果表明，这些词典可能不适用于分析公司文件。

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  • 来源
    《Journal of Accounting Research》 |2010年第5期|p.1049-1102|共54页
  • 作者

    FENG LI;

  • 作者单位

    Ross School of Business, University of Michigan;

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