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

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One of the first papers focusing on analysing financial documents
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12/3/2021, 6:39:21 PM
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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.
The Journal of Accounting Research publishes original research using analytical, empirical, experimental, and field study methods in accounting research. The journal had been published since 1963 by the Accounting Research Center (ARC) at the University of Chicago Booth School of Business. Beginning in 2001, the Journal of Accounting Research has been published by the ARC in partnership with Blackwell Publishing. JSTOR provides a digital archive of the print version of Journal of Accounting Research. The electronic version of Journal of Accounting Research is available at http://www.interscience.wiley.com. Authorized users may be able to access the full text articles at this site.
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