Disclosure Sentiment: Machine Learning vs. Dictionary Methods

Richard Frankel (), Jared Jennings () and Joshua Lee ()
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Richard Frankel: Olin Business School, Washington University in St. Louis, St. Louis, Missouri 63130
Jared Jennings: Olin Business School, Washington University in St. Louis, St. Louis, Missouri 63130
Joshua Lee: Marriott School of Business, Brigham Young University, Provo, Utah 84602

Management Science, 2022, vol. 68, issue 7, 5514-5532

Abstract: We compare the ability of dictionary-based and machine-learning methods to capture disclosure sentiment at 10-K filing and conference-call dates. Like Loughran and McDonald [Loughran T, McDonald B (2011) When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. J. Finance 66(1):35–65.], we use returns to assess sentiment. We find that measures based on machine learning offer a significant improvement in explanatory power over dictionary-based measures. Specifically, machine-learning measures explain returns at 10-K filing dates, whereas measures based on the Loughran and McDonald dictionary only explain returns at 10-K filing dates during the time period of their study. Moreover, at conference-call dates, machine-learning methods offer an improvement over the Loughran and McDonald dictionary method of a greater magnitude than the improvement of the Loughran and McDonald dictionary over the Harvard Psychosociological Dictionary. We further find that the random-forest-regression-tree method better captures disclosure sentiment than alternative algorithms, simplifying the application of the machine-learning approach. Overall, our results suggest that machine-learning methods offer an easily implementable, more powerful, and reliable measure of disclosure sentiment than dictionary-based methods.

Keywords: textual analysis; machine learning; disclosure; conference calls (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (13)

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