Textual Analysis of Corporate Disclosures: A Survey of the Literature

By Feng Li. 2011

642 citations recorded by the source corpus.

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Citations in the corpus

Deep Learning for Financial Applications : A Survey (2020, neutral): “[160], Loughran et al.”
Decision support from financial disclosures with deep neural networks and transfer learning (2017, background): “Recent literature surveys [2, 3, 4, 5] provide a detailed overview of explanatory works, ﬁnding that these commonly count the instances of polarity cues, predeﬁned by manually created dictionaries.”
The perceived credibility of forward-looking performance disclosures (2014, background): “Li (2010a) finds that firms with better current performance, smaller size, less return volatility and lower market-to-book ratios issue more positive forward-looking disclosures.”
Hyperbole or Reality? Investor Response to Extreme Language in Earnings Conference Calls (2019, methodology): “on individual word counts using Loughran and McDonald’s (2011) dictionary and then compare the estimated word power weights and extremity rankings of those words.”
Readability of Narrative Disclosures in 10-K Reports: Does Managerial Ability Matter? (2018, unknown): “This Panel reports the results from the OLS regression of the readability of narrative disclosures in 10-K reports on alternative measure of managerial ability and controls for firm characteristics as in Li (2008) and Lo et al.”
The Acquisitive Nature of Extraverted CEOs (2017, background): “Because they are held quarterly over multiple years, these calls capture various contextual settings over time, allowing us to assess long-term stable traits of CEOs (Li, 2010).”
Are CSR disclosures relevant for investors? Empirical evidence from Germany (2016, unknown): “Content analysis is a method of codifying written text (Conley and Tosti-Kharas, 2014; Li, 2010) into various groups or categories on the basis of selected criteria.”
Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research (2016, methodology): “Li (2010a, 2010b) used a ‘Naıve Bayesian machine learning [classification] algorithm’ to show that changes in the tone of MD&As were ‘positively correlated with future [firm] performance’ (p 0.01).”
Where is research on massive open online courses headed? A data analysis of the MOOC research initiative (2014, methodology): “…the automation of content analysis has been suggested by many authors and this is primarily achieved through the use of scientometric methods (Brent, 1984; Cheng et al., 2014; Hoonlor, Szymanski, & Zaki, 2013; Kinshuk, Huang, Sampson, & Chen, 2013; Li, 2010; Sari, Suharjito, & Widodo, 2012).”
Disclosure Readability and the Sensitivity of Investors' Valuation Judgments to Outside Information (2017, unknown): “investors to extract information contained within a disclosure, and Rennekamp (2012) argues that less readable disclosures could lead investors to view that information as less reliable such that they react less to that information, even if they acquire it. Consistent with these arguments, You and Z”
Textual risk disclosures and investors’ risk perceptions (2013, unknown): “Li (2010b) finds that the tone in forwardlooking statements in the MD&A section is associated with future earnings where statements related to risk and uncertainty are a (negative) category of tone.”
What drives mandatory and voluntary risk reporting variations across Germany, UK and US? (2015, unknown): “For a recent review see Li (2010).”
Does concrete language in disclosures increase willingness to invest? (2014, unknown): “While prior literature has examined the importance of such features as tone, vividness, and readability (see Li 2010 for a review), our study suggests that the concreteness of language is also an important determinant of investors’ decisions in evaluating investments.”