Text Mining to Uncover the Intensity of SEC Comment Letters and Its Association with the Probability of 10-K Restatement

Liu, Yue; Moffitt, Kevin

doi:10.2308/jeta-51438

Cited by 17 publications

(7 citation statements)

References 7 publications

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“…Quantitatively, prior literature has examined cross‐sectional variation in comment letters based on observable features such as the number of unique topics discussed (Heese et al 2017), the number of filings (Bozanic et al 2019), and the length of the conversation in days or rounds (Cassell et al 2013; Gunny and Hermis 2020). 23 Qualitatively, previous research using textual analysis of comment letters has focused on the length in the number of characters or words (Johnson et al 2020; Lowry et al 2020; Shen and Tan 2020), readability (Ballestero and Schmidt 2019; Cassell et al 2019), negative tone (Agarwal et al 2017; Chantziaras et al 2021; Ege et al 2020), linguistic strength (Liu and Moffitt 2016), parsing of staff information (Ege et al 2020; Kubic 2021), or identification of specific topics (Dechow et al 2016). More sophisticated incorporation of textual analysis includes latent Dirichlet allocation to classify comment letters textually as important or unimportant based on their ability to predict future restatements and write‐downs (Ryans 2021) and Kullback‐Leibler divergence to map textual components of comment letters to changes in future disclosures (Lowry et al 2020).…”

Section: Commentary On the Sec Filing Review Process Literaturementioning

confidence: 99%

“…Intelligize(Kubic 2021), SeekiNF(Liu and Moffitt 2016), and hand-collected data from FOIA requests (e.g.,Duro et al 2019). …”

mentioning

confidence: 99%

“…26 26 AAs is the most used database in prior literature. Other comment letter data sources used in prior literature include Intelligize (Kubic 2021), SeekiNF (Liu and Moffitt 2016), and hand‐collected data from FOIA requests (e.g., Duro et al 2019). …”

mentioning

confidence: 99%

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The SEC Filing Review Process: A Survey and Future Research Opportunities*

Cunningham

Leidner

2022

Contemporary Accting Res

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As part of its goal to monitor and enhance the quality of information available to investors, the SEC reviews companies' filings to ensure compliance with applicable financial reporting and disclosure requirements. Increased public interest and the substantial costs for both the SEC in reviewing and the companies in responding have led to a rapidly growing body of accounting literature that examines the filing review process. We survey and comment on 80 published and unpublished academic research papers in this literature and identify significant gaps that future research should address. We also summarize the institutional features of the filing review process, aiming to inform future academic studies. Our survey should be of interest to both academics and market participants evaluating the effectiveness and efficiency of the filing review process.

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Section: Commentary On the Sec Filing Review Process Literaturementioning

confidence: 99%

“…Intelligize(Kubic 2021), SeekiNF(Liu and Moffitt 2016), and hand-collected data from FOIA requests (e.g.,Duro et al 2019). …”

mentioning

confidence: 99%

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The SEC Filing Review Process: A Survey and Future Research Opportunities*

Cunningham

Leidner

2022

Contemporary Accting Res

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“…Negative disclosures are more likely to create opportunities for narrative obfuscation and complexity (Li, 2008). Furthermore, several studies have applied this word list and its variants to examine disclosure Tone (Loughran and McDonald 2015;Henry and Leone 2016;Liu and Moffitt 2016).…”

Section: Tonementioning

confidence: 99%

Year and Industry-level Accounting Narrative Analysis: Readability and Tone Variation

Efretuei¹

2020

SSRN Journal

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In this study, I examine variations in the textual complexity of annual report narrative disclosures using the Fog Readability Index and Fin-Neg word list Tone Index given year and industry effects. I analyse accounting narrative Readability and Tone based on firm years, associations between the two narrative measures, and industry data. Tests of the relationship between Readability and Tone show that negative narratives have higher Readability scores, supporting the obfuscation hypothesis that bad news tends to be more difficult to read. A year analysis shows that the negative relationship between Readability and Tone increases in significance over time (2006)(2007)(2008)(2009)(2010)(2011). An industry analysis shows that the observed obfuscation tends to persist in basic materials; consumer services; financial; technology; and utilities industries. This study shows that considering the effect of variations between industry and firm years can inform annual report textual complexity research and associated empirical analyses.

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“…In order to explore text data, Liu and Moffitt (2016) examine the association between SEC comment letters and the probability of a company 10-K restatement using text mining. Bochkay and Levine (2013) examine how the use of text analytics on MD&A information from SEC filings can improve earnings forecasts.…”

Section: Integration Of Evidencementioning

confidence: 99%

Research Ideas for Artificial Intelligence in Auditing: The Formalization of Audit and Workforce Supplementation

Issa

Sun²,

Vasarhelyi³

2016

Journal of Emerging Technologies in Accounting

257

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After decades of frustration with long “AI Winters,” various business industries are witnessing the arrival of AI's “Spring,” with its massive and compelling benefits. Auditing will also evolve with the application of AI. Recently, there has been a progressive evolution of technology aimed at creating “artificially intelligent” devices. Although this evolution has been permeated with false starts and exaggerated claims, there is some convergence on the fact that substantive progress has been obtained in the last few years with the adoption of deep learning in conjunction with much faster machines and dimensionally larger storage spaces (and samples). The area of auditing has lagged business adoption in the past (Oldhouser 2016), but is prime for partial automation due to its labor intensiveness and range of decision structures. Several accounting firms have disclosed substantive investments in the AI fields. This paper proposes various areas of AI-related research to examine where this emerging technology is most promising. Moreover, this paper raises a series of methodological and evolutionary research questions aiming to study the AI-driven transformation of today's world of audit into the assurance of the future.

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Text Mining to Uncover the Intensity of SEC Comment Letters and Its Association with the Probability of 10-K Restatement

Cited by 17 publications

References 7 publications

The SEC Filing Review Process: A Survey and Future Research Opportunities*

The SEC Filing Review Process: A Survey and Future Research Opportunities*

Year and Industry-level Accounting Narrative Analysis: Readability and Tone Variation

Research Ideas for Artificial Intelligence in Auditing: The Formalization of Audit and Workforce Supplementation

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