Publication Type

Journal Article

Version

publishedVersion

Publication Date

3-2020

Abstract

We use a machine learning technique to assess whether the thematic content of financial statement disclosures (labeled topic) is incrementally informative in predicting intentional misreporting. Using a Bayesian topic modeling algorithm, we determine and empirically quantify the topic content of a large collection of 10‐K narratives spanning 1994 to 2012. We find that the algorithm produces a valid set of semantically meaningful topics that predict financial misreporting, based on samples of Securities and Exchange Commission (SEC) enforcement actions (Accounting and Auditing Enforcement Releases [AAERs]) and irregularities identified from financial restatements and 10‐K filing amendments. Our out‐of‐sample tests indicate that topic significantly improves the detection of financial misreporting by as much as 59% when added to models based on commonly used financial and textual style variables. Furthermore, models that incorporate topic significantly outperform traditional models when detecting serious revenue recognition and core expense errors. Taken together, our results suggest that the topics discussed in annual report filings and the attention devoted to each topic are useful signals in detecting financial misreporting.

Keywords

topic modeling, disclosure, latent Dirichlet allocation, financial misreporting

Discipline

Accounting | Corporate Finance

Research Areas

Corporate Reporting and Disclosure

Publication

Journal of Accounting Research

Volume

58

Issue

1

First Page

237

Last Page

291

ISSN

0021-8456

Identifier

10.1111/1475-679X.12294

Publisher

Wiley

Copyright Owner and License

Publisher

Additional Files
BCE-Online-Appendix.pdf (461 kB)

Additional URL

https://doi.org/10.1111/1475-679X.12294

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