Classifying Restatements: An Application of Machine Learning and Textual Analytics

53 Pages Posted: 16 Jan 2016 Last revised: 2 May 2019

See all articles by B. Louise Hayes

B. Louise Hayes

University of Guelph - Gordon S. Lang School of Business and Economics

J. Efrim Boritz

University of Waterloo - School of Accounting and Finance

Date Written: February 15, 2019

Abstract

Restatements of audited financial statements are used for evaluating reporting quality, audit quality and for other evaluative purposes. Prior research shows that restatements that correct unintentional errors have different implications for statement preparers, users, auditors and regulators than restatements that correct intentional misstatements. However, manually classifying restatements into these categories can be tedious, time-consuming and inconsistently performed. Therefore, we constructed a Naïve Bayes machine learning algorithm to classify restatements by management intent based on the language in restatement announcements. Empirical tests of the algorithmically classified restatements show that this classification is an effective, efficient alternative to manual classification and more reliable than other commonly used automated methods such as classifying based on restatement direction or magnitude. Our method does not require a dictionary of words associated with management intent, is easily replicated and scalable and may be used to classify restatements disclosed at the same time as financial results.

Keywords: restatements, unintentional error, intentional misstatement, textual analysis, machine learning, naïve Bayes, data analytics

JEL Classification: G38, M41, M42, M48

Suggested Citation

Hayes, B. Louise and Boritz, Efrim, Classifying Restatements: An Application of Machine Learning and Textual Analytics (February 15, 2019). 2016 Canadian Academic Accounting Association (CAAA) Annual Conference. Available at SSRN: https://ssrn.com/abstract=2716166 or http://dx.doi.org/10.2139/ssrn.2716166

B. Louise Hayes (Contact Author)

University of Guelph - Gordon S. Lang School of Business and Economics ( email )

50 Stone Road East
Guelph, Ontario N1G 2W1
Canada

Efrim Boritz

University of Waterloo - School of Accounting and Finance ( email )

200 University Avenue West
Waterloo, Ontario N2L 3G1 N2L 3G1
Canada
519-888-4567 (Phone)
519-888-7562 (Fax)

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