What are You Saying? Using Topic to Detect Financial Misreporting

76 Pages Posted: 5 Jul 2016 Last revised: 26 Mar 2018

Nerissa C. Brown

University of Delaware - Accounting & MIS

Richard Crowley

Singapore Management University

W. Brooke Elliott

University of Illinois at Urbana-Champaign

Date Written: March 21, 2018

Abstract

This study uses a machine learning technique to assess whether the thematic content of financial statement disclosures (labeled as 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 the 1994 to 2012 period. We find that the algorithm produces a valid set of semantically meaningful topics that are predictive of financial misreporting based on samples of SEC enforcement actions (AAERs) and irregularity restatements arising from intentional GAAP violations. Our out-of-sample tests indicate that topic significantly improves the detection of financial misreporting when added to models based on commonly-used financial and textual style variables. Furthermore, we find that models including topic outperform traditional models when predicting long-duration misstatements. These results are robust to alternative topic definitions and regression specifications and various controls for firms with repeated instances of financial misreporting.

Keywords: Topic, Disclosure, Latent Dirichlet Allocation, Financial Misreporting

Suggested Citation

Brown, Nerissa C. and Crowley, Richard and Elliott, W. Brooke, What are You Saying? Using Topic to Detect Financial Misreporting (March 21, 2018). 27th Annual Conference on Financial Economics and Accounting Paper. Available at SSRN: https://ssrn.com/abstract=2803733 or http://dx.doi.org/10.2139/ssrn.2803733

Nerissa C. Brown (Contact Author)

University of Delaware - Accounting & MIS ( email )

Alfred Lerner College of Business and Economics
Newark, DE 19716
United States

Richard Crowley

Singapore Management University ( email )

60 Stamford Road
Singapore 178900
Singapore

HOME PAGE: http://rmc.link

W. Brooke Elliott

University of Illinois at Urbana-Champaign ( email )

1206 South Sixth Street
Champaign, IL 61820
United States

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