Using Machine Learning to Detect Misstatements

Review of Accounting Studies, Forthcoming

53 Pages Posted: 17 Dec 2019 Last revised: 25 Feb 2020

See all articles by Jeremy Bertomeu

Jeremy Bertomeu

Washington University in St. Louis - John M. Olin Business School

Edwige Cheynel

Washington University in St. Louis - John M. Olin Business School

Eric Floyd

University of California San Diego

Wenqiang Pan

Columbia University - Columbia Business School

Date Written: December 1, 2019

Abstract

Machine learning offers empirical methods to sift through accounting data sets with a large number of variables and limited a priori knowledge about functional forms. In this study, we show that these methods help detect and interpret patterns present in ongoing accounting misstatements. We use a wide set of variables from accounting, capital markets, governance, and auditing datasets to detect material misstatements. A primary insight of our analysis is that accounting variables, while they do not detect misstatements well on their own, become most important with suitable interactions with audit and market variables. We also analyze differences between misstatements and irregularities, compare algorithms, examine one-year and twoyear ahead predictions, and interpret groups at greater risk of misstatements.

Keywords: Machine Learning; Big Data; Analytics; Misstatements; AAERs; Accounting Fraud

JEL Classification: C63; D83; G38; K22; K42; M41

Suggested Citation

Bertomeu, Jeremy and Cheynel, Edwige and Floyd, Eric and Pan, Wenqiang, Using Machine Learning to Detect Misstatements (December 1, 2019). Review of Accounting Studies, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3496297

Jeremy Bertomeu (Contact Author)

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

Edwige Cheynel

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

Eric Floyd

University of California San Diego ( email )

CA
United States

Wenqiang Pan

Columbia University - Columbia Business School ( email )

New York, NY
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
648
Abstract Views
2,476
rank
50,831
PlumX Metrics