A Multilayered Perceptron Approach to the Prediction of the SEC's Investigation Targets

11 Pages Posted: 28 Jul 2008 Last revised: 4 Jan 2019

See all articles by Taek M. Kwon

Taek M. Kwon

University of Minnesota - Twin Cities - Electrical and Computer Engineering

Ehsan H. Feroz

University of Washington, Milgard School of Business-Accounting ; University of Illinois at Urbana-Champaign; Government of the United States of America - US GAO Advisory Council; University of Minnesota, Labovitz School of Business-Department of Accounting; University of Minnesota, Carlson School of Management-Department of Accounting; American Accounting Association

Date Written: July 26, 2008

Abstract

This paper predates the Sarbanes-Oxley Act of 2002 (Section 704: Study of Enforcement Actions) by six years. We used seven red flags which are composed of four financial red flags and three non-financial turnover red flags in order to predict the targets of the SEC's investigation of fraudulent financial reporting. The red flags are computed over 70 firms spread among various industrial sectors, and form the base data that is used used for developing the computational prediction model. Multilayered perceptron (MLP) computation of this data was able to predict the targets of the SEC investigation with an average of 88% accuracy in the cross validation test.

Another interesting finding is that the non-financial data such as the frequency of turnovers of CEOs, CFOs, and auditors of a firm can be more informative than the financial data alone in predicting which firms are likely to engage in fraudulent financial reporting. However both types of information are necessary to attain high levels of prediction accuracy.

Keywords: SEC, Financial Reporting Fraud, Red Flags, Sarbanes Oxley Act of 2002, Financial Engineering, Neural Networks, Political Economy

JEL Classification: C45, G18, G28, G38, K22, K23, L51, M41, P16

Suggested Citation

Kwon, Taek M. and Feroz, Ehsan H., A Multilayered Perceptron Approach to the Prediction of the SEC's Investigation Targets (July 26, 2008). IEEE Transactions on Neural Networks, Vol. 7, No. 5, pp.1286-1290, September 1996 . Available at SSRN: https://ssrn.com/abstract=1179275

Taek M. Kwon

University of Minnesota - Twin Cities - Electrical and Computer Engineering ( email )

Duluth, MN 55812
United States

Ehsan H. Feroz (Contact Author)

University of Washington, Milgard School of Business-Accounting ( email )

1900 Commerce Street, Campus Box 358420
Tacoma, WA 98402-3100
United States
(253) 692 4728 (Phone)
253 692 4523 (Fax)

HOME PAGE: http://www.tacoma.washington.edu/business

University of Illinois at Urbana-Champaign ( email )

515 East Gregory Drive# 2307
Champaign, IL 61820
United States

Government of the United States of America - US GAO Advisory Council ( email )

441 G Street NW
Washington, DC 20548-0001
United States

University of Minnesota, Labovitz School of Business-Department of Accounting ( email )

10 University Drive
Labovitz School of Business
Duluth, MN 55812
United States
218-726-6988 (Phone)
218-726-8510 (Fax)

University of Minnesota, Carlson School of Management-Department of Accounting ( email )

420 Delaware St. SE
Minneapolis, MN 55455
United States

American Accounting Association ( email )

5717 Bessie Drive
Sarasota, FL 34233-2399
United States

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