Bankruptcy Classification of Firms Investigated by the US Securities and Exchange Commission: An Evolutionary Ensemble Computing Model Approach

International Journal of Applied Decision Sciences, 2009, Vol.2, No.4, pp.360-388.

42 Pages Posted: 31 Aug 2009 Last revised: 20 Feb 2018

See all articles by Sergio Davalos

Sergio Davalos

University of Washington, Tacoma - Milgard School of Business

Fei Leng

University of Washington, Tacoma

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

Zhiyan Cao

University of Washington Tacoma

Date Written: August 26, 2009

Abstract

This paper develops an adaptive ensemble model for bankruptcy classification of firms cited in the SEC's Accounting and Auditing Enforcement Releases (AAER). We develop a Genetic Algorithm (GA) model for bankruptcy classification of AAER firms. Our research contributes to the bankruptcy literature in several ways. First of all, it fills a gap in the bankruptcy literature by developing a domain specific model for AAER firms. Secondly, by using financial and non-financial variables, the GA model generates and optimizes a set of 'if-then' comprehensible rules for the financial failure classification of AAER firms. A Genetic Algorithm model can provide a greater degree of accuracy in predicting financial failure of firms than classical statistical models. Thirdly, we develop a model using bagging that incorporates the output from different models or sources. Finally, we demonstrate the key role of the fitness function in determining the successful performance of a financial failure GA model.

Keywords: SEC, AAER, genetic algorithm, evolutionary computing, fitness function, concept learning, bagging, bankruptcy classification, ensemble, multiple classifier, hybrid classifier, accounting and political economy

JEL Classification: C45, C61, G18, G38, K22, L81, M40

Suggested Citation

Davalos, Sergio and Leng, Fei and Feroz, Ehsan H. and Cao, Zhiyan, Bankruptcy Classification of Firms Investigated by the US Securities and Exchange Commission: An Evolutionary Ensemble Computing Model Approach (August 26, 2009). International Journal of Applied Decision Sciences, 2009, Vol.2, No.4, pp.360-388., Available at SSRN: https://ssrn.com/abstract=1462565 or http://dx.doi.org/10.2139/ssrn.1462565

Sergio Davalos

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

1900 Commerce Street
Campus Box 358420
Tacoma, WA 98402-3100
United States

Fei Leng

University of Washington, Tacoma ( email )

1900 Commerce Street
Tacoma, WA 98402-3100
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

Zhiyan Cao

University of Washington Tacoma ( email )

1900 Commerce St, Campus Box 358420
Tacoma, WA 98402-3100
United States
(253) 692-4821 (Phone)
(253) 692-4523 (Fax)

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