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Rough sets bankruptcy prediction models versus auditor signalling rates


  • Thomas E. McKee

    (Visiting Professor, Department of Accounting and Legal Studies, College of Charleston, SC, USA, on leave from Department of Accountancy, East Tennessee State University, USA)


Both international and US auditing standards require auditors to evaluate the risk of bankruptcy when planning an audit and to modify their audit report if the bankruptcy risk remains high at the conclusion of the audit. Bankruptcy prediction is a problematic issue for auditors as the development of a cause-effect relationship between attributes that may cause or be related to bankruptcy and the actual occurrence of bankruptcy is difficult. Recent research indicates that auditors only signal bankruptcy in about 50% of the cases where companies subsequently declare bankruptcy. Rough sets theory is a new approach for dealing with the problem of apparent indiscernibility between objects in a set that has had a reported bankruptcy prediction accuracy ranging from 76% to 88% in two recent studies. These accuracy levels appear to be superior to auditor signalling rates, however, the two prior rough sets studies made no direct comparisons to auditor signalling rates and either employed small sample sizes or non-current data. This study advances research in this area by comparing rough set prediction capability with actual auditor signalling rates for a large sample of United States companies from the 1991 to 1997 time period. Prior bankruptcy prediction research was carefully reviewed to identify 11 possible predictive factors which had both significant theoretical support and were present in multiple studies. These factors were expressed as variables and data for 11 variables was then obtained for 146 bankrupt United States public companies during the years 1991-1997. This sample was then matched in terms of size and industry to 145 non-bankrupt companies from the same time period. The overall sample of 291 companies was divided into development and validation subsamples. Rough sets theory was then used to develop two different bankruptcy prediction models, each containing four variables from the 11 possible predictive variables. The rough sets theory based models achieved 61% and 68% classification accuracy on the validation sample using a progressive classification procedure involving three classification strategies. By comparison, auditors directly signalled going concern problems via opinion modifications for only 54% of the bankrupt companies. However, the auditor signalling rate for bankrupt companies increased to 66% when other opinion modifications related to going concern issues were included. In contrast with prior rough sets theory research which suggested that rough sets theory offered significant bankruptcy predictive improvements for auditors, the rough sets models developed in this research did not provide any significant comparative advantage with regard to prediction accuracy over the actual auditors' methodologies. The current research results should be fairly robust since this rough sets theory based research employed (1) a comparison of the rough sets model results to actual auditor decisions for the same companies, (2) recent data, (3) a relatively large sample size, (4) real world bankruptcy|non-bankruptcy frequencies to develop the variable classifications, and (5) a wide range of industries and company sizes. Copyright © 2003 John Wiley & Sons, Ltd.

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  • Thomas E. McKee, 2003. "Rough sets bankruptcy prediction models versus auditor signalling rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(8), pages 569-586.
  • Handle: RePEc:jof:jforec:v:22:y:2003:i:8:p:569-586
    DOI: 10.1002/for.875

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    References listed on IDEAS

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    1. Sun, Lili & Shenoy, Prakash P., 2007. "Using Bayesian networks for bankruptcy prediction: Some methodological issues," European Journal of Operational Research, Elsevier, vol. 180(2), pages 738-753, July.
    2. Hui Li & Jie Sun, 2010. "Forecasting business failure in China using case-based reasoning with hybrid case respresentation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(5), pages 486-501.
    3. Pablo de Llano Monelos & Manuel Rodríguez López & Carlos Piñeiro Sánchez, 2013. "Bankruptcy Prediction Models in Galician companies. Application of Parametric Methodologies and Artificial Intelligence," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 117-136.
    4. Araz Taeihagh, 2017. "Network-centric policy design," Policy Sciences, Springer;Society of Policy Sciences, vol. 50(2), pages 317-338, June.
    5. Samir Trabelsi & Roc He & Lawrence He & Martin Kusy, 2015. "A comparison of Bayesian, Hazard, and Mixed Logit model of bankruptcy prediction," Computational Management Science, Springer, vol. 12(1), pages 81-97, January.
    6. Kao-Yi Shen & Min-Ren Yan & Gwo-Hshiung Tzeng, 2017. "Exploring R&D Influences on Financial Performance for Business Sustainability Considering Dual Profitability Objectives," Sustainability, MDPI, Open Access Journal, vol. 9(11), pages 1-21, October.
    7. Tomasz Korol, 2018. "The Implementation of Fuzzy Logic in Forecasting Financial Ratios," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 12(2), June.
    8. Ruey-Ching Hwang & K. F. Cheng & Jack C. Lee, 2007. "A semiparametric method for predicting bankruptcy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(5), pages 317-342.
    9. Guan, Jian & Levitan, Alan S. & Kuhn, John R., 2013. "How AIS can progress along with ontology research in IS," International Journal of Accounting Information Systems, Elsevier, vol. 14(1), pages 21-38.
    10. Koen W. de Bock, 2017. "The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles," Post-Print hal-01588059, HAL.
    11. Sajad Abdipour & Ahmad Nasseri & Mojtaba Akbarpour & Hossein Parsian & Shahrzad Zamani, 2013. "Integrating Neural Network and Colonial Competitive Algorithm: A New Approach for Predicting Bankruptcy in Tehran Security Exchange," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 3(11), pages 1528-1539, November.
    12. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    13. Leila Bateni & Farshid Asghari, 2020. "Bankruptcy Prediction Using Logit and Genetic Algorithm Models: A Comparative Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 335-348, January.
    14. Nora Muñoz-Izquierdo & María-del-Mar Camacho-Miñano & María-Jesús Segovia-Vargas & David Pascual-Ezama, 2019. "Is the External Audit Report Useful for Bankruptcy Prediction? Evidence Using Artificial Intelligence," International Journal of Financial Studies, MDPI, Open Access Journal, vol. 7(2), pages 1-23, April.
    15. Bhimani, Alnoor & Gulamhussen, Mohamed Azzim & Lopes, Samuel, 2009. "The effectiveness of the auditor's going-concern evaluation as an external governance mechanism: Evidence from loan defaults," The International Journal of Accounting, Elsevier, vol. 44(3), pages 239-255, September.
    16. Kerstin Lopatta & Mario Albert Gloger & Reemda Jaeschke, 2017. "Can Language Predict Bankruptcy? The Explanatory Power of Tone in 10‐K Filings," Accounting Perspectives, John Wiley & Sons, vol. 16(4), pages 315-343, December.
    17. Yu-Chiang Hu & Jake Ansell, 2009. "Retail default prediction by using sequential minimal optimization technique," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(8), pages 651-666.
    18. Taeihagh, Araz & Bañares-Alcántara, René & Givoni, Moshe, 2014. "A virtual environment for the formulation of policy packages," Transportation Research Part A: Policy and Practice, Elsevier, vol. 60(C), pages 53-68.
    19. Josep Mª Argilés-Bosch & Josep García-Blandón & Diego Ravenda & Maika M. Valencia-Silva & Antonio D. Somoza, 2017. "The influence of the trade-off between profitability and future increases in sales on cost stickiness," Estudios de Economia, University of Chile, Department of Economics, vol. 44(1 Year 20), pages 81-104, June.
    20. Muñoz-Izquierdo, Nora & Segovia-Vargas, María Jesús & Camacho-Miñano, María-del-Mar & Pascual-Ezama, David, 2019. "Explaining the causes of business failure using audit report disclosures," Journal of Business Research, Elsevier, vol. 98(C), pages 403-414.
    21. Li, Hui & Hong, Lu-Yao & He, Jia-Xun & Xu, Xuan-Guo & Sun, Jie, 2013. "Small sample-oriented case-based kernel predictive modeling and its economic forecasting applications under n-splits-k-times hold-out assessment," Economic Modelling, Elsevier, vol. 33(C), pages 747-761.
    22. Antony Young & Yi Wang, 2010. "Multi-risk level examination of going concern modifications," Managerial Auditing Journal, Emerald Group Publishing, vol. 25(8), pages 756-791, September.

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