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Insolvency Prediction In The Presence Of Data Inconsistencies

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  • A. Mendes
  • R. L. Cardoso
  • P. C. Mário
  • A. L. Martinez
  • F. R. Ferreira

Abstract

In this paper we use data inconsistencies as an indicator of financial distress. Traditional models for insolvency prediction normally ignore inconsistent data, either by removing or replacing it. Instead of removing that information, we propose a new variable to capture it; using it together with traditional accounting variables (based on financial ratios) for the purpose of insolvency prediction. Computational tests use three datasets based on the financial results of 2033 Brazilian Health Maintenance Organizations over 7 years (2001 to 2007). Sixteen classification methods were used to evaluate whether or not the new variable impacted solvency prediction. Tests show a statistically significant improvement in classification accuracy – average results improve 1.3 (p = 0.003) and 1.8 (p = 0.006) percentage points, for 10‐fold and leave‐one‐out cross‐validations respectively. In addition, the analysis of false positives and false negatives shows that the new variable reduces the potentially harmful misclassification of false negatives (i.e. financially distressed companies being classified as financially healthy) and also reduces the estimated overall error rate. Regarding the extensibility of the results, even though this work uses data from Brazilian companies only, the calculation of the financial ratios variables, as well as the inconsistencies, could be extended to most companies worldwide subject to governmental accounting regulations aligned with the International Financial Reporting Standards. Copyright © 2014 John Wiley & Sons, Ltd.

Suggested Citation

  • A. Mendes & R. L. Cardoso & P. C. Mário & A. L. Martinez & F. R. Ferreira, 2014. "Insolvency Prediction In The Presence Of Data Inconsistencies," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 21(3), pages 155-167, July.
    • Handle: RePEc:wly:isacfm:v:21:y:2014:i:3:p:155-167
    DOI: 10.1002/isaf.1352
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    1. McKee, Thomas E. & Lensberg, Terje, 2002. "Genetic programming and rough sets: A hybrid approach to bankruptcy classification," European Journal of Operational Research, Elsevier, vol. 138(2), pages 436-451, April.
    2. Lee Benham, 2005. "Licit and Illicit Responses to Regulation," Springer Books, in: Claude Menard & Mary M. Shirley (ed.), Handbook of New Institutional Economics, chapter 23, pages 591-608, Springer.
    3. Kasanen, Eero & Kinnunen, Juha & Niskanen, Jyrki, 1996. "Dividend-based earnings management: Empirical evidence from Finland," Journal of Accounting and Economics, Elsevier, vol. 22(1-3), pages 283-312, October.
    4. Andreas Charitou & Evi Neophytou & Chris Charalambous, 2004. "Predicting corporate failure: empirical evidence for the UK," European Accounting Review, Taylor & Francis Journals, vol. 13(3), pages 465-497.
    5. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    6. Katherine A. Gunny, 2010. "The Relation Between Earnings Management Using Real Activities Manipulation and Future Performance: Evidence from Meeting Earnings Benchmarks," Contemporary Accounting Research, John Wiley & Sons, vol. 27(3), pages 855-888, September.
    7. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    8. Laughlin, Richard, 2007. "Critical reflections on research approaches, accounting regulation and the regulation of accounting," The British Accounting Review, Elsevier, vol. 39(4), pages 271-289.
    9. Fields, Thomas D. & Lys, Thomas Z. & Vincent, Linda, 2001. "Empirical research on accounting choice," Journal of Accounting and Economics, Elsevier, vol. 31(1-3), pages 255-307, September.
    10. Jones, Jj, 1991. "Earnings Management During Import Relief Investigations," Journal of Accounting Research, Wiley Blackwell, vol. 29(2), pages 193-228.
    11. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    12. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    13. Editors, 2014. "International Journal of Systems Science," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 1-1, December.
    14. Farshid Navissi, 1999. "Earnings Management under Price Regulation," Contemporary Accounting Research, John Wiley & Sons, vol. 16(2), pages 281-304, June.
    15. Denis Cormier & Michel Magnan & Bernard Morard, 2000. "The contractual and value relevance of reported earnings in a dividend-focused environment," European Accounting Review, Taylor & Francis Journals, vol. 9(3), pages 387-417.
    16. Claude Menard & Mary M. Shirley (ed.), 2005. "Handbook of New Institutional Economics," Springer Books, Springer, number 978-0-387-25092-2, June.
    17. Oliver Hart, 2000. "Different Approaches to Bankruptcy," NBER Working Papers 7921, National Bureau of Economic Research, Inc.
    18. Ligang Zhou & Kin Lai & Jerome Yen, 2014. "Bankruptcy prediction using SVM models with a new approach to combine features selection and parameter optimisation," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(3), pages 241-253.
    19. Kang, Sh & Sivaramakrishnan, K, 1995. "Issues In Testing Earnings Management And An Instrumental Variable Approach," Journal of Accounting Research, Wiley Blackwell, vol. 33(2), pages 353-367.
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    2. M. Naresh Kumar & V. Sree Hari Rao, 2015. "A New Methodology for Estimating Internal Credit Risk and Bankruptcy Prediction under Basel II Regime," Papers 1502.00882, arXiv.org.

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