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A statistical analysis of reliability of audit opinions as bankruptcy predictors

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  • Carlo Caserio
  • Delio Panaro
  • Sara Trucco

Abstract

Research measures the reliability of audit firms in predi cting bankruptcy for US-listed financial institutions. Object of the analysis is the Going Concern Opinion (GCO), widely considered a bankruptcy warning signal to stakeholders. The sample is composed of 42 US- listed financial companies that filed Chapter 11 between 1998 and 2011. To highlight differences between bankrupting and healthy firms, a matching sample composed by 42 randomly picked healthy US-listed financial companies is collected. We concentrate on financial institutions, whereas the existing literature pays considerably heavier attention to the industrial sector. This research imbalance is remarkable and particularly unexpected in the wake of recent financial scandals. Literature points out two main approaches on bankruptcy prediction: 1) purely mathematical; 2) approaches based on a combination of auditor knowledge, expertise and experience. The use of data mining techniques, allow us to benefit from the best features of both approaches. Statistical tools used in the analysis are: Logit regression, Support Vector Machines and an Adaboost Meta-algorithm. Findings show a quite low reliability of GCOs in predicting bankruptcy. It is likely that auditors consider further information in supporting their audit opinions, aside from financial - economic ratios. The scant predictive ability of auditors might be due to critical relationships with distressed clients, as suggested by recent literature.

Suggested Citation

  • Carlo Caserio & Delio Panaro & Sara Trucco, 2014. "A statistical analysis of reliability of audit opinions as bankruptcy predictors," Discussion Papers 2014/174, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
  • Handle: RePEc:pie:dsedps:2014/174
    Note: ISSN 2039-1854
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    File URL: https://www.ec.unipi.it/documents/Ricerca/papers/2014-174.pdf
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    References listed on IDEAS

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    1. Lennox, Clive, 1999. "Identifying failing companies: a re-evaluation of the logit, probit and DA approaches," Journal of Economics and Business, Elsevier, vol. 51(4), pages 347-364, July.
    2. Mehdi Divsalar & Habib Roodsaz & Farshad Vahdatinia & Ghassem Norouzzadeh & Amir Hossein Behrooz, 2012. "A Robust Data‐Mining Approach to Bankruptcy Prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(6), pages 504-523, September.
    3. 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.
    4. Davis, E. Philip & Karim, Dilruba, 2008. "Comparing early warning systems for banking crises," Journal of Financial Stability, Elsevier, vol. 4(2), pages 89-120, June.
    5. 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.
    6. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    7. Reynolds, J. Kenneth & Francis, Jere R., 2000. "Does size matter? The influence of large clients on office-level auditor reporting decisions," Journal of Accounting and Economics, Elsevier, vol. 30(3), pages 375-400, December.
    8. Mutchler, JF & Hopwood, W & McKeown, JM, 1997. "The influence of contrary information and mitigating factors on audit opinion decisions on bankrupt companies," Journal of Accounting Research, Wiley Blackwell, vol. 35(2), pages 295-310.
    9. Charles A. Malgwi & Emmanuel N. Emenyonu, 2004. "Audit effectiveness preceding bankruptcy in UK financial institutions," International Journal of Accounting, Auditing and Performance Evaluation, Inderscience Enterprises Ltd, vol. 1(4), pages 503-518.
    10. Holder-Webb, LM & Wilkins, MS, 2000. "The incremental information content of SAS No. 59 going-concern opinions," Journal of Accounting Research, Wiley Blackwell, vol. 38(1), pages 209-219.
    11. 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.
    12. Javier DE ANDRES & Fernando SÁNCHEZ-LASHERAS & Pedro LORCA & Francisco Javier DE COS JUEZ, 2011. "A Hybrid Device of Self Organizing Maps (SOM) and Multivariate Adaptive Regression Splines (MARS) for the Forecasting of Firms’ Bankruptcy," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 10(3), pages 351-374, September.
    13. Demyanyk, Yuliya & Hasan, Iftekhar, 2010. "Financial crises and bank failures: A review of prediction methods," Omega, Elsevier, vol. 38(5), pages 315-324, October.
    14. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    15. Pinches, George E & Mingo, Kent A & Caruthers, J Kent, 1973. "The Stability of Financial Patterns in Industrial Organizations," Journal of Finance, American Finance Association, vol. 28(2), pages 389-396, May.
    16. Beaver, Wh, 1968. "Market Prices, Financial Ratios, And Prediction Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 6(2), pages 179-192.
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    Cited by:

    1. Mihaela-Cristina Onica & Georgiana Grapa & Gabriela Manole, 2015. "Comparative Research On The Fundamental Analysis Of Shares From Companies Listed On Bucharest Stock Exchange," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 493-501.
    2. Mihaela Cristina ONICA, 2020. "Study of the Stock Market Performances through Stock Rates in the Context of Listed Companies," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 1, pages 161-166.

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    More about this item

    Keywords

    Bankruptcy; Financial institutions; Going Concern Opinion; Data Mining.;
    All these keywords.

    JEL classification:

    • M42 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Auditing
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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