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A re-evaluation of auditors’ opinions versus statistical models in bankruptcy prediction

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  • Lili Sun

Abstract

Existent empirical evidence on the relative performance of auditors’ going concern opinions versus statistical models in predicting bankruptcy is mixed. This study attempts to add new reliable evidence on this important issue by conducting the comparison based upon an improved statistical model. The improved statistical model incorporates some new developments advocated by recent bankruptcy prediction research (e.g., Shumway, 2001). First, the following non-traditional variables are added: a composite measure of financial distress, industry failure rate, abnormal stock returns, and market capitalization. Secondly, a hazard model is employed. The prediction ability of the hazard model with incorporation of non-financial-ratio variables is superior to that of auditors’ going concern opinions in the holdout sample. This suggests that a well-developed statistical model could serve as a decision aid for auditors to better make going-concern judgments. Further analyses reveal some evidence that industry failure rate does not have a significant impact upon auditors’ going concern judgments as it should be; auditors could improve their going concern judgments by considering industry-level information in addition to firm-specific information. Finally, we find that auditors’ opinions do have incremental contribution beyond stock-market information and industry failure rate in predicting bankruptcy. Copyright Springer Science+Business Media, LLC 2007

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  • Lili Sun, 2007. "A re-evaluation of auditors’ opinions versus statistical models in bankruptcy prediction," Review of Quantitative Finance and Accounting, Springer, vol. 28(1), pages 55-78, January.
    • Handle: RePEc:kap:rqfnac:v:28:y:2007:i:1:p:55-78
    DOI: 10.1007/s11156-006-0003-x
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    Cited by:

    1. Wikil Kwak & Yong Shi & Gang Kou, 2012. "Bankruptcy prediction for Korean firms after the 1997 financial crisis: using a multiple criteria linear programming data mining approach," Review of Quantitative Finance and Accounting, Springer, vol. 38(4), pages 441-453, May.
    2. Martin Kukuk & Michael Rönnberg, 2013. "Corporate credit default models: a mixed logit approach," Review of Quantitative Finance and Accounting, Springer, vol. 40(3), pages 467-483, April.
    3. Chiuling Lu & Ann Yang & Jui-Feng Huang, 2015. "Bankruptcy predictions for U.S. air carrier operations: a study of financial data," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(3), pages 574-589, July.
    4. Jian Cao & Thomas R. Kubick & Adi N. S. Masli, 2017. "Do corporate payouts signal going-concern risk for auditors? Evidence from audit reports for companies in financial distress," Review of Quantitative Finance and Accounting, Springer, vol. 49(3), pages 599-631, October.
    5. Gunter Löffler, 2013. "Can rating agencies look through the cycle?," Review of Quantitative Finance and Accounting, Springer, vol. 40(4), pages 623-646, May.
    6. Mroczek Teresa & Skica Tomasz & Rodzinka Jacek, 2018. "Application of Probabilistic Inference in Defining Impact of the General Government Sector’s Size on the Economy and Determining the Size of the Sector by the Economy in the EU," Financial Internet Quarterly (formerly e-Finanse), Sciendo, vol. 14(1), pages 1-11, March.
    7. Linda Myers & Jaime Schmidt & Michael Wilkins, 2014. "An investigation of recent changes in going concern reporting decisions among Big N and non-Big N auditors," Review of Quantitative Finance and Accounting, Springer, vol. 43(1), pages 155-172, July.
    8. Foster, Benjamin P. & Shastri, Trim, 2016. "Determinants of going concern opinions and audit fees for development stage enterprises," Advances in accounting, Elsevier, vol. 33(C), pages 68-84.
    9. Virág, Miklós & Nyitrai, Tamás, 2017. "Magyar vállalkozások felszámolásának előrejelzése pénzügyi mutatóik idősorai alapján [Predicting the liquidation of Hungarian firms using a time series of their financial ratios]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(3), pages 305-324.
    10. Jan-Henning Trustorff & Paul Konrad & Jens Leker, 2011. "Credit risk prediction using support vector machines," Review of Quantitative Finance and Accounting, Springer, vol. 36(4), pages 565-581, May.
    11. Antonio Pelaez-Verdet & Pilar Loscertales-Sanchez, 2021. "Key Ratios for Long-Term Prediction of Hotel Financial Distress and Corporate Default: Survival Analysis for an Economic Stagnation," Sustainability, MDPI, vol. 13(3), pages 1-17, January.

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