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Identification of corporate distress in UK industrials: a conditional probability analysis approach

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  • L. Lin
  • J. Piesse

Abstract

Multivariate discriminant analysis (MDA) has long been used to classify failing and non-failing firms with high accuracy rates, although a number of methodological flaws are well known. The alternative approach based on conditional probability analysis (CPA) models have been applied to forecast mergers and acquisitions and extended to the prediction of corporate failure. This is used here to distinguish between distressed and non-distressed companies in the UK industrial sector for the period 1985-1994. Results show that the CPA model is both efficient and consistent, has high accuracy levels and avoids the biased sampling problems that have been identified in MDA studies.

Suggested Citation

  • L. Lin & J. Piesse, 2004. "Identification of corporate distress in UK industrials: a conditional probability analysis approach," Applied Financial Economics, Taylor & Francis Journals, vol. 14(2), pages 73-82.
  • Handle: RePEc:taf:apfiec:v:14:y:2004:i:2:p:73-82
    DOI: 10.1080/0960310042000176344
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    References listed on IDEAS

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    1. Eisenbeis, Robert A, 1977. "Pitfalls in the Application of Discriminant Analysis in Business, Finance, and Economics," Journal of Finance, American Finance Association, vol. 32(3), pages 875-900, June.
    2. Wood, Douglas & Piesse, Jennie, 1988. "The information value of failure predictions in credit assessment," Journal of Banking & Finance, Elsevier, vol. 12(2), pages 275-292, June.
    3. Lev, Baruch & Sunder, Shyam, 1979. "Methodological issues in the use of financial ratios," Journal of Accounting and Economics, Elsevier, vol. 1(3), pages 187-210, December.
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    Cited by:

    1. Malcolm J. Beynon & Mark A. Clatworthy & Michael John Jones, 2004. "The prediction of profitability using accounting narratives: a variable‐precision rough set approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 12(4), pages 227-242, October.
    2. Korol, Tomasz, 2013. "Early warning models against bankruptcy risk for Central European and Latin American enterprises," Economic Modelling, Elsevier, vol. 31(C), pages 22-30.
    3. 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.
    4. Khan, Muhammad Kamran & Nouman, Mohammad & Imran, Muhammad, 2015. "Determinants of financial performance of financial sectors (An assessment through economic value added)," MPRA Paper 81281, University Library of Munich, Germany.
    5. Malcolm J. Beynon, 2005. "Optimizing object classification under ambiguity/ignorance: application to the credit rating problem," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 13(2), pages 113-130, June.
    6. Muqaddas Khalid & Qaisar Abbas & Fizzah Malik & Shahid Ali, 2020. "Impact of audit committee attributes on financial distress: Evidence from Pakistan," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 7(01), pages 1-19, March.
    7. Lin Lin & Hsien-Chang Kuo & I-Liang Lin, 2008. "Merger and optimal number of firms: an integrated simulation approach," Applied Economics, Taylor & Francis Journals, vol. 40(18), pages 2413-2421.
    8. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    9. Guanping Zhou, 2019. "Financial distress prevention in China: Does gender of board of directors matter?," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 9(6), pages 1-8.
    10. Tseng, Fang-Mei & Lin, Lin, 2005. "A quadratic interval logit model for forecasting bankruptcy," Omega, Elsevier, vol. 33(1), pages 85-91, February.

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