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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.

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  • 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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    Cited by:

    1. 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.
    2. Alberto Tron & Maurizio Dallocchio & Salvatore Ferri & Federico Colantoni, 2023. "Corporate governance and financial distress: lessons learned from an unconventional approach," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(2), pages 425-456, June.
    3. 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.
    4. Feng Zheng & Feng Ye, 2013. "Early Warning Model of Local Governments¡¯ Debt Risk in China Based on the Financial Perspective," Business and Management Research, Business and Management Research, Sciedu Press, vol. 2(4), pages 129-135, December.
    5. 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.
    6. Julia A. Tarasova & Ekaterina S. Fevraleva, 2021. "Forecasting of Bankruptcy: Evidence from Insurance Companies in Russia," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 4, pages 75-90, August.
    7. 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.
    8. 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.
    9. Şaban Çelik & Bora Aktan & Bruce Burton, 2022. "Firm dynamics and bankruptcy processes: A new theoretical model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 567-591, April.
    10. Tomasz Korol, 2020. "Assessment of Trajectories of Non-bankrupt and Bankrupt Enterprises," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 1113-1135.
    11. 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.
    12. 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.
    13. 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.
    14. Egor O. Bukharin & Sofia I. Mangileva & Vladislav V. Afanasev, 2024. "Default Prediction for Russian Food Service Firms: Contribution of Non-Financial Factors and Machine Learning," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 23(1), pages 206-226.
    15. 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.
    16. Martina Mokrišová & Jarmila Horváthová, 2023. "Domain Knowledge Features versus LASSO Features in Predicting Risk of Corporate Bankruptcy—DEA Approach," Risks, MDPI, vol. 11(11), pages 1-18, November.
    17. 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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