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Factors Affecting the Probability of Bankruptcy

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  • Maurice Peat

    (Discipline of Finance, University of Sydney)

Abstract

The majority of classification models developed have used a pool of financial ratios combined with statistical variable selection techniques to maximise the accuracy of the classifier being employed. Rather than follow an "ad hoc" variable selection process, this paper seeks to provide an economic basis for the selection of variables for inclusion in bankruptcy models, which are based on accounting information. Variables which occur in bankruptcy probability expressions derived from the solution of an stochastic optimising model for a firm are 'proxied' by variables constructed from financial statement data. The random nature of the life time of a single firm provides the rationale for the use of duration or hazard-based statistical methods in the validation of the derived bankruptcy probability expressions. The Cox (1972) proportional hazards model is used to estimate the coefficients and standard errors that are required for the validation of the derived bankruptcy probability expressions. Results of the validation exercise confirm that the variables included in the empirical hazard formulation behave in a way that is consistent with the model of the firm.

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  • Maurice Peat, 2003. "Factors Affecting the Probability of Bankruptcy," Working Paper Series 130, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    • Handle: RePEc:uts:wpaper:130
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    File URL: http://www.finance.uts.edu.au/research/wpapers/wp130.pdf
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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. Scott, James, 1981. "The probability of bankruptcy: A comparison of empirical predictions and theoretical models," Journal of Banking & Finance, Elsevier, vol. 5(3), pages 317-344, September.
    3. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    4. Mossman, Charles E, et al, 1998. "An Empirical Comparison of Bankruptcy Models," The Financial Review, Eastern Finance Association, vol. 33(2), pages 35-53, May.
    5. 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.
    6. Grice, John Stephen & Ingram, Robert W., 2001. "Tests of the generalizability of Altman's bankruptcy prediction model," Journal of Business Research, Elsevier, vol. 54(1), pages 53-61, October.
    7. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    8. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    9. Maurice Peat, 2001. "Bankruptcy Probability: A Theoretical and Empirical Examination," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 20, July-Dece.
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    1. Bhimani, Alnoor & Gulamhussen, Mohamed Azzim & Lopes, Samuel Da-Rocha, 2010. "Accounting and non-accounting determinants of default: An analysis of privately-held firms," Journal of Accounting and Public Policy, Elsevier, vol. 29(6), pages 517-532, November.
    2. Maria H. Kim & Graham Partington, 2015. "Dynamic forecasts of financial distress of Australian firms," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 135-160, February.
    3. Kenth Skogsvik, 2007. "Discussion of Peat," Abacus, Accounting Foundation, University of Sydney, vol. 43(3), pages 325-331, September.
    4. James Routledge & David Morrison, 2012. "Insolvency administration as a strategic response to financial distress," Australian Journal of Management, Australian School of Business, vol. 37(3), pages 441-459, December.
    5. Carlos Serrano-Cinca & Yolanda Fuertes-Call鮠 & Bego uti鲲ez-Nieto & Beatriz Cuellar-Fernᮤez, 2014. "Path modelling to bankruptcy: causes and symptoms of the banking crisis," Applied Economics, Taylor & Francis Journals, vol. 46(31), pages 3798-3811, November.
    6. Salwa Kessioui & Michalis Doumpos & Constantin Zopounidis, 2023. "A Bibliometric Overview of the State-of-the-Art in Bankruptcy Prediction Methods and Applications," World Scientific Book Chapters, in: Emilios Galariotis & Alexandros Garefalakis & Christos Lemonakis & Marios Menexiadis & Constantin Zo (ed.), Governance and Financial Performance Current Trends and Perspectives, chapter 6, pages 123-153, World Scientific Publishing Co. Pte. Ltd..
    7. Sailesh Tanna & Ibrahim Yousef & Matthias Nnadi, 2020. "Probability of mergers and acquisitions deal failure," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 13(1), pages 1-30, May.
    8. Alessandra Amendola & Francesco Giordano & Maria Lucia Parrella & Marialuisa Restaino, 2017. "Variable selection in high‐dimensional regression: a nonparametric procedure for business failure prediction," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(4), pages 355-368, August.
    9. Elsayed, Mohamed & Elshandidy, Tamer, 2020. "Do narrative-related disclosures predict corporate failure? Evidence from UK non-financial publicly quoted firms," International Review of Financial Analysis, Elsevier, vol. 71(C).
    10. Antonio Blanco-Oliver & Ana Irimia-Dieguez & María Oliver-Alfonso & Nicholas Wilson, 2015. "Systemic Sovereign Risk and Asset Prices: Evidence from the CDS Market, Stressed European Economies and Nonlinear Causality Tests," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 65(2), pages 144-166, April.
    11. David Johnstone, 2007. "Discussion of Altman and Sabato," Abacus, Accounting Foundation, University of Sydney, vol. 43(3), pages 358-362, September.
    12. Ana Paula Matias Gama & Helena Susana Amaral Geraldes, 2012. "Credit risk assessment and the impact of the New Basel Capital Accord on small and medium‐sized enterprises," Management Research Review, Emerald Group Publishing Limited, vol. 35(8), pages 727-749, July.
    13. Sumon Bhaumik & Pranab Kumar Das & Subal C. Kumbhakar, 2011. "Firm Investment & Credit Constraints in India, 1997 ??? 2006: A stochastic frontier approach," William Davidson Institute Working Papers Series wp1010, William Davidson Institute at the University of Michigan.
    14. Alessandro Zeli, 2014. "The financial distress indicators trend in Italy: an analysis of medium-size enterprises," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 4(2), pages 199-221, December.
    15. Elizabeth Carson & Neil Fargher & Yuyu Zhang, 2016. "Trends in Auditor Reporting in Australia: A Synthesis and Opportunities for Research," Australian Accounting Review, CPA Australia, vol. 26(3), pages 226-242, September.

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