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Factors Affecting the Probability of Bankruptcy

  • Maurice Peat

    (Discipline of Finance, University of Sydney)

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    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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    File URL: http://www.finance.uts.edu.au/research/wpapers/wp130.pdf
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    Paper provided by Finance Discipline Group, UTS Business School, University of Technology, Sydney in its series Working Paper Series with number 130.

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    Date of creation: 01 Sep 2003
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    Publication status: Published as: Peat, M., 2007, "Factors Affecting the Probability of Bankruptcy: A Managerial Decision Based Approach", Abacus, 43(3), 303-324.
    Handle: RePEc:uts:wpaper:130
    Contact details of provider: Postal: PO Box 123, Broadway, NSW 2007, Australia
    Phone: +61 2 9514 7777
    Fax: +61 2 9514 7711
    Web page: http://www.uts.edu.au/about/uts-business-school/finance

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    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 389-96, May.
    6. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-24, January.
    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.
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