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Factors Affecting the Probability of Bankruptcy: A Managerial Decision Based Approach

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  • Maurice Peat
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    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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    Bibliographic Info

    Article provided by Accounting Foundation, University of Sydney in its journal Abacus.

    Volume (Year): 43 (2007)
    Issue (Month): 3 ()
    Pages: 303-324

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    Handle: RePEc:bla:abacus:v:43:y:2007:i:3:p:303-324

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
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    Cited by:
    1. 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.
    2. 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.
    3. 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.
    4. David Johnstone, 2007. "Discussion of Altman and Sabato," Abacus, Accounting Foundation, University of Sydney, vol. 43(3), pages 358-362.

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