IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this article or follow this journal

Predicting corporate failure: empirical evidence for the UK

  • Andreas Charitou
  • Evi Neophytou
  • Chris Charalambous
Registered author(s):

    The main purpose of this study is to examine the incremental information content of operating cash flows in predicting financial distress and thus develop reliable failure prediction models for UK public industrial firms. Neural networks and logit methodology were employed to a dataset of fifty-one matched pairs of failed and non-failed UK public industrial firms over the period 1988-97. The final models are validated using an out-of-sample-period ex-ante test and the Lachenbruch jackknife procedure. The results indicate that a parsimonious model that includes three financial variables, a cash flow, a profitability and a financial leverage variable, yielded an overall correct classification accuracy of 83% one year prior to the failure. In summary, our models can be used to assist investors, creditors, managers, auditors and regulatory agencies in the UK to predict the probability of business failure.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL: http://www.tandfonline.com/doi/abs/10.1080/0963818042000216811
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Article provided by Taylor & Francis Journals in its journal European Accounting Review.

    Volume (Year): 13 (2004)
    Issue (Month): 3 ()
    Pages: 465-497

    as
    in new window

    Handle: RePEc:taf:euract:v:13:y:2004:i:3:p:465-497
    Contact details of provider: Web page: http://www.tandfonline.com/REAR20

    Order Information: Web: http://www.tandfonline.com/pricing/journal/REAR20

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    as in new window
    1. Peel, M. J. & Peel, D. A., 1988. "A multilogit approach to predicting corporate failure--Some evidence for the UK corporate sector," Omega, Elsevier, vol. 16(4), pages 309-318.
    2. Johnsen, Thomajean & Melicher, Ronald W., 1994. "Predicting corporate bankruptcy and financial distress: Information value added by multinomial logit models," Journal of Economics and Business, Elsevier, vol. 46(4), pages 269-286, October.
    3. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    4. Taffler, Richard J., 1984. "Empirical models for the monitoring of UK corporations," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 199-227, June.
    5. J.E. Boritz & D.B. Kennedy & Augusto de Miranda e Albuquerque, 1995. "Predicting Corporate Failure Using a Neural Network Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 4(2), pages 95-111, 06.
    6. Julian R. Franks & Kjell G. Nyborg & Walter N. Torous, 1996. "A Comparison of UK, US and German Insolvency Codes," Financial Management, Financial Management Association, vol. 25(3), Fall.
    7. Dambolena, Ismael G & Khoury, Sarkis J, 1980. " Ratio Stability and Corporate Failure," Journal of Finance, American Finance Association, vol. 35(4), pages 1017-26, September.
    8. Peel, MJ & Peel, DA & Pope, PF, 1986. "Predicting corporate failure-- Some results for the UK corporate sector," Omega, Elsevier, vol. 14(1), pages 5-12.
    9. Warner, Jerold B, 1977. "Bankruptcy Costs: Some Evidence," Journal of Finance, American Finance Association, vol. 32(2), pages 337-47, May.
    10. Nicholas Wilson & Kwee Chong & Michael Peel & A. N. Kolmogorov, 1995. "Neural Network Simulation and the Prediction of Corporate Outcomes: Some Empirical Findings," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 2(1), pages 31-50.
    11. Andreas Charitou & Nikos Vafeas, 1998. "The Association Between Operating Cash Flows and Dividend Changes: An Empirical Investigation," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 25(1&2), pages 225-249.
    Full references (including those not matched with items on IDEAS)

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:taf:euract:v:13:y:2004:i:3:p:465-497. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty)

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.