IDEAS home Printed from https://ideas.repec.org/a/bla/ausecp/v46y2007i4p375-388.html
   My bibliography  Save this article

Predicting Financial Distress In The Australian Financial Service Industry

Author

Listed:
  • JULIANA YIM
  • HEATHER MITCHELL

Abstract

This paper looks at the ability of a relatively new technique, a non-linear extension of the Granger thick model concept, hybrid ANN's, to predict failure of financial service firms in Australia. These models are compared with traditional statistical techniques and conventional ANN models. The results suggest that hybrid neural networks outperform all other models in predicting failure for up to two years prior to the event. This suggests that for researchers, policymakers and others interested in early warning systems, hybrid network may be a useful tool for predicting firm failure. Copyright 2007 The Authors Journal compilation 2007 Blackwell Publishing Ltd/ University of Adelaide and Flinders University .

Suggested Citation

  • Juliana Yim & Heather Mitchell, 2007. "Predicting Financial Distress In The Australian Financial Service Industry," Australian Economic Papers, Wiley Blackwell, vol. 46(4), pages 375-388, December.
  • Handle: RePEc:bla:ausecp:v:46:y:2007:i:4:p:375-388
    as

    Download full text from publisher

    File URL: http://www.blackwell-synergy.com/links/doi/10.1111/j.1467-8454.2007.00326.x
    File Function: link to full text
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Graciela L. Kaminsky, 1998. "Currency and banking crises: the early warnings of distress," International Finance Discussion Papers 629, Board of Governors of the Federal Reserve System (U.S.).
    2. repec:bla:joares:v:23:y:1985:i:1:p:146-160 is not listed on IDEAS
    3. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    4. Sinkey, Joseph F, Jr, 1975. "A Multivariate Statistical Analysis of the Characteristics of Problem Banks," Journal of Finance, American Finance Association, vol. 30(1), pages 21-36, March.
    5. Pamela K. Coats & L. Franklin Fant, 1993. "Recognizing Financial Distress Patterns Using a Neural Network Tool," Financial Management, Financial Management Association, vol. 22(3), Fall.
    6. McAdam, Peter & McNelis, Paul, 2005. "Forecasting inflation with thick models and neural networks," Economic Modelling, Elsevier, vol. 22(5), pages 848-867, September.
    7. Santomero, Anthony M. & Vinso, Joseph D., 1977. "Estimating the probability of failure for commercial banks and the banking system," Journal of Banking & Finance, Elsevier, vol. 1(2), pages 185-205, October.
    8. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    9. repec:bla:joares:v:18:y:1980:i:1:p:109-131 is not listed on IDEAS
    10. Izan, H. Y., 1984. "Corporate distress in Australia," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 303-320, June.
    11. Warner, Jerold B, 1977. "Bankruptcy Costs: Some Evidence," Journal of Finance, American Finance Association, vol. 32(2), pages 337-347, May.
    12. A. D. Castagna & Z. P. Matolcsy, 1981. "The Prediction of Corporate Failure: Testing the Australian Experience," Australian Journal of Management, Australian School of Business, vol. 6(1), pages 23-50, June.
    13. Juliana Yim & Heather Mitchell, 2005. "A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis," Nova Economia, Economics Department, Universidade Federal de Minas Gerais (Brazil), vol. 15(1), pages 73-93, January-A.
    14. Granger, Clive W. J. & Jeon, Yongil, 2004. "Thick modeling," Economic Modelling, Elsevier, vol. 21(2), pages 323-343, March.
    15. repec:bla:joares:v:12:y:1974:i:1:p:1-25 is not listed on IDEAS
    16. repec:bla:joares:v:10:y:1972:i:1:p:167-179 is not listed on IDEAS
    17. 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.
    18. Lincoln, Mervyn, 1984. "An empirical study of the usefulness of accounting ratios to describe levels of insolvency risk," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 321-340, June.
    19. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Situm Mario, 2014. "Inability of Gearing-Ratio as Predictor for Early Warning Systems," Business Systems Research, De Gruyter Open, vol. 5(2), pages 23-45, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:ausecp:v:46:y:2007:i:4:p:375-388. 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: (Wiley-Blackwell Digital Licensing) or (Christopher F. Baum). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0004-900X .

    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 CitEc recognized a reference but did not link an item in RePEc 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 RePEc Author Service 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.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.