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Predicting Financial Distress In The Australian Financial Service Industry

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  • 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.

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
    DOI: 10.1111/j.1467-8454.2007.00326.x
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    Cited by:

    1. Situm Mario, 2014. "Inability of Gearing-Ratio as Predictor for Early Warning Systems," Business Systems Research, Sciendo, vol. 5(2), pages 23-45, September.
    2. Tania Hamid & Farzana Akter & Naharin Rab, 2016. "Prediction of Financial Distress of Non-Bank Financial Institutions of Bangladesh using Altman’s Z Score Model," International Journal of Business and Management, Canadian Center of Science and Education, vol. 11(12), pages 261-261, November.
    3. Seema Miglani & Kamran Ahmed & Darren Henry, 2020. "Corporate governance and turnaround: Evidence from Australia," Australian Journal of Management, Australian School of Business, vol. 45(4), pages 549-578, November.

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