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Dynamic forecasts of financial distress of Australian firms

Author

Listed:
  • Maria H. Kim

    (Faculty of Business, University of Wollongong, Australia)

  • Graham Partington

    (Business School, The University of Sydney, Australia)

Abstract

Dynamic forecasts of financial distress have received far less attention than static forecasts, particularly in Australia. This study, therefore, investigates dynamic probability forecasts for Australian firms. Novel features of the modelling are the use of time-varying variables in forecasts from a Cox model. Not only is this one of relatively few studies to apply dynamic variables in forecasting financial distress, but to the authors’ knowledge it is the first to provide forecasts of survival probabilities using the Cox model with time-varying variables. Forecast accuracy is evaluated using receiver operating characteristics curves and the Brier Score. It was found that the dynamic model had superior predictive power, in out-of-sample forecasts, to the traditional Cox model and to the logit model.

Suggested Citation

  • Maria H. Kim & Graham Partington, 2015. "Dynamic forecasts of financial distress of Australian firms," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 135-160, February.
    • Handle: RePEc:sae:ausman:v:40:y:2015:i:1:p:135-160
    DOI: 10.1177/0312896213514237
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    References listed on IDEAS

    as
    1. Bandopadhyaya, Arindam, 1994. "An Estimation of the Hazard Rate of Firms under Chapter 11 Protection," The Review of Economics and Statistics, MIT Press, vol. 76(2), pages 346-350, May.
    2. John M. Griffin & Michael L. Lemmon, 2002. "Book‐to‐Market Equity, Distress Risk, and Stock Returns," Journal of Finance, American Finance Association, vol. 57(5), pages 2317-2336, October.
    3. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    4. Grice, John Stephen & Dugan, Michael T, 2001. "The Limitations of Bankruptcy Prediction Models: Some Cautions for the Researcher," Review of Quantitative Finance and Accounting, Springer, vol. 17(2), pages 151-166, September.
    5. David A. Hensher & Stewart Jones, 2007. "Forecasting Corporate Bankruptcy: Optimizing the Performance of the Mixed Logit Model," Abacus, Accounting Foundation, University of Sydney, vol. 43(3), pages 241-264, September.
    6. Bonfim, Diana, 2009. "Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 281-299, February.
    7. Howard Chan & Robert Faff & Paul Kofman, 2011. "Is default risk priced in Australian equity? Exploring the role of the business cycle," Australian Journal of Management, Australian School of Business, vol. 36(2), pages 217-246, August.
    8. Maurice Peat, 2007. "Factors Affecting the Probability of Bankruptcy: A Managerial Decision Based Approach," Abacus, Accounting Foundation, University of Sydney, vol. 43(3), pages 303-324, September.
    9. 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.
    10. David E Allen & Robert Powell, 2012. "The fluctuating default risk of Australian banks," Australian Journal of Management, Australian School of Business, vol. 37(2), pages 297-325, August.
    11. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    12. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    13. Harvey R. Crapp & Maxwell Stevenson, 1987. "Development of a Method to Assess the Relevant Variables and the Probability of Financial Distress," Australian Journal of Management, Australian School of Business, vol. 12(2), pages 221-236, December.
    14. 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.
    15. Alex Frino & Stewart Jones & Jin Boon Wong, 2007. "Market behaviour around bankruptcy announcements: evidence from the Australian Stock Exchange," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 47(4), pages 713-730, December.
    16. Julie Fitzpatrick & Joseph P. Ogden, 2011. "The Detection and Dynamics of Financial Distress," International Review of Finance, International Review of Finance Ltd., vol. 11(1), pages 87-121, March.
    17. Suparatana Tanthanongsakkun & Sirimon Treepongkaruna, 2008. "Explaining Credit Ratings of Australian Companies—An Application of the Merton Model," Australian Journal of Management, Australian School of Business, vol. 33(2), pages 261-275, December.
    18. Malcolm Baker & Jeffrey Wurgler, 2002. "Market Timing and Capital Structure," Journal of Finance, American Finance Association, vol. 57(1), pages 1-32, February.
    19. 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.
    20. Brad Wong & Graham Partington & Maxwell Stevenson & Violet Torbey, 2007. "Surviving Chapter 11 Bankruptcies: Duration and Payoff?," Abacus, Accounting Foundation, University of Sydney, vol. 43(3), pages 363-387, September.
    21. Jia Liu, 2004. "Macroeconomic determinants of corporate failures: evidence from the UK," Applied Economics, Taylor & Francis Journals, vol. 36(9), pages 939-945.
    22. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    23. Philip Gharghori & Howard Chan & Robert Faff, 2006. "Investigating the Performance of Alternative Default-Risk Models: Option-Based Versus Accounting-Based Approaches," Australian Journal of Management, Australian School of Business, vol. 31(2), pages 207-234, December.
    24. Sudheer Chava & Robert A. Jarrow, 2008. "Bankruptcy Prediction with Industry Effects," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 21, pages 517-549, World Scientific Publishing Co. Pte. Ltd..
    25. Chae Woo Nam & Tong Suk Kim & Nam Jung Park & Hoe Kyung Lee, 2008. "Bankruptcy prediction using a discrete-time duration model incorporating temporal and macroeconomic dependencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(6), pages 493-506.
    26. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    27. 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.
    28. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. "Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-291, March.
    29. Michael D. McKenzie, 2007. "International Evidence on the Determinants of the Decision to List," Australian Journal of Management, Australian School of Business, vol. 32(1), pages 1-28, June.
    30. Gharghori, Philip & Chan, Howard & Faff, Robert, 2009. "Default risk and equity returns: Australian evidence," Pacific-Basin Finance Journal, Elsevier, vol. 17(5), pages 580-593, November.
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