Predicting corporate failure: empirical evidence for the UK
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.
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Volume (Year): 13 (2004)
Issue (Month): 3 ()
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- 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.
- 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.
- 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.
- 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.
- 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.
- Taffler, Richard J., 1984. "Empirical models for the monitoring of UK corporations," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 199-227, June.
- 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.
- 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.
- 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.
- Warner, Jerold B, 1977. "Bankruptcy Costs: Some Evidence," Journal of Finance, American Finance Association, vol. 32(2), pages 337-347, May.
- Dambolena, Ismael G & Khoury, Sarkis J, 1980. " Ratio Stability and Corporate Failure," Journal of Finance, American Finance Association, vol. 35(4), pages 1017-1026, September.
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