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Predicting corporate failure: empirical evidence for the UK

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  • Andreas Charitou
  • Evi Neophytou
  • Chris Charalambous
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    Abstract

    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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    File URL: http://www.tandfonline.com/doi/abs/10.1080/0963818042000216811
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    Bibliographic Info

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

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

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    Handle: RePEc:taf:euract:v:13:y:2004:i:3:p:465-497

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    References

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    1. Taffler, Richard J., 1984. "Empirical models for the monitoring of UK corporations," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 199-227, June.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Warner, Jerold B, 1977. "Bankruptcy Costs: Some Evidence," Journal of Finance, American Finance Association, vol. 32(2), pages 337-47, May.
    7. 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.
    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. 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.
    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.
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    Cited by:
    1. du Jardin, Philippe & Séverin, Eric, 2011. "Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model," MPRA Paper 44262, University Library of Munich, Germany.
    2. Ha-Thu Nguyen, 2014. "Default Predictors in Credit Scoring - Evidence from France’s Retail Banking Institution," EconomiX Working Papers 2014-26, University of Paris West - Nanterre la Défense, EconomiX.
    3. Abbas, Qaiser & Rashid, Abdul, 2011. "Modeling Bankruptcy Prediction for Non-Financial Firms: The Case of Pakistan," MPRA Paper 28161, University Library of Munich, Germany.
    4. Lana Ivicic & Sasa Cerovac, 2009. "Credit Risk Assessment of Corporate Sector in Croatia," Financial Theory and Practice, Institute of Public Finance, vol. 33(4), pages 373-399.
    5. du Jardin, Philippe, 2010. "Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy," MPRA Paper 44375, University Library of Munich, Germany.
    6. Dionysios Polemis & Dimitrios Gounopoulos, 2012. "Prediction of distress and identification of potential M&As targets in UK," Managerial Finance, Emerald Group Publishing, vol. 38(11), pages 1085-1104, November.
    7. Evžen Kocenda & Martin Vojtek, 2009. "Default Predictors and Credit Scoring Models for Retail Banking," CESifo Working Paper Series 2862, CESifo Group Munich.
    8. du Jardin, Philippe & Séverin, Eric, 2012. "Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time," European Journal of Operational Research, Elsevier, vol. 221(2), pages 378-396.
    9. Shuk-Wern Ong & Voon Choong Yap & Roy W.L. Khong, 2011. "Corporate failure prediction: a study of public listed companies in Malaysia," Managerial Finance, Emerald Group Publishing, vol. 37(6), pages 553-564, June.
    10. Grammenos, Costas Th. & Papapostolou, Nikos C., 2012. "US shipping initial public offerings: Do prospectus and market information matter?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 276-295.
    11. García-Gallego, Ana & Mures-Quintana, María-Jesús, 2013. "La muestra de empresas en los modelos de predicción del fracaso: influencia en los resultados de clasificación || The Sample of Firms in Business Failure Prediction Models: Influence on Classificati," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 15(1), pages 133-150, June.
    12. Fen-May Liou, 2008. "Fraudulent financial reporting detection and business failure prediction models: a comparison," Managerial Auditing Journal, Emerald Group Publishing, vol. 23(7), pages 650-662, July.

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