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Neural Network Simulation and the Prediction of Corporate Outcomes: Some Empirical Findings


  • Nicholas Wilson
  • Kwee Chong
  • Michael Peel
  • A. N. Kolmogorov


Neural Networks (NN's) involve an innovative method of simulating and analysing complex and constantly changing systems of relationships. Originally developed to mimic the neural architecture and functioning of the human brain, NN techniques have recently been applied to many different business fields and have demonstrated a capability to solve complex problems. This paper investigates the use of NN techniques as a tool for the modelling and prediction of corporate bankruptcy and other corporate outcomes. The within and out-of-sample accuracy of trained NNs are compared with those of standard logit and multilogit techniques. The results of the study suggest that, from a pure predictive point of view, NN simulation produces a higher predictive accuracy and is more robust than conventional logit and multilogit models.

Suggested Citation

  • 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.
  • Handle: RePEc:taf:ijecbs:v:2:y:1995:i:1:p:31-50
    DOI: 10.1080/758521095

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    Cited by:

    1. Christian A. Johnson, 2005. "Modelos de alerta temprana para pronosticar crisis bancarias: desde la extracción de señales a las redes neuronales," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 20(1), pages 95-121, June.
    2. Christian A. Johnson & Rodrigo Vergara, 2005. "The implementation of monetary policy in an emerging economy: the case of Chile," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 20(1), pages 45-62, June.
    3. Natalia Isachenkova & John Hunter, 2002. "A Panel Analysis Of UK Industrial Company Failure," Working Papers wp228, Centre for Business Research, University of Cambridge.
    4. du Jardin, Philippe, 2008. "Bankruptcy prediction and neural networks: The contribution of variable selection methods," MPRA Paper 44384, University Library of Munich, Germany.
    5. Andreas Charitou & Evi Neophytou & Chris Charalambous, 2004. "Predicting corporate failure: empirical evidence for the UK," European Accounting Review, Taylor & Francis Journals, vol. 13(3), pages 465-497.
    6. Geppert, John M. & Ivanov, Stoyu I. & Karels, Gordon V., 2010. "Analysis of the probability of deletion of S&P 500 companies: Survival analysis and neural networks approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(2), pages 191-201, May.
    7. Curry, B. & Morgan, P., 1997. "Neural networks: a need for caution," Omega, Elsevier, vol. 25(1), pages 123-133, February.
    8. Hunter, John & Isachenkova, Natalia, 2001. "Failure risk: A comparative study of UK and Russian firms," Journal of Policy Modeling, Elsevier, vol. 23(5), pages 511-521, July.
    9. Philippe Jardin & David Veganzones & Eric Séverin, 2019. "Forecasting Corporate Bankruptcy Using Accrual-Based Models," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 7-43, June.
    10. Michael Dietrich, 2006. "Neural networks and the evolution of firms and industries: An application to UK SIC34 and SIC72," Working Papers 2006007, The University of Sheffield, Department of Economics, revised May 2006.

    More about this item


    Corporate failure; Neural networks; Multi-outcome models; JEL classifications: G33; G34; C53;

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • G34 - Financial Economics - - Corporate Finance and Governance - - - Mergers; Acquisitions; Restructuring; Corporate Governance
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods


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