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

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Author Info

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

Abstract

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.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal International Journal of the Economics of Business.

Volume (Year): 2 (1995)
Issue (Month): 1 ()
Pages: 31-50

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Handle: RePEc:taf:ijecbs:v:2:y:1995:i:1:p:31-50

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Related research

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

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Cited by:
  1. Christian A Johnson & Rodrigo Vergara, 2005. "The Implementation of Monetary Policy in an Emerging Economy: The Case of Chile," Documentos de Trabajo 291, Instituto de Economia. Pontificia Universidad Católica de Chile..
  2. JOHN HUNTER & Natalia Isachenkova, 2003. "A Panel Analysis Of Uk Industrial Company Failure," Economics and Finance Discussion Papers 03-10, Economics and Finance Section, School of Social Sciences, Brunel University.
  3. 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.
  4. Curry, B. & Morgan, P., 1997. "Neural networks: a need for caution," Omega, Elsevier, vol. 25(1), pages 123-133, February.
  5. 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, Ilades-Georgetown University, Universidad Alberto Hurtado/School of Economics and Bussines, vol. 20(1), pages 95-121, June.
  6. 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.
  7. du Jardin, Philippe, 2008. "Bankruptcy prediction and neural networks: The contribution of variable selection methods," MPRA Paper 44384, University Library of Munich, Germany.

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