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Enhanced Methods in Computer Security, Biometric and Artificial Intelligence Systems pp 321–328Cite as

How to deal with the data in a bankruptcy modelling

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Abstract

This article presents results of a survey that has been led in this year in the University of Szczecin. The aim of the survey was to find a neural model that would be able to predict a bankruptcy of a firm with a high rate of precision. The problem of bankruptcy prediction is broadly discussed in the economic literature and a lot of highly efficient models built via different modelling techniques have been developed in this field so far. The reason why this problem is once more touched in this article is connected with the data involved to the model.

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References

  1. Back B. Laitinen T. Sere K. Wezel M. 1996. ‘Choosing Bankruptcy Predictors Using Discriminant Analysis, Logit Analysis, and Genetic Algorithms’. Technical Raport No 40. Turku Centre for Computer Science.

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  3. Piegat A. 1999. ‘Fuzzy Modelling and Control’. Physica-Verlag. New, York.

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  4. Poddig T. 1995. ‘Bankruptcy Prediction: A Comparison with Discriminant Analysis’. Neural Networks in the Capital Markets. John Wiley$Sons. Chichester.

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  5. Sugeno M. Yasukawa T. A. 1993. ‘A Fuzzy-Logic-Based Approach to Qualitative Modelling’. IEEE Transaction on Fuzyy Systems. vol. 1, no. 1, February.

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Author information

Authors and Affiliations

  1. Uuniversity of Szczecin, Mickiewicza 64, Szczecin

    Rejer Izabela

Authors
  1. Rejer Izabela
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Editor information

Editors and Affiliations

  1. Technical University of Szczecin, Poland

    Jerzy Pejaś  & Andrzej Piegat  & 

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© 2005 Springer Science+Business Media, Inc.

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Izabela, R. (2005). How to deal with the data in a bankruptcy modelling. In: Pejaś, J., Piegat, A. (eds) Enhanced Methods in Computer Security, Biometric and Artificial Intelligence Systems. Springer, Boston, MA. https://doi.org/10.1007/0-387-23484-5_31

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  • DOI: https://doi.org/10.1007/0-387-23484-5_31

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4020-7776-0

  • Online ISBN: 978-0-387-23484-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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