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A Fuzzy Decision Aiding Method for the Assessment of Corporate Bankruptcy


Author Info

  • Matsatsinis, M.
  • Kosmidou, K.
  • Doumpos, M.
  • Zopounidis, C.

    (Technical University of Crete)

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    In many real world problems it is often difficult to find dependencies between the variables of a process or more general of a system, dependencies which can be used for controlling a plant, forecasting a value or classifying a group of objects into pre-defined classes. Since in many cases, analytic dependencies are unknown or very difficult to set up, the formulation of dependencies with the help of fuzzy rules offers a useful alternative. This paper presents the combined use of a fuzzy rule generation method and a data mining technique for financial risk assessment. The case of business failure is considered here and the classification of the firms into two classes is sought. Initially, a method for the generation of fuzzy rules is used. Then these rules are imported to a data mining technique so as the firms can be classified into as bankrupt or non-bankrupt. The fuzzy method supports the discovery of relevant dependencies by the automatic generation of if/then rules on the basis of expert knowledge, while the data mining technique, with the help of a fuzzy rule-based classifier, assigns an object to different classes on the basis of various different characteristics (financial ratios). Finally, a thorough comparison with discriminant analysis, logit and probit analysis is performed based on the same sample.

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    Bibliographic Info

    Article provided by International Association for Fuzzy-set Management and Economy (SIGEF) in its journal FUZZY ECONOMIC REVIEW.

    Volume (Year): VIII (2003)
    Issue (Month): 1 (May)
    Pages: 13-23

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    Handle: RePEc:fzy:fuzeco:v:viii:y:2003:i:1:p:13-23

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    Related research

    Keywords: Fuzzy set theory; Bankruptcy prediction; Data mining; Multivariate statistical analysis; Decision support systems;

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    Cited by:
    1. Morillas, Antonio & Díaz, Bárbara, 2007. "Qualitative Answering Surveys And Soft Computing," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(1), pages 3-19, May.


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