A Fuzzy Decision Aiding Method for the Assessment of Corporate Bankruptcy
AbstractIn 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 InfoArticle 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)
Fuzzy set theory; Bankruptcy prediction; Data mining; Multivariate statistical analysis; Decision support systems;
Find related papers by JEL classification:
- G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
- D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
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- 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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