Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis

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Bankruptcy prediction is one of the major business classification problems. In this paper we use three different techniques: (1) Multivariate discriminant analysis, (2) case-based forecasting, and (3) neural network to predict Korean bankrupt and nonbankrupt firms. The average hit ratios of three methods range from 81.5 to 83.8%. Neural network performs better allan discriminant analysis and the case-based forecasting system. (C) 1997 Elsevier Science Ltd.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
1997
Language
English
Article Type
Article
Keywords

FAILURE; MODELS

Citation

EXPERT SYSTEMS WITH APPLICATIONS, v.13, no.2, pp.97 - 108

ISSN
0957-4174
DOI
10.1016/S0957-4174(97)00011-0
URI
http://hdl.handle.net/10203/3782
Appears in Collection
MT-Journal Papers(저널논문)
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