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

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 than discriminant analysis and the case-based forecasting system. ? 1997 Elsevier Science Ltd.
Publisher
Pergamon Press Ltd.
Issue Date
1997-08
Language
ENG
Citation

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

ISSN
0957-4174
URI
http://hdl.handle.net/10203/3782
Appears in Collection
MT-Journal Papers(저널논문)
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