Article ID: | iaor19982393 |
Country: | Netherlands |
Volume: | 18 |
Issue: | 1 |
Start Page Number: | 63 |
End Page Number: | 72 |
Publication Date: | Sep 1996 |
Journal: | Decision Support Systems |
Authors: | Lee Kun Chang, Han Ingoo, Kwon Youngsig |
Keywords: | finance & banking |
The objective of this paper is to develop the hybrid neural network models for bankruptcy prediction. The proposed hybrid neural network models are (1) a MDA-assisted neural network, (2) an ID3-assisted neural network, and (3) a SOFM(self organizing feature map)-assisted neural network. Both the MDA-assisted neural network and the ID3-assisted neural network are the neural network models operating with the input variables selected by the MDA method and ID3 respectively. The SOFM-assisted neural network combines a backpropagation model (supervised learning) with a SOFM model (unsupervised learning). The performance of the hybrid neural network model is evaluated using MDA and ID3 as a benchmark. Empirical results using Korean bankruptcy data show that hybrid neural network models are very promising neural network models for bankruptcy prediction in terms of predictive accuracy and adaptability.