Hybrid neural network models for bankruptcy predictions

Hybrid neural network models for bankruptcy predictions

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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: , ,
Keywords: finance & banking
Abstract:

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.

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