Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

Hybrid Neural Network Bankruptcy Prediction: An Integration of Financial Ratios, Intellectual Capital Ratios, MDA, and Neural Network Learning

Authors
Wen-Kuei Hsieh 0, Shang-Ming Liu, Sung-Yi Hsieh
Corresponding Author
Wen-Kuei Hsieh
0Department of Finance, De Lin Institute of Technology
Available Online October 2006.
DOI
https://doi.org/10.2991/jcis.2006.323How to use a DOI?
Keywords
Bankruptcy prediction; Neural network; Hybrid neural network; MDA
Abstract
One purpose of this paper is to propose hybrid neural network models for bankruptcy prediction. The proposed hybrid neural network models are, respecitvely, a MDA model integrated with financial ratios, a MDA model integrated with financial ratios and intellectual capital ratios, a MDA-assisted neural network model integrated with financial ratios, and a MDA-assissted neural network model integrated with financial ratios and intellectual capital ratios. The performance of the hybrid neural network model is compared with MDA model integrated with financial ratios as a benchmark. Empirical results using Taiwan bankruptcy data show that hybrid neural network models are very promising ones in terms of accuracy and adaptability.
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Proceedings
9th Joint International Conference on Information Sciences (JCIS-06)
Part of series
Advances in Intelligent Systems Research
Publication Date
October 2006
ISBN
978-90-78677-01-7
ISSN
1951-6851
DOI
https://doi.org/10.2991/jcis.2006.323How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Wen-Kuei Hsieh
AU  - Shang-Ming Liu
AU  - Sung-Yi Hsieh
PY  - 2006/10
DA  - 2006/10
TI  - Hybrid Neural Network Bankruptcy Prediction: An Integration of Financial Ratios, Intellectual Capital Ratios, MDA, and Neural Network Learning
BT  - 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.323
DO  - https://doi.org/10.2991/jcis.2006.323
ID  - Hsieh2006/10
ER  -