A comparison of corporate failure models in Australia: hybrid neural networks, logit models and discriminant analysis

Yim, J and Mitchell, H 2003, 'A comparison of corporate failure models in Australia: hybrid neural networks, logit models and discriminant analysis', in P. W. H. Chungn, C. Hinde and M. Ali (ed.) Developments in Applied Artificial Intelligence: 16th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2003, Loughborough, UK, 1 January 2003, pp. 241-249.


Document type: Conference Paper
Collection: Conference Papers

Title A comparison of corporate failure models in Australia: hybrid neural networks, logit models and discriminant analysis
Author(s) Yim, J
Mitchell, H
Year 2003
Conference name Developments in Applied Artificial Intelligence: 16th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2003
Conference location Loughborough, UK
Conference dates 1 January 2003
Proceedings title Developments in Applied Artificial Intelligence: 16th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2003
Editor(s) P. W. H. Chungn
C. Hinde
M. Ali
Publisher Springer
Place of publication Berlin, Germany
Start page 241
End page 249
Total pages 9
Abstract This study investigated whether two artificial neural networks (ANNs), multilayer perceptron (MLP) and hybrid networks using statistical and ANN approaches, can outperform traditional statistical models for predicting corporate failures in Australia one year prior to the financial distress. The results suggest that hybrid neural networks outperform all other models. Therefore, hybrid neural network model is a very promising tool for failure prediction. This supports the conclusion that for shareholders, policymakers and others interested in early warning systems, hybrid networks would be useful.
Subjects Econometric and Statistical Methods
DOI - identifier 10.1007/3-540-45034-3_35
Copyright notice © Springer-Verlag Berlin Heidelberg 2003
ISBN 978-3-540-40455-2
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