Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction
Journal of Management Information Systems - Special section: Data mining
Ensemble prediction of commercial bank failure through diversification of input features
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Finding robust models using a stratified design
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Bankruptcy analysis with self-organizing maps in learning metrics
IEEE Transactions on Neural Networks
| Hi-index | 0.01 |
This paper describes the development of a predictive model for corporate insolvency risk in Australia. The model building methodology is empirical with out-of-sample future year test sets. The regression method used is logistic regression after pre-processing by quantisation of interval (or numeric) attributes. We show that logistic regression matches the performance of ensemble methods, such as random forests and ada boost, provided that pre-processing and variable selection is performed. A distinctive feature of the insolvency risk model described in this paper is its breadth; since we are using income tax return data we are able to risk score one million companies across all industries, all corporation types (public, private) and all sizes, as measured either by assets or number of employees. This is an application paper that uses standard credit scoring methodology on a new data source. The contribution is to demonstrate that insolvency risk can be estimated using income tax return data. The corporate insolvency prediction model is still in development and so we welcome suggestions for improvements on the current methodology.