Fuzzy ARTMAP Neural Network for Classifying the Financial Health of a Firm

Authors:
Anatoli Nachev
Affiliations:
Information Systems, Dept. of Accountancy & Finance, National University of Ireland, Galway, Ireland
Venue:
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Year:
2008

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Abstract

In this paper, an application, based on data from a popular dataset, shows in an empirical form the strengths and weaknesses of fuzzy ARTMAP neural networks as predictor of corporate bankruptcy. This is an advantageous approach enabling fast learning, self-determination of the network structure and high prediction accuracy. Experiments showed that the fuzzy ARTMAP outperforms statistical techniques and the most popular backpropagation MLP neural networks, all applied to the same dataset. An exhaustive search procedure over the Altman's financial ratios leads to the conclusion that two of them are enough to obtain the highest prediction accuracy. The experiments also showed that the model is not sensitive to outliers of the dataset. Our research is the first to use fuzzy ARTMAP neural networks for bankruptcy prediction.