도산 예측을 위한 러프집합이론과 인공신경망 통합방법론

소개

Rough Set Theory & Neural Network

목차

Abstract
1. Introduction

2. Rough sets and neural networks
2.1 Rough sets
2.2 Neural networks

3. Research model development
3.1 Rough set data preprocessing
3.2 The hybrid models

4. Experiment
4.1 Research data
4.2 Neural network configuration
4.3 Experiment and results
4.5 Analysis of the results

5. Conclusions
References

본문내용

This paper proposes a hybrid intelligent system that predicts the failure of firms based on the past financial performance data, combining neural network and rough set approach. We can get reduced information table, which implies that the number of evaluation criteria such as financial ratios and qualitative variables and objects (i.e., firms) is reduced with no information loss through rough set approach. And then, this reduced information is used to develop classification rules and train neural network to infer appropriate parameters. Through the reduction of information table, it is expected that the performance of the neural network improve.
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참고자료

· Altman, E.I., “Financial Ratios, Discriminant Analysis and Prediction of Corporate Bankruptcy,” The Journal of Finance, 23,1968, pp.589-609.
· Altman, E.I., Haldeman, R.G., and Narayanan, P., “Zeta Analysis,” Journal of Banking and Finance, June 1977, pp.29-51.
· Altman, E.I., Marco, G., and Varetto, F., “Corporate Distress Diagnosis: Comparisons using Discriminant Analysis and Neural Networks (the Italian Experience),” Journal of Banking and Finance, 18, 1994, pp. 505-529.
· Beaver, W.H., “Financial Ratios as Predictors of Failure, Empirical Research in Accounting: Selected Studies,” Journal of Accounting Research, Supplement to Vol. 5, 1990, pp. 179-199.
· Blum, M., “Failing Company Discriminant Analysis,” Journal of Accounting Research, spring 1974, pp.1-25.