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The Hybrid Systems for Credit Rating

Goo, Han-In    (Graduate School of Management, Korea Advanced Institute of Science and Technology   ); Jo, Hong-Kyuo    (Graduate School of Management, Korea Advanced Institute of Science and Technology   ); Shin, Kyung-Shik    (Graduate School of Management, Korea Advanced Institute of Science and Technology  );
  • 초록

    Although numerous studies demonstrate that one technique outperforms the others for a given data set, it is hard to tell a priori which of these techniques will be the most effective to solve a specific problem. It has been suggested that the better approach to classification problem might be to integrate several different forecasting techniques by combining their results. The issues of interest are how to integrate different modeling techniques to increase the predictive performance. This paper proposes the post-model integration method, which tries to find the best combination of the results provided by individual techniques. To get the optimal or near optimal combination of different prediction techniques, Genetic Algorithms (GAs) are applied, which are particularly suitable for multi-parameter optimization problems with an object function subject to numerous hard and soft constraints. This study applies three individual classification techniques (Discriminant analysis, Logit model and Neural Networks) as base models for the corporate failure prediction. The results of composite predictions are compared with the individual models. Preliminary results suggests that the use of integrated methods improve the performance of business classification.


  • 참고문헌 (12)

    1. Current techniques in bankruptcy prediction , Jones, F. , Journal of Accounting Literature / v.,pp.131-164,
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    5. Boritz, J.E. , The going concern assumption / v.,pp.,
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    7. Combining neural networks and statistical prediction to solve the classification problem in discriminant analysis , Markham, I. S.;C. T. Ragsdale , Decision Science / v.26,pp.229-242,
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    10. Deboeck, G.J. , Trading On The Edge(ed) / v.,pp.,
    11. An inductive learning-assisted neural network approach to bankruptcy prediction: Comparison with MDA, inductive learning, and neural network models , Lee, K.C.;H. Kim;M. Kim , Korean Management Study / v.,pp.,
    12. Altman, E. , Corporate financial distress: A complete guide to predicting, avoiding, and dealing with bankruptcy / v.,pp.,
  • 이 논문을 인용한 문헌 (3)

    1. Shin, Kyung-shik 2001. "A GA-based Rule Extraction for Bankruptcy Prediction Modeling" 한국 지능정보시스템학회논문지 = Journal of intelligent information systems, 7(2): 83~93     
    2. 2003. "Using GA based Input Selection Method for Artificial Neural Network Modeling Application to Bankruptcy Prediction" 한국 지능정보시스템학회논문지 = Journal of intelligent information systems, 9(1): 227~249     
    3. 2003. "Using GA based Input Selection Method for Artificial Neural Network Modeling Application to Bankruptcy Prediction" 한국 지능정보시스템학회논문지 = Journal of intelligent information systems, 9(1): 227~249     

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