지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2011.12
- 수록면
- 219 - 228 (10page)
이용수
초록· 키워드
오류제보하기
The prediction of bankruptcy has been steadily studied in the accounting and finance field. In a previous study on bankruptcy, many researchers have focused on developing a bankruptcy prediction model to prevent bankruptcy from occurring. However, there are few studies that classify the specific bankruptcy type.
We propose a hybrid approach using a backpropagation neural network (BPN) and selforganizing map (SOM) for the classification of bankruptcy type. We develop a back-propagation model for bankruptcy prediction and construct a Self-organizing map model to divide bankruptcy data into several types. The experimental result shows that each of five bankruptcy types has different characteristics according to eight financial ratios used in this study. By using the proposed approach, it is possible to perform both bankruptcy prediction and bankruptcy type classification.
We propose a hybrid approach using a backpropagation neural network (BPN) and selforganizing map (SOM) for the classification of bankruptcy type. We develop a back-propagation model for bankruptcy prediction and construct a Self-organizing map model to divide bankruptcy data into several types. The experimental result shows that each of five bankruptcy types has different characteristics according to eight financial ratios used in this study. By using the proposed approach, it is possible to perform both bankruptcy prediction and bankruptcy type classification.
목차
Abstract
1. Introduction
2. Previous Research
3. Research Methodology
4. Model Development
5. Results and Analysis
6. Concluding Remarks
References
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UCI(KEPA) : I410-ECN-0101-2015-003-001492395