| 基于改进支持向量机的上市公司财务困境判别研究 |
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| 引用本文： | 韩立岩,宋晓东,姚伟龙.基于改进支持向量机的上市公司财务困境判别研究[J].管理评论,2011(5). |
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| 作者姓名： | 韩立岩 宋晓东 姚伟龙 |
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| 作者单位： | 北京航空航天大学经济管理学院; |
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| 基金项目： | 国家自然科学基金项目(70831001;70821061); 北京市自然基金项目(9102013) |
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| 摘 要： | 针对上市公司财务困境判别研究的不足,本文构建了财务困境判别的双隶属模糊支持向量机模型,使每个训练样本依双隶属度同时隶属于两个类别;考虑到财务困境判别研究中两类样本非平衡的问题,本文构建了一种基于非平衡数据的改进支持向量机模型。实证结果表明,与已有的支持向量机模型相比,本文构建的改进支持向量机模型在对上市公司财务困境进行判别时精度更高,具有良好的应用价值。
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| 关 键 词： | 非平衡 支持向量机 双隶属度 财务困境 上市公司 |
An Improved-SVM-based Research on the Discrimination of Listed Companies' Financial Distress |
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| Han Liyan,Song Xiaodong , Yao Weilong.An Improved-SVM-based Research on the Discrimination of Listed Companies' Financial Distress[J].Management Review,2011(5). |
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| Authors: | Han Liyan Song Xiaodong Yao Weilong |
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| Institution: | Han Liyan,Song Xiaodong and Yao Weilong (School of Economics and Management,Beihang University,Beijing 100191) |
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| Abstract: | Based on the existing researches,we use dual membership fuzzy support vector machine(SVM) model to analyze listed companies financial distress.In this model,each sample belongs to two classes according to its dual membership.To solve the class imbalance problem in financial distress,we propose a new SVM model called CI-FSVM.Empirical results show that the discrimination accuracy of CI-FSVM is significantly better than other existing SVM models and the new model has great application value. |
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| Keywords: | class imbalance support vector machine dual membership financial crisis listed companies |
| 本文献已被 CNKI 等数据库收录！ |
