Research of Financial Crisis Prediction Based on FCM-PCA-SVM Model

摘要：

Financial crises prediction is an important and widely studied topic in the last three decades. Recently, the support vector machine (SVM) has been applied to the problem of financial crises prediction. Fuzzy c-means clustering (FCM) is among considerable techniques for data reduction. In addition, principal component analysis (PCA) is a powerful technique for feather extraction. This paper proposes using fuzzy c-means clustering algorithm, principle component analysis to make SVM more effective. The structure proposed in this paper, FCM-PCA-SVM composed of three subnetworks: fuzzy classifier, layer of feather extraction with principal component analysis and support vector machine. Empirical results using Chinese listed companies show that the hybrid model is very promising for financial crises in terms of predictive accuracy.

关键词： Financial crises prediction fuzzy c-means clustering principal component analysis support vector machine

作者: Ping Yao

作者单位: School of Economics &Management,Heilongjiang Institute of Science and Technology, 150027, China

会议类型: 国际会议

会议名称: The Second International Workshop on Information Systems for Crisis Response and Management(ISCRAM CHINA 2007)(第二届国际危机响应与管理信息系统中国研讨会)

会议地点: 哈尔滨

会议语种:英文

页码: 476-481

在线出版日期: 2007-08-26（万方平台首次上网日期，不代表论文的发表时间）