摘要
本文以上证综指和深证成指为研究对象,将随机欠采样(RU)、合成少数类过采样(SMOTE)与传统支持向量机(SVM)相结合,提出了一种改进的SVM模型——RU-SMOTE-SVM模型来预测我国金融市场极端风险,并与传统SVM、SMOTE-SVM、RU-SMOTE-NN和RU-SMOTE-DT进行比较。实证结果表明,RU-SMOTE-SVM既优于传统SVM模型,又比SMOTE-SVM具有更高的预测精度,同时还展示出比RU-SMOTE-NN和RU-SMOTE-DT更为优越的预测性能。
Taking the Shanghai Stock Exchange Composite Index and Shenzhen Stock Exchange Component Index as the objects of research, this paper combines Random Under-Sampling(RU), Synthetic Minority Over-Sampling Technique (SMOTE) with Support Vector Machine (SVM) to establish an improvement SVM RU-SMOTE-SVM, which is applied to predict the extreme risk in Chinese financial market and compared with conventional SVM, SMOTE-SVM, RU-SMOTE-NN and RU-SMOTE-DT. The result of investigation illustrates that RU-SMOTE-SVM not only outperforms conventional SVM, but also has a higher predictive accuracy than SMOTE-SVM, simultaneously, has a more excellent predictive performance than RU-SMOTE-NN and RU-SMOTE-DT.
出处
《预测》
CSSCI
北大核心
2013年第4期15-20,共6页
Forecasting
基金
国家自然科学基金资助项目(71171025)
国家社会科学基金资助项目(12BGL024)
教育部人文社会科学青年基金资助项目(10YJCZH086)
成都理工大学中青年科研骨干教师培养计划资助项目(KYGG201207)
成都理工大学"金融与投资"优秀创新团队计划资助项目