Support Vector Machine Model of Financial Early Warning

被引：0

作者：

Chen Hong ^{[1
]}

Liu Jingshu ^{[1
]}

机构：

[1] Zhongyuan Univ Technol, Sch Econ & Management, Zhengzhou, Peoples R China

来源：

2011 AASRI CONFERENCE ON APPLIED INFORMATION TECHNOLOGY (AASRI-AIT 2011), VOL 1 | 2011年

关键词：

Financial early warning; Support vector machine; Empirical analysis;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The article is selected 66 listed companies of 13 industries of Shanghai and Shenzhen stock market as samples, and selected three-year consecutive financial statement data in the year of 2007-2009. We use support vector machine to set up a new financial early warning to predict the selected listed companies. In order to test the validity of SVM prediction, we compare it with BP Neural Network, which is more accepted in nowadays. We find that because of its small sample research and unlimited dimension, SVM model has more advantages and higher accuracy than BP Neural Network.

引用

页码：45 / 48

页数：4