东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2010, Vol. 31 ›› Issue (4): 601-604.DOI: -

• 论著 • 上一篇    下一篇

基于支持向量机的财务危机预警模型

吴冬梅;朱俊;庄新田;杨霖;   

  1. 东北大学工商管理学院;
  • 收稿日期:2013-06-20 修回日期:2013-06-20 出版日期:2010-04-15 发布日期:2013-06-20
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    教育部高等学校博士学科点专项科研基金资助项目(20060145001)

A model based on support vector machine for early warning financial crisis

Wu, Dong-Mei (1); Zhu, Jun (1); Zhuang, Xin-Tian (1); Yang, Lin (1)   

  1. (1) School of Business Administration, Northeastern University, Shenyang 110004, China
  • Received:2013-06-20 Revised:2013-06-20 Online:2010-04-15 Published:2013-06-20
  • Contact: Wu, D.-M.
  • About author:-
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摘要： 首先利用因子分析、均值检验和相关性分析分别对财务指标和公司治理变量进行筛选,得到具有代表性的指标变量,然后利用支持向量机方法进行实证分析.研究结果表明,支持向量机模型对于企业破产风险有较强的预测能力.通过与财务指标下的模型结果进行比较,发现引入公司治理变量(流通股比例、第一大股东持股比例和股权集中度)后,模型的预测能力更强,该方法具有一定的实际应用价值.

关键词: 支持向量机, 因子分析, 财务危机, 预警, 公司治理

Abstract: Financial indicators and corporate governance variables were sieved separately to get representative variables via factor analysis, mean value test and correlation analysis. Then, an empirical analysis was done by support vector machine (SVM). The results showed that the SVM model is superior in predicting the financial bankruptcy risk to other methods. Comparing the SVM model with the model based on financial indicators, it is found that the model introducing corporate governance variables in it is more predictable, where the variables include the proportions of circulating shares, shares held by the biggest shareholders and share ownership concentration. This method is worthy of practical applications to a certain extent.

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