Q-高斯核支持向量机的财务危机预报  

Financial failure prediction using support vector machine with Q-Gaussian kernel

作　　者：刘遵雄[1] 黄志强[1] 晏峰[2] 张恒[1] 

机构地区：[1]华东交通大学信息工程学院,南昌330013 [2]江西理工大学软件学院,南昌330013

出　　处：《计算机应用》2013年第6期1767-1770,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61065003);教育部人文社会科学研究规划基金资助项目(10YJC630379);教育部人文社会科学研究基金项目(12YJCZH078)

摘　　要：针对科学实践、经济生活等诸多领域数据分布相对复杂的分类问题,使用传统支持向量机(SVM)无法很好地刻画其变量间的相关性,从而影响分类性能。对于这一情况,提出使用经典高斯函数的参数推广形式——Q-高斯函数作为SVM的核函数构建财务危机预警模型。结合沪深股市A股制造业上市公司的财务数据分别建立T-2和T-3财务预警模型进行实证分析,采用显著性检验筛选出合适的财务指标并利用交叉验证方法确定模型参数。相比高斯核SVM财务危机预警模型,使用Q-高斯核SVM建立的T-2和T-3模型的预报准确率都提高了大约3%,而且成本较高的第Ⅰ类错误最多降低了14.29%。Concerning the classification problems of complex data distribution of scientific practice, economic fife and many other fields, the correlation between variables could not be well described by using traditional Support Vector Machine （SVM）, which would influence the classification performance. For this situation, Q-Gaussian function that was a parametric generalization of Gaussian function was put forward as the kernel function of SVM, and a financial early-warning model based on SVM with Q-Gaussian kernel was presented. Based on the financial data of A-share manufacturing listed companies of the Shanghai and Shenzhen stock markets, T- 2 and T- 3 financial early-warning model were constructed in experiments, the significance test was used to select some suitable indicators and the Cross Validation （CV） was used to determine model parameters. Compared to SVM model with Gaussian kernel, the forecasting accuracies of T- 2 and T- 3 model constructed by SVM with Q-Gaussian kernel were improved about 3%, and high-cost type I errors were reduced by at most 14.29%.

关 键 词：财务危机预警 支持向量机 Q-高斯核 显著性检验 交叉验证 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]