基于SVM的中小企业集合债券融资个体信用风险度量研究

Research on Credit Risk Assessment of SMES Assemble Bond Financier Based on Support Vector Machines

中小企业集合债券融资个体的信用风险度量面临小样本、非线性、高维数等现实问题,传统的评估方法很难适用。为了弥补传统评估方法的不足,提高信用风险度量的准确性,建立了适用性更强的信用风险评估指标体系,并引入基于统计学习理论的SVM模型对融资个体信用风险进行度量。选取径向基核函数作为支持向量机的核函数,通过数据的转化与缩放、参数的优选,最终获得了分类效果比较好的中小企业集合债券融资个体信用风险度量模型。经实际数据检验,模型的预测准确率为90.77%,具有较强的适用性。

The small and median enterprises assemble bond financing of individual credit risk facing many realistic problems, such as small samples, nonlinear, high dimensions and so on, to which the traditional evaluation method is difficult to apply. In order to make up for the shortcomings of the traditional evaluation methods, and enhance the credit risk measurement accuracy, this paper established a more applicable credit risk evaluation index system, and introduced support vector machine model based on statistical learning theory. Having selected the radial basis kernel function as the kernel function of support vector machine, through the data conversion and scaling, parameter optimization, the authors finally obtained good classification effect of small and median enterprises assemble bond financing of individual credit risk measurement model. After the test of actual data, the forecasting accuracy reached 90.77%, the model has strong applicability.