基于支持向量机的大学财务困境预警模型

Financial Crisis Early Warning Model for Universities Based on Support Vector Machine

为了解决大学的财务风险预警模型由于样本稀疏造成预测准确率偏低的问题,该文提出了一种基于支持向量机(SVM)的预警模型的构造方法。选取教育部64所大学的7个财务评价指标并将其分为轻警、重警和巨警3种类别进行了训练计算。与误差反向传播(BP)神经网络模型相比较,在小样本数据条件下,基于SVM的大学财务困境预警模型是大学财务困境预警的有效方法。研究结果可以较好地对大学财务困境进行预警监测。

In order to solve the problem of low prediction accuracy of the financial risk early warning （FREW） model for universities due to sample sparse, this paper presents a method based on support vector machine to construct a FREW model. Seven financial indexes from the 64 universities of China are selected. These index values divided into 3 categories--low, middle and heigh alarming are trained and tested. Compared with the error back propagation neural network model, the FREW model based on support vector machine is a more efficient way in the small sample data conditions. The results can provide an early warming monitor for the financial distress of universities.