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### 含图结构的GR-LDA 方法及其信用违约预警应用

• 出版日期:2021-07-25 发布日期:2021-07-25

### GR-LDA Model with Graph Structure and Its Application in Credit Default Warning

Wang Xiaoyan Zhang Zhongyan

• Online:2021-07-25 Published:2021-07-25

Abstract: Credit risk management concerns the survival of the loan industry, and risk indicator selection is the essential content. Existing studies show that the correlation information among indicators can improve indicator selection. Therefore, based on the complex network theory, this paper constructs a graph structure for the indicators to incorporate their correlations. Combined with the L0 penalty, a new linear discriminant analysis (GR-LDA) model is proposed to select indicators. It has theoretically proved that the loss function of the proposed model can be transformed into a least-square function, in which the computation is quite convenient. A simulation study shows that compared with the benchmarks (Lasso-LDA, L0-LDA, Elastic Net Logistic, and Lasso-SVM), the proposed GR-LDA has certain advantages in terms of variable selection and classification accuracy. The graph structure of indicators can significantly improve the performance of the model in classification and indicator selection. Moreover, the advantage of graph structure becomes more and more significant with the increase of the correlation between indicators. The empirical analysis of P2P online loan data shows that the proposed GR-LDA model has satisfactory prediction performance and identifies the important indicators in the graph structure.