Research on Corporate Default Risk Prediction Based on Twin SVR

Abstract

Abstract:

This paper, taking companies that issue short-term financing bond as the objects of research, establishes a suitable method based on the credit spread to analyze the corporate default risk, then applies Twin-SVR (Twin Support Vector Regression) to predict the corporate default risk and finally explores the ability to interpret the characteristic indicators that affect the predictive performance of Twin-SVR default risk model. The empirical results are as follows:During the construction of the Twin-SVR model, we find that the RBF kernel function exhibits better prediction performance than the Polynomial, Linear, and Sigmoid kernel functions; compared with the traditional SVR, BPNN and Logistic corporate default risk prediction models, the Twin-SVR model not only has the best prediction performance and stability performance in the overall industry, but also has excellent generalization performance in the sub-industry; for the interpretation ability of the top two characteristic indicators that affect the prediction performance of Twin-SVR model, we find that the most explanatory two indicators in the overall industry and non-ferrous metal industry are credit rating and SOE/non-SOE background, and in the coal and steel industry, the top two indicators are credit rating and ROE.

Key words: Twin SVR, corporate default risk, short-term financing bond, prediction

Lin Yu, Wu Qinghe, Li Hao, Tang Xiaohua. Research on Corporate Default Risk Prediction Based on Twin SVR[J]. , 2019, 31(11): 33-43.

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