A Study on Forecasting the Default Risk of Bond Based on XGboost Algorithm and Over-Sampling Method

Yan Zhang,Lin Chen

doi:10.4236/tel.2021.112019

Abstract

China’s bond market is an emerging market. The number of bond defaults has been increasing in recent years, but the data set is severely imbalanced. Based on financial data of total 6731 corporate bond issuers which 50 bond issuers had defaulted, this paper uses the XGboost algorithm and an Over-sampling method named SMOTE to predict the default of bond issuers. The results show that the XGboost algorithm has advantages over the traditional algorithm in processing imbalanced data, and SMOTE is one of the effective methods to deal with imbalanced samples. Then, this is an effective way to predict the default risk of bond issuers in an emerging market.

Highlights

Corporate credit risk is one of the most key risks for financial institutions and investors
Based on financial data of total 6731 corporate bond issuers which 50 bond issuers had defaulted, this paper uses the XGboost algorithm and an Oversampling method named SMOTE to predict the default of bond issuers
The results show that the XGboost algorithm has advantages over the traditional algorithm in processing imbalanced data, and SMOTE is one of the effective methods to deal with imbalanced samples

Summary

Introduction

Corporate credit risk is one of the most key risks for financial institutions and investors. The XGBoost has been used in many fields such as disease diagnosis (Liu et al, 2021; Zhang et al 2020b; Zhang, Deng, and Jia, 2020a; Zhang and Gong, 2020), image recognition (Zhao, Guo et al, 2020; Zhao, Zeng et al, 2020), traffic flow prediction (Zhang and Zhang, 2020), sports analytics (Yigit, Samak, and Kaya, 2020), power system (Xue and Wu, 2020; Raichura, Chothani, and Patel, 2021; Li et al, 2018), e-commerce (Song and Liu, 2020), personal credit (Li et al, 2020; Ma et al, 2018; Xia, Liu, and Liu, 2017; Chang, Chang, and Wu, 2018) forecasting of weather (Jin et al, 2020; Fan et al, 2020; Zheng and Wu, 2019), public security (Feng et al, 2020), fault detection (Zhang et al, 2018; Chen et al, 2019; Lei et al, 2019; Lin et al, 2019) and so on.

XGboost Algorithm and SMOTE Algorithm

Data Set

Effectiveness Analysis of XGboost Algorithm

Findings

Conclusion