TITLE:
A Study on Forecasting the Default Risk of Bond Based on XGboost Algorithm and Over-Sampling Method
AUTHORS:
Yan Zhang, Lin Chen
KEYWORDS:
XGboost, Default Risk, Bond Issuer, Imbalanced Data, SMOTE
JOURNAL NAME:
Theoretical Economics Letters,
Vol.11 No.2,
April
13,
2021
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.