Early Warning Research on Bond Default Risk Based on LR-RF-XGBoost
With the rapid development of the market economy,the Chinese bond market has also grown and gradually developed into an integral part of the market economy.However,in the course of this development,bond defaults have been common and increasing,greatly hampering market dynamics.This paper takes companies issuing corporate bonds,corporate bonds,short-term financing bonds and medium-term bonds as the research subjects,and proposes the LR-RF-XGBoost bond default warning model,which is based on a soft voting method combining Logistic Regression,Random Forest and Extreme Gradient Boosting algorithm,study of financial indicators as well as non-financial indicators for the sample.Results show that the LR-RF-XGBoost fusion model has a higher generalisation capability than other single warning models,with an accuracy of 95.3%.The method is useful in providing investors and bond market supervisory authorities with reliable predictive information,and can better help companies themselves to identify risks early,providing protection for the healthy development of the bond market.
bond defaultslogistic regressionrandom forestextreme gradient boosting
NETL
NSTL
万方数据
维普
