Since 2018,China's real estate enterprise bonds have defaulted frequently,and the number of defaulted bonds and the amount of defaults far exceed those of other industries.Therefore,based on machine learning technique,an early warning model for bond default of real estate enterprises is constructed to identify bonds that may default in advance.The results show that:the importance of the indicators used for prediction is different at different default prediction time points,and the con-structed early warning indicator group has the best prediction performance for default half a year before default occurs;adding the"three red lines"financial indicators to the index system improves the performance of bond default prediction of real estate enterprises;the characteristics of bond transaction data are introduced as the influencing factors of default prediction,which has a good predictive effect and explanatory significance for default.
关键词
机器学习/债券违约风险/XGBoost/房地产企业/债券交易数据
Key words
machine learning/bond default risk/XGBoost/real estate enterprise/bond transaction data
维普
万方数据