金融科技时代Issue(5)：28-42,15.
集成学习在债务风险预警中的效力
The Efficacy of Ensemble Learning in Debt Risk Early-Warning:Evidence from Chinese Listed Real Estate Companies
摘要
Abstract
This paper tests the efficacy of ensemble learning in debt risk early warning of listed real estate enterprises.We use 89 real estate enterprises listed in China Mainland and Hong Kong between 2008 to 2022.The results show that the early warning framework based on the LASSO-GA-XGBoost model outperforms others.Stock price performance and pre-sales are the most important indicator dimensions in the debt risk early warning indicator system of real estate enterprises.Contracted sales amount,annualized stock yield,stock closing price and annualized stock volatility all rank high in importance for debt risk early warning of real estate enterprises.The conclusions provide valuable policy insights on how to utilize artificial intelligence models to enhance the enterprise credit risk early warning and monitoring system,improve the foresight of financial risk prevention and control,and achieve intelligent supervision.关键词
房地产企业/债务违约/风险预警/集成学习Key words
Real Estate/Debt Default/Risk Early-warning/Ensemble Learning分类
管理科学引用本文复制引用
钟永红,钟俊豪..集成学习在债务风险预警中的效力[J].金融科技时代,2026,(5):28-42,15.基金项目
本文感谢国家社会科学基金一般项目(23BJY118)、广东省自然科学基金项目粤惠联合基金青年项目(2024A1515110111)的资助. （23BJY118）
