“去刚兑”背景下的企业债券违约风险:机器学习预警和经济机制探究  

Research on an Early Warning Model of Corporate Bond Default and its Economic Mechanism Based on Machine Learning

作　　者：姜富伟 林奕皓 马甜 JIANG Fuwei;LIN Yihao;MA Tian(School of Finance,Central University of Finance and Economics;School of Economics,Minzu University of China)

机构地区：[1]中央财经大学金融学院,北京100081 [2]中央民族大学经济学院,北京100081

出　　处：《金融研究》2023年第10期85-103,共19页Journal of Financial Research

基　　金：国家社会科学基金重大项目(22&ZD063);国家自然科学基金面上项目(72072193,71872195,72342019);国家自然科学基金青年项目(72303271);中央财经大学青年科研创新团队支持计划的资助。

摘　　要：本文构建了包含1245个变量的宏观经济-微观企业混合大数据集,并结合10种机器学习算法,开展基于大数据和机器学习的债券违约风险预警,探究其背后经济机制。实证结果表明:相比经典Altman模型、Merton模型、信用评级模型,机器学习模型能够更好地预测我国债券市场违约风险,非线性机器学习模型表现更佳。异质性分析表明,机器学习模型对信用评级低、发行期限长、票面利率高、非国有企业、银行间市场的债券,以及在经济政策不确定性(公众基于媒体报道对政府经济政策未来走向的预期的不确定性)高的时期,具有更强的预测能力。机制分析表明,机器学习模型通过违约债券样本识别、短期信号识别(债券交易量)、长期特征识别(融资约束、内部控制)实现精准预测。本文对于债券违约风险预警、维护金融稳定、信用评级体系完善、金融科技创新和金融服务实体经济提供了有益的政策启示。In recent years,the bond market has played an important role in serving the real economy,optimizing resource allocation,and supporting macroeconomic policy regulation.However,since China terminated rigid payments in 2014,bond defaults have occurred frequently.In this context,the identification of bond default risk has become a new and key issue for the capital market and economic development.At the same time,financial technology(fintech)is becoming an important method to enhance the prevention and control of financial risks.In this context,this paper proposes the use of fintech,such as big data and machine learning,to develop an early warning model for bond default risk that fits the current context.This paper systematically explores the performance of big data and machine learning models in predicting bond default risk.In terms of data,in this paper,we examine the general enterprise bonds,enterprise bonds,medium-term notes,and commercial papers issued by China's A-share listed companies in the interbank and exchange markets.We construct a macro and micro mixed big dataset with a total of 1,245 variables,including 15 macroeconomic indicators,70 enterprise characteristic variables,and 12 bond characteristic variables.Specifically,this paper adds macroeconomic indicators that reflect the willingness and ability of local governments to rescue enterprises subject to bond defaults.In addition,we construct enterprise characteristic variables based on six categories of indicators,including valuation and growth,investment,profit,inertia,transaction friction,and intangible assets.Furthermore,we cross-multiply macro and micro indicators to construct interactive indicators.In terms of model construction,we select 10 machine learning models,including PCA,PLS,Ridge,LASSO,ENet,SVR,RF,GBDT,XGBoost,and AdaBoost.Based on the above models,we examine an early warning model of bond default risk based on big data and machine learning,and explore the economic mechanism behind machine learning.The empirical results show that a machine

关 键 词：去刚兑 违约风险 预警模型 机器学习 金融大数据 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] F275[自动化与计算机技术—控制科学与工程]