基于Stacking算法集成的我国信用债违约预测  

Default Prediction of Credit Bond in China Based onStacking Algorithm Integrated Model

作　　者：刘晓 周荣喜[2] 李玉茹 LIU Xiao;ZHOU Rongxi;LI Yuru(School of Economics and Management,North China University of Technology,Beijing 100144,China;School of Banking and Finance,University of International Business and Economics,Beijing 100029,China)

机构地区：[1]北方工业大学经济管理学院,北京100144 [2]对外经济贸易大学金融学院,北京100029

出　　处：《运筹与管理》2023年第3期163-170,共8页Operations Research and Management Science

基　　金：国家自然科学基金资助项目(71871062,71631005)。

摘　　要：通过对2014~2019年我国信用债违约案例的原因分析及相关文献综述,从债券资质、债务主体、财务数据、宏观因素四个维度构建债券违约的指标体系,利用随机森林算法优化,研究发现当影响因素选择18项与37项时,样本内外预测结果达到均衡。基于不同角度的七种算法对比分析,择优选取三种作为底层算法:随机森林算法、梯度提升决策树算法与贝叶斯算法,并结合逻辑回归算法为次级训练算法融合构建基于Stacking算法集成的债券违约预测模型。实证结果表明,第一,Stacking算法的双重集成作用相对底层的单次集成总体精确度提升了1%到8%;第二,对不同指标数量的Stacking算法集成模型的评估表明所构建的指标体系提高了预测水平;第三,基于样本内外预测均衡的底层算法选择方法有效可取,分别纳入相对劣势的底层算法时,会逐渐影响模型稳定性。研究成果可以为我国债券市场风险管理提供技术支持与参考。Many scholars pay more attention to financial risk warning analysis of debt default,and the comprehensive impact analysis of cross-level and multi-angle influencing factors is less.The existing bond default data is obviously unbalanced,and the overall number is relatively small,and the acquisition of data information becomes difficult.The research of credit risk models and the mining of various models have shortcomings in model setting.In the face of the increasingly serious trend of credit debt default in China,how to effectively predict it so as to achieve timely supervision in advance and prevent risk aggregation has important theoretical significance and application value.We select the data of credit bonds that have defaulted between January 1,2014 and September 30,2019,and the bonds that have been in normal existence for two years or more as the normal sample,including 453 default samples.We select the default bond data in the period from May 2019 to September 2019,and intercept the bond data in the last five months of the normal existing bond data.A total of 411 bonds contain 90 default samples,as the prediction sample data of the evaluation model.Through the analysis of the causes of the default cases of credit bonds in China and the review of relevant literature,the indicator system of bond default is constructed from the four dimensions of bond qualification,debt subject,financial data and macro factors.First of all,the Pearson correlation coefficient and Spear-man correlation coefficient are used to test the correlation between default and 43 consecutive indicators,and the importance score of their impact degree is ranked.The stochastic forest algorithm model is used to determine the optimal parameters of the continuity indicators,and the model training and evaluation are carried out by eliminating the indicators one by one to obtain the optimal impact factor combination.Secondly,the underlying algorithm is built by weighted fusion,and a certain algorithm is combined as a secondary algorithm,and the out

关 键 词：信用风险 债券违约预测 机器学习 Stacking算法 算法集成 

分 类 号：F830.91[经济管理—金融学]