非均衡数据下制造型企业金融信用风险研究--基于压力测试和机器学习  

Research on Financial Credit Risk of Manufacturing Enterprises Under Unbalanced Data:Based on Stress Testing and Machine Learning

作　　者：龙志 陈湘州[1,2] LONG Zhi;CHEN Xiangzhou(Business School,Hunan University of Science and Technology,Xiangtan Hunan 411201,China;Hunan Strategic Emerging Industries Research Base,Xiangtan Hunan 411201,China)

机构地区：[1]湖南科技大学商学院,湖南湘潭411201 [2]湖南省战略性新兴产业研究基地,湖南湘潭411201

出　　处：《重庆文理学院学报（社会科学版）》2025年第1期26-45,共20页Journal of Chongqing University of Arts and Sciences（Social Sciences Edition)

基　　金：国家社会科学基金一般项目“持续调控背景下房地产市场利益分配协调机制及政策研究”(13BJY057)。

摘　　要：如何有效评估企业金融信用风险状况是当前风险预警领域的研究重点。以我国制造型企业为例,首先通过主成分分析和K均值聚类对企业金融信用风险进行综合打分和等级划分,并深入探究指标重要性;然后使用SMOTE过采样方法解决类别不均衡问题,以提升机器学习模型的预测效果;最后评估各机器学习模型的预测效果,将表现出色的模型作为压力传导模型,通过压力测试分析不同细分行业中企业的抗压能力。研究发现:1)不同信用指标对制造型企业金融信用风险的影响程度存在显著差异,影响最大的是行业偿债能力,影响最小的是企业经营能力;2)在压力测试中,相较于其他模型,MLP模型的整体预测效果最佳,在逐级升压情境下,其P_(MLP)下降幅度和C_(VMLP)上升幅度最小;3)随着压力因素的增加,各细分行业下制造型企业的抗压能力曲线显著下降,若以下降幅度为标准,通用设备制造企业具备较强的抗压能力,而专用设备制造企业的抗压能力较小。研究结果可以帮助利益相关者更有效地评估和管理制造型企业的金融信用风险,降低风险暴露的可能性,促进企业健康发展。How to effectively assess the financial credit risk status of enterprises is the current research focus in the field of risk warning.Taking Chinese manufacturing enterprises as an example,firstly,through the principal component analysis and K-mean clustering to comprehensively score and classify the financial credit risk of enterprises,an exploration was made on the importance of indicators in depth;then,SMOTE oversampling was used to solve the problem of category imbalance in order to improve the prediction effect of the machine learning model;finally,an evaluation was made on the prediction effect of each machine learning model,and taking the model with outstanding performance as the stress transfer model,an analysis was made on the stress resistance of enterprises under different segments through stress testing.The study found that:1)there is a significant difference in the degree of influence of each credit indicator on the financial credit risk of manufacturing companies.For example,the most influential is the industry solvency,and the least influential is the enterprise operating ability;2)in the stress test,compared with other models,the MLP model has the best overall prediction effect,with the smallest decrease in P_(MLP) and the smallest increase in C_(VMLP);3)with the increase of stress factors,the stress resistance curve of manufacturing enterprises under each sub-sector decreases significantly.Taking the decline as a criterion,the general equipment manufacturing enterprises have stronger stress resistance,while the specialized equipment manufacturing enterprises have smaller stress resistance.The research results can help stakeholders more effectively assess and manage the financial credit risk of manufacturing enterprises,reduce risk exposure,and promote the healthy development of enterprises.

关 键 词：制造型企业 金融信用风险 压力测试 机器学习 非均衡数据 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] F274[自动化与计算机技术—控制科学与工程] F832.4[经济管理—企业管理]