| 基于最大指标区分度与最优相对隶属度的上市公司信用风险研究 |
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| 引用本文： | 李哲,迟国泰. 基于最大指标区分度与最优相对隶属度的上市公司信用风险研究[J]. 中国管理科学, 2021, 29(4): 1-15. DOI: 10.16381/j.cnki.issn1003-207x.2019.1095 |
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| 作者姓名： | 李哲 迟国泰 |
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| 作者单位： | 大连理工大学经济管理学院, 辽宁 大连 116024 |
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| 基金项目： | 国家自然科学基金重点资助项目（71731003）；国家自然科学基金面上资助项目（72071026，71873103，71971051，71971034）；国家自然科学基金青年科学基金资助项目（71901055，71903019，71902077）；国家社会科学基金重大资助项目（18ZDA095） |
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| 摘 要： | 信用风险又称违约风险，是评价一笔债务由于某些因素遭受损失的可能性，因此信用评价体系既要有违约鉴别能力，又能给出违约产生的原因。本文涉及的问题一是如何在众多指标中筛选出对违约状态鉴别最大的指标组合，二是如何对指标赋予权重，使得权重体现指标真实的重要程度，三是如何选取兼顾分类性能和可解释性的模型。本文的方法一是通过指标区分度最大的广义F-Score、指标子集间冗余度最小的平均相关系数遴选出对违约状态鉴别能力最大的指标体系；二是通过构建组合赋权模型，以指标信息含量最大、赋权结果一致性最大为目标函数，得到一组最优的指标权重；三是以客户与类别的广义加权距离最小为目标函数，求解非线性目标规划得到客户在不同类别中的相对隶属度，以此得到客户所属类别。上市公司样本实证分析表明：（1）从610个指标中筛选出的31个指标既具有违约状态的区分能力，又符合"信用5C"原则，是一个科学、合理的指标体系；（2）通过组合赋权得到31个指标的权重，企业内部财务因素对信用评价影响最大，"营运资本周转率"的权重值最大，是影响企业信用评价的关键指标，"监事会会议次数"、"派息比"、"审计意见类型"等指标都是影响信用评价的重要指标；（3）与逻辑回归、支持向量机等8种模型对比发现，本文提出的相对隶属度模型不仅具有良好的分类性能，而且是一个平衡分类性能、可解释性的模型。
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| 关 键 词： | 信用评价 指标组合筛选 组合赋权 最优相对隶属度 |
| 收稿时间： | 2019-07-25 |
| 修稿时间： | 2020-01-22 |
Research on the Listed Companies' Credit Risk Based on Maximum Discrimination and Optimal Relative Membership Degree |
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| LI Zhe,CHI Guo-tai. Research on the Listed Companies' Credit Risk Based on Maximum Discrimination and Optimal Relative Membership Degree[J]. Chinese Journal of Management Science, 2021, 29(4): 1-15. DOI: 10.16381/j.cnki.issn1003-207x.2019.1095 |
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| Authors: | LI Zhe CHI Guo-tai |
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| Affiliation: | School of Economics and Management, Dalian University of Technology, Dalian 116024, China |
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| Abstract: | Credit risk, also known as default risk, is to evaluate the possibility of a debt suffering losses due to certain factors. Therefore, the credit evaluation system must be able to not only have the ability to identify defaults, but also be interpretable and provide reasons for defaults.The construction of the listed companies' credit risk system involves at least three problems. The first problem is selecting a combination of features, as 2m-1 feature combinations exist for m features. Further, different feature combinations from the same sample will have different evaluation results. Therefore, a scientific issue that this article aims to explore and answer is how to build a feature selection model and select a group of features with the greatest discriminative ability. The second problem is determining the feature weights, as the classification results obtained by assigning different weights for the same set of features will differ, or even oppose one another. In a classification model that includes feature weights, the weight setting directly affects the model's classification ability. Therefore, another issue that this article aims to address is how to reasonably weigh the system features. The third problem involves constructing an optimal classification model. Traditional classification models, such as the support vector machines and neural network models, among others, have a theoretical classification threshold of 0.5. As a sample's optimal classification threshold will differ, determining whether its selection is reasonable will directly affect the model's classification performance. Further, the previously mentioned models are "black box" models, and cannot explain the causes of defaults. Consequently, another issue that must be addressed is how to choose an interpretable, highly accurate classification model.The following solutions for these three problems are proposed in this work. Firstly, the feature system with the largest discrimination ability is selected by the generalized F-Score with the largest discrimination and the smallest Pearson correlation coefficient among the features. Secondly, a set of optimal weights is obtained by constructing a combination weighting model, taking the maximum feature information content and the maximum consistency of weighting results as the objective function. Thirdly, through minimizing the generalized weighted distance between samples and categories, the relative membership degrees of the customer in different categories are obtained by solving the non-linear goal programming, and then the category of the customer is obtained.An empirical analysis of the listed company samples shows that:(1) 31 features selected from 610 features have the ability to distinguish the defaults and non-defaults and conform to the principle of five Cs of credit, which is a scientific and reasonable feature system; (2) Weights of 31 features are obtained by combination weighting model, and financial factors within enterprises have the greatest impact on credit risk, and working capital turnover ratio having the greatest weight is a key feature affecting the credit risk. The number of supervisory committee meeting, dividend payout ratio, type of audit opinion are all important features affecting the credit rating; (3) Compared with eight models such as logistic regression and support vector machine, etc., it is found that the model proposed in this paper not only has good classification performance, but also is a model balancing classification accuracy and complexity. |
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| Keywords: | credit risk feature combination selection combination weighting optimal relative membership degree |
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