摘要
【目的】立足分类监管理念,通过刻画违规上市公司的多维特征,辅助监管部门进行风险苗头识别和违法违规线索发现。【方法】以因违法违规受罚的我国制造业上市公司为研究对象,引入RFM模型进行违规风险指数评价,在此基础上,从盈利能力、偿债能力、营运能力、分红能力、资本结构、公司治理6个维度进行系统聚类,进而对违规上市公司进行画像。【结果】根据RFM分值,将违规上市公司划分为低风险类、中风险类、次高风险类和高风险类,不同风险等级的违规上市公司体现出不同的特征,较弱的营运能力、偿债能力和公司治理能力以及高资本结构往往意味着高违规风险,这一结果符合违规行为发生的内在逻辑。【结论】监管部门应依据上市公司的违规风险及其特征分类施策,从而进一步提升监管精准度。
[Objective]Based on the concept of classified supervision,this paper depicts the multidimen-sional characteristics of illegal listed companies so as to assist the regulatory authorities in iden-tifying signs of risk and discovering clues of illegal activities.[Methods]Taking China’s listed manufacturing companies punished for violations as the research object,the RFM model is in-troduced to evaluate the violation risk index to reflect their violation severity and regulatory concern.On this basis,a hierarchical cluster analysis is performed in six dimensions:profitabili-ty,solvency,operation ability,dividend ability,capital structure,and corporate governance,and the violation portrait of listed companies is drawn.[Results]According to the RFM score,the illegal listed companies are divided into four categories:low risk,medium risk,sub high risk,and high risk.The listed companies with different violation risk levels have different character-istics.Weak operating capacity,solvency and corporate governance capacity,and high capital structure often mean high violation risk,which is consistent with the internal logic of violations.[Conclusions]The regulatory authorities should take differentiated measures according to the violation risks and characteristics of listed compa-nies so as to further improve the regulatory accuracy.
作者
徐静
袁慧
XU Jing;YUAN Hui(School of Management,Beijing Union University,Beijing 100101,China;Beijing Wuzi University,Beijing 101149,China)
出处
《数据与计算发展前沿》
CSCD
2023年第6期20-30,共11页
Frontiers of Data & Computing
基金
北京市社会科学基金规划项目“大数据审计模式下财务报表审计线索发现研究”(21GLB015)。
关键词
RFM模型
违规行为
分类监管
系统聚类
企业画像
RFM model
violation
classified supervision
hierarchical clustering
enterprise portrait
