Hybrid models based on rough set classifiers for setting credit rating decision rules in the global banking industry
Hybrid models based on rough set classifiers for setting credit rating decision rules in the global banking industry
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DOI：
10.1016/j.knosys.2012.11.004
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发表时间：
2013-02
期刊：
影响因子：
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通讯作者：
You-Shyang Chen;Ching-Hsue Cheng
中科院分区：
文献类型：
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作者：
You-Shyang Chen;Ching-Hsue Cheng
银行对国家乃至全球经济稳定至关重要。银行资不抵债或破产后的银行恐慌，特别是大银行，可能严重危及经济稳定。因此，发行人和投资者迫切需要一个信用评级指标来帮助识别银行的财务状况和经营能力。信用评级为金融实体提供了对信用价值、投资风险和违约概率的评估。虽然已经提出了许多模型来解决信用评级问题，但它们具有以下缺点：（1）缺乏解释力;（2）依赖于统计技术的限制性假设;（3）变量众多，导致多维和复杂的数据。为了克服这些缺点，本文采用了两种混合模型来解决信用评级分类中的实际问题。为了验证模型，这项工作使用了从Bankscope数据库收集的1998-2007年期间的实验数据集。实验结果表明，所提出的混合模型的信用评级分类优于上市模型在这项工作中。一组决策规则分类信用评级提取。最后，研究结果和管理的影响提供给学者和从业人员。
Banks are important to national, and even global, economic stability. Banking panics that follow bank insolvency or bankruptcy, especially of large banks, can severely jeopardize economic stability. Therefore, issuers and investors urgently need a credit rating indicator to help identify the financial status and operational competence of banks. A credit rating provides financial entities with an assessment of credit worthiness, investment risk, and default probability. Although numerous models have been proposed to solve credit rating problems, they have the following drawbacks: (1) lack of explanatory power; (2) reliance on the restrictive assumptions of statistical techniques; and (3) numerous variables, which result in multiple dimensions and complex data. To overcome these shortcomings, this work applies two hybrid models that solve the practical problems in credit rating classification. For model verification, this work uses an experimental dataset collected from the Bankscope database for the period 1998–2007. Experimental results demonstrate that the proposed hybrid models for credit rating classification outperform the listing models in this work. A set of decision rules for classifying credit ratings is extracted. Finally, study findings and managerial implications are provided for academics and practitioners.