基于主成分—粗糙集理论的企业信贷风险预警研究

Application of Principal Component Analysis and Rough Set Theory on Business Cried Risk Early Warning

针对预测数据分布的非正态性,采用主成分—粗糙集理论对企业信贷风险预警进行研究。首先,通过主成分分析法与粗糙集属性约简功能建立一套定量分析与定性分析相结合的指标体系;其次,利用Rosetta商用软件对指标体系处理生成信贷风险评估规则;最后,运用此评估规则对企业信贷风险进行评估。实例验证表明:选用的财务指标、非财务指标及利用粗糙集理论导出的评估规则对我国银行信贷违约风险评估具有较好的预警作用,可为银行的信贷决策提供参考。

Based on the nonnormal distribution of economic data, the assessment of credit risk of business was carried out by using the principal component analysis and rough set theory,. The step as follows, the first, an index system for financial management was established by qualitative analysis and quantitative analysis through principal component method and rough set attribute reduction. The second, the credit risk evaluation rule is maken by using Rosetta commercial software to deal with index system. Finally, credit risk evaluation is done with this rule. Practice proves that the selected financial index , non - financial index and evaluation rule have a good effect in pre - warning of Chinese bank＇ s credit default risk and are useful for the banks credit determination.