不同规模中小型企业不良贷款回收率结构特征研究

依托于国内最大不良贷款回收情况数据库LossMetrics ~(TM)数据库中以中小型企业债务人为主体的数据,针对不同规模注册资本金的中小企业不良贷款数据,从银行贷款金额和转让协议金额两方面分析了不同规模中小型企业资产和债务间的关系;利用聚类的方式划分出有相似回收率结构特征的四类不同规模企业,并用Logistic分类方法对不同规模的企业分别建模,进一步研究影响每一类企业回收率结构的关键因素,给出不同企业不良贷款的质量判别,发现不同规模企业间的回收信用风险差异十分显著.实证研究结果表明:形成不良贷款的大部分中小型企业都存在资不抵债、贷款数量过于庞大的问题;注册资本金在1000万到3000万之间的企业的不良贷款数量最为庞大,应引起银行的重点监控;Logistic模型中以企业的经营情况、不良贷款的处置方式及有效抵质押情况为最重要的判别因素.研究结果对处于金融危机的中小型企业贷款融资和银行业风险管理提供了帮助和参考指标.

Recovery rate structural analysis of non-performing loan for different-size small and medium enterprises

Based on debtors of small and medium enterprises in LossMetrics^TM, focusing debtors of different registered capital, we analyze the relationship of assets and debts for different small and medium enterprises through the amount of loans and transferred money by agreement. We classify the enterprises into four similar group though clustering approach, construct models on recovery rate of different groups by logistic method and give quality discriminant of non-performing loans. The results turn out that the enterprises which have non-performing loans often have too large debts, even more than their assets, the enterprises of 10--30 million registered capital have the largest amount of non-performing loans, banks should pay more attention to them, debtors' financial situation, disposaling methods of non-performing loans, debts' credit guarantees are most significant variables in the models. The results will be helpful to small and medium enterprises and risk management of banks.