商业银行信贷业务风险预警——基于灰色系统理论与Logistic回归的实证检验  

Credit Risk Early Warning of Commercial Banks——Empirical Test Based on the Grey System Theory & Logistic Regression

作　　者：李森[1] 刘媛华[2] 于明亮[1] 

机构地区：[1]上海理工大学公共实验中心,上海200093 [2]上海理工大学管理学院,上海200093

出　　处：《科技与经济》2015年第6期15-18,73,共5页Science & Technology and Economy

基　　金：教育部人文社会科学基金项目--"新媒体时代企业声誉的影响因素与管理模式研究"(项目编号:12YJC630127;项目负责人:刘媛华)成果之一;沪江基金人文社科项目--"基于灰色系统理论与logistic回归的信贷风险预警问题研究"(项目编号:15HJSK-YB22;项目负责人:李森)成果之一

摘　　要：商业银行信贷业务在给银行带来丰厚收益的同时,也带来了很高的风险。其主要原因在于银行对违约企业的前期预警能力不足。研究以2011年制造型企业上市公司年报数据为样本,采用样本配比的抽样法,建立了基于灰色系统理论的logistic违约预警模型,并进行了实证检验。结果表明,引入灰色系统理论的logistic违约预警模型具有较好的拟合度,对违约企业具有较强的预测能力和预测精度,达到了对违约企业进行前期预警的目的。该模型的运用将显著降低银行的信贷业务风险,提升银行的经营效益。The commercial bank credit has brought huge benefits to banks, but it also brings a high risk. The main reason is the lack of early warning capability to default enterprises. This paper uses annual report data of listed manufacturing enterprises in 2011 as samples. Using the sample proportion as the sampling method, this paper establishes a logistic default warning model based on the gray system theory and makes an empirical inspection. The results show that the logistic default warning model based on the gray system theory has a relatively good degree of fitting. The model has strong prediction ability and accuracy, and can achieve the purpose of the pre-warning of default enter- prises. Using this model will significantly reduce bank＇s credit business risk and improve operational efficiency of banks.

关 键 词：灰色系统理论 LOGISTIC回归 制造型企业 信贷风险预警 

分 类 号：F832.4[经济管理—金融学]