基于多元Logit模型的小微企业信用评级分类研究  

Research on Credit Rating Classification of Small and Micro Enterprises Based on Multinomial Logit Model

作　　者：赵星然 邓晓卫[1] 吕学斌[1] 

机构地区：[1]南京工业大学,江苏 南京

出　　处：《金融》2022年第6期582-589,共8页Finance

摘　　要：首先用Python语言对123家小微企业共约三十七万条交易信息进行分类整理;然后用主成分分析将多种信息归为四类主要因子,并引入定性变量;最后建立多元Logit模型对企业进行由好至差的A、B、C、D四类评级。结果显示:整体评级准确率达到72%;交易信息离评级时点越近,评级的准确率越高;该模型对D级的评级准确率达到100%,说明该方法能有效甄别最差级别企业,为商业银行规避不良贷款发生提供了一种可行性方法。First, use Python to classify and sort a total of about 370,000 transaction information from 123 small and micro enterprises;then use principal component analysis to classify a variety of infor-mation into four main factors, and introduce qualitative variables;finally, establish Multinomial Logit model to rank companies from good to bad in four categories: A, B, C and D. The result shows: the overall rating accuracy rate reaches 72%;the closer the transaction information is to the rating time point, the higher the accuracy of the rating;the accuracy rate of the D-level rating of this model reaches 100%, it shows that this method can effectively identify the worst-level enterprises, which provides a feasible method for commercial banks to avoid the occurrence of non-performing loans.

关 键 词：信用评级 PYTHON语言 主成分分析 多元LOGIT模型 

分 类 号：F832[经济管理—金融学]