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

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.