Evaluation of credit risk on pharmaceutical supply chain finance based on KMV model

Abstract

During the period of the epidemic, uncertain factors intensified the problem of credit risk in the pharmaceutical supply chain. This paper selects the stock data of 292 listed companies in the nodes of the pharmaceutical supply chain from January 20, 2020 to January 19, 2021 and uses Python software and KMV model for quantitative research. Then it obtains that the average expected default frequencies of supplier class, core manufacturing class, pharmaceutical circulation class and logistics class are 7.57%,4.59%,5.73%and 0.39% respectively. The average default distances of traditional Chinese medicine industry, chemical pharmacy industry, biological product industry, medical device industry, medical service industry and pharmaceutical business industry are 2.4363, 2.0418, 1.9911, 1.9006, 1.8341 and 1.4070 respectively. The results show that the default probability of core manufacturing enterprises is higher than that of logistics enterprises and lower than that of upstream and downstream enterprises and there is a certain gap in the credit quality of various industries in both pharmaceutical core manufacturing class and pharmaceutical circulation class.