Supply Chain Financial Default Risk Early Warning System Based on Particle Swarm Optimization Algorithm

Abstract

With the advancement of the linkage between financial markets, the probability of credit risk infection is also increasing. Traditional financing methods, which mostly relied on corporate credit to give credit to the whole supply chain, have been replaced by supply chain finance. This paper studies the supply chain financial credit risk through the logistic model and chooses the financial data and supply chain financial operation indicators of relevant listed companies from 2014 to 2016 for analysis. Because not all of companies can find the bad debt rate of accounts receivable from 2014 to 2016, and some agricultural listed companies only have one or two years of relevant data, this paper creates an unbalanced panel data with 91 sample sizes, which is larger than previous studies. Binary logistic regression and principal component analysis are mainly used to accurately calculate the compliance probability of cooperative customers in agricultural supply chain financial products. Unlike the existing literature, which mainly uses s.t to define whether an enterprise defaults, this paper uses $Z$ value to define the default risk of listed companies in agricultural supply chain finance. In terms of the default risk value of the company, $Z$ value not only has high accurate value but also has advantages in accurate prediction, which effectively complements and improves the existing research on supply chain finance.