摘要
供应链金融作为一种产融结合的新型融资模式,在解决企业融资问题的同时,也给传统金融行业带来了新的利润增长,因此自提出以来,便受到了学术界、企业界以及金融界的高度关注。但是由于中小企业普遍存在规模较小、信息披露程度低等问题,银行在实际业务开展中,很难对业务风险进行准确的评估,进而产生较高的业务风险,其中主要来源于信用风险,因此建立有效的信用风险评估方法对供应链金融的发展起着关键性的作用。文中使用KMV模型计算的违约距离代替财务性指标作为融资企业经济绩效的衡量标准,有效避免了现有指标体系因过度使用财务性指标而导致的滞后性问题,进而提升供应链金融信用风险评估的准确性。
As a new financing mode combining industry and finance,supply chain finance not only solves the problem of enterprise financing,but also brings new profit growth to the traditional financial industry.Therefore,it has been highly concerned by the academic,business and financial circles since it was put forward.However,due to the common problems of small scale and low degree of information disclosure in small and medium-sized enterprises,it is difficult for banks to accurately evaluate the business risk in the actual business development,resulting in higher business risk,which mainly comes from credit risk.Therefore,the establishment of effective credit risk evaluation methods plays a key role in the development of supply chain finance.This paper uses the default distance calculated by KMV model instead of financial indicators as the measurement standard of the economic performance of financing enterprises,which effectively avoids the lag problem caused by the excessive use of financial indicators in the existing index system,and then improves the accuracy of supply chain financial credit risk assessment.
作者
祝锡永
赵甜甜
ZHU Xi-yong;ZHAO Tian-tian(School of Economics and Management,Zhejiang Sci-Tech University,Hangzhou 310000,China)
出处
《物流工程与管理》
2022年第10期48-51,共4页
Logistics Engineering and Management
基金
浙江省自然科学基金项目(LY18G020017)
浙江理工大学共享经济研究院项目(18GXJJ07)。
关键词
供应链金融
信用风险评估
KMV模型
粒子群算法
supply chain finance
credit risk assessment
KMV model
particle swarm optimization
