Assessment of associated credit risk in the supply chain based on trade credit risk contagion

Abstract

Assessment of associated credit risk in the supply chain is a challenge in current credit risk management practices. This paper proposes a new approach for assessing associated credit risk in the supply chain based on graph theory and fuzzy preference theory. First, we classified the credit risk of firms in the supply chain into two types, namely firms’ “own credit risk” and “credit risk contagion”; second, we designed a system of indicators for assessing the credit risks of firms in the supply chain and used fuzzy preference relations to obtain the fuzzy comparison judgment matrix of credit risk assessment indicators, on which basis we constructed the basic model for assessing the own credit risk of firms in the supply chain; third, we established a derivative model for assessing credit risk contagion. On this basis, we carried out a comprehensive assessment of the credit risk of firms in the supply chain by combining the two assessment results, revealing the contagion effect of associated credit risk in the supply chain based on trade credit risk contagion (TCRC). The case study shows that the credit risk assessment method proposed in this paper enables banks to accurately identify the credit risk status of firms in the supply chain, which helps curb the accumulation and outbreak of systemic financial risks.

Analysis of the Relationship Between Cross Capital Flows and Stock Exchange Index with Machine Learning

“…The securities markets, functioning as critical barometers of economic sentiment, play an indispensable role in the international movement of capital. Their dynamics, as highlighted by Xie et al (2023), are crucial for understanding the broader economic landscape. This study is dedicated to enhancing our understanding of these dynamics, focusing on the interplay between stock exchange indices and global capital transfers.…”