Credit rating analysis based on the network of trading information

Ximei Wang; Boualem Djehiche; Xiaoming Hu

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In this paper, credit rating problems based on the network of trading information are studied.
Assortativity analysis, community detection, centrality metric including betweenness, PageRank, and closeness for are introduced to investigate the topology properties of the network.
The correlation between sovereign ratings conducted by Standard & Poors (S&P) and network properties are analyzed.
A simulation study is conducted to compare the credit rating prediction accuracy result with and without network information, respectively.

Abstract

In this paper, we investigate a credit rating problem based on the network of trading information (NoTI). First, several popular tools, such as assortativity analysis, community detection and centrality measurement, are introduced for analyzing the topology structures and properties of the NoTI. Then, the correlation between the characteristics of the network and the credit ratings is investigated to illustrate the feasibility of credit risk analysis based on the NoTI. Sovereign rating based on the world trade network is analyzed as a case study. The correlation between the centrality metrics and the sovereign ratings conducted by Standard & Poor’s clearly shows that highly ranked economies with vigorous economic trading links usually have higher credit ratings. Finally, a simulation is conducted to illustrate the degree of improvement in credit rating prediction accuracy if the NoTI is considered as an additional attribute.

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