DOI: 10.1142/s021848852340010x ISSN: 0218-4885

Integrating Network Information Into Credit Classification Models

Le T. T. An, Hoang T. K. Anh, Luu M. Anh, Mai T. Mien, Phan T. K. Oanh
  • Artificial Intelligence
  • Information Systems
  • Control and Systems Engineering
  • Software

Some qualitative studies have mentioned the impact of trading networks on the credit levels of countries or businesses. However, only a few studies quantified network information on simulated or actual data. This research studied the relationship between network characteristics and sovereign credit levels. Using a data set for 114 countries, we developed and compared the performance of five credit classification models with and without integrating network information.

We used several measures to quantify the trading network information, such as Trading Weight, Closeness, Betweenness, PageRank, and Modularity. The analysis showed that they were closely related to credit levels. In particular, the Trading Weight usually belonged to the top five critical features for almost all classifiers. Overall, we saw that the correct prediction of the network models was consistently higher than those without a network. It meant we could improve the sovereign credit classifications with available trading network information.

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