[2J4-GS-10] AI application: economics

[2J4-GS-10-05] Predicting economic damage spillover in supply chains using graph neural network

〇Shaofeng Yang¹, Yoshiki Ogawa¹, Koji Ikeuchi¹, Ryosuke Shibasaki¹ (1. Univ. of Tokyo)

Keywords:graph neural network, supply chain management, inter-firm transaction data, economic damage spillover

This study proposes an economic damage estimation model based on the Graph Neural Network (GNN) method, assuming that economic damage spreads through networks among firms when firms are affected by natural disasters. In the proposed model, various inter-firm networks (seven types in total, such as business relationship, investment relationship, same industry, same region, etc.) are extracted from the corporate credit survey data set. Next, graph data is created from the features of individual firms and the network structure of the extracted firms, and a learning model based on the GNN is constructed. To test the effectiveness of the model, we trained seven types of networks from actual company data for the period from 2009 to 2019 and validated their forecasting accuracy. As a result, we found that the prediction error (MSE) of the networks among firms with investments, business partners, and the same municipality was relatively small, and in particular, the model of the relationship with the investee had the smallest prediction error. This implies that the damage spillover of the transaction value of firms is more likely to be influenced by each firm's investees, business partners, and firms in the same city.