[3D4-GS-10] AI application: prediction 1

[3D4-GS-10-04] Predicting the influence of event news transferring between countries using LSTM-Graph Neural Networks

〇Akito Suzuki¹, Akihiro Tsuji¹, Yusuke Tashiro¹, Sintaro Suda¹, Tokuma Suzuki¹, Ryo Ito² (1. Mitsubishi UFJ Trust Investment TEChnology Institute, 2. Fiah Co., Ltd.)

[[Online]]

Keywords:GNN, GAT, News, Graph, LSTM

In financial markets, there is a lot of news coming out every day and affecting asset prices. To understand how information about specific events in news articles propagates from a country to other countries, we focus on predicting the change of the amount of news articles in each country. While previous studies utilized GAT (graph attention networks) to capture cross-country dependencies, they aggregated past information and did not consider temporal structures. In this paper, we extend GAT model to LSTM-GAT for modelling the change of information propagation across time. Our experiment shows that LSTM-GAT improves the prediction accuracy compared to other baseline methods, which capture only one of cross-country and temporal dependencies.