Predicting the influence of event news transferring between countries using LSTM-Graph Neural Networks

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  • LSTM-GNNを用いたイベントニュースの国間の波及予測

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<p>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.</p>

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