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Incorporating related text information has proven successful in stock marketprediction. However, it is a huge challenge to utilize texts in the enormousforex (foreign currency exchange) market because the associated texts are tooredundant. In this work, we propose a BERT-based Hierarchical Aggregation Modelto summarize a large amount of finance news to predict forex movement. Wefirstly group news from different aspects: time, topic and category. Then weextract the most crucial news in each group by the SOTA extractivesummarization method. Finally, we conduct interaction between the news and thetrade data with attention to predict the forex movement. The experimentalresults show that the category based method performs best among three groupingmethods and outperforms all the baselines. Besides, we study the influence ofessential news attributes (category and region) by statistical analysis andsummarize the influence patterns for different currency pairs.

Group, Extract and Aggregate: Summarizing a Large Amount of Finance News for Forex Movement Prediction