  Incorporating related text information has proven successful in stock market
prediction. However, it is a huge challenge to utilize texts in the enormous
forex (foreign currency exchange) market because the associated texts are too
redundant. In this work, we propose a BERT-based Hierarchical Aggregation Model
to summarize a large amount of finance news to predict forex movement. We
firstly group news from different aspects: time, topic and category. Then we
extract the most crucial news in each group by the SOTA extractive
summarization method. Finally, we conduct interaction between the news and the
trade data with attention to predict the forex movement. The experimental
results show that the category based method performs best among three grouping
methods and outperforms all the baselines. Besides, we study the influence of
essential news attributes (category and region) by statistical analysis and
summarize the influence patterns for different currency pairs.
