2019/10/11 by Deli Chen, Chen, Deli, Shuming Ma +9 · 1 citation
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Advanced Text Analysis Techniques #Complex Systems and Time Series Analysis #Computation and Language (cs.CL) #FOS: Computer and information sciences #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.1910.05032
openalex publication_date 2019/10/11 · openalex created_date 2019/10/18 · openalex updated_date 2026/07/28
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.