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

arXiv: https://arxiv.org/abs/1910.05032 · HTML: https://mlsanity.com/paper/1910.05032/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [FiNER: Financial Numeric Entity Recognition for XBRL Tagging](https://mlsanity.com/paper/2203.06482/) (2022)
- [Financial Numeric Extreme Labelling: A Dataset and Benchmarking for XBRL Tagging](https://mlsanity.com/paper/2306.03723/) (2023)
- [Fine-Tuning Large Language Models for Stock Return Prediction Using Newsflow](https://mlsanity.com/paper/2407.18103/) (2024)
- [Exploring the Synergy of Quantitative Factors and Newsflow Representations from Large Language Models for Stock Return Prediction](https://mlsanity.com/paper/2510.15691/) (2025)
- [Do Deep Learning Models and News Headlines Outperform Conventional Prediction Techniques on Forex Data?](https://mlsanity.com/paper/2205.10743/) (2022)
- [Regex-augmented Domain Transfer Topic Classification based on a Pre-trained Language Model: An application in Financial Domain](https://mlsanity.com/paper/2305.18324/) (2023)

## Abstract (arXiv)

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.