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

Abstract

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.

Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction

“…ence the stock market, we first filter the news with the "RIC" label provided in the data by Reuters, which are the stock codes that the news may influence. Then we filter the news with some financial keywords described in the paper of Chen et al [2019b]. Because much of the news is not related to the market price, we choose the keywords in the category of earnings, affairs, business, ratings and corporate.…”

“…Xu and Cohen [2018] propose to use sequential tweets and market data to predict the stock movement. Apart from stock movement prediction, Chen et al [2019a] propose a hierarchical framework to predict the Forex movement by grouping and summarizing a large amount of finance text.…”

From Text Representation to Financial Market Prediction: A Literature Review

“…In this survey, we study methods for identifying the contextual information published in social media related to financial markets. Text mining techniques, such as a sentiment analysis [10][11][12][13], part of speech tagging (POS) [14,15], text representation, such as transformer-based word embedding [16][17][18][19][20][21][22], and machine learning techniques [23][24][25][26][27][28][29][30][31], have been used in this area after 2006. Recently, researchers have focused on using deep learning-based natural language processing (NLP), such as Bidirectional Encoder Representations from Transformer (BERT) [18,21,[32][33][34] or seq2seq architecture with an attention mechanism [20,[35][36][37][38], to structure textual web data.…”

“…Text mining techniques, such as a sentiment analysis [10][11][12][13], part of speech tagging (POS) [14,15], text representation, such as transformer-based word embedding [16][17][18][19][20][21][22], and machine learning techniques [23][24][25][26][27][28][29][30][31], have been used in this area after 2006. Recently, researchers have focused on using deep learning-based natural language processing (NLP), such as Bidirectional Encoder Representations from Transformer (BERT) [18,21,[32][33][34] or seq2seq architecture with an attention mechanism [20,[35][36][37][38], to structure textual web data. BERT-contextualized word embedding, announced by Google in 2018, is used as a word sense disambiguation technique for summarizing and selecting important news for investors' behavior analysis [21,25].…”

“…Recently, researchers have focused on using deep learning-based natural language processing (NLP), such as Bidirectional Encoder Representations from Transformer (BERT) [18,21,[32][33][34] or seq2seq architecture with an attention mechanism [20,[35][36][37][38], to structure textual web data. BERT-contextualized word embedding, announced by Google in 2018, is used as a word sense disambiguation technique for summarizing and selecting important news for investors' behavior analysis [21,25]. Recent works have investigated the use of an attention mechanism in a deep encoder-decoder network that applies transfer learning from pre-trained models or calculating the impact of each news feed as well as assigning heavy weights for important time intervals in a time series [19,39,40].…”