Considering event structure information has proven helpful in text-based
stock movement prediction. However, existing works mainly adopt the
coarse-grained events, which loses the specific semantic information of diverse
event types. In this work, we propose to incorporate the fine-grained events in
stock movement prediction. Firstly, we propose a professional finance event
dictionary built by domain experts and use it to extract fine-grained events
automatically from finance news. Then we design a neural model to combine
finance news with fine-grained event structure and stock trade data to predict
the stock movement. Besides, in order to improve the generalizability of the
proposed method, we design an advanced model that uses the extracted
fine-grained events as the distant supervised label to train a multi-task
framework of event extraction and stock prediction. The experimental results
show that our method outperforms all the baselines and has good
generalizability.

使用领域专家构建金融专业事件字典，并自动从金融新闻中提取精细的事件信息，并设计神经模型将金融新闻与精细的事件结构和股票交易数据相结合，预测股票运动，实验结果表明，该方法胜过所有基准线，并具有良好的泛化能力。

股票价格预测中细粒度事件的引入

Incorporating Fine-grained Events in Stock Movement Prediction

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.

本研究提出了一种基于 BERT 的分层聚合模型，通过最先进的抽取式摘要方法，从大量的金融新闻中提取最关键的新闻，并与交易数据相互作用来预测外汇市场的动向。实验结果表明，类别为基础的方法在三种分组方法中表现最佳，并在所有基线模型中表现最优。此外，研究了关键新闻属性（类别和地区）的影响模式，总结了不同货币对的影响模式。