In this paper, we introduce an event-driven trading strategy that predicts stock movements by detecting corporate events from news articles. Unlike existing models that utilize textual features (e.g., bag-of-words) and sentiments to directly make stock predictions, we consider corporate events as the driving force behind stock movements and aim to profit from the temporary stock mispricing that may occur when corporate events take place. The core of the proposed strategy is a bi-level event detection model. The low-level event detector identifies events' existences from each token, while the high-level event detector incorporates the entire article's representation and the low-level detected results to discover events at the article-level. We also develop an elaborately-annotated dataset EDT for corporate event detection and news-based stock prediction benchmark. EDT includes 9721 news articles with token-level event labels as well as 303893 news articles with minute-level timestamps and comprehensive stock price labels. Experiments on EDT indicate that the proposed strategy outperforms all the baselines in winning rate, excess returns over the market, and the average return on each transaction.
翻译:在本文中,我们引入了一种由事件驱动的贸易战略,通过从新闻报道中发现公司事件来预测股票流动。与使用文字特征(例如一袋字)和直接预测股票情绪的现有模式不同,我们认为公司事件是股票流动的驱动力,目的是从公司事件发生时可能发生的临时股票定价错误中获利。拟议战略的核心是双级事件检测模式。低级事件检测器从每个象征物中查明事件的存在,而高级别事件检测器则包含整个文章的代表性和在文章一级发现事件时发现的低级别结果。我们还为公司事件检测和基于新闻的股票预测基准开发了一套详细附加说明的EDT数据集。EDT包括有象征性事件标签的9721篇新闻文章,以及带有分钟级时标和全面股票价格标签的303893篇新闻文章。EDT实验显示,拟议的战略在赢得率、超额市场回报和每项交易的平均回报方面超过了所有基线。