论文标题
事件驱动的股票市场系统行为的学习
Event-Driven Learning of Systematic Behaviours in Stock Markets
论文作者
论文摘要
据报道，金融新闻，尤其是新闻中表达的金融事件，为投资者的长/短期决定提供信息，并影响股市的变动。在此激励的基础上，我们利用金融事件流来培训一个分类神经网络，该神经网络检测潜在的事件储备联系和股票市场在美国股票市场的系统行为。我们提出的管道包括（1）使用开放信息提取和神经共同参考分辨率的合并事件提取方法，（2）Bert/Albert增强了事件的表示，以及（3）扩展的层次结构注意网络，其中包括有关事件，新闻和时间级别的关注。当应用于预测标准\＆Poor 500，Dow Jones，Dow Jones，Nasdaq指数和10个单独的股票时，我们的管道比最先进的模型实现了比最先进的模型更好的准确性和更高的模拟年度收益。
It is reported that financial news, especially financial events expressed in news, provide information to investors' long/short decisions and influence the movements of stock markets. Motivated by this, we leverage financial event streams to train a classification neural network that detects latent event-stock linkages and stock markets' systematic behaviours in the U.S. stock market. Our proposed pipeline includes (1) a combined event extraction method that utilizes Open Information Extraction and neural co-reference resolution, (2) a BERT/ALBERT enhanced representation of events, and (3) an extended hierarchical attention network that includes attentions on event, news and temporal levels. Our pipeline achieves significantly better accuracies and higher simulated annualized returns than state-of-the-art models when being applied to predicting Standard\&Poor 500, Dow Jones, Nasdaq indices and 10 individual stocks.
