论文标题
股票市场预测的新闻新闻（DRNEWS）的新颖分布式表示
A Novel Distributed Representation of News (DRNews) for Stock Market Predictions
论文作者
论文摘要
在这项研究中，新闻（DRNEWS）模型的新颖分布式表示形式是在基于深度学习的股票市场预测中开发和应用的。凭借整合上下文信息和跨社会知识的优点，DRNEWS模型创建了新闻向量，可以通过归因的新闻网络描述新闻事件之间的语义信息和潜在联系。在基于注意力的长期长期记忆（LSTM）网络的框架内实施了两项股票市场预测任务，即短期股票移动预测和股票危机预警。建议Drnews大大增强了两项任务的结果，与五个新闻嵌入模型的基线相比。此外，注意机制表明，短期股票趋势和股票市场危机都受到每日新闻的影响，前者对与股票市场有关的信息表现出更为关键的反应{\ em本质}，而后者对银行业和经济政策提出了更多关注。
In this study, a novel Distributed Representation of News (DRNews) model is developed and applied in deep learning-based stock market predictions. With the merit of integrating contextual information and cross-documental knowledge, the DRNews model creates news vectors that describe both the semantic information and potential linkages among news events through an attributed news network. Two stock market prediction tasks, namely the short-term stock movement prediction and stock crises early warning, are implemented in the framework of the attention-based Long Short Term-Memory (LSTM) network. It is suggested that DRNews substantially enhances the results of both tasks comparing with five baselines of news embedding models. Further, the attention mechanism suggests that short-term stock trend and stock market crises both receive influences from daily news with the former demonstrates more critical responses on the information related to the stock market {\em per se}, whilst the latter draws more concerns on the banking sector and economic policies.
