论文标题
新闻驱动的股票预测，基于注意力的嘈杂经常性状态过渡
News-Driven Stock Prediction With Attention-Based Noisy Recurrent State Transition
论文作者
论文摘要
我们考虑在新闻驱动的股票移动预测中随着时间的推移将基本股票价值运动序列进行直接建模。构建了一个经常性的状态过渡模型，该模型通过建模过去和未来的价格移动之间的相关性来更好地捕获库存移动的逐步逐步捕获。通过分离新闻和噪声的影响，还根据复发状态明确拟合嘈杂的随机因素。结果表明，所提出的模型的表现优于强基础。由于对新闻事件的关注使用，我们的模型也可以解释。据我们所知，我们是第一个在新闻驱动的股票移动预测的基本股票价值状态下明确对事件和噪音进行建模的人。
We consider direct modeling of underlying stock value movement sequences over time in the news-driven stock movement prediction. A recurrent state transition model is constructed, which better captures a gradual process of stock movement continuously by modeling the correlation between past and future price movements. By separating the effects of news and noise, a noisy random factor is also explicitly fitted based on the recurrent states. Results show that the proposed model outperforms strong baselines. Thanks to the use of attention over news events, our model is also more explainable. To our knowledge, we are the first to explicitly model both events and noise over a fundamental stock value state for news-driven stock movement prediction.
