英文标题：
《Leveraging Financial News for Stock Trend Prediction with
  Attention-Based Recurrent Neural Network》
---
作者：
Huicheng Liu
---
最新提交年份：
2018
---
英文摘要：
  Stock market prediction is one of the most attractive research topic since the successful prediction on the market\'s future movement leads to significant profit. Traditional short term stock market predictions are usually based on the analysis of historical market data, such as stock prices, moving averages or daily returns. However, financial news also contains useful information on public companies and the market. Existing methods in finance literature exploit sentiment signal features, which are limited by not considering factors such as events and the news context. We address this issue by leveraging deep neural models to extract rich semantic features from news text. In particular, a Bidirectional-LSTM are used to encode the news text and capture the context information, self attention mechanism are applied to distribute attention on most relative words, news and days. In terms of predicting directional changes in both Standard & Poor\'s 500 index and individual companies stock price, we show that this technique is competitive with other state of the art approaches, demonstrating the effectiveness of recent NLP technology advances for computational finance.
---
中文摘要：
股票市场预测是最有吸引力的研究课题之一，因为对市场未来走势的成功预测会带来巨大的利润。传统的短期股市预测通常基于对历史市场数据的分析，如股价、移动平均值或每日收益率。然而，金融新闻也包含关于上市公司和市场的有用信息。金融文献中的现有方法利用情绪信号特征，但由于没有考虑事件和新闻背景等因素，这些特征受到限制。我们通过利用深层神经模型从新闻文本中提取丰富的语义特征来解决这个问题。特别是，采用双向LSTM对新闻文本进行编码并捕获上下文信息，采用自我注意机制对大多数相关词、新闻和日期进行注意力分配。在预测标准普尔500指数和个别公司股票价格的方向性变化方面，我们表明，该技术与其他最先进的方法具有竞争力，证明了最近NLP技术进步对计算金融的有效性。
---
分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
--
一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
--

---
PDF下载：
-->