论文标题
使用公司特定的头条和卷积神经网络来预测库存波动
Using Company Specific Headlines and Convolutional Neural Networks to Predict Stock Fluctuations
论文作者
论文摘要
这项工作提出了卷积神经网络（CNN），用于使用公司特定的新闻头条预测第二天的股票波动。报告了使用单词插头和卷积滤波器宽度的各种配置来评估模型性能的实验。所使用的卷积过滤器的总数远远少于常见，从而降低了任务的维度而不会丧失准确性。此外，采用多个具有降低维度的隐藏层。分类精度为61.7 \％是使用预制的嵌入来实现的，这些嵌入在训练过程中经过微调，以表示该任务的特定背景。还实现了多个滤波器宽度以检测分类关键的不同长度短语。交易模拟使用提出的分类结果进行。使用最佳分类配置和简单的交易策略，在838天测试期间，初始投资的三倍以上。提出了两种新颖的方法，以减少交易模拟的风险。使用多个类构成这些方法的基础，调整Sigmoid类阈值和重新标记头条。这些方法的结合被发现是基线模拟期间获得的平均贸易利润（ATP）的两倍以上。
This work presents a Convolutional Neural Network (CNN) for the prediction of next-day stock fluctuations using company-specific news headlines. Experiments to evaluate model performance using various configurations of word-embeddings and convolutional filter widths are reported. The total number of convolutional filters used is far fewer than is common, reducing the dimensionality of the task without loss of accuracy. Furthermore, multiple hidden layers with decreasing dimensionality are employed. A classification accuracy of 61.7\% is achieved using pre-learned embeddings, that are fine-tuned during training to represent the specific context of this task. Multiple filter widths are also implemented to detect different length phrases that are key for classification. Trading simulations are conducted using the presented classification results. Initial investments are more than tripled over a 838 day testing period using the optimal classification configuration and a simple trading strategy. Two novel methods are presented to reduce the risk of the trading simulations. Adjustment of the sigmoid class threshold and re-labelling headlines using multiple classes form the basis of these methods. A combination of these approaches is found to more than double the Average Trade Profit (ATP) achieved during baseline simulations.
