Stock price movement prediction is a challenging and essential problem in finance. While it is well established in modern behavioral finance that the share prices of related stocks often move after the release of news via reactions and overreactions of investors, how to capture the relationships between price movements and news articles via quantitative models is an active area research; existing models have achieved success with variable degrees. In this paper, we propose to improve stock price movement classification using news articles by incorporating regularization and optimization techniques from deep learning. More specifically, we capture the dependencies between news articles and stocks through embeddings and bidirectional recurrent neural networks as in recent models. We further incorporate weight decay, batch normalization, dropout, and label smoothing to improve the generalization of the trained models. To handle high fluctuations of validation accuracy of batch normalization, we propose dual-phase training to realize the improvements reliably. Our experimental results on a commonly used dataset show significant improvements, achieving average accuracy of 80.7% on the test set, which is more than 10.0% absolute improvement over existing models. Our ablation studies show batch normalization and label smoothing are most effective, leading to 6.0% and 3.4% absolute improvement, respectively on average.
翻译:股票价格变动预测是金融方面一个具有挑战性和至关重要的问题。虽然现代行为融资中已经明确证实,相关股票的股价在投资者反应和反应过度后往往在新闻发布后流动,但如何通过定量模型捕捉价格变动和新闻文章之间的关系是一项积极的领域研究;现有模型在不同程度上取得了成功。在本文中,我们提议通过纳入深层学习的正规化和优化技术,利用新闻报道改进股票价格变动分类。更具体地说,我们通过嵌入和最近模型中的双向经常性神经网络,捕捉新闻文章和股票之间的依赖性。我们进一步纳入重量衰减、批次正常化、退出和标签,以利改进经过培训的模式的普及。为了应对批次正常化认证准确性高度波动,我们建议进行双阶段培训,以可靠的方式实现改进。我们关于常用数据集的实验结果显示显著改进,在测试集上达到平均80.7%的准确度,比现有模型高出10.0%的绝对改进率。我们的通货膨胀研究表明,批次正常化和标签最为有效,分别导致6.0%和3.4%的绝对改进率。