Abstract
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.
Abstract (translated)
股票价格预测是 finance 中具有挑战性和重要性的问题。虽然现代行为金融学已经证明,相关股票的价格通常在新闻发布后通过投资者的反应和过度反应而移动,但如何通过量化模型捕捉价格运动和新闻之间的关系是一个活跃的研究领域。在本文中,我们提议利用新闻来改善股票价格运动分类,并借鉴深度学习中的 Regularization 和 Optimization 技术。具体来说,我们使用最近模型中的嵌入和双向循环神经网络来捕捉新闻和股票之间的依赖关系。我们还添加权重衰减、批量归一化、 dropout 和标签平滑来改善训练模型的泛化能力。为了处理批量归一化验证准确性的高波动性,我们提议采用双阶段训练来实现可靠的改进。我们使用常用的数据集进行实验,结果表明,我们的改进显著性很高,在测试集上的平均准确率达到 80.7%,比现有模型的 absolute 改进率高得多。我们的亚单位研究结果表明,批量归一化和标签平滑最有效,平均导致 6.0% 和 3.4% 的 absolute 改进。
URL
https://arxiv.org/abs/2301.10458
