融合新闻情绪与股价数据，用图神经网络预测股市走势。
A Hybrid Model for Stock Market Forecasting: Integrating News Sentiment and Time Series Data with Graph Neural Networks
- 用LSTM和语言模型分别处理股价与新闻，构建异构图结构。
- 在美股和Bloomberg数据集上，准确率达53%，精度提升4%。
- 短标题比全文更有效，新闻多的公司预测更准。
股票市场预测是金融领域长期挑战，准确预测有助于投资决策。传统模型主要依赖历史价格，近年研究发现财经新闻可提供有用外部信号。本文提出一种多模态方法，将公司新闻文章与历史股价数据结合以提升预测性能。采用图神经网络（GNN）模型对比基准LSTM模型：每家公司历史数据通过LSTM编码，新闻标题由语言模型嵌入，构成异构图中的节点，使用GraphSAGE捕捉文章、公司与行业间的交互关系。评估两个目标：二分类方向变化标签与基于显著性的标签。在美股市价和Bloomberg数据集上的实验表明，GNN优于LSTM基线，在第一项任务中达到53%准确率，在第二项任务中精度提升4%。结果还显示，关联新闻越多的公司预测准确率越高；且新闻标题比全文包含更强预测信号，说明简明摘要对短期市场反应具有关键作用。
原文摘要 · Abstract (English)
Stock market prediction is a long-standing challenge in finance, as accurate forecasts support informed investment decisions. Traditional models rely mainly on historical prices, but recent work shows that financial news can provide useful external signals. This paper investigates a multimodal approach that integrates companies' news articles with their historical stock data to improve prediction performance. We compare a Graph Neural Network (GNN) model with a baseline LSTM model. Historical data for each company is encoded using an LSTM, while news titles are embedded with a language model. These embeddings form nodes in a heterogeneous graph, and GraphSAGE is used to capture interactions between articles, companies, and industries. We evaluate two targets: a binary direction-of-change label and a significance-based label. Experiments on the US equities and Bloomberg datasets show that the GNN outperforms the LSTM baseline, achieving 53% accuracy on the first target and a 4% precision gain on the second. Results also indicate that companies with more associated news yield higher prediction accuracy. Moreover, headlines contain stronger predictive signals than full articles, suggesting that concise news summaries play an important role in short-term market reactions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。