跳至正文
返回文库全部文档

利用公司与高管知识图谱预测股票走势

文章 arXiv papers · 作者: Yu Zhao et al.
阅读原文

总结

本文介绍一种旨在捕捉上市公司之间动量溢出的股票走势预测方法。该方法构建市场知识图谱，包含公司和高管两类实体，并结合两者之间的显式关系和隐式联系。

一种名为DanSmp的双重注意力网络利用该图谱学习溢出信号，以预测股票走势。文档报告称，该方法在构建的数据集上与九种基准方法进行了实证比较，并改善了预测效果。文档没有提供数据集细节、具体表现指标、交易成本或实盘部署信息，因此报告的预测增益并不能证明该方法能够产生盈利交易。

核心观点

  • 图谱表示上市公司及其相关高管。
  • 市场图谱结合了显式关系和隐式关系。
  • 双重注意力网络从图谱中学习动量溢出信号。
  • 该方法在构建的数据集上与九种基准方法进行评估。
  • 仅凭预测效果改善，无法证明交易策略能够盈利。

标签

股票动量机器学习统计学
全文
# Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks


# Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks









Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets. Recent financial studies show that the momentum spillover effect plays a significant role in stock fluctuation. However, previous studies typically only learn the simple connection information among related companies, which inevitably fail to model complex relations of listed companies in the real financial market. To address this issue, we first construct a more comprehensive Market Knowledge Graph (MKG) which contains bi-typed entities including listed companies and their associated executives, and hybrid-relations including the explicit relations and implicit relations. Afterward, we propose DanSmp, a novel Dual Attention Networks to learn the momentum spillover signals based upon the constructed MKG for stock prediction. The empirical experiments on our constructed datasets against nine SOTA baselines demonstrate that the proposed DanSmp is capable of improving stock prediction with the constructed MKG.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。