计算机与现代化 ›› 2024, Vol. 0 ›› Issue (07): 21-25.doi: 10.3969/j.issn.1006-2475.2024.07.004

• 人工智能 • 上一篇    下一篇

   基于业务内容构建股票关联关系的股价预测

  



  1. (1.江苏科技大学计算机学院,江苏 镇江 212100; 2.东京大学工学系研究科,东京 113-8654)





  • 出版日期:2024-07-25 发布日期:2024-08-07
  • 基金资助:
    国家自然科学基金资助项目(62261029)

Stock Price Prediction Based on Business Content to Construct Stock Association Relationships

  1. (1. School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212100, China;
    2. Department of Bioengineering, School of Engineering, The University of Tokyo, Tokyo 113-8654, Japan)
  • Online:2024-07-25 Published:2024-08-07

摘要: 传统的股价预测方法大多基于单只股票的时间序列,而忽视了股票间复杂的相互影响关系。针对该问题,从构建更有效的股票组合角度出发,提出一种基于业务内容构建股票关联关系的股价预测方法。模型包含3个组件:关联关系构建组件、时序特征提取组件和关联关系捕捉组件。关联关系构建组件通过改进的TF-IDF提取上市公司年报中业务内容关键字的相似度来构建股票关联关系;时序特征提取组件利用LSTM提取股票交易数据的时序特征;关联关系捕捉组件利用GCN捕获股票间相互作用的高维特征,最后通过全连接层输出预测的股价。在中国A股市场的实验结果表明,该模型与用单只股票和基于行业关系的预测方法相比误差最小,拟合度最优,能更有效地预测股价,是一种能更充分捕捉股票间相互影响关系的股价预测模型。

关键词: 股票价格预测, 业务内容, 股票关联关系, 词频-逆向文件频率, 长短期记忆神经网络, 图卷积神经网络

Abstract: Traditional stock price prediction methods are mostly based on the time series of a single stock, ignoring the complex interrelationships between stocks. In response to this issue, the article proposes a stock price prediction method based on business content to construct stock correlation relationships from the perspective of building a more effective stock portfolio. The model consists of three components: the association relationship construction component, the temporal feature extraction component and the association capture component. The association relationship construction component uses improved TF-IDF to extract the similarity of business content keywords in the annual reports of listed companies to construct stock correlation relationships. The temporal feature extraction component uses LSTM to extract temporal features of stock trading data. The association capture component utilizes GCN to capture high-dimensional features of stock interactions, and finally outputs the predicted stock price through the fully connected layer. The experimental results in the Chinese A-share market indicate that this model has the smallest error, the better fit, and can more effectively predict stock prices compared to single stocks and industry relationship based prediction methods. It is a stock price prediction model that captures the mutual influence between stocks more fully.

Key words: stock price forecast, business content, stock related relationships, term frequency-inverse document frequency, long and short term memory neural network, graph convolution neural network

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