首页|基于业务内容构建股票关联关系的股价预测

基于业务内容构建股票关联关系的股价预测

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  • 万方数据
  • 维普
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.
Stock Price Prediction Based on Business Content to Construct Stock Association Relationships
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 busi-ness 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 compo-nent and the association capture component.The association relationship construction component uses improved TF-IDF to ex-tract the similarity of business content keywords in the annual reports of listed companies to construct stock correlation relation-ships.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 relation-ship based prediction methods.It is a stock price prediction model that captures the mutual influence between stocks more fully.

stock price forecastbusiness contentstock related relationshipsterm frequency-inverse document frequencylong and short term memory neural networkgraph convolution neural network

杨江、孙晓梅、许韬

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江苏科技大学计算机学院,江苏 镇江 212100

东京大学工学系研究科,东京 113-8654

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

国家自然科学基金资助项目

62261029

2024

10.3969/j.issn.1006-2475.2024.07.004

计算机与现代化
江西省计算机学会 江西省计算技术研究所

计算机与现代化

CSTPCD
影响因子：0.472
ISSN：1006-2475
年,卷(期)：2024.(7)
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