School of Management Science and Engineering, Southwestern University of Finance and Economics, Chengdu, China.
School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, China.
PLoS One. 2022 Aug 11;17(8):e0272083. doi: 10.1371/journal.pone.0272083. eCollection 2022.
The stock market is an important part of the capital market, and the research on the price fluctuation of the stock market has always been a hot topic for scholars. As a dynamic and complex system, the stock market is affected by various factors. However, with the development of information technology, information presents multisource and heterogeneous characteristics, and the transmission speed and mode of information have changed greatly. The explanation and influence of multi-source and heterogeneous information on stock market price fluctuations need further study. In this paper, a graph fusion and embedding method for multi-source heterogeneous information of Chinese stock market is established. Relational dimension information is introduced to realize the effective fusion of multi-source heterogeneous data information. A multi-attention graph neural network based on nodes and semantics is constructed to mine the implied semantics of fusion graph data and capture the influence of multi-source heterogeneous information on stock market price fluctuations. Experiments show that the proposed multi-source heterogeneous information fusion methods is superior to tensor or vector fusion method, and the constructed multi-attention diagram neural network has a better ability to explain stock market price fluctuations.
股票市场是资本市场的重要组成部分，对股票市场价格波动的研究一直是学者们关注的热点。股票市场作为一个动态复杂的系统，受到各种因素的影响。然而，随着信息技术的发展，信息呈现出多源异质的特点，信息的传递速度和方式发生了很大的变化。多源异质信息对股票市场价格波动的解释和影响需要进一步研究。本文建立了一种中文股票市场多源异质信息的图融合与嵌入方法。引入关系维度信息，实现多源异质数据信息的有效融合。构建了一种基于节点和语义的多注意力图神经网络，挖掘融合图数据的隐含语义，捕捉多源异质信息对股票市场价格波动的影响。实验表明，所提出的多源异质信息融合方法优于张量或向量融合方法，所构建的多注意力图神经网络对股票市场价格波动具有更好的解释能力。
