基于多重注意力机制的图神经网络股市波动预测方法

股票市场是金融市场关键组成部分，因此对股票市场波动的研究对合理化控制金融市场风险、提高投资收益提供了重要支持，一直以来都是学术界和相关业界的关注焦点，然而，股票市场会受到各种因素的影响。面对股票市场中多源化、异构化的信息，如何高效挖掘、融合股票市场的多源异构数据具有挑战性。为了充分解释不同信息及信息间相互作用对于股票市场价格波动的影响，提出一种基于多重注意力机制的图神经网络来预测股票市场的价格波动。首先，引入关系维度构建股票市场交易数据和新闻文本的异构子图，并利用多重注意力机制实现图数据的融合；其次，通过图神经网络门控循环单元（GRU）进行图分类，在此基础上完成对股票市场中上证综合指数、沪深300指数、深证成份指数这三个重要指数波动的预测。实验结果表明，从异构信息特性角度，相较于股票市场交易数据，股市新闻信息对于股票价格影响存在滞后性；从异构信息融合角度，所提方法与支持向量机（SVM）、随机森林、多核k-means （MKKM）聚类等算法相比，预测准确率分别提升了17.88个百分点、30.00个百分点和38.00个百分点，并进行了模型交易策略的量化投资模拟。

Stock market volatility prediction method based on graph neural network with multi-attention mechanism

Stock market is an essential element of financial market， therefore， the study on volatility of stock market plays a significant role in taking effective control of financial market risks and improving returns on investment. For this reason， it has attracted widespread attention from both academic circle and related industries. However， there are multiple influencing factors for stock market. Facing the multi-source and heterogeneous information in stock market， it is challenging to find how to mine and fuse multi-source and heterogeneous data of stock market efficiently. To fully explain the influence of different information and information interaction on the price changes in stock market， a graph neural network based on multi-attention mechanism was proposed to predict price fluctuation in stock market. First of all， the relationship dimension was introduced to construct heterogeneous subgraphs for the transaction data and news text of stock market， and multi-attention mechanism was adopted for fusion of the graph data. Then， the graph neural network Gated Recurrent Unit （GRU） was applied to perform graph classification. On this basis， prediction was made for the volatility of three important indexes： Shanghai Composite Index， Shanghai and Shenzhen 300 Index， Shenzhen Component Index. Experimental results show that from the perspective of heterogeneous information characteristics， compared with the transaction data of stock market， the news information of stock market has the lagged influence on stock volatility； from the perspective of heterogeneous information fusion， compared with algorithms such as Support Vector Machine （SVM）， Random Forest （RF） and Multiple Kernel k-Means （MKKM） clustering， the proposed method has the prediction accuracy improved by 17.88 percentage points， 30.00 percentage points and 38.00 percentage points respectively； at the same time， the quantitative investment simulation was performed according to the model trading strategy.