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

doi:10.11772/j.issn.1001-9081.2021081487

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (7): 2265-2273.DOI: 10.11772/j.issn.1001-9081.2021081487

• Frontier and comprehensive applications • Previous Articles Next Articles

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

Xiaohan LI¹(), Jun WANG¹, Huading JIA¹, Liu XIAO²

^1.School of Economic Information Engineering，Southwestern University of Finance and Economics，Chengdu Sichuan 611130，China
^2.Housing Finance Industry Department，Sichuan Jiuyuan Yinhai Software Company Limited，Chengdu Sichuan 610063，China

Received:2021-08-19 Revised:2021-11-30 Accepted:2021-12-03 Online:2022-01-07 Published:2022-07-10
Contact: Xiaohan LI
About author:WANG Jun， born in 1987， Ph. D.， associate professor. His research interests include financial technology， financial intelligence.
JIA Huading， born in 1956， Ph. D.， professor. His research interests include machine learning， algorithmic trading， spread spectrum sequence design.
XIAO Liu， born in 1986， senior engineer. His research interests include machine learning， big data，social security， economics and finance.
Supported by:
Science and Technology Program of Sichuan Province(2020JDJQ0061)

基于多重注意力机制的图神经网络股市波动预测方法

李晓寒¹(), 王俊¹, 贾华丁¹, 萧刘²

^1.西南财经大学经济信息工程学院，成都 611130
^2.四川久远银海软件股份有限公司住房金融行业部，成都 610063

通讯作者: 李晓寒
作者简介:王俊（1987—），男，山东青岛人，副教授，博士，CCF会员，主要研究方向：金融科技、金融智能
贾华丁（1956—），男，四川成都人，教授，博士，CCF高级会员，主要研究方向：机器学习、算法交易、扩频序列设计
萧刘（1986—），男，四川成都人，高级工程师，主要研究方向：机器学习、大数据、社会保障经济与金融。
基金资助:
四川省科技计划项目(2020JDJQ0061)

Abstract

Abstract:

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.

Key words: stock market prediction, multi-attention mechanism, graph neural network, stock market news, graph data

摘要：

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。

关键词: 股市预测, 多重注意力机制, 图神经网络, 股市新闻, 图数据

CLC Number:

TP391.7

Xiaohan LI, Jun WANG, Huading JIA, Liu XIAO. Stock market volatility prediction method based on graph neural network with multi-attention mechanism[J]. Journal of Computer Applications, 2022, 42(7): 2265-2273.

李晓寒, 王俊, 贾华丁, 萧刘. 基于多重注意力机制的图神经网络股市波动预测方法[J]. 《计算机应用》唯一官方网站, 2022, 42(7): 2265-2273.

Figures/Tables 10

References 48

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真实值	预测值
真实值	上涨	下跌
上涨	TP	FN
下跌	FP	TN

真实值	预测值
真实值	上涨	下跌
上涨	TP	FN
下跌	FP	TN

方法	卷积名称	Input size	Out size	Drop Rate	Aggregator Type
本文方法	GRATConv1	200	128	0.1	LSTM
本文方法	GRATConv2	128	64	0.0	LSTM
GAT	GATConv1	200	128	0.1	num_heads=3
GAT	GATConv2	128	64	0.0	num_heads=3
RelGraph	RelGraphConv1	200	128	0.1	regularizer=basis
RelGraph	RelGraphConv2	128	64	0.0	regularizer=basis
Edge	EdgeConv1	200	128	0.1
Edge	EdgeConv2	128	64	0.0
SAGE	SAGEConv1	200	128	0.1	LSTM
SAGE	SAGEConv2	128	64	0.0	LSTM

方法	卷积名称	Input size	Out size	Drop Rate	Aggregator Type
本文方法	GRATConv1	200	128	0.1	LSTM
本文方法	GRATConv2	128	64	0.0	LSTM
GAT	GATConv1	200	128	0.1	num_heads=3
GAT	GATConv2	128	64	0.0	num_heads=3
RelGraph	RelGraphConv1	200	128	0.1	regularizer=basis
RelGraph	RelGraphConv2	128	64	0.0	regularizer=basis
Edge	EdgeConv1	200	128	0.1
Edge	EdgeConv2	128	64	0.0
SAGE	SAGEConv1	200	128	0.1	LSTM
SAGE	SAGEConv2	128	64	0.0	LSTM

方法	参数
SVM	C=0.8；kernel=liner；max_iter=1 000
RF	Max_ feature=none；min_samples_split=10；n_estimators=3
MKKM	View=3；kernel=RBF；gamma=1/3；k=2
TeSIA	Tensor_order=3；tensor_size（i=5， j=1，k=10）；Max_iter=5 000

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

基于多重注意力机制的图神经网络股市波动预测方法

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Figures/Tables 10

References 48

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Metrics

方法	上证综合指数						沪深300指数						深证成份指数
	交易日T+1		交易日T+2		交易日T+3		交易日T+1		交易日T+2		交易日T+3		交易日T+1		交易日T+2		交易日T+3
	召回率	准确率	召回率	准确率	召回率	准确率	召回率	准确率	召回率	准确率	召回率	准确率	召回率	准确率	召回率	准确率	召回率	准确率
本文方法	0.65	0.58	0.75	0.73	0.61	0.60	0.75	0.76	0.90	0.85	0.72	0.63	0.65	0.64	0.89	0.91	0.66	0.73
GATConv	0.53	0.58	0.57	0.51	0.46	0.43	0.69	0.53	0.49	0.45	0.62	0.47	0.48	0.52	0.63	0.61	0.42	0.45
RelGraphConv	0.52	0.53	0.49	0.50	0.47	0.39	0.64	0.54	0.63	0.49	0.55	0.47	0.44	0.47	0.45	0.53	0.47	0.50
EdgeConv	0.39	0.37	0.61	0.56	0.31	0.38	0.50	0.51	0.54	0.53	0.59	0.53	0.47	0.45	0.34	0.44	0.38	0.41
SAGEConv	0.48	0.45	0.50	0.48	0.52	0.49	0.43	0.38	0.39	0.49	0.52	0.38	0.38	0.47	0.30	0.38	0.31	0.33
SVM	0.58	0.56	0.58	0.55	0.54	0.53	0.46	0.46	0.47	0.48	0.46	0.45	0.44	0.47	0.57	0.58	0.43	0.40
RF	0.56	0.60	0.60	0.59	0.55	0.52	0.56	0.55	0.51	0.53	0.51	0.48	0.57	0.52	0.52	0.43	0.53	0.46
MKKM	0.58	0.56	0.62	0.60	0.60	0.59	0.68	0.66	0.68	0.70	0.62	0.57	0.62	0.56	0.63	0.70	0.65	0.58
TeSIA	0.52	0.58	0.63	0.62	0.58	0.57	0.62	0.67	0.69	0.73	0.68	0.56	0.61	0.59	0.63	0.57	0.62	0.59

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