ST-Trader: A Spatial-Temporal Deep Neural Network for Modeling Stock Market Movement

doi:10.1109/JAS.2021.1003976

CASIA OpenIR > 学术期刊 > IEEE/CAA Journal of Automatica Sinica

	ST-Trader: A Spatial-Temporal Deep Neural Network for Modeling Stock Market Movement
	Xiurui Hou; Kai Wang; Cheng Zhong; Zhi Wei
发表期刊	IEEE/CAA Journal of Automatica Sinica
ISSN	2329-9266
	2021
卷号	8 期号:5 页码:1015-1024
摘要	Stocks that are fundamentally connected with each other tend to move together. Considering such common trends is believed to benefit stock movement forecasting tasks. However, such signals are not trivial to model because the connections among stocks are not physically presented and need to be estimated from volatile data. Motivated by this observation, we propose a framework that incorporates the inter-connection of firms to forecast stock prices. To effectively utilize a large set of fundamental features, we further design a novel pipeline. First, we use variational autoencoder (VAE) to reduce the dimension of stock fundamental information and then cluster stocks into a graph structure (fundamentally clustering). Second, a hybrid model of graph convolutional network and long-short term memory network (GCN-LSTM) with an adjacency graph matrix (learnt from VAE) is proposed for graph-structured stock market forecasting. Experiments on minute-level U.S. stock market data demonstrate that our model effectively captures both spatial and temporal signals and achieves superior improvement over baseline methods. The proposed model is promising for other applications in which there is a possible but hidden spatial dependency to improve time-series prediction.
关键词	Graph convolution network long-short term memory network stock market forecasting variational autoencoder (VAE)
DOI	10.1109/JAS.2021.1003976
引用统计	被引频次：46[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/43963
专题	学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Xiurui Hou,Kai Wang,Cheng Zhong,et al. ST-Trader: A Spatial-Temporal Deep Neural Network for Modeling Stock Market Movement[J]. IEEE/CAA Journal of Automatica Sinica,2021,8(5):1015-1024.
APA	Xiurui Hou,Kai Wang,Cheng Zhong,&Zhi Wei.(2021).ST-Trader: A Spatial-Temporal Deep Neural Network for Modeling Stock Market Movement.IEEE/CAA Journal of Automatica Sinica,8(5),1015-1024.
MLA	Xiurui Hou,et al."ST-Trader: A Spatial-Temporal Deep Neural Network for Modeling Stock Market Movement".IEEE/CAA Journal of Automatica Sinica 8.5(2021):1015-1024.

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