A Hybrid Long Short-Term Memory-Graph Convolutional Network Model for Enhanced Stock Return Prediction: Integrating Temporal and Spatial Dependencies

doi:10.3390/math13071142

	A Hybrid Long Short-Term Memory-Graph Convolutional Network Model for Enhanced Stock Return Prediction: Integrating Temporal and Spatial Dependencies
	Shi, Songze 1; Li, Fan 2; Li, Wei 3
	2025-03-31
发表期刊	MATHEMATICS
卷号	13 期号:7 页码:13
摘要	Stock return prediction is a pivotal yet intricate task in financial markets, challenged by volatility and multifaceted dependencies. This study proposes a hybrid model integrating long short-term memory (LSTM) networks and graph convolutional networks (GCNs) to enhance accuracy by capturing both temporal dynamics and spatial inter-stock relationships. Tested on the Dow Jones Industrial Average (DJIA), Shanghai Stock Exchange 50 (SSE50), and China Securities Index 100 (CSI 100), our LSTM-GCN model outperforms baselines-LSTM, GCN, RNN, GRU, BP, decision tree, and SVM-achieving the lowest mean squared error (e.g., 0.0055 on DJIA), mean absolute error, and highest R2 values. This superior performance stems from the synergistic interaction of spatio-temporal features, offering a robust tool for investors and policymakers. Future enhancements could incorporate sentiment analysis and dynamic graph structures.
关键词	LSTM GCN machine learning temporal information spatial information stock return prediction
DOI	10.3390/math13071142
收录类别	SCI
语种	英语
WOS研究方向	Mathematics
WOS类目	Mathematics
WOS记录号	WOS:001463959700001
出版者	MDPI
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40671
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Wei
作者单位	1.Univ Macau, Fac Business Adm, Macau, Peoples R China 2.Hong Kong Polytech Univ, Fac Business, Hong Kong, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Shi, Songze,Li, Fan,Li, Wei. A Hybrid Long Short-Term Memory-Graph Convolutional Network Model for Enhanced Stock Return Prediction: Integrating Temporal and Spatial Dependencies[J]. MATHEMATICS,2025,13(7):13.
APA	Shi, Songze,Li, Fan,&Li, Wei.(2025).A Hybrid Long Short-Term Memory-Graph Convolutional Network Model for Enhanced Stock Return Prediction: Integrating Temporal and Spatial Dependencies.MATHEMATICS,13(7),13.
MLA	Shi, Songze,et al."A Hybrid Long Short-Term Memory-Graph Convolutional Network Model for Enhanced Stock Return Prediction: Integrating Temporal and Spatial Dependencies".MATHEMATICS 13.7(2025):13.