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MF-DAT: a stock trend prediction of the double-graph attention network based on multisource information fusion

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A Correction to this article was published on 15 June 2024

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Abstract

Stock forecasting research, which aims to predict the future price movement of stocks, has been the focus of investors and scholars. This is important for practical applications related to human-centric computing and information sciences. Previous research has generally only considered market information other than the relationship between stocks, and it is challenging to learn a better representation of stock characteristics by considering the relationship between stocks. In the existing methods of combining market information with stock relationship modeling, most of them use predefined industry relationships to construct stock relationship diagrams, which inevitably ignores the potential interactions between stocks, especially the hidden relationships between stock groups. To this end, a new dual-graph attention model (MF-DAT) based on multisource information fusion is designed. Specifically, first, multiple features are fused by the LMF module, then the long-term and short-term state characteristics of stocks are learned through the first layer of the graph attention layer, and finally the node representation of the stock relationship network constructed by the mining stock cluster structure through community detection is updated. Our model takes into account both stock time-series information and potential relationships between stocks. Experiments on the S &P 500 and NASDAQ datasets show that our MF-DAT has better performance than the 8 SOTA methods that are now more popular.

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Acknowledgements

This work was supported in part by the Key R &D program of Zhejiang Province (2022C01083), the National Science Foundation of China (62102262), the Development Project of Xinjiang Production and Construction Corps 12th (no. SR202103), the Practice Conditions and Practice Base Construction of Ministry of Education (no.SR202102624032).

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Authors and Affiliations

  1. Eit Data Science and Communication College, Zhejiang Yuexiu University, Shaoxing, 312000, China

    Kun Huang

  2. College of International Business, Zhejiang Yuexiu University, Shaoxing, 312000, China

    Xiaoming Li & Yihe Yang

  3. Department of Computer, Mathematical and Physical Sciences, Sul Ross State University, Alpine, TX, 79830, USA

    Neal Xiong

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  1. Kun Huang
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  2. Xiaoming Li
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  3. Neal Xiong
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  4. Yihe Yang
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Contributions

Kun Huang: conceptualization, methodology, writing—original draft. Xiaoming Li: validation, software. Yihe Yang: visualization, writing—reviewing and editing. Neal Xiong: supervision, data curation, investigation, formal analysis.

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Correspondence to Neal Xiong.

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Communicated by J. Gao.

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Huang, K., Li, X., Xiong, N. et al. MF-DAT: a stock trend prediction of the double-graph attention network based on multisource information fusion. Multimedia Systems 30, 136 (2024). https://doi.org/10.1007/s00530-024-01333-9

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