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A multi-view time series model for share turnover prediction

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Abstract

Share turnover is a key indicator for investing in the stock market, which represents how easy or difficult it is to trade a stock. Several techniques have been proposed to predict share turnover values. However, they are often inaccurate because they utilize single-view models that have an incomplete picture of the temporal dynamics. To address this issue, a multi-view time series model (MvT) is proposed to capture temporal dynamics using three views on two data groups. The temporal dynamics of target turnover data and exogenous turnover data are captured by a view generation component. The component generates three views in three different aspects. The predictions are then made by a view combination component and a full connected layer. Extensive experiments on two real stock datasets show the effectiveness and efficiency of the proposed MvT model, when compared with ten algorithms on four groups of stock data in terms of three metrics.

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Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Notes

  1. Southbound trading refers to the trading of certain SEHK securities from mainland investors. It is likely named so because Hong Kong is located south of Shanghai and Shenzhen.

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Acknowledgments

This work was supported in part by Jimei University (nos. ZP2021013, ZQ2018008 and ZP2020043), the Science project of Xiamen City (no. 3502Z20193048), the Education Department of Fujian Province (CN) (nos. JAT200277, JAT200232, JAT170327 and JAT200273), and the Natural Science Foundation of Fujian Province (CN) (nos. 2019J05099 and 2021J01859).

The authors would like to thank the editor and anonymous reviewers for their helpful comments in improving the manuscript quality.

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Authors and Affiliations

  1. Computer Engineering College, Jimei University, Yinjiang Road 185, Xiamen, 361021, China

    Zhijin Wang, Qiankun Su, Bing Cai, Yaohui Huang & Yonggang Fu

  2. School of Computer Science and Technology, Harbin Institute of Technology, 2 West Culture Road, Weihai, 264209, People’s Republic of China

    Guoqing Chao

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  1. Zhijin Wang
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  2. Qiankun Su
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  3. Guoqing Chao
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  4. Bing Cai
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  5. Yaohui Huang
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Correspondence to Qiankun Su.

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This article belongs to the Topical Collection: Special Issue on Multi-view Learning

Guest Editors: Guoqing Chao, Xingquan Zhu, Weiping Ding, Jinbo Bi and Shiliang Sun

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Wang, Z., Su, Q., Chao, G. et al. A multi-view time series model for share turnover prediction. Appl Intell 52, 14595–14606 (2022). https://doi.org/10.1007/s10489-021-02979-y

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