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
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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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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DOI: https://doi.org/10.1007/s10489-021-02979-y