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Quantifying the effects of long-term news on stock markets on the basis of the multikernel Hawkes process

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Abstract

Recent studies have revealed that long-term financial news can affect on stock markets. However, previous research mainly focuses on modeling the short-term effects of financial news and suffers from the weak ability of quantifying the time-decaying influence of financial news. To fill this gap, this study introduces the Hawkes process to estimate the time-decaying influence of long-term financial news. However, the performance of the conventional Hawkes process is sensitive to the choice of kernel functions. Hence, we propose a novel multikernel-powered Hawkes process framework, which uses multiple kernels to model different time-decaying rates, thus alleviating the instability of our proposed Hawkes process based prediction model. Experimental results show that the proposed framework yields state-of-the-art stock market prediction accuracies on 515 listed companies and gains more profits in market trading simulation compared with baseline methods. News-based stock prediction can complement studies on price-volume-based stock prediction.

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Acknowledgements

This work was supported by National Key Research and Development Program of China (Grant No. 2018AAA0101901) and National Natural Science Foundation of China (Grant Nos. 61976073, 61702137). We thank the anonymous reviewers for their constructive comments.

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Authors and Affiliations

  1. Research Center for Social Computing and Information Retrieval, Harbin, 150001, China

    Xiao Ding, Jihao Shi, Junwen Duan, Bing Qin & Ting Liu

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  1. Xiao Ding
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  2. Jihao Shi
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  3. Junwen Duan
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  4. Bing Qin
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  5. Ting Liu
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Correspondence to Ting Liu.

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Ding, X., Shi, J., Duan, J. et al. Quantifying the effects of long-term news on stock markets on the basis of the multikernel Hawkes process. Sci. China Inf. Sci. 64, 192102 (2021). https://doi.org/10.1007/s11432-020-3064-4

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  • DOI: https://doi.org/10.1007/s11432-020-3064-4

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