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Market impact analysis via deep learned architectures

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Abstract

How to deeply process market data sources and build systems to process accurate market impact analysis is an attractive problem. In this paper, we build up a system that exploits deep learning architecture to improve feature representations, and adopt state-of-the-art supervised learning algorithm—extreme learning machine—to predict market impacts. We empirically evaluate the performance of the system by comparing different configurations of representation learning and classification algorithms, and conduct experiments on the intraday tick-by-tick price data and corresponding commercial news archives of stocks in Hong Kong Stock Exchange. From the results, we find that in order to make system achieve good performance, both the representation learning and the classification algorithm play important roles, and comparing with various benchmark configurations of the system, deep learned feature representation together with extreme learning machine can give the highest market impact prediction accuracy.

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Notes

  1. www.finet.hk.

  2. www.hkex.com.hk.

  3. www.hsi.com.hk.

  4. http://cn.mathworks.com/matlabcentral/fileexchange/38310-deep-learning-toolbox.

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Acknowledgements

The work described in this paper was partially supported by National Natural Science Foundation of China under the Grant Nos. 61602149 and 61502360, partially supported by the Fundamental Research Funds for the Central Universities under the Grant No. 2016B01714, and partially supported by Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Authors and Affiliations

  1. College of Computer and Information, Hohai University, Nanjing, China

    Xiaodong Li

  2. School of Logistics Engineering, Wuhan University of Technology, Wuhan, China

    Jingjing Cao

  3. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Zhaoqing Pan

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  1. Xiaodong Li
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  2. Jingjing Cao
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  3. Zhaoqing Pan
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Correspondence to Jingjing Cao.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work; there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, “Market Impact Analysis via Deep Learned Architectures.”

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Li, X., Cao, J. & Pan, Z. Market impact analysis via deep learned architectures. Neural Comput & Applic 31, 5989–6000 (2019). https://doi.org/10.1007/s00521-018-3415-3

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