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EPSO-GHSOM Stock Selecting and Trading Strategy on Big Data

Received: 30 June 2015     Accepted: 2 December 2015     Published: 25 March 2017
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Abstract

Extracting trading information from the stock market to construct accurate forecasting models that filter signals and noise is a challenge. This research employs big data analytics to construct a computation platform for stock selection and trading strategies. It adopts elite particle swarm optimization (EPSO) to elucidate optimal trading opportunities and combines growing hierarchical self-organizing map (GHSOM) and EPSO in its stock selection strategy. EPSO–GHSOM distinguishes companies’ operating profitability, identifies price signals, and sets decision rules for buying and selling.

Published in International Journal of Intelligent Information Systems (Volume 6, Issue 2)
DOI 10.11648/j.ijiis.20170602.11
Page(s) 7-20
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Particle Swarm Optimization (PSO), Growing Hierarchical Self-Organizing Map (GHSOM), Big Data Analytics, Stock Trading Strategies, Stock Market Forecasting, Stock Market Predicting

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Cite This Article
  • APA Style

    Wenqing Liu, Tingyu Chen, Mike Y. J. Lee. (2017). EPSO-GHSOM Stock Selecting and Trading Strategy on Big Data. International Journal of Intelligent Information Systems, 6(2), 7-20. https://doi.org/10.11648/j.ijiis.20170602.11

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    ACS Style

    Wenqing Liu; Tingyu Chen; Mike Y. J. Lee. EPSO-GHSOM Stock Selecting and Trading Strategy on Big Data. Int. J. Intell. Inf. Syst. 2017, 6(2), 7-20. doi: 10.11648/j.ijiis.20170602.11

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    AMA Style

    Wenqing Liu, Tingyu Chen, Mike Y. J. Lee. EPSO-GHSOM Stock Selecting and Trading Strategy on Big Data. Int J Intell Inf Syst. 2017;6(2):7-20. doi: 10.11648/j.ijiis.20170602.11

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  • @article{10.11648/j.ijiis.20170602.11,
      author = {Wenqing Liu and Tingyu Chen and Mike Y. J. Lee},
      title = {EPSO-GHSOM Stock Selecting and Trading Strategy on Big Data},
      journal = {International Journal of Intelligent Information Systems},
      volume = {6},
      number = {2},
      pages = {7-20},
      doi = {10.11648/j.ijiis.20170602.11},
      url = {https://doi.org/10.11648/j.ijiis.20170602.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20170602.11},
      abstract = {Extracting trading information from the stock market to construct accurate forecasting models that filter signals and noise is a challenge. This research employs big data analytics to construct a computation platform for stock selection and trading strategies. It adopts elite particle swarm optimization (EPSO) to elucidate optimal trading opportunities and combines growing hierarchical self-organizing map (GHSOM) and EPSO in its stock selection strategy. EPSO–GHSOM distinguishes companies’ operating profitability, identifies price signals, and sets decision rules for buying and selling.},
     year = {2017}
    }
    

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    T1  - EPSO-GHSOM Stock Selecting and Trading Strategy on Big Data
    AU  - Wenqing Liu
    AU  - Tingyu Chen
    AU  - Mike Y. J. Lee
    Y1  - 2017/03/25
    PY  - 2017
    N1  - https://doi.org/10.11648/j.ijiis.20170602.11
    DO  - 10.11648/j.ijiis.20170602.11
    T2  - International Journal of Intelligent Information Systems
    JF  - International Journal of Intelligent Information Systems
    JO  - International Journal of Intelligent Information Systems
    SP  - 7
    EP  - 20
    PB  - Science Publishing Group
    SN  - 2328-7683
    UR  - https://doi.org/10.11648/j.ijiis.20170602.11
    AB  - Extracting trading information from the stock market to construct accurate forecasting models that filter signals and noise is a challenge. This research employs big data analytics to construct a computation platform for stock selection and trading strategies. It adopts elite particle swarm optimization (EPSO) to elucidate optimal trading opportunities and combines growing hierarchical self-organizing map (GHSOM) and EPSO in its stock selection strategy. EPSO–GHSOM distinguishes companies’ operating profitability, identifies price signals, and sets decision rules for buying and selling.
    VL  - 6
    IS  - 2
    ER  - 

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Author Information
  • Department of Management Information Systems, National Chengchi University, Taipei, Taiwan

  • Department of Management Information Systems, National Chengchi University, Taipei, Taiwan

  • Department of Business Administration, China University of Technology, Taipei, Taiwan

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