EPSO-GHSOM Stock Selecting and Trading Strategy on Big Data

Wenqing Liu; Tingyu Chen; Mike Y. J. Lee

doi:doi:10.11648/j.ijiis.20170602.11

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EPSO-GHSOM Stock Selecting and Trading Strategy on Big Data

Wenqing Liu, Tingyu Chen, Mike Y. J. Lee

Published in International Journal of Intelligent Information Systems (Volume 6, Issue 2)

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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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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TY  - JOUR
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

Wenqing Liu

Department of Management Information Systems, National Chengchi University, Taipei, Taiwan
Tingyu Chen

Department of Management Information Systems, National Chengchi University, Taipei, Taiwan
Mike Y. J. Lee

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

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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

Copy | Download

@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}
}

Copy | Download

TY  - JOUR
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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