Volume 6, Issue 2, April 2017, Page: 7-20
EPSO-GHSOM Stock Selecting and Trading Strategy on Big Data
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
Received: Jun. 30, 2015;       Accepted: Dec. 2, 2015;       Published: Mar. 25, 2017
DOI: 10.11648/j.ijiis.20170602.11      View  2565      Downloads  149
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.
Particle Swarm Optimization (PSO), Growing Hierarchical Self-Organizing Map (GHSOM), Big Data Analytics, Stock Trading Strategies, Stock Market Forecasting, Stock Market Predicting
To cite this article
Wenqing Liu, Tingyu Chen, Mike Y. J. Lee, EPSO-GHSOM Stock Selecting and Trading Strategy on Big Data, International Journal of Intelligent Information Systems. Vol. 6, No. 2, 2017, pp. 7-20. doi: 10.11648/j.ijiis.20170602.11
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