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

References

[1]	Silver, Nate, The signal and the noise: Why so many predictions fail-but some don't. Penguin, 2012.
[2]	Teixeira, Lamartine Almeida, and Adriano Lorena Inacio De Oliveira. “A method for automatic stock trading combining technical analysis and nearest neighbor classification.” Expert systems with applications, 2010, vol.37, no.10, pp. 6885-6890.
[3]	Fama, Eugene F., “Efficient capital markets: A review of theory and empirical work*.” The journal of Finance, 1970, vol.25, no.2, pp. 383-417.
[4]	Haugen, Robert A., The inefficient stock market: What pays off and why. Upper Saddle River, NJ: Prentice Hall, 1999.
[5]	Los, Cornelis A., “Nonparametric efficiency testing of Asian stock markets using weekly data.” Centre for Research in Financial Services Working Paper, 1998, pp. 99-01.
[6]	Ohkawa, E., Chen, Y., Mabu, S., Shimada, K., & Hirasawa, K. “Evaluation of varying portfolio construction of stocks using Genetic Network Programming with control nodes.” SICE Annual Conference, 2008.
[7]	Kwon, Yung-Keun, and Byung-Ro Moon., “A hybrid neurogenetic approach for stock forecasting.” Neural Networks, IEEE Transactions on, 2007, vol. 18, no. 3, pp. 851-864.
[8]	Chavarnakul, Thira, and David Enke., “A hybrid stock trading system for intelligent technical analysis-based equivolume charting.” Neurocomputing, 2009, vol. 72, no. 16, pp. 3517-3528.
[9]	Rodríguez-González, A., García-Crespo, Á., Colomo-Palacios, R., Iglesias, F. G., & Gómez-Berbís, J. M. “CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator.” Expert systems with Applications, 2011, vol. 38, no. 9, pp. 11489-11500.
[10]	Wang, Lipo, and Shekhar Gupta., “Neural networks and wavelet de-noising for stock trading and prediction.” Time Series Analysis, Modeling and Applications. Springer Berlin Heidelberg, 2013, pp. 229-247.
[11]	Lin, Xiaowei, Zehong Yang, and Yixu Song., “Intelligent stock trading system based on improved technical analysis and Echo State Network.” Expert systems with Applications, 2011, vol. 38, no. 9, pp. 11347-11354.
[12]	Olatunji, S. O., Al-Ahmadi, M. S., Elshafei, M., & Fallatah, Y. A. "Forecasting the Saudi Arabia Stock Prices Based on Artificial Neural Networks Model." International Journal of Intelligent Information Systems, 2013, vol. 2, no. 5, pp. 77.
[13]	Rath, Smita, Alok Kumar Jagadev, and Manoj Ranjan Nayak., "Performance Analysis of Stock Market Using Artificial Neural Network." International Journal of Applied Engineering Research, 2015, vol. 10, no. 4.
[14]	Chien, Ya-Wen Chang, and Yen-Liang Chen., “Mining associative classification rules with stock trading data–A GA-based method.” Knowledge-Based Systems, 2010, vol. 23, no. 6, pp. 605-614.
[15]	Hsu, L. Y., Horng, S. J., He, M., Fan, P., Kao, T. W., Khan, M. K.,... & Chen, R. J. “Mutual funds trading strategy based on particle swarm optimization.” Expert Systems with Applications, 2011, vol. 38, no. 6, pp. 7582-7602.
[16]	Gunasekarage, Abeyratna, and David M. Power., “The profitability of moving average trading rules in South Asian stock markets.” Emerging Markets Review, 2001, vol. 2, no. 1, pp. 17-33.
[17]	Kennedy, James., “Particle swarm optimization.” Encyclopedia of Machine Learning. Springer US, 2010, pp. 760-766.
[18]	Dittenbach, Michael, Andreas Rauber, and Dieter Merkl. “Uncovering hierarchical structure in data using the growing hierarchical self-organizing map.” Neurocomputing, 2002, vol. 48, no. 1, pp. 199-216.
[19]	Wang, Jar-Long, and Shu-Hui Chan., "Stock market trading rule discovery using pattern recognition and technical analysis." Expert Systems with Applications, 2007, vol. 33, no. 2, pp. 304-315.
[20]	Peng, Hsin-Tsung, Hahn-Ming Lee, and Jan-Ming Ho., “Trading decision maker: Stock trading decision by price series smoothing and tendency transition inference.” -Technology, e-Commerce and e-Service, 2005. EEE'05. Proceedings. The 2005 IEEE International Conference on. IEEE, 2005.
[21]	Chang, Pei-Chann, Chin-Yuan Fan, and Chen-Hao Liu., “Integrating a piecewise linear representation method and a neural network model for stock trading points prediction. Systems,” Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 2009, vol. 39, no. 1, pp. 80-92.
[22]	Lin, Nana, Xinwei Zhang, and Siqi Lv. “CAN WEB NEWS MEDIA SENTIMENTS IMPROVE STOCK TRADING SIGNAL PREDICTION?”. 2014.
[23]	Butler, Matthew, and Dimitar Kazakov., “Testing implications of the adaptive market hypothesis via computational intelligence.” Computational Intelligence for Financial Engineering & Economics (CIFEr), 2012 IEEE Conference on. IEEE, 2012.
[24]	Pham, H. V., Cooper, E. W., Cao, T., & Kamei, K. “Hybrid Kansei-SOM model using risk management and company assessment for stock trading.” Information Sciences, 2014, vol. 256, pp. 8-24.
[25]	Kohonen, Teuvo., “Self-organizing maps.” Springer Science & Business Media, 2001, pp. 30.
[26]	Fama, Eugene F., and Kenneth R. French., “Size and book‐to-market factors in earnings and returns.” The Journal of Finance, 1995, vol. 50, no. 1, pp. 131-155.
[27]	Graham, Benjamin, and David L. Dodd., Security analysis: principles and technique. McGraw-Hill, 1934.
[28]	Buffett, Warren, and Carol Loomis., “Warren Buffett on the stock market.” Fortune, 2001.
[29]	Hagstrom, Robert G., The Warren Buffett way: Investment strategies of the world's greatest investor. John Wiley & Sons, 1997.
[30]	Zhong Hua., “Buffett stock valuation investment strategy applied research.” Securities bimonthly, 2011, vol. 586, pp. 80-109.
[31]	Piotroski, Joseph D., “Value investing: The use of historical financial statement information to separate winners from losers.” Journal of Accounting Research, 2000. pp. 1-41.
[32]	Mohanram, Partha S., “Separating Winners from Losers among Low Book-to-Market Stocks using Financial Statement Analysis.” Review of Accounting Studies, 2005, vol. 10, no. 2-3, pp. 133-170.
[33]	Liaw, Siqin., Technical Analysis: An Asian Perspective. 2012.
[34]	Gartner. http://www.gartner.com/it-glossary/big-data/.
[35]	Zikopoulos, P. C., Eaton, C., DeRoos, D., Deutsch, T., & Lapis, G. Understanding big data. New York et al: McGraw-Hill, 2012.
[36]	Zikopoulos, P., Parasuraman, K., Deutsch, T., Giles, J., & Corrigan, D. Harness the Power of Big Data The IBM Big Data Platform. McGraw Hill Professional, 2012.
[37]	Xindong Wu, Xingquan Zhu, Gong-Qing Wu, and Wei Ding., “Data Mining with Big Data.” Transactions on Knowledge and Data Engineering, 2014, vol. 26, no. 1, pp. 1041-4347.
[38]	Chen, CL Philip, and Chun-Yang Zhang., “Data-intensive applications, challenges, techniques and technologies: A survey on Big Data.” Information Sciences, 2014, vol. 275, pp. 314-347.
[39]	EMC Institute, Vmware research team, Hu Jia Xi(translation)., Next step of Big Data: Big Data new strategy, technology and large-scale Web applications. Saatchi Times Press, 2014.
[40]	Palvia, P., Leary, D., Mao, E., Midha, V., Pinjani, P., & Salam, A. F. “Research methodologies in MIS: an update.” Communications of the Associaton for Information Systems, 2004, vol. 14, no. 1, pp. 24.
[41]	Hong Ruitai., Buffett stock magic book. Smart Inv Press, 2004.
[42]	Liu, Wenqing, Tingyu Chen, and Mike YJ Lee., “A Method for Stock Trading Strategy Combining Technical Analysis and Particle Swarm Optimization. “Journal of Convergence Information Technology, 2014, vol. 9, no. 5.

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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

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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}
}

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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