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Intelligent Systems'2014 pp 871–882Cite as

An Efficient System for Stock Market Prediction

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 323))

Abstract

This paper presents an efficient system for accurate, confident, general and responsive stock market prediction, employing Artificial Neural Networks (ANN). For technical indicators, Multi-Layer Perceptron (MLP) ANN is used and trained with Kullback Leibler Divergence (KLD) learning algorithm because it converges fast in addition to offering generalization in the learning mechanism. On the other hand, Radial Basis Function Neural Network (RBFNN) trained with Localized Generalization Error (L-GEM) is used for candlesticks patterns. The accuracy, generalization and statistical-significance of the developed system were confirmed through various local and international data sets. Next, sensitivity analysis was conducted for the different parameters that influence the system efficiency metrics. In order to have responsive prediction, the proposed system was evolved, employing concurrent programming to get benefit from the off-the-shelf multi-core architectures. Then, the performance of the developed system was evaluated to confirm acceptance scalability and utilization.

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References

  1. Pimentel, M.A.F., Clifton, D.A., Clifton, L., Tarassenko, L.: Review: A Review of Novelty Detection. Journal of Signal Processing 99, 215–249 (2014)

    Google Scholar 

  2. Kristjanpoller, W., Fadic, A., Minutolo, M.C.: Volatility Forecast using Hybrid Neural Network Models. International Journal of Expert Systems with Applications 41(5), 2437–2442 (2014)

    Article  Google Scholar 

  3. Hamed, I.M., Hussein, A.S., Tolba, M.F.: An Intelligent Model for Stock Market Prediction. International Journal Computational Intelligence Systems 5(4), 639–652 (2012)

    Article  Google Scholar 

  4. Rout, M., Majhi, B., Mohapatra, U.M., Mahapatra, R.: Stock Indices Prediction using Radial Basis Function Neural Network. In: 3rd International Conference on Swarm, Evolutionary, and Memetic Computing, pp. 285–293 (2012)

    Google Scholar 

  5. Li, Y., Ma, W.: Applications of Artificial Neural Networks in Financial Economics: A Survey. In: 2010 International Symposium on Computational Intelligence and Design (ISCID 2010), vol. 10, pp. 211–214 (2010)

    Google Scholar 

  6. Xiao, W., Ng, W., Firth, M., Yeung, D.S., Cai, G.Y., Li, J.C., Sun, B.: L-GEM Based MCS Aided Candlestick Pattern Investment Strategy in the Shenzhen Stock Market. In: International Conference on Machine Learning and Cybernetics, vol. 1, pp. 243–248 (2009)

    Google Scholar 

  7. Quah, T.S.: Using Neural Network for DJIA Stock Selection. Engineering Letters 15(1), 126–133 (2007)

    Google Scholar 

  8. White, H.: Economic Prediction using Neural Networks: The Case of IBM Daily Stock Returns. In: IEEE International Conference on Neural Networks, vol. 2, pp. 451–458 (1988)

    Google Scholar 

  9. Lo, A.W., Mamaysky, H., Wang, J.: Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. Journal of Finance 55(4), 1765–1770 (2000)

    Article  Google Scholar 

  10. Murphy, J.: Technical Analysis of the Futures Markets: A Comprehensive Guide to Trading Methods and Applications. Prentice-Hal, New York (1986)

    Google Scholar 

  11. Ahmad, K., Taskaya-Temizel, T., Cheng, D., Gillam, L., Ahmad, S., Traboulsi, H., Nan-kervis, J.: Financial Information Grid –an ESRC e-Social Science Pilot. In: 3rd UK e-Science Programme All Hands Meeting, Nottingham, United Kingdom (2004)

    Google Scholar 

  12. Fung, P.C., Yu, X., Lam, W.: Stock Prediction: Integrating Text Mining. In: IEEE International Conference on Computational Intelligence for Financial Engineering, pp. 395–402 (2003)

    Google Scholar 

  13. Hwang, H., Oh, J.: Fuzzy Models for Predicting Time Series Stock Price Index. International Journal of Control, Automation and Systems 8(3), 702–706 (2010)

    Article  Google Scholar 

  14. Nguyen, M.N., Omkar, U., Shi, D., Hayfron-Acquah, J.B.: Stock Market Price Prediction using Cyclic Self-Organizing Hierarchical CMAC. In: 9th International Conference on Control, Automation, Robotics and Vision, pp. 1–6 (2006)

    Google Scholar 

  15. Huang, C., Liao, J., Yang, D., Chang, T., Luo, Y.: Realization of a News Dissemination Agent Based on Weighted Association Rules and Text Mining Techniques. Expert Systems with Applications 37(9), 6409–6413 (2010)

    Article  Google Scholar 

  16. Fu, T., Chung, F., Luk, R., Ng, C.: Stock Time Series Pattern Matching: Template-based vs. Rule-based Approaches. Engineering Applications of Artificial Intelligence 20(3), 347–364 (2007)

    Article  Google Scholar 

  17. Li, H., Ng, W.W.Y., Lee, J.W.T., Binbin, S., Yeung, D.S.: Quantitative Study on Candle Stick Pattern for Shenzhen Stock Market. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2008, pp. 54–59 (2008)

    Google Scholar 

  18. Jasemi, M., Kimiagari, A.M., Memariani, A.: A Modern Neural Network Model to Do Stock Market Timing on the Basis of the Ancient Investment Technique of Japanese Candlestick. Expert Systems with Applications 38(4), 3884–3890 (2011)

    Google Scholar 

  19. Okaz (2014) https://www.okazinvest.com/

  20. Nison, S.: Japanese Candlestick Charting Techniques, 2nd edn. Prentice Hall Press (2001)

    Google Scholar 

  21. Bigalow, S.: High Profit Candlestick Patterns. Profit Publishing LLC (2005)

    Google Scholar 

  22. Wilder, J.W.: New Concepts in Technical Trading Systems, 1st edn. Trend Research, Greensboro (1978)

    Google Scholar 

  23. Person, J.L.: A Complete Guide to Technical Trading Tactics: How to Profit using Pivot Points, Candlesticks & other Indicators, pp. 144–145. Wiley, Hoboken (2004)

    Google Scholar 

  24. Appel, G.: Technical Analysis Power Tools for Active Investors, p. 166. Financial Times Prentice Hall (1999)

    Google Scholar 

  25. Egeli, B., Ozturan, M., Badur, B.: Stock Market Prediction using Artificial Neural Net-Works. In: International Conference on Business, Hawaii (2003)

    Google Scholar 

  26. Jang, J.S.: ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man and Cybernetics 23(3), 665–685 (1993)

    Article  Google Scholar 

  27. Grosan, C., Abraham, A., Ramos, V., Han, S.Y.: Stock Market Prediction using Multi Expression Programming. In: Portuguese Conference on Artificial intelligence, pp. 73–78 (2005)

    Google Scholar 

  28. Johnson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis, 5th edn. Prentice Hall Upper Saddle River, NJ (2002)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Arab Open University, Headquarters, P.O. Box: 3322, Al-Safat, 13033, Kuwait

    Ashraf S. Hussein

  2. Ain Shams University, Cairo, 11566, Egypt

    Ibrahim M. Hamed & Mohamed F. Tolba

Authors
  1. Ashraf S. Hussein
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  2. Ibrahim M. Hamed
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  3. Mohamed F. Tolba
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Corresponding author

Correspondence to Ashraf S. Hussein .

Editor information

Editors and Affiliations

  1. Ford Motor Company, Research & Advanced Engineering, Dearborn, Mississippi, USA

    D. Filev

  2. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    J. Jabłkowski

  3. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    J. Kacprzyk

  4. Polish Academy of Sciences and WIT - Warsaw School of Information Technology, Systems Research Institute, Warsaw, Poland

    M. Krawczak

  5. Bulgarian Academy of Sciences, Institute of Information and Communication, Sofia, Bulgaria

    I. Popchev

  6. Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland

    L. Rutkowski

  7. Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Sofia, Bulgaria

    V. Sgurev

  8. Department of Computer and Information Technologies, “Prof. Assen Zlatarov" University Faculty of Technical Sciences, Bourgas, Bulgaria

    E. Sotirova

  9. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    P. Szynkarczyk

  10. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    S. Zadrozny

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© 2015 Springer International Publishing Switzerland

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Hussein, A.S., Hamed, I.M., Tolba, M.F. (2015). An Efficient System for Stock Market Prediction. In: Filev, D., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_76

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  • DOI: https://doi.org/10.1007/978-3-319-11310-4_76

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11309-8

  • Online ISBN: 978-3-319-11310-4

  • eBook Packages: EngineeringEngineering (R0)

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