Intelligence Trading System for Thai Stock Index

  • Monruthai Radeerom
  • Hataitep Wongsuwarn
  • M. L. Kulthon Kasemsan
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 351)

Abstract

Stock investment has become an important investment activity in Thailand. However, investors often lose money due to unclear investment objectives. Therefore, an investment decision support system to assist investors in making good decisions has become an important research issue. Thus, this paper introduces an intelligent decision-making model, based on the application of Neurofuzzy system (NFs) technology. Our proposed system can decide a trading strategy for each day and produce a high profit for of each stock. Our decision-making model is used to capture the knowledge in technical indicators for making decisions such as buy, hold and sell. Finally, the experimental results have shown higher profits than the Neural Network (NN) and “Buy & Hold” models for each stock index. The results are very encouraging and can be implemented in a Decision- Trading System during the trading day.

Keywords

Hide Layer Stock Market Stock Price Trading System Stock Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. 1.
    Babuska, A.R.: Neuro-fuzzy methods for modeling and identification. In: Recent Advances in intelligent Paradigms and Application, pp. 161–186 (2002)Google Scholar
  2. 2.
    Cardon, O., Herrera, F., Villar, P.: Analysis and Guidelines to Obtain A Good Uniform Fuzzy rule Based System Using simulated Annealing. Int’l J. of Approximated Reason 25(3), 187–215 (2000)CrossRefGoogle Scholar
  3. 3.
    Chapman, A.J.: Stock market reading systems through neural networks: developing a model. Int’l J. of Apply Expert Systems 2(2), 88–100 (1994)Google Scholar
  4. 4.
    Chen, A.S., Leuny, M.T., Daoun, H.: Application of Neural Networks to an Emerging Financial Market: Forecasting and Trading The Taiwan Stock Index. Computers and Operations Research 30, 901–902 (2003)MATHCrossRefGoogle Scholar
  5. 5.
    Conner, N.O., Madden, M.: A Neural Network Approach to Pre-diction Stock Exchange Movements Using External Factor. Knowledge Based System 19, 371–378 (2006)CrossRefGoogle Scholar
  6. 6.
    Liu, J.N.K., Kwong, R.W.M.: Automatic Extraction and Identification of chart Patterns Towards Financial Forecast. Applied soft Computing 1, 1–12 (2006)MATHGoogle Scholar
  7. 7.
    Doeksen, B., Abraham, A., Thomas, J., Paprzycki, M.: Real Stock Trading Using Soft Computing Models. In: IEEE Int’l Conf. on Information Technology: Coding and Computing, Las Vegas, Nevada, USA, pp. 123–129 (2005)Google Scholar
  8. 8.
    Dutta, S., Shekhar, S.: Bond rating: A non-conservative application of neural networks. In: IEEE Int’l Conf. on Neural Networks, San Diego, CA, USA, pp. 124–130 (1990)Google Scholar
  9. 9.
    Farber, J.D., Sidorowich, J.J.: Can new approaches to nonlinear modeling improve economic forecasts? The Economy As An Evolving Complex System, 99–115 (1988)Google Scholar
  10. 10.
    Hiemstra, Y.: Modeling Structured Nonlinear Knowledge to Predict Stock Markets: Theory. In: Evidena and Applications, Irwin, pp. 163–175 (1995)Google Scholar
  11. 11.
    Hutchinson, J.M., Lo, A., Poggio, T.: A nonparametric approach to pricing and hedging derivative securities via learning networks. Int’l J. of Finance 49, 851–889 (1994)Google Scholar
  12. 12.
    James, N.K., Raymond, W.M., Wong, K.: Automatic Extraction and Identification of chart Patterns towards Financial Forecast. Applied soft Computing 1, 1–12 (2006)Google Scholar
  13. 13.
    LeBaron, B., Weigend, A.S.: Evaluating neural network predictors by bootstrapping. In: Int’l Conf. on Neural Information Process, Seoul, Korea, pp. 1207–1212 (1994)Google Scholar
  14. 14.
    Li, R.-J., Xiong, Z.-B.: Forecasting Stock Market with Fuzzy Neural Network. In: 4th Int’l Conf. on Machine Learning and Cybernetics, Guangzhou, China, pp. 3475–3479 (2005)Google Scholar
  15. 15.
    Radeerom, M., Srisa-an, C.: Prediction Method for Real Thai Stock Index Based on Neurofuzzy Approach. In: Trends in Intelligent Systems and Computer Engineering. LNEE, vol. 6, pp. 327–347 (2008)Google Scholar
  16. 16.
    Refenes, P., Abu-Mustafa, Y., Moody, J.E., Weigend, A.S. (eds.): Neural Networks in Financial Engineering. World Scientific, Singapore (1996)MATHGoogle Scholar
  17. 17.
    Tanigawa, T., Kamijo, K.: Stock price pattern matching system: dynamic programming neural network approach. In: Int’l J. Conf. on on Neural Networks, vol. 2, pp. 59–69 (1992)Google Scholar
  18. 18.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its application to modeling and control. IEEE Trans. on System, Man and Cybernetics 5, 116–132 (1985)Google Scholar
  19. 19.
    Trippi, R., Lee, K.: Artificial Intelligence in Finance & Investing. Chicago, IL, Irwin (1996)Google Scholar
  20. 20.
    Tsaih, R., Hsn, V.R., Lai, C.C.: Forecasting S&P500 Stock Index Future with A Hybrid AI System. Decision Support Systems 23, 161–174 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Monruthai Radeerom
    • 1
  • Hataitep Wongsuwarn
    • 2
  • M. L. Kulthon Kasemsan
    • 1
  1. 1.Faculty of Information TechnologyRangsit UniversityPathumtaniThailand
  2. 2.ME, Faculty of Engineering at Kasetsart University (Kamphaeng Saen)NakhonpathomThailand

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