[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Intelligence Decision Trading Systems for Stock Index

  • Conference paper
Intelligent Information and Database Systems (ACIIDS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7198))

Included in the following conference series:

Abstract

This paper introduces an intelligent decision-making model, based on the application of Fuzzy Logic and Neurofuzzy system (NFs) technology. Our proposed system can decide a trading strategy for each day and produce a high profit for each stock. Our decision-making model is used to capture the knowledge in technical indicators for making decisions such as buy, hold and sell. Moreover, we compared with 3 our proposed scenario of Intelligence Trading System model. Finally, the experimental results have shown higher profits than the Neural Network (NN) and “Buy & Hold” models for each stock index. And, some models which were including volume indicator and predicted close price on next day have profit batter than other models. The results are very encouraging and can be implemented in a Decision- Trading System during the trading day.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 35.99
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 44.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  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. Cardon, O., Herrera, F., Villar, P.: Analysis and Guidelines to Obtain A Good Uniform Fuzzy rule Based System Using simulated Annealing. J. Approximated Reason. 25(3), 187–215 (2000)

    Article  MATH  Google Scholar 

  3. Chapman, A.J.: Stock market reading systems through neural networks: developing a model. J. Apply Expert Systems 2(2), 88–100 (1994)

    Google Scholar 

  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)

    Article  Google Scholar 

  5. Conner, N.O., Madden, M.: A Neural Network Approach to Prediction Stock Exchange Movements Using External Factor. Knowledge Based System 19, 371–378 (2006)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. Hiemstra, Y.: Modeling Structured Nonlinear Knowledge to Predict Stock Markets: Theory. In: Evidena and Applications, Irwin, pp. 163–175 (1995)

    Google Scholar 

  11. Hutchinson, J.M., Lo, A., Poggio, T.: A nonparametric approach to pricing and hedging derivative securities via learning networks. J. Finance 49, 851–889 (1994)

    Article  Google Scholar 

  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. 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. 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. Radeerom, M., Srisaan, C.K., Kasemsan, M.L.K.: 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. Refenes, P., Abu-Mustafa, Y., Moody, J.E., Weigend, A.S. (eds.): Neural Networks in Financial Engineering. World Scientific, Singapore (1996)

    Google Scholar 

  17. Tanigawa, T., Kamijo, K.: Stock price pattern matching system: dynamic programming neural network approach. In: Int’l Conf. on Neural Networks, vol. 2, pp. 59–69 (1992)

    Google Scholar 

  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)

    Article  MATH  Google Scholar 

  19. Trippi, R., Lee, K.: Artificial Intelligence in Finance & Investing, Chicago, IL, Irwin (1996)

    Google Scholar 

  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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Radeerom, M., Wongsuwarn, H., Kasemsan, M.L.K. (2012). Intelligence Decision Trading Systems for Stock Index. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Intelligent Information and Database Systems. ACIIDS 2012. Lecture Notes in Computer Science(), vol 7198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28493-9_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28493-9_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28492-2

  • Online ISBN: 978-3-642-28493-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics