Skip to main content

Effective Utilization of Neural Networks for Constructing an Intelligent Decision Support System for Dealing Stocks

  • Conference paper
Knowledge-Based and Intelligent Information and Engineering Systems (KES 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5712))

  • 733 Accesses

Abstract

In this paper, we propose a new decision support system for dealing stocks which utilizes the predictions (obtained by NNs) concerning the occurrence of the “Golden Cross (GC) and Dead Cross (DC)”, those (also obtained by NNs) concerning the rate of change of the future stock price several weeks ahead, and that (also obtained by NNs) concerning the relative position of the stock price versus “GC” and “DC”. Computer simulation results concerning the dealings of the TOPIX for the last 15 years confirm the effectiveness of our approach.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • 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.

References

  1. Rumelhart, D.E., et al.: Parallel Distributed Processing. MIT Press, Cambridge (1986)

    Google Scholar 

  2. Haykin, S.: Neural Networks. Prentice-Hall, Englewood Cliffs (1998)

    MATH  Google Scholar 

  3. Baba, N., Kozaki, M.: An intelligent forecasting system of stock price using neural network. In: Proceedings of IJCNN 1992, pp. 371–377 (1992)

    Google Scholar 

  4. Refenes, A.-P.N., et al.: Neural Networks in Financial Engineering: A Study in Methodology. IEEE Trans. NNs, 1222–1267 (1997)

    Google Scholar 

  5. Baba, N., Nomura, T.: An Intelligent Utilization of Neural Networks for Improving the Traditional Technical Analysis in the Stock Markets. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS, vol. 3681, pp. 8–14. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  6. Baba, N., Nin, K.: Prediction of Golden Cross and Dead Cross by Neural Networks and Its Utilization. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part II. LNCS, vol. 4693, pp. 642–648. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  7. Baba, N., Nin, K.: Prediction of Golden Cross and Dead Cross by Artificial Neural Networks Could Contribute a Lot for Constructing an Intelligent Decision Support System for Dealing Stocks. In: Proc. of ICCAS 2008, pp. 2547–2550 (2008)

    Google Scholar 

  8. Zurada, J.M., et al.: Sensitivity analysis for minimization of joint data dimension for feed forward neural network. In: Proceedings of the IEEE International Symposium on Circuits and Systems, pp. 447–450 (1994)

    Google Scholar 

  9. Harvey, A.C.: Time Series Models. Prentice Hall / Harvester Wheatsheaf (1993)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Baba, N., Nin, K. (2009). Effective Utilization of Neural Networks for Constructing an Intelligent Decision Support System for Dealing Stocks. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2009. Lecture Notes in Computer Science(), vol 5712. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04592-9_94

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04592-9_94

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04591-2

  • Online ISBN: 978-3-642-04592-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics