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International Conference on Knowledge-Based and Intelligent Information and Engineering Systems

KES 2011: Knowlege-Based and Intelligent Information and Engineering Systems pp 366–371Cite as

An Effective Utilization of Many Neural Networks for Improving the Traditional Technical Analysis in the Stock Market

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6882))

Abstract

In this paper, we propose a new decision support system for dealing stocks which utilizes information regarding 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 Nikkei-225 for the last 16 years confirm the effectiveness of our approach.

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References

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Author information

Authors and Affiliations

  1. Information Science, Osaka Kyoiku University, Asahiga-Oka, 4-698-1, Kashiwara City, Osaka Prefecture, 582-8582, Japan

    Norio Baba, Kokutan Liu, Lee Chen Han, Takao Mitsuda & Kou Ro

  2. Toshiba Solution Company, Minatoku, Shibaura 1-1-1, Tokyo, Japan

    Kou Ninn

Authors
  1. Norio Baba
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  2. Kokutan Liu
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  3. Lee Chen Han
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  4. Takao Mitsuda
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  5. Kou Ro
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  6. Kou Ninn
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Editor information

Editors and Affiliations

  1. Institute of Integrated Sensor Systems, University of Kaiserslautern, Erwin-Schroedinger-str. 12, 67663, Kaiserslautern, Germany

    Andreas König

  2. Knowledge-Based Systems Group, Department of Computer Science, University of Kaiserslautern, P.O. Box 3049, 67653, Kaiserslautern, Germany

    Andreas Dengel

  3. School of Business, University of Applied Sciences Northwestern Switzerland, Riggenbachstr. 16, 4600, Olten, Switzerland

    Knut Hinkelmann

  4. Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, 599-8531, Sakai,, Osaka, Japan

    Koichi Kise

  5. KES International, P.O. Box 2115, BN43 9AF, Shoreham-by-sea, UK

    Robert J. Howlett

  6. University of South Australia, Adelaide, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2011 Springer-Verlag Berlin Heidelberg

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Cite this paper

Baba, N., Liu, K., Han, L.C., Mitsuda, T., Ro, K., Ninn, K. (2011). An Effective Utilization of Many Neural Networks for Improving the Traditional Technical Analysis in the Stock Market. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds) Knowlege-Based and Intelligent Information and Engineering Systems. KES 2011. Lecture Notes in Computer Science(), vol 6882. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23863-5_37

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  • DOI: https://doi.org/10.1007/978-3-642-23863-5_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23862-8

  • Online ISBN: 978-3-642-23863-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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