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International Conference on Artificial Neural Networks

ICANN 2006: Artificial Neural Networks – ICANN 2006 pp 664–671Cite as

Testing the Random Walk Hypothesis with Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4132))

Abstract

Although, there is an ongoing belief in the investment community that technical analysis can be used to infer the direction of future prices, the academic community always treated it (at best) with skepticism. However, if there is a degree of effectiveness in technical analysis, that necessarily lies in direct contrast with the efficient market hypothesis. In this paper, we use neural network estimators to infer from technical trading rules how to extrapolate future price movements. To the extend that the total return of a technical trading strategy can be regarded as a measure of predictability, technical analysis can be seen as a test of the independent increments version of random walk.

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References

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Author information

Authors and Affiliations

  1. Accounting and Finance Department, University of Macedonia, 156 Egnatia St, 540 06, Thessaloniki, Greece

    Achilleas Zapranis

Authors
  1. Achilleas Zapranis
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Editor information

Editors and Affiliations

  1. School of Electrical and Computer Engineering, Image, Video and Multimedia Systems Laboratory, National Technical University of Athens, 157 80, Zographou, GR, Greece

    Stefanos Kollias

  2. Department of Electrical and Computer Engineering, National Technical University of Athens, 15780, Zographou, Greece

    Andreas Stafylopatis

  3. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  4. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

    Erkki Oja

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© 2006 Springer-Verlag Berlin Heidelberg

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Zapranis, A. (2006). Testing the Random Walk Hypothesis with Neural Networks. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_69

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  • DOI: https://doi.org/10.1007/11840930_69

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38871-5

  • Online ISBN: 978-3-540-38873-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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