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A Technical Trading Indicator Based on Dynamical Consistent Neural Networks

  • Conference paper
Artificial Neural Networks – ICANN 2006 (ICANN 2006)

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

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Abstract

In econometrics, the behaviour of financial markets is described by quantitative variables. Mathematical and statistical methods are used to explore economic relationships and to forecast the future market development. However, econometric modeling is often limited to a single financial market. In the age of globalisation financial markets are highly interrelated and thus, single market analyses are misleading. In this paper we present a new way to model the dynamics of coherent financial markets. Our approach is based on so-called dynamical consistent neural networks (DCNN), which are able to map multiple scales and different sub-dynamics of the coherent market movement. Unlikely to standard econometric methods, small market movements are not treated as noise but as valuable market information. We apply the DCNN to forecast monthly movements of major foreign exchange (FX) rates. Based on the DCNN forecasts we develop a technical trading indicator to support investment decisions.

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Author information

Authors and Affiliations

  1. Information & Communications, Learning Systems, Siemens AG, Corporate Technology, 81739, Munich, Germany

    Hans Georg Zimmermann, Ralph Grothmann, Anton Maximilian Schäfer & Christoph Tietz

  2. JPMorgan, London, UK

    Lorenzo Bertolini

  3. DekaBank, Frankfurt, Germany

    Lorenzo Bertolini

  4. University of Ulm, Optimisation and Operations Research, Germany

    Anton Maximilian Schäfer

Authors
  1. Hans Georg Zimmermann
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  2. Lorenzo Bertolini
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  3. Ralph Grothmann
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  4. Anton Maximilian Schäfer
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  5. Christoph Tietz
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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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Zimmermann, H.G., Bertolini, L., Grothmann, R., Schäfer, A.M., Tietz, C. (2006). A Technical Trading Indicator Based on Dynamical Consistent 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_68

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  • DOI: https://doi.org/10.1007/11840930_68

  • 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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