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Natural Computing in Computational Finance pp 147–167Cite as

Modeling Turning Points in Financial Markets with Soft Computing Techniques

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 293))

Summary

Two independent evolutionary modeling methods, based on fuzzy logic and neural networks respectively, are applied to predicting trend reversals in financial time series of the financial instruments S&P 500, crude oil and gold, and their performances are compared. Both methods are found to give essentially the same results, indicating that trend reversals are partially predictable.

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Author information

Authors and Affiliations

  1. Dipartimento di Tecnologie dell’Informazione, Università degli Studi di Milano, via Bramante 65, I-26013, Crema, Italy

    Antonia Azzini, Célia da Costa Pereira & Andrea G. B. Tettamanzi

Authors
  1. Antonia Azzini
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  2. Célia da Costa Pereira
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  3. Andrea G. B. Tettamanzi
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Editor information

Editors and Affiliations

  1. Quinn School of Business, University College Dublin , Belfield, Dublin 4, Ireland

    Anthony Brabazon

  2. UCD CASL , 8 BelfieldOffice Park, Beaver Row, Clonskeagh, Dublin 4, Ireland

    Michael O’Neill

  3. University of Basel Wirtschaftswissenschaftliches Zentrum (WWZ) Abteilung Quantitative Methoden , Peter Merian-Weg 6, 4002, Basel, Switzerland

    Dietmar G. Maringer

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Azzini, A., da Costa Pereira, C., Tettamanzi, A.G.B. (2010). Modeling Turning Points in Financial Markets with Soft Computing Techniques. In: Brabazon, A., O’Neill, M., Maringer, D.G. (eds) Natural Computing in Computational Finance. Studies in Computational Intelligence, vol 293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13950-5_9

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  • DOI: https://doi.org/10.1007/978-3-642-13950-5_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13949-9

  • Online ISBN: 978-3-642-13950-5

  • eBook Packages: EngineeringEngineering (R0)

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