IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v47y2016i2d10.1007_s10614-015-9482-y.html
   My bibliography  Save this article

Financial Time Series Modeling and Prediction Using Postfix-GP

Author

Listed:
  • Vipul K. Dabhi

    () (Dharmsinh Desai University)

  • Sanjay Chaudhary

    () (Ahmedabad University)

Abstract

Abstract Financial time series prediction is considered as a challenging task. The task becomes difficult due to inherent nonlinear and non-stationary characteristics of financial time series. This article proposes a combination of wavelet and Postfix-GP, a postfix notation based genetic programming system, for financial time series prediction. The discrete wavelet transform approach is used to smoothen the time series by separating the fluctuations from the trend of the series. Postx-GP is then employed to evolve models for the smoothen series. The out-of-sample prediction capability of evolved solutions is tested on two stocks price and two stock indexes series. The results are compared with those obtained using ECJ, a Java based evolutionary framework. The nonparametric statistical tests are applied to evaluate the significance of the obtained results.

Suggested Citation

  • Vipul K. Dabhi & Sanjay Chaudhary, 2016. "Financial Time Series Modeling and Prediction Using Postfix-GP," Computational Economics, Springer;Society for Computational Economics, vol. 47(2), pages 219-253, February.
  • Handle: RePEc:kap:compec:v:47:y:2016:i:2:d:10.1007_s10614-015-9482-y
    DOI: 10.1007/s10614-015-9482-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-015-9482-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Gioqinang & Hu, Michael Y., 1998. "Neural network forecasting of the British Pound/US Dollar exchange rate," Omega, Elsevier, vol. 26(4), pages 495-506, August.
    2. M. B. Porecha & P. K. Panigrahi & J. C. Parikh & C. M. Kishtawal & Sujit Basu, 2005. "Forecasting non-stationary financial time series through genetic algorithm," Papers nlin/0507037, arXiv.org.
    3. M. A. Kaboudan, 2000. "Genetic Programming Prediction of Stock Prices," Computational Economics, Springer;Society for Computational Economics, vol. 16(3), pages 207-236, December.
    Full references (including those not matched with items on IDEAS)

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:47:y:2016:i:2:d:10.1007_s10614-015-9482-y. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla) or (Rebekah McClure). General contact details of provider: http://www.springer.com .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.