IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v336y2004i3p538-548.html
   My bibliography  Save this article

Robust methods for stock market data analysis

Author

Listed:
  • Antoniou, I.
  • Akritas, P.
  • Burak, D.A.
  • Ivanov, V.V.
  • Kryanev, A.V.
  • Lukin, G.V.

Abstract

We consider the problem of extraction of trend and chaotic components from irregular stock market time series. The proposed methods also permit to extract a part of chaotic component, the so-called anomalous term, caused by the transient short-time surges with high amplitudes. This provides more accurate determination of the trend component. The methods are based on the M-evaluation with decision functions of Huber and Tukey type. The iterative numerical schemes for determination of trend and chaotic components are briefly presented, resulting in an acceptable solution within a finite number of iterations. The optimal level for extraction of the chaotic component is determined by a new numerical scheme based on the fractal dimension of the chaotic component of the analyzed series. Forecasting from the realized part of the analyzed series and a priori expert information is also discussed.

Suggested Citation

  • Antoniou, I. & Akritas, P. & Burak, D.A. & Ivanov, V.V. & Kryanev, A.V. & Lukin, G.V., 2004. "Robust methods for stock market data analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 538-548.
    • Handle: RePEc:eee:phsmap:v:336:y:2004:i:3:p:538-548
    DOI: 10.1016/j.physa.2003.12.052
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437103012330
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2003.12.052?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

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

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. J. E. Wesen & V. VV. Vermehren & H. M. de Oliveira, 2015. "Adaptive Filter Design for Stock Market Prediction Using a Correlation-based Criterion," Papers 1501.07504, arXiv.org.

    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:eee:phsmap:v:336:y:2004:i:3:p:538-548. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.