IDEAS home Printed from https://ideas.repec.org/a/taf/quantf/v1y2001i4p414-426.html
   My bibliography  Save this article

Conditional entropy and randomness in financial time series

Author

Listed:
  • M. D. London
  • A. K. Evans
  • M. J. Turner

Abstract

This paper investigates the fundamental question of whether or not financial time series can be predicted. It does so using the conditional entropy measure commonly used in information theory and statistical physics. This approach is appropriate because it will reveal patterns of arbitrary complexity. That is, this method will not just reveal linear correlation, or any specific nonlinear correlation, it will reveal any patterns in the data. Interesting discoveries include the fact that there is a degree of consistency amongst data sets and the ability to clearly distinguish between stock market indices and exchange rate time series. The fact that the magnitude of price movements is more correlated than the direction is verified and quantified in the context of daily data. The main result is that above certain time scales financial time series are random, but below this threshold there are situations where knowing a portion of the past can bring a statistically significant amount of certainty about the future.

Suggested Citation

  • M. D. London & A. K. Evans & M. J. Turner, 2001. "Conditional entropy and randomness in financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 1(4), pages 414-426.
    • Handle: RePEc:taf:quantf:v:1:y:2001:i:4:p:414-426
    DOI: 10.1088/1469-7688/1/4/302
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1088/1469-7688/1/4/302
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1088/1469-7688/1/4/302?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. Shternshis, Andrey & Mazzarisi, Piero & Marmi, Stefano, 2022. "Measuring market efficiency: The Shannon entropy of high-frequency financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    2. Zavala-Díaz, J.C. & Pérez-Ortega, J. & Hernández-Aguilar, J.A. & Almanza-Ortega, N.N. & Martínez-Rebollar, A., 2020. "Short-term prediction of the closing price of financial series using a ϵ-machine model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).

    More about this item

    Statistics

    Access and download statistics

    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:taf:quantf:v:1:y:2001:i:4:p:414-426. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RQUF20 .

    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.