IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-4-431-28915-9_37.html
   My bibliography  Save this book chapter

Explanation of binarized tick data using investor sentiment and genetic learning

In: Practical Fruits of Econophysics

Author

Listed:
  • Takashi Yamada

    (University of Tokyo)

  • Kazuhiro Ueda

    (University of Tokyo)

Abstract

Summary This paper attempts to clarify some time series properties of binarized tick data by investor sentiment and genetic algorithm. For this purpose, first we explore the conditions for genetic algorithm to describe investor sentiment. Then we calculate auto-correlations and conditional probabilities using binarized sample paths generated by estimated models of investor sentiment. The most fitted parameter set of genetic algorithm have the following implications: First, a herd behavior is likely to emerge. Second, traders try to perceive brand-new information even if it is not completely correct.

Suggested Citation

  • Takashi Yamada & Kazuhiro Ueda, 2006. "Explanation of binarized tick data using investor sentiment and genetic learning," Springer Books, in: Hideki Takayasu (ed.), Practical Fruits of Econophysics, pages 205-209, Springer.
    • Handle: RePEc:spr:sprchp:978-4-431-28915-9_37
    DOI: 10.1007/4-431-28915-1_37
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:sprchp:978-4-431-28915-9_37. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.