IDEAS home Printed from https://ideas.repec.org/p/sce/scecf9/712.html
   My bibliography  Save this paper

The Evolution of Trading Rules in an Artificial Stock Market

Author

Listed:
  • Mark Howard

    (University of Massachusetts)

Abstract

This paper applies evolutionary modeling to expectation formation of an asset's price. As a first step, I consider a population of n investors each of whom takes on one of two possible cultural variants. Every individual is a potential role model for all other individuals and can pass on their variant with a certain probability determined by the relative return to being that type. Different types of traders employ different 'models' which forecast future price and dividend movements. With the two basic types being traders who follow the fundamentals suggested by the CAPM model and those who follow technical trading rules (such as, sell if the price is above it's 50 day moving average). I show that given these two types of simple traders, prices can fluctuate between periods of low volume and volatility and periods of high volume and volatility. Results indicate that, given a random walk fundamental valuation, as the random fluctuations increase in magnitude, technical trading can become more profitable than fundamental trading and for a period dominate the market.

Suggested Citation

  • Mark Howard, 1999. "The Evolution of Trading Rules in an Artificial Stock Market," Computing in Economics and Finance 1999 712, Society for Computational Economics.
  • Handle: RePEc:sce:scecf9:712
    as

    Download full text from publisher

    File URL: http://www-unix.oit.umass.edu/~mmh/
    File Function: main text
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Laib, Fodil & Laib, M.S., 2007. "Some mathematical properties of the futures market platform," MPRA Paper 6126, University Library of Munich, Germany.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:sce:scecf9:712. 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: Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/sceeeea.html .

    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.