Advanced Search
MyIDEAS: Login

Complexity and the Character of Stock Returns: Empirical Evidence and a Model of Asset Prices Based on Complex Investor Learning

Contents:

Author Info

  • Scott C. Linn

    ()
    (Division of Finance, Michael F. Price College of Business, University of Oklahoma, 205 A Adams Hall, Norman, Oklahoma 73019)

  • Nicholas S. P. Tay

    ()
    (School of Business and Management, University of San Francisco, 2130 Fulton Street, Malloy Hall, San Francisco, California 94117-1045)

Abstract

Empirical evidence on the distributional characteristics of common stock returns indicates: (1) A power-law tail index close to three describes the behavior of the positive tail of the survivor function of returns (pr(r > x) ~ x -\alpha ), a reflection of fat tails; (2) general linear and nonlinear dependencies exist in the time series of returns; (3) the time-series return process is characterized by short-run dependence (short memory) in both returns as well as their volatility, the latter usually characterized in the form of autoregressive conditional heteroskedasticity; and (4) the time-series return process probably does not exhibit long memory, but the squared returns process does exhibit long memory. We propose a model of complex, self-referential learning and reasoning amongst economic agents that jointly produces security returns consistent with these general observed facts and which are supported here by empirical results presented for a benchmark sample of 50 stocks traded on the New York Stock Exchange. The market we postulate is populated by traders who reason inductively while compressing information into a few fuzzy notions that they can in turn process and analyze with fuzzy logic. We analyze the implications of such behavior for the returns on risky securities within the context of an artificial stock market model. Dynamic simulation experiments of the market are conducted, from which market-clearing prices emerge, allowing us to then compute realized returns. We test the effects of varying values of the parameters of the model on the character of the simulated returns. The results indicate that the model proposed in this paper can jointly account for the presence of a power-law characterization of the positive tail of the survivor function of returns with exponent on the order of three, for autoregressive conditional heteroskedasticity, for long memory in volatility, and for general nonlinear dependencies in returns.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://dx.doi.org/10.1287/mnsc.1060.0622
Download Restriction: no

Bibliographic Info

Article provided by INFORMS in its journal Management Science.

Volume (Year): 53 (2007)
Issue (Month): 7 (July)
Pages: 1165-1180

as in new window
Handle: RePEc:inm:ormnsc:v:53:y:2007:i:7:p:1165-1180

Contact details of provider:
Postal: 7240 Parkway Drive, Suite 300, Hanover, MD 21076 USA
Phone: +1-443-757-3500
Fax: 443-757-3515
Email:
Web page: http://www.informs.org/
More information through EDIRC

Related research

Keywords: learning; stock return distribution; power-law; nonlinear dependence; artificial stock market;

References

No references listed on IDEAS
You can help add them by filling out this form.

Citations

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:53:y:2007:i:7:p:1165-1180. 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: (Mirko Janc).

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 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.