IDEAS home Printed from https://ideas.repec.org/p/fda/fdaddt/2006-11.html
   My bibliography  Save this paper

Using machine learning algorithms to find patterns in stock prices

Author

Listed:
  • Nuno Garoupa

Abstract

This paper analyzes the regulation of access to, and activity of, the legal and medical professions. A critical assessment is offered of the economic theory of the regulation of professions in relation to the key issues of: (a) Why regulate, (b) How to regulate, and (c) What to regulate. We suggest a set of indicators to measure the quality of regulatory restrictions, and thereby expose comparative inefficiencies, in the medical and legal professional activities. We conclude that generally speaking the USA followed by Norway, the UK [England and Wales] and Belgium perform better in terms of efficient regulation, whereas Germany, Austria and Portugal perform badly for both legal and medical professionals. Other countries (including the Netherlands, Spain, France) vary. Our results are partly, but not entirely, consistent with previous findings.

Suggested Citation

  • Nuno Garoupa, 2006. "Using machine learning algorithms to find patterns in stock prices," Working Papers 2006-11, FEDEA.
  • Handle: RePEc:fda:fdaddt:2006-11
    as

    Download full text from publisher

    File URL: http://documentos.fedea.net/pubs/dt/2006/dt-2006-11.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    2. Jegadeesh, Narasimhan, 1990. " Evidence of Predictable Behavior of Security Returns," Journal of Finance, American Finance Association, vol. 45(3), pages 881-898, July.
    3. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
    Full references (including those not matched with items on IDEAS)

    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:fda:fdaddt:2006-11. 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: (Carmen Arias). General contact details of provider: http://www.fedea.net .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.