IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-03783347.html
   My bibliography  Save this paper

Adaptive strategy in Kelly's horse races model

Author

Listed:
  • Armand Despons

    (ESPCI Paris - Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris - PSL - Université Paris sciences et lettres, Gulliver (UMR 7083) - ESPCI Paris - Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris - PSL - Université Paris sciences et lettres - INC-CNRS - Institut de Chimie - CNRS Chimie - CNRS - Centre National de la Recherche Scientifique)

  • David Lacoste

    (ESPCI Paris - Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris - PSL - Université Paris sciences et lettres, Gulliver (UMR 7083) - ESPCI Paris - Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris - PSL - Université Paris sciences et lettres - INC-CNRS - Institut de Chimie - CNRS Chimie - CNRS - Centre National de la Recherche Scientifique)

  • Luca Peliti

    (Santa Marinella Research Institute)

Abstract

We formulate an adaptive version of Kelly's horse model in which the gambler learns from past race results using Bayesian inference. A known asymptotic scaling for the difference between the growth rate of the gambler and the optimal growth rate, known as the gambler'sregret, is recovered. We show how this adaptive strategy is related to the universal portfolio strategy, and we build improved adaptive strategies in which the gambler exploits information contained in the bookmaker odds distribution to reduce his/her initial loss of the capital during the learning phase.

Suggested Citation

  • Armand Despons & David Lacoste & Luca Peliti, 2022. "Adaptive strategy in Kelly's horse races model," Post-Print hal-03783347, HAL.
    • Handle: RePEc:hal:journl:hal-03783347
    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.

    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:hal:journl:hal-03783347. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.