IDEAS home Printed from https://ideas.repec.org/a/taf/eurjfi/v21y2015i4p337-351.html
   My bibliography  Save this article

Adaptive universal portfolios

Author

Listed:
  • Patrick O'Sullivan
  • David Edelman

Abstract

In this article, we consider Cover's universal portfolio and the problem of multi-period investment in a nonparametric setting. We show that Cover's universal portfolio is equivalent to a Bayes estimator of the optimal growth portfolio. However, as noted by Cover, it can take a long time for the universal portfolio to produce significant growth. Therefore, we propose the adaptive universal portfolio, which retains much of the qualitative nature of Cover's universal portfolio while enhancing early performance. An empirical study is carried out over a range of exchange traded funds over a 5 year period, which exhibits the enhanced early performance generated by the adaptive universal portfolio.

Suggested Citation

  • Patrick O'Sullivan & David Edelman, 2015. "Adaptive universal portfolios," The European Journal of Finance, Taylor & Francis Journals, vol. 21(4), pages 337-351, March.
    • Handle: RePEc:taf:eurjfi:v:21:y:2015:i:4:p:337-351
    DOI: 10.1080/1351847X.2013.788534
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/1351847X.2013.788534
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/1351847X.2013.788534?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Jin’an He & Shicheng Yin & Fangping Peng, 2024. "Weak aggregating specialist algorithm for online portfolio selection," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2405-2434, June.
    2. Xingyu Yang & Jin’an He & Hong Lin & Yong Zhang, 2020. "Boosting Exponential Gradient Strategy for Online Portfolio Selection: An Aggregating Experts’ Advice Method," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 231-251, January.
    3. Esther Mohr & Robert Dochow, 2017. "Risk management strategies for finding universal portfolios," Annals of Operations Research, Springer, vol. 256(1), pages 129-147, September.
    4. Yong Zhang & Hong Lin & Lina Zheng & Xingyu Yang, 2022. "Adaptive online portfolio strategy based on exponential gradient updates," Journal of Combinatorial Optimization, Springer, vol. 43(3), pages 672-696, April.

    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:taf:eurjfi:v:21:y:2015:i:4:p:337-351. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/REJF20 .

    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.