IDEAS home Printed from https://ideas.repec.org/a/taf/quantf/v7y2007i2p161-173.html
   My bibliography  Save this article

Constant rebalanced portfolios and side-information

Author

Listed:
  • E. Fagiuoli
  • F. Stella
  • A. Ventura

Abstract

In recent years much work has been carried out to design and analyse online investment strategies based on constant rebalanced portfolios. A constant rebalanced portfolio is a sequential investment strategy that maintains fixed through time, trading period by trading period, the wealth distribution among a set of assets. In this framework, Cover proposed the universal portfolio, which is competitive with the best constant rebalanced portfolio determined in hindsight, i.e. the constant rebalanced portfolio obtained by assuming perfect knowledge of future stock prices. However, the constant rebalanced portfolio is designed to deal with the portfolio selection problem in the case where no additional information concerning the stock market, is available. To overcome this limitation, Cover and Ordentlich proposed the state constant rebalanced portfolio, which is capable of appropriately exploiting the available side-information concerning the stock market. In this paper we study and analyse the topic introduced by Cover and Ordentlich and focus our attention on the interplay between constant rebalanced portfolios and side-information. We introduce a mathematical framework to deal with the constant rebalanced portfolio in the case where side-information, concerning the stock market, is available. The mathematical framework defines and analyses the mixture best constant rebalanced portfolio, which we propose as the investment benchmark to be considered in the case where side-information, concerning the stock market, is available. The mixture best constant rebalanced portfolio outperforms the best constant rebalanced portfolio by an exponential factor in terms of the achieved wealth and therefore offers an interesting opportunity for side-information specialized online investment algorithms. We describe a new online investment algorithm that exploits the definition of the mixture best constant rebalanced portfolio and the available side-information. The performance of the proposed online investment algorithm is investigated through a set of numerical experiments concerning four major stock market data sets, namely DJIA, S&P500, TSE and NYSE. The results emphasize the relevance of the proposed online investment strategy and underline the central role of the quality of the side-information in outperforming the best constant rebalanced portfolio.

Suggested Citation

  • E. Fagiuoli & F. Stella & A. Ventura, 2007. "Constant rebalanced portfolios and side-information," Quantitative Finance, Taylor & Francis Journals, vol. 7(2), pages 161-173.
    • Handle: RePEc:taf:quantf:v:7:y:2007:i:2:p:161-173
    DOI: 10.1080/14697680601157942
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/14697680601157942
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/14697680601157942?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. 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.
    2. Fabio Stella & Alfonso Ventura, 2011. "Defensive online portfolio selection," International Journal of Financial Markets and Derivatives, Inderscience Enterprises Ltd, vol. 2(1/2), pages 88-105.

    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:quantf:v:7:y:2007:i:2:p:161-173. 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/RQUF20 .

    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.