Advanced Search
MyIDEAS: Login to save this article or follow this journal

Boosting-Based Framework For Portfolio Strategy Discovery And Optimization


Author Info


    (Head of Quantitative Research, Alexandra Investment Management, 767 3-d Avenue New York, New York 10017, USA)

Registered author(s):


    Increasing availability of the multi-scale market data exposes limitations of the existing quantitative models such as low accuracy of the simplified analytical and statistical frameworks as well as insufficient interpretability and stability of the best machine learning algorithms. Boosting was recently proposed as a simple and robust framework for intelligent combination of the clarity and stability of the analytical and parsimonious statistical models with the accuracy of the adaptive data-driven models. Encouraging results of the boosting application to symbolic volatility forecasting have also been reported. However, accurate forecasting does not always warrant optimal decision making that leads to acceptable performance of the portfolio strategy. In this work, a boosting-based framework for a direct trading strategy and portfolio optimization is introduced. Due to inherent adaptive control of the parameter space dimensionality, this technique can work with very large pools of base strategies and financial instruments that are usually prohibitive for other portfolio optimization frameworks. Unlike existing approaches, this framework can be effectively used for the coupled optimization of the portfolio capital/asset allocation and dynamic trading strategies. Generated portfolios of trading strategies not only exhibit stable and robust performance but also remain interpretable. Encouraging preliminary results based on real market data are presented and discussed.

    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:
    Download Restriction: Access to full text is restricted to subscribers.

    File URL:
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Bibliographic Info

    Article provided by World Scientific Publishing Co. Pte. Ltd. in its journal New Mathematics and Natural Computation.

    Volume (Year): 02 (2006)
    Issue (Month): 03 ()
    Pages: 315-330

    as in new window
    Handle: RePEc:wsi:nmncxx:v:02:y:2006:i:03:p:315-330

    Contact details of provider:
    Web page:

    Order Information:

    Related research

    Keywords: Boosting; ensemble learning; portfolio optimization; trading strategies;


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


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

    Cited by:
    1. Kim, Min Jae & Kim, Sehyun & Jo, Yong Hwan & Kim, Soo Yong, 2011. "Dependence structure of the commodity and stock markets, and relevant multi-spread strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3842-3854.


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


    Access and download statistics


    When requesting a correction, please mention this item's handle: RePEc:wsi:nmncxx:v:02:y:2006:i:03:p:315-330. 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: (Tai Tone Lim).

    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.