IDEAS home Printed from https://ideas.repec.org/a/wsi/ijtafx/v13y2010i03ns0219024910005802.html
   My bibliography  Save this article

A Maximal Predictability Portfolio Using Dynamic Factor Selection Strategy

Author

Listed:
  • HIROSHI KONNO

    (Department of Industrial and Systems Engineering, Chuo University, Japan)

  • YOSHIHIRO TAKAYA

    (Department of Industrial and Systems Engineering, Chuo University, Japan)

  • REI YAMAMOTO

    (Department of Industrial and Systems Engineering, Chuo University, Japan;
    Mitsubishi UFJ Trust Investment Technology Institute Co., Ltd., Japan)

Abstract

In this paper, we will propose a practical method for improving the performance of a maximal predictability portfolio (MPP) model proposed by Lo and MacKinlay and later extended by the authors. We will employ an alternative version of MPP using absolute deviation instead of variance as a measure of fitting and apply a dynamic strategy for choosing the set of factors which fits best to the market data. It will be shown that this approach leads to a significantly better performance than the standard MPP and the index.

Suggested Citation

  • Hiroshi Konno & Yoshihiro Takaya & Rei Yamamoto, 2010. "A Maximal Predictability Portfolio Using Dynamic Factor Selection Strategy," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 13(03), pages 355-366.
    • Handle: RePEc:wsi:ijtafx:v:13:y:2010:i:03:n:s0219024910005802
    DOI: 10.1142/S0219024910005802
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219024910005802
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0219024910005802?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. Michael Pinelis & David Ruppert, 2023. "Maximizing Portfolio Predictability with Machine Learning," Papers 2311.01985, arXiv.org.
    2. Philippe Goulet Coulombe & Maximilian Goebel, 2023. "Maximally Machine-Learnable Portfolios," Papers 2306.05568, arXiv.org, revised Apr 2024.
    3. Philippe Goulet Coulombe & Maximilian Gobel, 2023. "Maximally Machine-Learnable Portfolios," Working Papers 23-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Apr 2023.

    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:wsi:ijtafx:v:13:y:2010:i:03:n:s0219024910005802. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijtaf/ijtaf.shtml .

    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.