IDEAS home Printed from https://ideas.repec.org/p/rza/wpaper/394.html
   My bibliography  Save this paper

Kalman Filtering and Online Learning Algorithms for Portfolio Selection

Author

Listed:
  • Alain Kabundi
  • Raphael Nkomo

Abstract

This paper proposes a new online learning algorithms for portfolio selection based on alternative measure of price relative called the Cyclically Adjusted Price Relative (CAPR). The CAPR is derived from a simple state-space model of stock prices and we prove that the CAPR, unlike the standard raw price relative widely used in the machine literature, […]

Suggested Citation

  • Alain Kabundi & Raphael Nkomo, 2013. "Kalman Filtering and Online Learning Algorithms for Portfolio Selection," Working Papers 394, Economic Research Southern Africa.
    • Handle: RePEc:rza:wpaper:394
    as

    Download full text from publisher

    File URL: https://econrsa.org/wp-content/uploads/2022/06/working_paper_394.pdf
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Chu, Gang & Zhang, Wei & Sun, Guofeng & Zhang, Xiaotao, 2019. "A new online portfolio selection algorithm based on Kalman Filter and anti-correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).

    More about this item

    Keywords

    Education; investment; Quantitative Methods;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:rza:wpaper:394. 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: Maggi Sigg (email available below). General contact details of provider: https://edirc.repec.org/data/ersacza.html .

    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.