IDEAS home Printed from https://ideas.repec.org/a/gam/jijfss/v11y2023i4p125-d1268092.html
   My bibliography  Save this article

Large-Scale Portfolio Optimization Using Biogeography-Based Optimization

Author

Listed:
  • Wendy Wijaya

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Ganesa Street No. 10, Bandung 40132, Indonesia)

  • Kuntjoro Adji Sidarto

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Ganesa Street No. 10, Bandung 40132, Indonesia)

Abstract

Portfolio optimization is a mathematical formulation whose objective is to maximize returns while minimizing risks. A great deal of improvement in portfolio optimization models has been made, including the addition of practical constraints. As the number of shares traded grows, the problem becomes dimensionally very large. In this paper, we propose the usage of modified biogeography-based optimization to solve the large-scale constrained portfolio optimization. The results indicate the effectiveness of the method used.

Suggested Citation

  • Wendy Wijaya & Kuntjoro Adji Sidarto, 2023. "Large-Scale Portfolio Optimization Using Biogeography-Based Optimization," IJFS, MDPI, vol. 11(4), pages 1-16, October.
  • Handle: RePEc:gam:jijfss:v:11:y:2023:i:4:p:125-:d:1268092
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7072/11/4/125/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7072/11/4/125/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. M. Bartholomew-Biggs & S. Kane, 2009. "A global optimization problem in portfolio selection," Computational Management Science, Springer, vol. 6(3), pages 329-345, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mansini, Renata & Ogryczak, Wlodzimierz & Speranza, M. Grazia, 2014. "Twenty years of linear programming based portfolio optimization," European Journal of Operational Research, Elsevier, vol. 234(2), pages 518-535.
    2. Buu-Chau Truong & Nguyen Van Thuan & Nguyen Huu Hau & Michael McAleer, 2019. "Applications of the Newton-Raphson Method in Decision Sciences and Education," Advances in Decision Sciences, Asia University, Taiwan, vol. 23(4), pages 52-80, December.
    3. Ralph Steuer & Markus Hirschberger & Kalyanmoy Deb, 2016. "Extracting from the relaxed for large-scale semi-continuous variable nondominated frontiers," Journal of Global Optimization, Springer, vol. 64(1), pages 33-48, January.

    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:gam:jijfss:v:11:y:2023:i:4:p:125-:d:1268092. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc 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 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.