IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v16y2025i2d10.1007_s13198-024-02610-6.html
   My bibliography  Save this article

TODIM with XGBOOST and MVO metaheuristic approach for portfolio optimization

Author

Listed:
  • Veena Jain

    (University of Delhi)

  • Rishi Rajan Sahay

    (University of Delhi)

  • Nupur

    (University of Delhi)

Abstract

This paper presents an innovative and comprehensive approach to portfolio optimization that integrates TODIM, a multi criteria decision making method, with the proposed forecasting model for asset selection and return prediction. The optimization process utilizes the multi-verse optimizer, a powerful metaheuristic algorithm. TODIM is initially employed to select the top 10 companies from the Nifty 100 index based on various financial indicators. Daily return predictions for the selected portfolio are enhanced through an ensemble forecasting approach, BN-XGBoost, which combines BiLSTM and N-BEATS models, with XGBoost serving as a meta-learner to improve accuracy. The portfolio optimization problem, framed as a higher-order asymmetric risk model, incorporates semi-variance, higher order moments of return, and entropy. The proposed optimization framework is compared against a variance-based model, demonstrating superior portfolio performance. This research provides valuable insights for investors seeking advanced strategies to navigate complex financial markets and achieve optimal portfolio outcomes.

Suggested Citation

  • Veena Jain & Rishi Rajan Sahay & Nupur, 2025. "TODIM with XGBOOST and MVO metaheuristic approach for portfolio optimization," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(2), pages 595-612, February.
    • Handle: RePEc:spr:ijsaem:v:16:y:2025:i:2:d:10.1007_s13198-024-02610-6
    DOI: 10.1007/s13198-024-02610-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-024-02610-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-024-02610-6?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.

    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:spr:ijsaem:v:16:y:2025:i:2:d:10.1007_s13198-024-02610-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.