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. Mavrotas, George & Florios, Kostas, 2013. "An improved version of the augmented epsilon-constraint method (AUGMECON2) for finding the exact Pareto set in Multi-Objective Integer Programming problems," MPRA Paper 105034, University Library of Munich, Germany.
    2. Andre F. Perold, 1984. "Large-Scale Portfolio Optimization," Management Science, INFORMS, vol. 30(10), pages 1143-1160, October.
    3. 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. Panos Xidonas & Haris Doukas & George Mavrotas & Olena Pechak, 2016. "Environmental corporate responsibility for investments evaluation: an alternative multi-objective programming model," Annals of Operations Research, Springer, vol. 247(2), pages 395-413, December.
    2. Zhang, Xili & Zhang, Weiguo & Xiao, Weilin, 2013. "Multi-period portfolio optimization under possibility measures," Economic Modelling, Elsevier, vol. 35(C), pages 401-408.
    3. Bo Zhang & Jin Peng & Shengguo Li, 2015. "Uncertain programming models for portfolio selection with uncertain returns," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(14), pages 2510-2519, October.
    4. Satya Tamby & Daniel Vanderpooten, 2021. "Enumeration of the Nondominated Set of Multiobjective Discrete Optimization Problems," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 72-85, January.
    5. H. Khorshidian & M. Akbarpour Shirazi & S. M. T. Fatemi Ghomi, 2019. "An intelligent truck scheduling and transportation planning optimization model for product portfolio in a cross-dock," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 163-184, January.
    6. Chen, Wei & Zhang, Wei-Guo, 2010. "The admissible portfolio selection problem with transaction costs and an improved PSO algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(10), pages 2070-2076.
    7. Ken Kobayashi & Yuichi Takano & Kazuhide Nakata, 2021. "Bilevel cutting-plane algorithm for cardinality-constrained mean-CVaR portfolio optimization," Journal of Global Optimization, Springer, vol. 81(2), pages 493-528, October.
    8. Walter Murray & Howard Shek, 2012. "A local relaxation method for the cardinality constrained portfolio optimization problem," Computational Optimization and Applications, Springer, vol. 53(3), pages 681-709, December.
    9. Kamesh Korangi & Christophe Mues & Cristi'an Bravo, 2024. "Large-scale Time-Varying Portfolio Optimisation using Graph Attention Networks," Papers 2407.15532, arXiv.org, revised Feb 2025.
    10. Kerstens, Kristiaan & Mounir, Amine & Van de Woestyne, Ignace, 2011. "Geometric representation of the mean-variance-skewness portfolio frontier based upon the shortage function," European Journal of Operational Research, Elsevier, vol. 210(1), pages 81-94, April.
    11. Hubáček, Ondřej & Šír, Gustav, 2023. "Beating the market with a bad predictive model," International Journal of Forecasting, Elsevier, vol. 39(2), pages 691-719.
    12. Schmidt, Adam & Albert, Laura A. & Zheng, Kaiyue, 2021. "Risk management for cyber-infrastructure protection: A bi-objective integer programming approach," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    13. Dimitris Bertsimas & Ryan Cory-Wright, 2022. "A Scalable Algorithm for Sparse Portfolio Selection," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1489-1511, May.
    14. Erfan Hassannayebi & Seyed Hessameddin Zegordi & Mohammad Reza Amin-Naseri & Masoud Yaghini, 2017. "Train timetabling at rapid rail transit lines: a robust multi-objective stochastic programming approach," Operational Research, Springer, vol. 17(2), pages 435-477, July.
    15. Petrelli, Marina & Fioriti, Davide & Berizzi, Alberto & Bovo, Cristian & Poli, Davide, 2021. "A novel multi-objective method with online Pareto pruning for multi-year optimization of rural microgrids," Applied Energy, Elsevier, vol. 299(C).
    16. Zhang, Wei-Guo & Zhang, Xi-Li & Xiao, Wei-Lin, 2009. "Portfolio selection under possibilistic mean-variance utility and a SMO algorithm," European Journal of Operational Research, Elsevier, vol. 197(2), pages 693-700, September.
    17. Zhi-Long Dong & Fengmin Xu & Yu-Hong Dai, 2020. "Fast algorithms for sparse portfolio selection considering industries and investment styles," Journal of Global Optimization, Springer, vol. 78(4), pages 763-789, December.
    18. Mavrotas, George & Figueira, José Rui & Siskos, Eleftherios, 2015. "Robustness analysis methodology for multi-objective combinatorial optimization problems and application to project selection," Omega, Elsevier, vol. 52(C), pages 142-155.
    19. Finke, Jonas & Bertsch, Valentin, 2022. "Implementing a highly adaptable method for the multi-objective optimisation of energy systems," MPRA Paper 115504, University Library of Munich, Germany.
    20. Sahar Moazzeni & Sobhan Mostafayi Darmian & Lars Magnus Hvattum, 2023. "Multiple criteria decision making and robust optimization to design a development plan for small and medium-sized enterprises in the east of Iran," Operational Research, Springer, vol. 23(1), pages 1-32, March.

    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.