IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v9y2018i1d10.1007_s13198-017-0574-z.html
   My bibliography  Save this article

Genetic algorithm designed for solving portfolio optimization problems subjected to cardinality constraint

Author

Listed:
  • Hemant Jalota

    (Indian Institute of Technology Mandi)

  • Manoj Thakur

    (Indian Institute of Technology Mandi)

Abstract

In the present study, a new algorithm named BEXPM-RM is proposed which require no constraint handling techniques to solve portfolio optimization problems subjected to budget, cardinality, and lower/upper bound constraints. The algorithm presented combines the BEX-PM (Thakur et al. in Appl Math Comput 235:292–317, 2014) genetic algorithm (GA) together with repair mechanism (RM) proposed by Chang et al. (Comput Oper Res 27(13):1271–1302, 2000). BEXPM GA tries to efficiently explore the search space whereas repair method suggested by Chang et al. (2000) ensures that a solution string is always feasible subject to the budget, cardinality, and lower/upper bound constraints. To analyze the performance of BEXPM-RM, six portfolio optimization problems are considered from the literature (Chang et al. 2000; Barak et al. in Eur J Oper Res 228(1):141–147, 2013). Among these one problem uses fuzzy set theory and others used probability theory to quantify attributes of a portfolio. In addition to these problems, a new portfolio model is formulated in fuzzy environment to analyze the effect of providing different sets of lower or/and upper bound to an asset.

Suggested Citation

  • Hemant Jalota & Manoj Thakur, 2018. "Genetic algorithm designed for solving portfolio optimization problems subjected to cardinality constraint," 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. 9(1), pages 294-305, February.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:1:d:10.1007_s13198-017-0574-z
    DOI: 10.1007/s13198-017-0574-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-017-0574-z
    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/s13198-017-0574-z?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    2. Barak, Sasan & Abessi, Masoud & Modarres, Mohammad, 2013. "Fuzzy turnover rate chance constraints portfolio model," European Journal of Operational Research, Elsevier, vol. 228(1), pages 141-147.
    3. Markowitz, Harry, 2014. "Mean–variance approximations to expected utility," European Journal of Operational Research, Elsevier, vol. 234(2), pages 346-355.
    4. Leon, T. & Liern, V. & Vercher, E., 2002. "Viability of infeasible portfolio selection problems: A fuzzy approach," European Journal of Operational Research, Elsevier, vol. 139(1), pages 178-189, May.
    5. N. J. Jobst & M. D. Horniman & C. A. Lucas & G. Mitra, 2001. "Computational aspects of alternative portfolio selection models in the presence of discrete asset choice constraints," Quantitative Finance, Taylor & Francis Journals, vol. 1(5), pages 489-501.
    6. Yong Fang & Kin Keung Lai & Shouyang Wang, 2008. "Fuzzy Portfolio Optimization," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-540-77926-1, December.
    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. Reinhard Madlener & Barbara Glensk & Veronika Weber, 2011. "Fuzzy Portfolio Optimization of Onshore Wind Power Plants," FCN Working Papers 10/2011, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    2. Bi, Junna & Jin, Hanqing & Meng, Qingbin, 2018. "Behavioral mean-variance portfolio selection," European Journal of Operational Research, Elsevier, vol. 271(2), pages 644-663.
    3. Briec, Walter & Kerstens, Kristiaan, 2009. "Multi-horizon Markowitz portfolio performance appraisals: A general approach," Omega, Elsevier, vol. 37(1), pages 50-62, February.
    4. Liu, Yong-Jun & Zhang, Wei-Guo, 2015. "A multi-period fuzzy portfolio optimization model with minimum transaction lots," European Journal of Operational Research, Elsevier, vol. 242(3), pages 933-941.
    5. Zhang, Wei-Guo & Xiao, Wei-Lin & Xu, Wei-Jun, 2010. "A possibilistic portfolio adjusting model with new added assets," Economic Modelling, Elsevier, vol. 27(1), pages 208-213, January.
    6. Milan Vaclavik & Josef Jablonsky, 2012. "Revisions of modern portfolio theory optimization model," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(3), pages 473-483, September.
    7. Castro, F. & Gago, J. & Hartillo, I. & Puerto, J. & Ucha, J.M., 2011. "An algebraic approach to integer portfolio problems," European Journal of Operational Research, Elsevier, vol. 210(3), pages 647-659, May.
    8. Gatzert, Nadine & Martin, Alexander & Schmidt, Martin & Seith, Benjamin & Vogl, Nikolai, 2021. "Portfolio optimization with irreversible long-term investments in renewable energy under policy risk: A mixed-integer multistage stochastic model and a moving-horizon approach," European Journal of Operational Research, Elsevier, vol. 290(2), pages 734-748.
    9. C Papahristodoulou & E Dotzauer, 2004. "Optimal portfolios using linear programming models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(11), pages 1169-1177, November.
    10. 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.
    11. Rui Pedro Brito & Hélder Sebastião & Pedro Godinho, 2016. "Efficient skewness/semivariance portfolios," Journal of Asset Management, Palgrave Macmillan, vol. 17(5), pages 331-346, September.
    12. Francesco Cesarone & Andrea Scozzari & Fabio Tardella, 2015. "Linear vs. quadratic portfolio selection models with hard real-world constraints," Computational Management Science, Springer, vol. 12(3), pages 345-370, July.
    13. Kassimatis, Konstantinos, 2021. "Mean-variance versus utility maximization revisited: The case of constant relative risk aversion," International Review of Financial Analysis, Elsevier, vol. 78(C).
    14. Aouni, Belaid & Colapinto, Cinzia & La Torre, Davide, 2014. "Financial portfolio management through the goal programming model: Current state-of-the-art," European Journal of Operational Research, Elsevier, vol. 234(2), pages 536-545.
    15. Piasecki, Krzysztof, 2011. "Rozmyte zbiory probabilistyczne jako narzędzie finansów behawioralnych [Fuzzy Probabilistic Sets as a Tool for Behavioural Finance]," MPRA Paper 46218, University Library of Munich, Germany.
    16. Ahmar, Ansari Saleh & Arifin, Andi Nurani Mangkawani, 2017. "Optimalisasi Risiko Saham Menggunakan Optimalisasi Portofolio Markowitz (Studi Kasus Saham Di Indonesia)," INA-Rxiv 5v27k, Center for Open Science.
    17. Walter Briec & Kristiaan Kerstens & Octave Jokung, 2007. "Mean-Variance-Skewness Portfolio Performance Gauging: A General Shortage Function and Dual Approach," Management Science, INFORMS, vol. 53(1), pages 135-149, January.
    18. Simaan, Majeed & Simaan, Yusif & Tang, Yi, 2018. "Estimation error in mean returns and the mean-variance efficient frontier," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 109-124.
    19. Angelelli, Enrico & Mansini, Renata & Speranza, M. Grazia, 2008. "A comparison of MAD and CVaR models with real features," Journal of Banking & Finance, Elsevier, vol. 32(7), pages 1188-1197, July.
    20. Hirschberger, Markus & Qi, Yue & Steuer, Ralph E., 2010. "Large-scale MV efficient frontier computation via a procedure of parametric quadratic programming," European Journal of Operational Research, Elsevier, vol. 204(3), pages 581-588, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:spr:ijsaem:v:9:y:2018:i:1:d:10.1007_s13198-017-0574-z. 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: 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.