IDEAS home Printed from https://ideas.repec.org/a/vrs/ekonom/v96y2017i2p66-78n5.html
   My bibliography  Save this article

Investment Portfolio Optimization by Applying a Genetic Algorithm-Based Approach

Author

Listed:
  • Dubinskas Petras
  • Urbšienė Laimutė

    (Businesss School, Vilnius University, Saulėtekio Ave. 22, LT-10225,Vilnius, Lithuania)

Abstract

The investment portfolio optimization issues have been widely discussed by scholars for more than 60 years. One of the key issues that emerge for researchers is to clarify which optimization approach helps to build the most efficient portfolio (in this case, the efficiency refers to the minimization of the investment risk and the maximization of the return). The objective of the study is to assess the fitness of a genetic algorithm approach in optimizing the investment portfolio. The paper analyzes the theoretical aspects of applying a genetic algorithm-based approach, then it adapts them to practical research. To build an investment portfolio, four Lithuanian enterprises listed on the OMX Baltics Stock Exchange Official List were selected in accordance with the chosen criteria. Then, by applying a genetic algorithm-based approach and using MatLab software, the optimum investment portfolio was constructed from the selected enterprises. The research results showed that the genetic algorithm-based portfolio in 2013 reached a better risk-return ratio than the portfolio optimized by the deterministic and stochastic programing methods. Also, better outcomes were achieved in comparison with the OMX Baltic Market Index. As a result, the hypothesis of the superiority of a portfolio, optimized on the basis of a genetic algorithm, is not rejected. However, it should be noted that in seeking for more reliable conclusions, further research should include more trial periods as the current study examined a period of one year. In this context, the operation of the approach in the context of a market downturn could be of particular interest.

Suggested Citation

  • Dubinskas Petras & Urbšienė Laimutė, 2017. "Investment Portfolio Optimization by Applying a Genetic Algorithm-Based Approach," Ekonomika (Economics), Sciendo, vol. 96(2), pages 66-78, February.
  • Handle: RePEc:vrs:ekonom:v:96:y:2017:i:2:p:66-78:n:5
    DOI: 10.15388/ekon.2017.2.10998
    as

    Download full text from publisher

    File URL: https://doi.org/10.15388/ekon.2017.2.10998
    Download Restriction: no

    File URL: https://libkey.io/10.15388/ekon.2017.2.10998?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
    ---><---

    References listed on IDEAS

    as
    1. Slimane Sefiane & Mohamed Benbouziane, 2012. "Portfolio Selection Using Genetic Algorithm," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 2(4), pages 1-9.
    2. Siddiqui, Afzal S. & Maribu, Karl, 2009. "Investment and upgrade in distributed generation under uncertainty," Energy Economics, Elsevier, vol. 31(1), pages 25-37, January.
    3. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    4. Svensson, Elin & Berntsson, Thore & Strömberg, Ann-Brith, 2009. "Benefits of using an optimization methodology for identifying robust process integration investments under uncertainty--A pulp mill example," Energy Policy, Elsevier, vol. 37(3), pages 813-824, March.
    5. Svensson, Elin & Berntsson, Thore & Strömberg, Ann-Brith & Patriksson, Michael, 2009. "An optimization methodology for identifying robust process integration investments under uncertainty," Energy Policy, Elsevier, vol. 37(2), pages 680-685, February.
    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. Milford, James & Henrion, Max & Hunter, Chad & Newes, Emily & Hughes, Caroline & Baldwin, Samuel F., 2022. "Energy sector portfolio analysis with uncertainty," Applied Energy, Elsevier, vol. 306(PA).
    2. Svensson, Elin & Berntsson, Thore, 2011. "Planning future investments in emerging energy technologies for pulp mills considering different scenarios for their investment cost development," Energy, Elsevier, vol. 36(11), pages 6508-6519.
    3. Clara Inés Pardo Martínez, 2010. "Investments and Energy Efficiency in Colombian Manufacturing Industries," Energy & Environment, , vol. 21(6), pages 545-562, October.
    4. Svensson, Elin & Berntsson, Thore, 2014. "The effect of long lead times for planning of energy efficiency and biorefinery technologies at a pulp mill," Renewable Energy, Elsevier, vol. 61(C), pages 12-16.
    5. Ana Espinola-Arredondo & Felix Munoz-Garcia & Dolores Garrido, 2023. "Measuring regulatory errors from environmental policy uncertainty," Journal of Regulatory Economics, Springer, vol. 64(1), pages 48-65, December.
    6. Tolis, Athanasios I. & Rentizelas, Athanasios A., 2011. "An impact assessment of electricity and emission allowances pricing in optimised expansion planning of power sector portfolios," Applied Energy, Elsevier, vol. 88(11), pages 3791-3806.
    7. Tolis, Athanasios & Doukelis, Aggelos & Tatsiopoulos, Ilias, 2010. "Stochastic interest rates in the analysis of energy investments: Implications on economic performance and sustainability," Applied Energy, Elsevier, vol. 87(8), pages 2479-2490, August.
    8. Tolis, Athanasios I. & Rentizelas, Athanasios A. & Tatsiopoulos, Ilias P., 2010. "Optimisation of electricity energy markets and assessment of CO2 trading on their structure: A stochastic analysis of the Greek Power Sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2529-2546, December.
    9. Svensson, Elin & Strömberg, Ann-Brith & Patriksson, Michael, 2011. "A model for optimization of process integration investments under uncertainty," Energy, Elsevier, vol. 36(5), pages 2733-2746.
    10. Akosah, Nana Kwame & Alagidede, Imhotep Paul & Schaling, Eric, 2020. "Testing for asymmetry in monetary policy rule for small-open developing economies: Multiscale Bayesian quantile evidence from Ghana," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    11. Cui, Xueting & Zhu, Shushang & Sun, Xiaoling & Li, Duan, 2013. "Nonlinear portfolio selection using approximate parametric Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 37(6), pages 2124-2139.
    12. Pichler, Anton & Poledna, Sebastian & Thurner, Stefan, 2021. "Systemic risk-efficient asset allocations: Minimization of systemic risk as a network optimization problem," Journal of Financial Stability, Elsevier, vol. 52(C).
    13. Peter A. Abken & Milind M. Shrikhande, 1997. "The role of currency derivatives in internationally diversified portfolios," Economic Review, Federal Reserve Bank of Atlanta, vol. 82(Q 3), pages 34-59.
    14. Dhanya Jothimani & Ravi Shankar & Surendra S. Yadav, 2018. "A Big data analytical framework for portfolio optimization," Papers 1811.07188, arXiv.org, revised Nov 2018.
    15. Leonard J. Mirman & Egas M. Salgueiro & Marc Santugini, 2013. "Integrating Real and Financial Decisions of the Firm," Cahiers de recherche 1333, CIRPEE.
    16. Dominique Guégan & Wayne Tarrant, 2012. "On the necessity of five risk measures," Annals of Finance, Springer, vol. 8(4), pages 533-552, November.
    17. Andriosopoulos, Kostas & Nomikos, Nikos, 2014. "Performance replication of the Spot Energy Index with optimal equity portfolio selection: Evidence from the UK, US and Brazilian markets," European Journal of Operational Research, Elsevier, vol. 234(2), pages 571-582.
    18. Raffestin, Louis, 2014. "Diversification and systemic risk," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 85-106.
    19. Sridhar, Shrihari & Naik, Prasad A. & Kelkar, Ajay, 2017. "Metrics unreliability and marketing overspending," International Journal of Research in Marketing, Elsevier, vol. 34(4), pages 761-779.
    20. Vithayasrichareon, Peerapat & MacGill, Iain F., 2013. "Assessing the value of wind generation in future carbon constrained electricity industries," Energy Policy, Elsevier, vol. 53(C), pages 400-412.

    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:vrs:ekonom:v:96:y:2017:i:2:p:66-78:n:5. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.