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Efficient Portfolio Construction with the Use of Multiobjective Evolutionary Algorithms: Best Practices and Performance Metrics

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  • K. Liagkouras

    (Decision Support Systems Laboratory, Department of Informatics, University of Piraeus, 80, Karaoli & Dimitriou Str., 18534 Piraeus, Greece)

  • K. Metaxiotis

    (Decision Support Systems Laboratory, Department of Informatics, University of Piraeus, 80, Karaoli & Dimitriou Str., 18534 Piraeus, Greece)

Abstract

This paper provides a systematic study of the technologies and algorithms associated with the implementation of multiobjective evolutionary algorithms (MOEAs) for the solution of the portfolio optimization problem. Based on the examination of the state-of-the art we provide the best practices for dealing with the complexities of the constrained portfolio optimization problem (CPOP). In particular, rigorous algorithmic and technical treatment is provided for the efficient incorporation of a wide range of real-world constraints into the MOEAs. Moreover, we address special configuration issues related to the application of MOEAs for solving the CPOP. Finally, by examining the state-of-the-art we identify the most appropriate performance metrics for the evaluation of the relevant results from the implementation of the MOEAs to the solution of the CPOP.

Suggested Citation

  • K. Liagkouras & K. Metaxiotis, 2015. "Efficient Portfolio Construction with the Use of Multiobjective Evolutionary Algorithms: Best Practices and Performance Metrics," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(03), pages 535-564.
    • Handle: RePEc:wsi:ijitdm:v:14:y:2015:i:03:n:s0219622015300013
    DOI: 10.1142/S0219622015300013
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    Citations

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    Cited by:

    1. Liagkouras, Konstantinos & Metaxiotis, Konstantinos, 2021. "Improving multi-objective algorithms performance by emulating behaviors from the human social analogue in candidate solutions," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1019-1036.
    2. K. Liagkouras & K. Metaxiotis, 2018. "A new efficiently encoded multiobjective algorithm for the solution of the cardinality constrained portfolio optimization problem," Annals of Operations Research, Springer, vol. 267(1), pages 281-319, August.
    3. Yuanyuan Zhang & Xiang Li & Sini Guo, 2018. "Portfolio selection problems with Markowitz’s mean–variance framework: a review of literature," Fuzzy Optimization and Decision Making, Springer, vol. 17(2), pages 125-158, June.
    4. Massimiliano Kaucic & Mojtaba Moradi & Mohmmad Mirzazadeh, 2019. "Portfolio optimization by improved NSGA-II and SPEA 2 based on different risk measures," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-28, December.
    5. K. Liagkouras & K. Metaxiotis & G. Tsihrintzis, 2022. "Incorporating environmental and social considerations into the portfolio optimization process," Annals of Operations Research, Springer, vol. 316(2), pages 1493-1518, September.
    6. Fernando García & Jairo González-Bueno & Javier Oliver & Nicola Riley, 2019. "Selecting Socially Responsible Portfolios: A Fuzzy Multicriteria Approach," Sustainability, MDPI, vol. 11(9), pages 1-14, April.
    7. K. Liagkouras & K. Metaxiotis, 2019. "Improving the performance of evolutionary algorithms: a new approach utilizing information from the evolutionary process and its application to the fuzzy portfolio optimization problem," Annals of Operations Research, Springer, vol. 272(1), pages 119-137, January.
    8. Massimiliano Kaucic & Filippo Piccotto & Gabriele Sbaiz & Giorgio Valentinuz, 2023. "Optimal Portfolio with Sustainable Attitudes under Cumulative Prospect Theory," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 13(4), pages 1-4.

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