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Portfolio optimization by improved NSGA-II and SPEA 2 based on different risk measures

Author

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  • Massimiliano Kaucic

    (University of Trieste, Piazzale Europa 1)

  • Mojtaba Moradi

    (University of Guilan)

  • Mohmmad Mirzazadeh

    (University of Guilan)

Abstract

In this study, we analyze three portfolio selection strategies for loss-averse investors: semi-variance, conditional value-at-risk, and a combination of both risk measures. Moreover, we propose a novel version of the non-dominated sorting genetic algorithm II and of the strength Pareto evolutionary algorithm 2 to tackle this optimization problem. The effectiveness of these algorithms is compared with two alternatives from the literature from five publicly available datasets. The computational results indicate that the proposed algorithms in this study outperform the others for all the examined performance metrics. Moreover, they are able to approximate the Pareto front even in cases in which all the other approaches fail.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:fininn:v:5:y:2019:i:1:d:10.1186_s40854-019-0140-6
    DOI: 10.1186/s40854-019-0140-6
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    2. Jaydip Sen & Abhishek Dutta, 2022. "Design and Analysis of Optimized Portfolios for Selected Sectors of the Indian Stock Market," Papers 2210.03943, arXiv.org.
    3. Georgios Mamanis, 2021. "Analyzing the Performance of a Two-Tail-Measures-Utility Multi-objective Portfolio Optimization Model," SN Operations Research Forum, Springer, vol. 2(4), pages 1-18, December.
    4. Bedoui, Rihab & Benkraiem, Ramzi & Guesmi, Khaled & Kedidi, Islem, 2023. "Portfolio optimization through hybrid deep learning and genetic algorithms vine Copula-GARCH-EVT-CVaR model," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    5. A. Garcia-Bernabeu & J. V. Salcedo & A. Hilario & D. Pla-Santamaria & Juan M. Herrero, 2019. "Computing the Mean-Variance-Sustainability Nondominated Surface by ev-MOGA," Complexity, Hindawi, vol. 2019, pages 1-12, December.
    6. Abhiraj Sen & Jaydip Sen, 2023. "Performance Evaluation of Equal-Weight Portfolio and Optimum Risk Portfolio on Indian Stocks," Papers 2309.13696, arXiv.org.
    7. Marco Di Francesco, 2021. "Portfolio optimization under solvency II: a multi-objective approach incorporating market views and real-world constraints," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(1), pages 269-294, June.
    8. Wang, Xianhe & Ouyang, Yuliang & Li, You & Liu, Shu & Teng, Long & Wang, Bo, 2023. "Multi-objective portfolio selection considering expected and total utility," Finance Research Letters, Elsevier, vol. 58(PD).
    9. Yin, Hao & Wu, Fei & Meng, Xin & Lin, Yicheng & Fan, Jingmin & Meng, Anbo, 2020. "Crisscross optimization based short-term hydrothermal generation scheduling with cascaded reservoirs," Energy, Elsevier, vol. 203(C).
    10. 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.
    11. Adolfo Hilario-Caballero & Ana Garcia-Bernabeu & Jose Vicente Salcedo & Marisa Vercher, 2020. "Tri-Criterion Model for Constructing Low-Carbon Mutual Fund Portfolios: A Preference-Based Multi-Objective Genetic Algorithm Approach," IJERPH, MDPI, vol. 17(17), pages 1-15, August.

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