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Multi-Objective Portfolio Optimization: An Application of the Non-Dominated Sorting Genetic Algorithm III

Author

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  • John Weirstrass Muteba Mwamba

    (Haute École de Commerce de Kinshasa—HEC, Gombe Gombe 1003071, Democratic Republic of the Congo
    School of Economics, University of Johannesburg, P.O. Box 524, Auckland Park, Johannesburg 2006, South Africa)

  • Leon Mishindo Mbucici

    (School of Economics, University of Johannesburg, P.O. Box 524, Auckland Park, Johannesburg 2006, South Africa)

  • Jules Clement Mba

    (School of Economics, University of Johannesburg, P.O. Box 524, Auckland Park, Johannesburg 2006, South Africa)

Abstract

This study evaluates the effectiveness of the Non-dominated Sorting Genetic Algorithm III (NSGA-III) in comparison to the traditional Mean–Variance optimization method for financial portfolio management. Leveraging a dataset of global financial assets, we applied both approaches to optimize portfolios across multiple objectives, including risk, return, skewness, and kurtosis. The findings reveal that NSGA-III significantly outperforms the Mean–Variance method by generating a more diverse set of Pareto-optimal portfolios. Portfolios optimized with NSGA-III exhibited superior performance, achieving higher Sharpe ratios, more favorable skewness, and reduced kurtosis, indicating a better balance between risk and return. Moreover, NSGA-III’s capability to handle conflicting objectives underscores its utility in navigating complex financial environments and enhancing portfolio resilience. In contrast, while the Mean–Variance method effectively balances risk and return, it demonstrates limitations in addressing higher-order moments of the return distribution. These results emphasize the potential of NSGA-III as a robust and comprehensive tool for portfolio optimization in modern financial markets characterized by multifaceted objectives.

Suggested Citation

  • John Weirstrass Muteba Mwamba & Leon Mishindo Mbucici & Jules Clement Mba, 2025. "Multi-Objective Portfolio Optimization: An Application of the Non-Dominated Sorting Genetic Algorithm III," IJFS, MDPI, vol. 13(1), pages 1-18, January.
    • Handle: RePEc:gam:jijfss:v:13:y:2025:i:1:p:15-:d:1578885
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    References listed on IDEAS

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    1. Bushra Naqvi & Nawazish Mirza & Waqar Azeem Naqvi & S. K. A. Rizvi, 2017. "Portfolio optimisation with higher moments of risk at the Pakistan Stock Exchange," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 30(1), pages 1594-1610, January.
    2. Ali, Fahad & Bouri, Elie & Naifar, Nader & Shahzad, Syed Jawad Hussain & AlAhmad, Mohammad, 2022. "An examination of whether gold-backed Islamic cryptocurrencies are safe havens for international Islamic equity markets," Research in International Business and Finance, Elsevier, vol. 63(C).
    3. Alan Moreira & Tyler Muir, 2017. "Volatility-Managed Portfolios," Journal of Finance, American Finance Association, vol. 72(4), pages 1611-1644, August.
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