Multi-Objective Portfolio Optimization: An Application of the Non-Dominated Sorting Genetic Algorithm III
John Weirstrass Muteba Mwamba, Leon Mishindo Mbucici, Jules Clément
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.