Version 1
: Received: 11 September 2024 / Approved: 12 September 2024 / Online: 12 September 2024 (15:39:44 CEST)
How to cite:
MUTEBA MWAMBA, J. W.; MBUCICI, L. M. Optimizing the Complexity of Higher-Order Moment Inclusion in Portfolio Management with the Non-Dominated Sorting Genetic Algorithm III. Preprints2024, 2024091019. https://doi.org/10.20944/preprints202409.1019.v1
MUTEBA MWAMBA, J. W.; MBUCICI, L. M. Optimizing the Complexity of Higher-Order Moment Inclusion in Portfolio Management with the Non-Dominated Sorting Genetic Algorithm III. Preprints 2024, 2024091019. https://doi.org/10.20944/preprints202409.1019.v1
MUTEBA MWAMBA, J. W.; MBUCICI, L. M. Optimizing the Complexity of Higher-Order Moment Inclusion in Portfolio Management with the Non-Dominated Sorting Genetic Algorithm III. Preprints2024, 2024091019. https://doi.org/10.20944/preprints202409.1019.v1
APA Style
MUTEBA MWAMBA, J. W., & MBUCICI, L. M. (2024). Optimizing the Complexity of Higher-Order Moment Inclusion in Portfolio Management with the Non-Dominated Sorting Genetic Algorithm III. Preprints. https://doi.org/10.20944/preprints202409.1019.v1
Chicago/Turabian Style
MUTEBA MWAMBA, J. W. and Leon Mishindo MBUCICI. 2024 "Optimizing the Complexity of Higher-Order Moment Inclusion in Portfolio Management with the Non-Dominated Sorting Genetic Algorithm III" Preprints. https://doi.org/10.20944/preprints202409.1019.v1
Abstract
.This paper explores the effectiveness of the Non-dominated Sorting Genetic Algorithm III (NSGA-III) and traditional Mean-Variance optimization in financial portfolio management. Using a dataset comprising global financial assets, we applied both methodologies to optimize portfolios based on multiple objectives, including risk, return, skewness, and kurtosis. The findings reveal that NSGA-III outperforms the Mean-Variance method in achieving a more diverse set of Pareto-optimal portfolios. NSGA-III portfolios exhibited superior performance in balancing risk and return, demonstrated by higher Sharpe ratios, more favorable skewness, and lower kurtosis. Additionally, NSGA-III's ability to simultaneously optimize across multiple conflicting objectives highlights its robustness in navigating complex financial landscapes, offering enhanced portfolio resilience. In contrast, the Mean-Variance approach, while effective in achieving balanced risk and return, was limited in addressing higher-order moments of the return distribution. These results underscore NSGA-III's potential as a powerful tool for portfolio optimization, providing a comprehensive alternative to traditional methods in modern financial markets where multiple objectives must be considered.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
