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Financial Portfolio Optimization using Simulation and Evolutionary Artificial Intelligence

by Yousef Alajrosh, Mohamed El-dosuky
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 107
Year of Publication: 2026
Authors: Yousef Alajrosh, Mohamed El-dosuky
10.5120/ijcaa3c84767540f

Yousef Alajrosh, Mohamed El-dosuky . Financial Portfolio Optimization using Simulation and Evolutionary Artificial Intelligence. International Journal of Computer Applications. 187, 107 ( May 2026), 17-22. DOI=10.5120/ijcaa3c84767540f

@article{ 10.5120/ijcaa3c84767540f,
author = { Yousef Alajrosh, Mohamed El-dosuky },
title = { Financial Portfolio Optimization using Simulation and Evolutionary Artificial Intelligence },
journal = { International Journal of Computer Applications },
issue_date = { May 2026 },
volume = { 187 },
number = { 107 },
month = { May },
year = { 2026 },
issn = { 0975-8887 },
pages = { 17-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number107/financial-portfolio-optimization-using-simulation-and-evolutionary-artificial-intelligence/ },
doi = { 10.5120/ijcaa3c84767540f },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-05-21T00:17:02.068759+05:30
%A Yousef Alajrosh
%A Mohamed El-dosuky
%T Financial Portfolio Optimization using Simulation and Evolutionary Artificial Intelligence
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 107
%P 17-22
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Modern Portfolio Theory (MPT), also known as the mean–variance approach, focuses on constructing portfolios that minimize risk for a desired return or maximize return for a defined risk level. This paper integrates simulation and artificial intelligence techniques to enhance portfolio optimization, providing improved decision-making by effectively balancing risk and return. Monte Carlo simulation served as the primary simulation method. The results showed that combining MPT with advanced optimization techniques, including Monte Carlo simulation and NSGA-II that produces robust and efficient portfolios. All methods delivered consistent outcomes, with NSGA-II achieving a high Sharpe ratio of 2.113 and exhibiting early, stable convergence. Pareto-front analysis emphasized the dominance of large-cap technology stocks, particularly Apple and Amazon, with Microsoft contributing stability and Tesla appearing mainly in higher-risk allocations. The convergence of these techniques underscores the portfolio’s robustness and reflects a market environment favoring major tech leaders, illustrating the value of blending simulation and AI to balance risk and return in financial strategy.

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Index Terms

Computer Science
Information Sciences

Keywords

Modern Portfolio Theory Monte Carlo simulation Artificial Intelligence NSGA-II

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