Keywords
Portfolio Management
Sharpe Ratio Optimization
How to Cite
Abstract
This study evaluates the performance of the Grey Wolf Optimizer (GWO) in optimizing investment portfolios, comparing it with gradient-based methods and other metaheuristics. While various variants of this algorithm exist, no studies have directly compared it to these approaches within the same comparative framework. To this end, Markowitz's mean-variance model is employed, analyzing its capacity to maximize expected returns within an acceptable risk level based on the Sharpe ratio and convergence time. The study follows a quantitative-longitudinal design, examining investment strategies using historical data sourced from Investing.com. Two portfolios are considered: one with 20 assets for diversification and another with 10 high-volatility assets. Optimization is implemented in Matlab, comparing the fmincon, genetic algorithm, particle swarm optimization, pattern search, and GWO methods with 4, 10, and 20 agents. The results highlight that GWO achieves a balance between performance and computational efficiency, positioning itself as a robust alternative compared to the other evaluated methods.
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