فهرست نشریات علمی دسترسی آزاد ایران

Stock portfolio selection represents a critical area within the broader field of investment management. Owing to the extensive range of options available in the stock market, the optimal allocation of assets remains a major concern for investment firms. Accordingly, these firms frequently employ portfolio selection models to enhance decision-making processes. Portfolio management strategies and risk hedging techniques, which serve as essential mechanisms for constructing optimal portfolios, require rigorous planning and analysis. Given the computational complexity involved, the application of metaheuristic algorithms significantly improves both the speed and precision of portfolio optimization processes. This study aims to optimize portfolio value through the implementation of the Ant Colony Optimization (ACO) algorithm. To this end, data pertaining to companies listed on the Tehran Stock Exchange (TSE) over the period 2010–2021 were utilized to construct and optimize stock portfolios. The respective weight of each stock within the optimal portfolio, as well as the corresponding risk and return measures, were calculated using the Python programming language and executed in the Visual Studio Code environment. The empirical results demonstrate that employing the Ant Colony Optimization algorithm for portfolio management and risk hedging purposes, particularly under conditions characterized by economic shocks and sanctions, leads to enhanced portfolio performance and mitigation of associated risks.