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Optimizing Stock Portfolio Using the Particle Swarm Optimization Algorithm and Assessing PSO and Other Algorithms
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Author(s): Samaneh Mohammadi Jarchelou (Department of Statistics, Islamic Azad University, North Tehran Branch, Tehran, Iran), Kianoush Fathi Vajargah (Department of Statistics, Islamic Azad University, North Tehran Branch, Tehran, Iran)and Parvin Azhdari (Department of Statistics, Islamic Azad University, North Tehran Branch, Tehran, Iran)
Copyright: 2024
Volume: 15
Issue: 1
Pages: 13
Source title:
International Journal of Applied Metaheuristic Computing (IJAMC)
Editor(s)-in-Chief: Peng-Yeng Yin (Ming Chuan University, Taiwan)
DOI: 10.4018/IJAMC.362001
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Abstract
When it comes to making financial decisions, choosing stocks is crucial to building a successful portfolio. Stocks are evaluated according to lower risk, and the best stocks are chosen to produce assets that are then utilized to construct the portfolio. In this study, we have compared the integrated particle swarm method to four other algorithms for stock selection and optimization: the genetic algorithm, the Pareto search algorithm, the pattern search algorithm, and quadratic programming in the Matlab toolbox. Six particular stock firms are taken into consideration for this reason during a given time period. First, we will use the aforementioned Matlab toolbox techniques to conduct Markowitz's mean variance model. Additionally, the usual embedded particle swarm methodology and the penalty function approach will be used to create this model. The difference between the averages at ten distinct levels of predicted values will be examined in the next research based on the returns in the chosen portfolios. Statistical tests will be employed to differentiate noteworthy distinctions between the suggested approach and the alternative algorithms. MSC: 65K10,91B05.
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