Selecting and optimizing stock portfolio is one of the main concerns of investors and dealers in the stock market. The present study aims to select an optimized portfolio using genetic and particle swarm algorithms and comparing the related results. The statistical sample included 146 companies active in the Tehran Stock Exchange. Considering monthly share prices of the companies between 2001 and 2009, a model was developed to select and optimize their stock portfolios. In this line, trough adding a number of the real world limitations to the mean-semi variance base model of Markowitz, the developed model was prepared and its related algorithms were designed using MATLAB software. Finally, the algorithms were carried out for different sizes of stock portfolios. Results of the study prove high stability and optimization for both genetic and particle swarm algorithms in different frequencies and acceptable times. Also, results of the study indicate that the particle swarm algorithm has higher speed compared to the genetic algorithm, booth in number of generations and implementation time.
کلید واژگان :Optimizing stock portfolio, genetic algorithm, particle swarm algorithm, semi variance, risk and efficiency.
ارزش ریالی : 600000 ریال
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