Optimization of Multi-Objective Portfolios Based on Mean, Variance, Entropy and Particle Swarm Algorithm

Document Type : applied


1 Prof. Finance Department, Tehran University, Tehran, Iran

2 Assistant Prof,. Finance Department,Tehran University,Tehran, Iran

3 MSc. Student in Finance, Tehran university, Tehran, Iran


Most optimization problems in the real world have several goals that are usually in conflict with each other. Investors in the capital market are also pursuing several goals for optimizing the stock portfolio. The purpose of this paper was to optimize multi-objective portfolios based on ARMA-GARCH predictions and entropy. to provide solutions using the method of particle swarm algorithm. The statistical sample of this study includes the top 30 Tehran Stock Exchange (TSE). Initially, Autoregressive integrated moving average (ARIMA) efficiency series was modeled. Then, in order to evaluate the asset portfolio risk, we first calculated the risk based on generalized autoregressive conditional heteroskedasticity (GARCH) models. Also, the results of this study show that the multi-objective optimization algorithm based on the method of particle swarm algorithm is successful in creating stock portfolios. According to the findings of the research, the application of the Particle Swarm Algorithm (PSO) in the selection and optimization of stock portfolios is recommended.
JEL: G10, G17, G19
How to cite this paper: Raei, R., Bajelan, S., Habibi, M., & Nikahd, A. (2017). Optimization of Multi-Objective Portfolios Based on Mean, Variance, Entropy and Particle Swarm Algorithm. Quarterly Journal of Risk Modeling and Financial Engineering, 2(3), 362–379. (In Persian)


Main Subjects

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