TY - JOUR ID - 50715 TI - Optimal Portfolio Prediction in Tehran Stock Market using Multi-Objective Evolutionary Algorithms, NSGA-II and MOPSO JO - Financial Research Journal JA - FRJ LA - en SN - 1024-8153 AU - Rajabi, Mahsa AU - Khaloozadeh, Hamid AD - PhD., Electrical and Control Engineering, K. N. Toosi University of Technology AD - Prof. , K. N. Toosi University of Technology Y1 - 2014 PY - 2014 VL - 16 IS - 2 SP - 253 EP - 270 KW - Optimal Portfolio Prediction KW - Multi-Objective Evolutionary Algorithms KW - Conditional Value at Risk KW - NSGA-II KW - MOPSO DO - 10.22059/jfr.2014.50715 N2 - Despite the growing use of evolutionary multi-objective optimization algorithms in different categories of science, these algorithms as a powerful tool in portfolio optimization and specially solving multi-objective portfolio optimization problem is still in its early stages. In this paper, MOEAs have been used for solving multi-objective portfolio optimization problem in Tehran stock market. For this purpose, Non-dominated Sorting Genetic Algorithm (NSGA_II) and Multi-objective Particle Swarm Optimization (MOPSO), as two common approaches, were compared with each other. Using pareto front, investors can choose optimal portfolio based on different risks and returns. Two objectives of the problem are return and risk of portfolio and CVaR is the risk metric. In order to solve the problem, three real-world constraints were considered. The results indicate that these approaches have a high performance in constraint portfolio optimization. UR - https://jfr.ut.ac.ir/article_50715.html L1 - https://jfr.ut.ac.ir/article_50715_585cb96acf8f5ee88526fdd187a14bd5.pdf ER -