چکیده:
Portfolio optimization and selection is one of the most important issues in the financial world, so investors are trying to make decisions that are most in line with the real world. But the uncertainty in data and parameters, and the contradiction in the investor's goals, adds to the complexity of the stock portfolio optimization problem, and the other hand because of the efficient market, it is necessary to use multi-period models that, unlike single-period models, allow the investor to review their wealth at the beginning of each period. This paper introduces a new approach to optimizing a multi-period portfolio optimization based on fuzzy general theory and using scenario tree to deal with uncertainties. In addition to considering all of the above constraints, It has made it possible for the investor to be able to apply his manner by changing the parameter to optimistic-pessimistic, and there is no need to model in credibility, necessity or possibility mode. Then the proposed model is solved by the Epsilon constraint method. Finally, using the data of 17 companies from different industries operating in the Tehran Stock Exchange Market in 1398, we examine the validity of the model and its efficiency.
خلاصه ماشینی:
Designing a multi-period investment portfolio optimization model with a new approach to fuzzy uncertainty Zahra Khandan Barkousaraei 1 Date received: 2020/07/19 Date accepted: 2020/10/12 Omran Mohammadi 2 Farzad Movahedi Sobhani 3 Abstract Portfolio optimization and selection is one of the most important issues in the financial world; therefore, investors strive to make decisions with the highest compatibility with the real world.
Keywords Investment portfolio, multi-period optimization, uncertainty, fuzzy general measure, scenario tree, epsilon-constraint 1- Industrial Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran.
[7] Najafi and Mousakhian (2015) in an article named "Stochastic Multi-period Investment Portfolio Optimization Model: Mean-Semi-Variance-Conditional Value at Risk Considering Transaction Costs" addressed solving the model using a combination of genetic algorithm and particle swarm optimization, and since the efficiency of the algorithms depends on the correct selection of parameters, the Taguchi method 8 is used to tune the algorithm parameters.
[10] Mohebbi and Najafi (2017) in an article titled "Credit Optimization of Multi-period Stock Portfolio Based on Scenario Tree" propose a multi-period stock portfolio model considering transaction costs and the possibility of risk-free investment, and they model financial market uncertainty through the integration of scenario trees and fuzzy credit theory in this article.
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