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Multi-period and Multi-objective Stock Selection Optimization Model Based on Fuzzy Interval Approach

Year: 1398
COI: JR_IJE-32-9_011
Language: EnglishView: 246
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Azadeh Kameli - Department of Financial Engineering, Kooshyar Higher Education Institute, Rasht, Iran
Nikbakhsh Javadian - Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran
Allahyar Daghbandan - Department of Industrial Engineering, Gilan University, Rasht, Iran


The optimization of investment portfolios is the most important topic in financial decision making, and many relevant models can be found in the literature.  According to importance of portfolio optimization in this paper, deals with novel solution approaches to solve new developed portfolio optimization model. Contrary to previous work, the uncertainty of future returns of a given portfolio is modeled using LR-FUZZY numbers while the function of its return are evaluated using possibility theory. We used a novel Lp-metric method to solve the model. The efficacy of the proposed model is tested on criterion problems of portfolio optimization  on LINGO provides a framework to optimize objectives when creating the loan portfoliso, in a search for a dynamic markets decision. In addition to, the performance of the proposed efficiently encoded multi-objective portfolio optimization solver is assessed in comparison with two well-known MOEAs, namely NSGAII and ICA. To the best of our knowledge, there is no research that considered NSGAΠ, ICA fuzzy simultaneously. Due to improve the performance of algorithm, the performance of this approach more study is probed by using a dataset of assets from the Iran’s stock market for three years historical data and PRE method. The results are analyzed through novel performance parameters RPD method. Thus, the potential of our comparison led to improve different portfolios in different generations.


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Kameli, Azadeh and Javadian, Nikbakhsh and Daghbandan, Allahyar,1398,Multi-period and Multi-objective Stock Selection Optimization Model Based on Fuzzy Interval Approach,

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