Multi-period and Multi-objective Stock Selection Optimization Model Based on Fuzzy Interval Approach
Author(s) -
Azadeh Kameli,
Nikbakhsh Javadian,
Allahyar Daghbandan
Publication year - 2019
Publication title -
international journal of engineering. transactions c: aspects
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.213
H-Index - 17
ISSN - 2423-7167
DOI - 10.5829/ije.2019.32.09c.11
Subject(s) - portfolio optimization , computer science , portfolio , mathematical optimization , solver , fuzzy logic , loan , metric (unit) , optimization problem , genetic algorithm , performance metric , machine learning , artificial intelligence , mathematics , economics , finance , operations management , management
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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