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Multi-objective genetic algorithm based on the fuzzy MULTIMOORA method for solving the cardinality constrained portfolio optimization

Multi-objective genetic algorithm based on the fuzzy MULTIMOORA method for solving the... Over the recent decades, Markowitz mean-variance models with additional constraints and objectives have still attracted the attention of many researchers. Stock evaluation and portfolio optimization have a decision-making process that contains tangible and intangible factors under vague parameters. This study presents an integrated approach including the fuzzy MULTIMOORA based on Correlation Coefficient and Standard Deviation (CCSD) method and the mean–variance-ranking cardinality constrained portfolio optimization (MVRCCPO), which is the extension of classical mean-variance cardinality constrained portfolio optimization model. The ranking obtained from the fuzzy MULTIMOORA based on the CCSD method is handled as one of the objective functions in the proposed model. The MVRCCPO problem is an NP-Complete problem when taking cardinality constraints into account. In this case, existing exact algorithms such as quadratic programming may not be efficient for solving the problem. Due to the complexity of the problem and the nature of the multiple objectives, we used the multi-objective genetic algorithm based on three different scalarization techniques, involving conic, tchebycheff, and weighted-sum. The performance of the multi-objective genetic algorithm with scalarization variants is investigated across forty-nine benchmark instances consisting of seven different objective weight combinations and seven different instances from ‘small’ to ‘large’ size derived from a large real-life problem. The computational results indicate that the multi-objective genetic algorithm utilizing weighted-sum in accordance with the weighted distance to the ideal point outperforms the rest in point of the obtained results in most cases. Moreover, the multi-objective genetic algorithm using conic in accordance with the weighted distance to the reference point is better performance than the multi-objective genetic algorithm using tchebycheff. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

Multi-objective genetic algorithm based on the fuzzy MULTIMOORA method for solving the cardinality constrained portfolio optimization

Applied Intelligence , Volume 53 (12) – Jun 1, 2023

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References (114)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
0924-669X
eISSN
1573-7497
DOI
10.1007/s10489-022-04240-6
Publisher site
See Article on Publisher Site

Abstract

Over the recent decades, Markowitz mean-variance models with additional constraints and objectives have still attracted the attention of many researchers. Stock evaluation and portfolio optimization have a decision-making process that contains tangible and intangible factors under vague parameters. This study presents an integrated approach including the fuzzy MULTIMOORA based on Correlation Coefficient and Standard Deviation (CCSD) method and the mean–variance-ranking cardinality constrained portfolio optimization (MVRCCPO), which is the extension of classical mean-variance cardinality constrained portfolio optimization model. The ranking obtained from the fuzzy MULTIMOORA based on the CCSD method is handled as one of the objective functions in the proposed model. The MVRCCPO problem is an NP-Complete problem when taking cardinality constraints into account. In this case, existing exact algorithms such as quadratic programming may not be efficient for solving the problem. Due to the complexity of the problem and the nature of the multiple objectives, we used the multi-objective genetic algorithm based on three different scalarization techniques, involving conic, tchebycheff, and weighted-sum. The performance of the multi-objective genetic algorithm with scalarization variants is investigated across forty-nine benchmark instances consisting of seven different objective weight combinations and seven different instances from ‘small’ to ‘large’ size derived from a large real-life problem. The computational results indicate that the multi-objective genetic algorithm utilizing weighted-sum in accordance with the weighted distance to the ideal point outperforms the rest in point of the obtained results in most cases. Moreover, the multi-objective genetic algorithm using conic in accordance with the weighted distance to the reference point is better performance than the multi-objective genetic algorithm using tchebycheff.

Journal

Applied IntelligenceSpringer Journals

Published: Jun 1, 2023

Keywords: Portfolio optimization; Fuzzy MULTIMOORA; Scalarization methods; Markowitz mean-variance model; Cardinality constraints

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