A multi-objective evolutionary algorithm for a class of mean-variance portfolio selection problems 机翻标题: 暂无翻译,请尝试点击翻译按钮。

来源
Expert Systems with Application
年/卷/期
2019 / 133 / Nov.
页码
225-241
ISSN号
0957-4174
作者单位
Univ Fed Paraiba, Ctr Tecnol, Dept Engn Prod, Campus 1,Cidade Univ, BR-58059900 Joao Pessoa, PB, Brazil|Ctr Univ Joao Pessoa, BR 230-22 Agua Fria, BR-58053000 Joao Pessoa, PB, Brazil;Univ Fed Paraiba, Ctr Tecnol, Dept Engn Prod, Campus 1,Cidade Univ, BR-58059900 Joao Pessoa, PB, Brazil;Univ Fed Paraiba, Dept Sistemas Comp, Ctr Informat, Rua Escoteiros S-N, BR-58055000 Joao Pessoa, Paraiba, Brazil;
作者
Silva, Yuri Laio T. V.;Herthel, Ana Beatriz;Subramanian, Anand;
摘要
The portfolio selection problem (PSP) concerns the resource allocation to a finite number of assets. In its classic approach, the problem aims at overcoming a trade-off between the risk and expected return of the portfolio. In recent years, additional constraints identified in financial markets have been incorporated into the literature, as an attempt to close the gap between theory and practice. In view of this, this paper introduces a unified multi-objective particle swarm optimization approach capable of solving a class of mean-variance PSPs. An adaptive ranking procedure is also developed, which is based on three mechanisms, including a new one. Extensive computational experiments were carried out in five PSP variants and the results obtained were compared with those found by problem-specific methods from the literature. The proposed approach was capable of finding highly competitive results in all problems and in most of the multi-objective metrics considered. (C) 2019 Elsevier Ltd. All rights reserved.
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关键词/主题词
Portfolio selection problem;Mean-variance;Multi-objective optimization;Particle swarm optimization;Unified algorithm;
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