An Artificial Bee Colony Algorithm for Uncertain Portfolio Selection

Portfolio selection is an important issue for researchers and practitioners. In this paper, underthe assumption that security returns are given by experts’ evaluations rather than historical data,we discuss the portfolio adjusting problem which takes transaction costs and diversification degree ofpo...

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Published inTheScientificWorld Vol. 2014; no. 2014; pp. 1 - 12
Main Author Chen, Wei
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2014
John Wiley & Sons, Inc
Wiley
Subjects
Online AccessGet full text
ISSN2356-6140
1537-744X
1537-744X
DOI10.1155/2014/578182

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Abstract Portfolio selection is an important issue for researchers and practitioners. In this paper, underthe assumption that security returns are given by experts’ evaluations rather than historical data,we discuss the portfolio adjusting problem which takes transaction costs and diversification degree ofportfolio into consideration. Uncertain variables are employed to describe the security returns. In theproposed mean-variance-entropy model, the uncertain mean value of the return is used to measureinvestment return, the uncertain variance of the return is used to measure investment risk, and theentropy is used to measure diversification degree of portfolio. In order to solve the proposed model,a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is givento illustrate the modelling idea and the effectiveness of the proposed algorithm.
AbstractList Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts' evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm.
Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts' evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm.
Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts' evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm.Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts' evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm.
Portfolio selection is an important issue for researchers and practitioners. In this paper, underthe assumption that security returns are given by experts’ evaluations rather than historical data,we discuss the portfolio adjusting problem which takes transaction costs and diversification degree ofportfolio into consideration. Uncertain variables are employed to describe the security returns. In theproposed mean-variance-entropy model, the uncertain mean value of the return is used to measureinvestment return, the uncertain variance of the return is used to measure investment risk, and theentropy is used to measure diversification degree of portfolio. In order to solve the proposed model,a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is givento illustrate the modelling idea and the effectiveness of the proposed algorithm.
Audience Academic
Author Chen, Wei
AuthorAffiliation School of Information, Capital University of Economics and Business, Beijing 100070, China
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CitedBy_id crossref_primary_10_1109_TFUZZ_2018_2856120
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ContentType Journal Article
Copyright Copyright © 2014 Wei Chen.
COPYRIGHT 2014 John Wiley & Sons, Inc.
Copyright © 2014 Wei Chen. Wei Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright © 2014 Wei Chen. 2014
Copyright_xml – notice: Copyright © 2014 Wei Chen.
– notice: COPYRIGHT 2014 John Wiley & Sons, Inc.
– notice: Copyright © 2014 Wei Chen. Wei Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
– notice: Copyright © 2014 Wei Chen. 2014
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Snippet Portfolio selection is an important issue for researchers and practitioners. In this paper, underthe assumption that security returns are given by experts’...
Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts’...
Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts'...
Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts'...
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StartPage 1
SubjectTerms Algorithms
Economic models
Entropy
Expected values
Fuzzy logic
Fuzzy sets
Heuristic
Investment policy
Investments
Mathematical optimization
Methods
Models, Theoretical
Portfolio management
Random variables
Science
Securities markets
Social sciences
Theory
Uncertainty
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Title An Artificial Bee Colony Algorithm for Uncertain Portfolio Selection
URI https://search.emarefa.net/detail/BIM-1050166
https://dx.doi.org/10.1155/2014/578182
https://www.ncbi.nlm.nih.gov/pubmed/25089292
https://www.proquest.com/docview/1552853294
https://www.proquest.com/docview/1551336096
https://pubmed.ncbi.nlm.nih.gov/PMC4098991
https://downloads.hindawi.com/journals/tswj/2014/578182.pdf
https://doaj.org/article/238c67d1490e4cde9bfe34d99dc0c8a3
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