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Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization

In the field of asset allocation, how to balance the returns of an investment portfolio and its fluctuations is the core issue. Capital asset pricing model, arbitrage pricing theory, and Fama–French three-factor model were used to quantify the price of individual stocks and portfolios. Based on the...

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Detalles Bibliográficos
Autores principales: Zhai, Q. H., Ye, T., Huang, M. X., Feng, S. L., Li, H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7474746/
https://www.ncbi.nlm.nih.gov/pubmed/32908478
http://dx.doi.org/10.1155/2020/8834162
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author Zhai, Q. H.
Ye, T.
Huang, M. X.
Feng, S. L.
Li, H.
author_facet Zhai, Q. H.
Ye, T.
Huang, M. X.
Feng, S. L.
Li, H.
author_sort Zhai, Q. H.
collection PubMed
description In the field of asset allocation, how to balance the returns of an investment portfolio and its fluctuations is the core issue. Capital asset pricing model, arbitrage pricing theory, and Fama–French three-factor model were used to quantify the price of individual stocks and portfolios. Based on the second-order stochastic dominance rule, the higher moments of return series, the Shannon entropy, and some other actual investment constraints, we construct a multiconstraint portfolio optimization model, aiming at comprehensively weighting the returns and risk of portfolios rather than blindly maximizing its returns. Furthermore, the whale optimization algorithm based on FTSE100 index data is used to optimize the above multiconstraint portfolio optimization model, which significantly improves the rate of return of the simple diversified buy-and-hold strategy or the FTSE100 index. Furthermore, extensive experiments validate the superiority of the whale optimization algorithm over the other four swarm intelligence optimization algorithms (gray wolf optimizer, fruit fly optimization algorithm, particle swarm optimization, and firefly algorithm) through various indicators of the results, especially under harsh constraints.
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spelling pubmed-74747462020-09-08 Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization Zhai, Q. H. Ye, T. Huang, M. X. Feng, S. L. Li, H. Comput Intell Neurosci Research Article In the field of asset allocation, how to balance the returns of an investment portfolio and its fluctuations is the core issue. Capital asset pricing model, arbitrage pricing theory, and Fama–French three-factor model were used to quantify the price of individual stocks and portfolios. Based on the second-order stochastic dominance rule, the higher moments of return series, the Shannon entropy, and some other actual investment constraints, we construct a multiconstraint portfolio optimization model, aiming at comprehensively weighting the returns and risk of portfolios rather than blindly maximizing its returns. Furthermore, the whale optimization algorithm based on FTSE100 index data is used to optimize the above multiconstraint portfolio optimization model, which significantly improves the rate of return of the simple diversified buy-and-hold strategy or the FTSE100 index. Furthermore, extensive experiments validate the superiority of the whale optimization algorithm over the other four swarm intelligence optimization algorithms (gray wolf optimizer, fruit fly optimization algorithm, particle swarm optimization, and firefly algorithm) through various indicators of the results, especially under harsh constraints. Hindawi 2020-08-28 /pmc/articles/PMC7474746/ /pubmed/32908478 http://dx.doi.org/10.1155/2020/8834162 Text en Copyright © 2020 Q. H. Zhai et al. http://creativecommons.org/licenses/by/4.0/ 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.
spellingShingle Research Article
Zhai, Q. H.
Ye, T.
Huang, M. X.
Feng, S. L.
Li, H.
Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization
title Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization
title_full Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization
title_fullStr Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization
title_full_unstemmed Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization
title_short Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization
title_sort whale optimization algorithm for multiconstraint second-order stochastic dominance portfolio optimization
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7474746/
https://www.ncbi.nlm.nih.gov/pubmed/32908478
http://dx.doi.org/10.1155/2020/8834162
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