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Robust Portfolio Mean-Variance Optimization for Capital Allocation in Stock Investment Using the Genetic Algorithm: A Systematic Literature Review

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dc.contributor.authorDiandra Chika Fransisca
dc.contributor.authorSukono
dc.contributor.authorDiah Chaerani
dc.contributor.authorNurfadhlina Abdul Halim
dc.date.accessioned2024-09-05T17:04:57Z
dc.date.available2024-09-05T17:04:57Z
dc.date.issued2024
dc.date.submitted2024-8-31
dc.descriptionComputation Volume 12 Issue 8
dc.description.abstractTraditional mean-variance (MV) models, considered effective in stable conditions, often prove inadequate in uncertain market scenarios. Therefore, there is a need for more robust and better portfolio optimization methods to handle the fluctuations and uncertainties in asset returns and covariances. This study aims to perform a Systematic Literature Review (SLR) on robust portfolio mean-variance (RPMV) in stock investment utilizing genetic algorithms (GAs). The SLR covered studies from 1995 to 2024, allowing a thorough analysis of the evolution and effectiveness of robust portfolio optimization methods over time. The method used to conduct the SLR followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The result of the SLR presented a novel strategy to combine robust optimization methods and a GA in order to enhance RPMV. The uncertainty parameters, cardinality constraints, optimization constraints, risk-aversion parameters, robust covariance estimators, relative and absolute robustness, and parameters adopted were unable to develop portfolios capable of maintaining performance despite market uncertainties. This led to the inclusion of GAs to solve the complex optimization problems associated with RPMV efficiently, as well as fine-tuning parameters to improve solution accuracy. In three papers, the empirical validation of the results was conducted using historical data from different global capital markets such as Hang Seng (Hong Kong), Data Analysis Expressions (DAX) 100 (Germany), the Financial Times Stock Exchange (FTSE) 100 (U.K.), S&P 100 (USA), Nikkei 225 (Japan), and the Indonesia Stock Exchange (IDX), and the results showed that the RPMV model optimized with a GA was more stable and provided higher returns compared with traditional MV models. Furthermore, the proposed method effectively mitigated market uncertainties, making it a valuable tool for investors aiming to optimize portfolios under uncertain conditions. The implications of this study relate to handling uncertainty in asset returns, dynamic portfolio parameters, and the effectiveness of GAs in solving portfolio optimization problems under uncertainty, providing near-optimal solutions with relatively lower computational time.
dc.identifier.citationDiandra Chika Fransisca, Sukono, Diah Chaerani, & Nurfadhlina Abdul Halim. (2024). Robust Portfolio Mean-Variance Optimization for Capital Allocation in Stock Investment Using the Genetic Algorithm: A Systematic Literature Review. Computation, 12(8). https://doi.org/10.3390/computation12080166
dc.identifier.doi10.3390/computation12080166
dc.identifier.epage18
dc.identifier.issn2079-3197
dc.identifier.issue166
dc.identifier.spage2
dc.identifier.urihttps://www.mdpi.com/2079-3197/12/8/166
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/22468
dc.identifier.volume12
dc.language.isoen_US
dc.publisherMDPI
dc.relation.ispartofComputation
dc.relation.issn2079-3197
dc.relation.journalComputation
dc.subjectrobust portfolio
dc.subjectmean-variance
dc.subjectstock
dc.subjectliterature review
dc.subjectgenetic algorithm
dc.titleRobust Portfolio Mean-Variance Optimization for Capital Allocation in Stock Investment Using the Genetic Algorithm: A Systematic Literature Review
dc.typetext::journal::journal article::research article
dspace.entity.typePublication
oaire.citation.endPage8
oaire.citation.issue8
oaire.citation.startPage1
oaire.citation.volume12
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation#PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliationUniversiti Sains Islam Malaysia

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