Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies
This survey paper provides an overview of current developments for the Portfolio Optimisation Problem (POP) based on articles published from 2018 to 2022. It reviews the latest solution methodologies utilised in addressing POPs in terms of mechanisms and performance. The methodologies are categorise...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10087257/ |
| _version_ | 1797850974827577344 |
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| author | Zi Xuan Loke Say Leng Goh Graham Kendall Salwani Abdullah Nasser R. Sabar |
| author_facet | Zi Xuan Loke Say Leng Goh Graham Kendall Salwani Abdullah Nasser R. Sabar |
| author_sort | Zi Xuan Loke |
| collection | DOAJ |
| description | This survey paper provides an overview of current developments for the Portfolio Optimisation Problem (POP) based on articles published from 2018 to 2022. It reviews the latest solution methodologies utilised in addressing POPs in terms of mechanisms and performance. The methodologies are categorised as Metaheuristic, Mathematical Optimisation, Hybrid Approaches, Matheuristic and Machine Learning. The datasets (benchmark, real-world, and hypothetical) utilised in portfolio optimisation research are provided. The state-of-the-art methodologies for benchmark datasets are presented accordingly. Population-based metaheuristics are the most preferred techniques among researchers in addressing the POP. Hybrid approaches is an emerging trend (2018 onwards). The OR-Library is the most widely used benchmark dataset for researchers to compare their methodologies in addressing POP. The research challenges and opportunities are discussed. The summarisation of the published papers in this survey provides an insight to researchers in identifying emerging trends and gaps in this research area. |
| first_indexed | 2024-04-09T19:10:16Z |
| format | Article |
| id | doaj.art-70c17615361140b1aba0b631dcc9597a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T19:10:16Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-70c17615361140b1aba0b631dcc9597a2023-04-06T23:00:32ZengIEEEIEEE Access2169-35362023-01-0111331003312010.1109/ACCESS.2023.326319810087257Portfolio Optimization Problem: A Taxonomic Review of Solution MethodologiesZi Xuan Loke0Say Leng Goh1https://orcid.org/0000-0003-4562-2519Graham Kendall2Salwani Abdullah3https://orcid.org/0000-0003-0037-841XNasser R. Sabar4https://orcid.org/0000-0002-0276-4704Optimisation and Visual Analytics Research Group, Faculty of Computing and Informatics, Universiti Malaysia Sabah Labuan International Campus, Labuan, MalaysiaOptimisation and Visual Analytics Research Group, Faculty of Computing and Informatics, Universiti Malaysia Sabah Labuan International Campus, Labuan, MalaysiaSchool of Computer Science, University of Nottingham Malaysia, Semenyih, Selangor, MalaysiaFaculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, MalaysiaDepartment of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, AustraliaThis survey paper provides an overview of current developments for the Portfolio Optimisation Problem (POP) based on articles published from 2018 to 2022. It reviews the latest solution methodologies utilised in addressing POPs in terms of mechanisms and performance. The methodologies are categorised as Metaheuristic, Mathematical Optimisation, Hybrid Approaches, Matheuristic and Machine Learning. The datasets (benchmark, real-world, and hypothetical) utilised in portfolio optimisation research are provided. The state-of-the-art methodologies for benchmark datasets are presented accordingly. Population-based metaheuristics are the most preferred techniques among researchers in addressing the POP. Hybrid approaches is an emerging trend (2018 onwards). The OR-Library is the most widely used benchmark dataset for researchers to compare their methodologies in addressing POP. The research challenges and opportunities are discussed. The summarisation of the published papers in this survey provides an insight to researchers in identifying emerging trends and gaps in this research area.https://ieeexplore.ieee.org/document/10087257/Hybrid approachesmachine learningmathematical optimisationmatheuristicmetaheuristic |
| spellingShingle | Zi Xuan Loke Say Leng Goh Graham Kendall Salwani Abdullah Nasser R. Sabar Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies IEEE Access Hybrid approaches machine learning mathematical optimisation matheuristic metaheuristic |
| title | Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies |
| title_full | Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies |
| title_fullStr | Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies |
| title_full_unstemmed | Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies |
| title_short | Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies |
| title_sort | portfolio optimization problem a taxonomic review of solution methodologies |
| topic | Hybrid approaches machine learning mathematical optimisation matheuristic metaheuristic |
| url | https://ieeexplore.ieee.org/document/10087257/ |
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