A self-adaptive step-size search algorithm for the cardinality constrained portfolio optimisation problem
This paper proposes a Self-adaptive Step-size Search (SASS) algorithm to address a Cardinality Constrained Portfolio Optimisation Problem (CCPOP). The proposed methodology is tested using five datasets from OR-Library. Experiments are conducted to test different settings of the particles in the SASS...
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| Main Authors: | , , |
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| Format: | Proceedings |
| Language: | en en |
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IEEE
2024
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| Online Access: | https://eprints.ums.edu.my/id/eprint/42111/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/42111/2/FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/42111/ https://ieeexplore.ieee.org/document/10525478 |
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| author | Zi, Xuan Loke Say, Leng Goh Jonathan Likoh Juis @ Juise |
| author_facet | Zi, Xuan Loke Say, Leng Goh Jonathan Likoh Juis @ Juise |
| author_sort | Zi, Xuan Loke |
| building | UMS Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Sabah |
| content_source | UMS Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This paper proposes a Self-adaptive Step-size Search (SASS) algorithm to address a Cardinality Constrained Portfolio Optimisation Problem (CCPOP). The proposed methodology is tested using five datasets from OR-Library. Experiments are conducted to test different settings of the particles in the SASS algorithm. The computational results are compared in terms of performance measures. The SASS algorithm achieves a lower value for most of the performance measures when the number of particles increases. |
| format | Proceedings |
| id | my.ums.eprints-42111 |
| institution | Universiti Malaysia Sabah |
| language | en en |
| publishDate | 2024 |
| publisher | IEEE |
| record_format | eprints |
| spelling | my.ums.eprints-421112024-12-04T07:27:59Z https://eprints.ums.edu.my/id/eprint/42111/ A self-adaptive step-size search algorithm for the cardinality constrained portfolio optimisation problem Zi, Xuan Loke Say, Leng Goh Jonathan Likoh Juis @ Juise QA75.5-76.95 Electronic computers. Computer science T1-995 Technology (General) This paper proposes a Self-adaptive Step-size Search (SASS) algorithm to address a Cardinality Constrained Portfolio Optimisation Problem (CCPOP). The proposed methodology is tested using five datasets from OR-Library. Experiments are conducted to test different settings of the particles in the SASS algorithm. The computational results are compared in terms of performance measures. The SASS algorithm achieves a lower value for most of the performance measures when the number of particles increases. IEEE 2024 Proceedings PeerReviewed text en https://eprints.ums.edu.my/id/eprint/42111/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/42111/2/FULL%20TEXT.pdf Zi, Xuan Loke and Say, Leng Goh and Jonathan Likoh Juis @ Juise (2024) A self-adaptive step-size search algorithm for the cardinality constrained portfolio optimisation problem. https://ieeexplore.ieee.org/document/10525478 |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science T1-995 Technology (General) Zi, Xuan Loke Say, Leng Goh Jonathan Likoh Juis @ Juise A self-adaptive step-size search algorithm for the cardinality constrained portfolio optimisation problem |
| title | A self-adaptive step-size search algorithm for the cardinality constrained portfolio optimisation problem |
| title_full | A self-adaptive step-size search algorithm for the cardinality constrained portfolio optimisation problem |
| title_fullStr | A self-adaptive step-size search algorithm for the cardinality constrained portfolio optimisation problem |
| title_full_unstemmed | A self-adaptive step-size search algorithm for the cardinality constrained portfolio optimisation problem |
| title_short | A self-adaptive step-size search algorithm for the cardinality constrained portfolio optimisation problem |
| title_sort | self-adaptive step-size search algorithm for the cardinality constrained portfolio optimisation problem |
| topic | QA75.5-76.95 Electronic computers. Computer science T1-995 Technology (General) |
| url | https://eprints.ums.edu.my/id/eprint/42111/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/42111/2/FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/42111/ https://ieeexplore.ieee.org/document/10525478 |
| url_provider | http://eprints.ums.edu.my/ |