Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem
► We model a multi-objective portfolio optimization problem. ► Particle Swarm Optimization (PSO) algorithm has been used to solve the problem. ► The PSO model is tested on various risk investment portfolios. ► The results show that the PSO model are more effective than GAs and VBA solvers. One of th...
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| Veröffentlicht in: | Expert systems with applications 2011-08, Vol.38 (8), p.10161-10169 |
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| container_end_page | 10169 |
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| container_title | Expert systems with applications |
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| creator | Zhu, Hanhong Wang, Yi Wang, Kesheng Chen, Yun |
| description | ► We model a multi-objective portfolio optimization problem. ► Particle Swarm Optimization (PSO) algorithm has been used to solve the problem. ► The PSO model is tested on various risk investment portfolios. ► The results show that the PSO model are more effective than GAs and VBA solvers.
One of the most studied problems in the financial investment expert system is the intractability of portfolios. The non-linear constrained portfolio optimization problem with multi-objective functions cannot be efficiently solved using traditionally approaches. This paper presents a meta-heuristic approach to portfolio optimization problem using Particle Swarm Optimization (PSO) technique. The model is tested on various restricted and unrestricted risky investment portfolios and a comparative study with Genetic Algorithms is implemented. The PSO model demonstrates high computational efficiency in constructing optimal risky portfolios. Preliminary results show that the approach is very promising and achieves results comparable or superior with the state of the art solvers. |
| doi_str_mv | 10.1016/j.eswa.2011.02.075 |
| format | Article |
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One of the most studied problems in the financial investment expert system is the intractability of portfolios. The non-linear constrained portfolio optimization problem with multi-objective functions cannot be efficiently solved using traditionally approaches. This paper presents a meta-heuristic approach to portfolio optimization problem using Particle Swarm Optimization (PSO) technique. The model is tested on various restricted and unrestricted risky investment portfolios and a comparative study with Genetic Algorithms is implemented. The PSO model demonstrates high computational efficiency in constructing optimal risky portfolios. Preliminary results show that the approach is very promising and achieves results comparable or superior with the state of the art solvers.</description><identifier>ISSN: 0957-4174</identifier><identifier>EISSN: 1873-6793</identifier><identifier>DOI: 10.1016/j.eswa.2011.02.075</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Computational efficiency ; Constraints ; Expert system ; Expert systems ; Financing ; Investment ; Optimal portfolio ; Optimization ; Particle Swarm Optimization (PSO) ; Portfolio management (PM) ; Sharp Ratio (SR) ; Solvers ; Swarm Intelligence (SI)</subject><ispartof>Expert systems with applications, 2011-08, Vol.38 (8), p.10161-10169</ispartof><rights>2011 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c431t-2b55e58d4206f4921f010ad7086abcaeb7a8e1fdf7ca7ddb6e2831be9c7c16483</citedby><cites>FETCH-LOGICAL-c431t-2b55e58d4206f4921f010ad7086abcaeb7a8e1fdf7ca7ddb6e2831be9c7c16483</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0957417411002818$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Zhu, Hanhong</creatorcontrib><creatorcontrib>Wang, Yi</creatorcontrib><creatorcontrib>Wang, Kesheng</creatorcontrib><creatorcontrib>Chen, Yun</creatorcontrib><title>Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem</title><title>Expert systems with applications</title><description>► We model a multi-objective portfolio optimization problem. ► Particle Swarm Optimization (PSO) algorithm has been used to solve the problem. ► The PSO model is tested on various risk investment portfolios. ► The results show that the PSO model are more effective than GAs and VBA solvers.
One of the most studied problems in the financial investment expert system is the intractability of portfolios. The non-linear constrained portfolio optimization problem with multi-objective functions cannot be efficiently solved using traditionally approaches. This paper presents a meta-heuristic approach to portfolio optimization problem using Particle Swarm Optimization (PSO) technique. The model is tested on various restricted and unrestricted risky investment portfolios and a comparative study with Genetic Algorithms is implemented. The PSO model demonstrates high computational efficiency in constructing optimal risky portfolios. Preliminary results show that the approach is very promising and achieves results comparable or superior with the state of the art solvers.</description><subject>Computational efficiency</subject><subject>Constraints</subject><subject>Expert system</subject><subject>Expert systems</subject><subject>Financing</subject><subject>Investment</subject><subject>Optimal portfolio</subject><subject>Optimization</subject><subject>Particle Swarm Optimization (PSO)</subject><subject>Portfolio management (PM)</subject><subject>Sharp Ratio (SR)</subject><subject>Solvers</subject><subject>Swarm Intelligence (SI)</subject><issn>0957-4174</issn><issn>1873-6793</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LxDAQQIMouK7-AU-9qYfWmTZtWvAi4hcsrKCeQ5pOMUvbrEnWRX-9LevB057m8t4M8xg7R0gQsLheJeS3KkkBMYE0AZEfsBmWIosLUWWHbAZVLmKOgh-zE-9XACgAxIy9vSgXjO4oet0q10fLdTC9-VHB2CG6fHldXkWtdVH4oEjbwQenzEBNtLYutLYzNrL_hbWzdUf9KTtqVefp7G_O2fvD_dvdU7xYPj7f3S5izTMMcVrnOeVlw1MoWl6l2AKCagSUhaq1olqokrBtWqGVaJq6oLTMsKZKC40FL7M5u9jtHe9-bsgH2RuvqevUQHbjZVlUeZlxnMjLveRUo6oQOB_RdIdqZ7131Mq1M71y3xJBTrHlSk6x5RRbQirH2KN0s5NofPfLkJNeGxo0NcaRDrKxZp_-CwrLiVM</recordid><startdate>20110801</startdate><enddate>20110801</enddate><creator>Zhu, Hanhong</creator><creator>Wang, Yi</creator><creator>Wang, Kesheng</creator><creator>Chen, Yun</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20110801</creationdate><title>Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem</title><author>Zhu, Hanhong ; Wang, Yi ; Wang, Kesheng ; Chen, Yun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c431t-2b55e58d4206f4921f010ad7086abcaeb7a8e1fdf7ca7ddb6e2831be9c7c16483</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Computational efficiency</topic><topic>Constraints</topic><topic>Expert system</topic><topic>Expert systems</topic><topic>Financing</topic><topic>Investment</topic><topic>Optimal portfolio</topic><topic>Optimization</topic><topic>Particle Swarm Optimization (PSO)</topic><topic>Portfolio management (PM)</topic><topic>Sharp Ratio (SR)</topic><topic>Solvers</topic><topic>Swarm Intelligence (SI)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhu, Hanhong</creatorcontrib><creatorcontrib>Wang, Yi</creatorcontrib><creatorcontrib>Wang, Kesheng</creatorcontrib><creatorcontrib>Chen, Yun</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Expert systems with applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhu, Hanhong</au><au>Wang, Yi</au><au>Wang, Kesheng</au><au>Chen, Yun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem</atitle><jtitle>Expert systems with applications</jtitle><date>2011-08-01</date><risdate>2011</risdate><volume>38</volume><issue>8</issue><spage>10161</spage><epage>10169</epage><pages>10161-10169</pages><issn>0957-4174</issn><eissn>1873-6793</eissn><abstract>► We model a multi-objective portfolio optimization problem. ► Particle Swarm Optimization (PSO) algorithm has been used to solve the problem. ► The PSO model is tested on various risk investment portfolios. ► The results show that the PSO model are more effective than GAs and VBA solvers.
One of the most studied problems in the financial investment expert system is the intractability of portfolios. The non-linear constrained portfolio optimization problem with multi-objective functions cannot be efficiently solved using traditionally approaches. This paper presents a meta-heuristic approach to portfolio optimization problem using Particle Swarm Optimization (PSO) technique. The model is tested on various restricted and unrestricted risky investment portfolios and a comparative study with Genetic Algorithms is implemented. The PSO model demonstrates high computational efficiency in constructing optimal risky portfolios. Preliminary results show that the approach is very promising and achieves results comparable or superior with the state of the art solvers.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.eswa.2011.02.075</doi><tpages>9</tpages></addata></record> |
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| language | eng |
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| source | Elsevier ScienceDirect Journals |
| subjects | Computational efficiency Constraints Expert system Expert systems Financing Investment Optimal portfolio Optimization Particle Swarm Optimization (PSO) Portfolio management (PM) Sharp Ratio (SR) Solvers Swarm Intelligence (SI) |
| title | Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem |
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