A Population-Based Incremental Learning Method for Constrained Portfolio Optimisation
This paper investigates a hybrid algorithm which utilizes exact and heuristic methods to optimise asset selection and capital allocation in portfolio optimisation. The proposed method is composed of a customised population based incremental learning procedure and a mathematical programming applicati...
Uloženo v:
| Vydáno v: | 2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing s. 212 - 219 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
01.09.2014
|
| Témata: | |
| ISBN: | 9781479984473, 1479984477 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | This paper investigates a hybrid algorithm which utilizes exact and heuristic methods to optimise asset selection and capital allocation in portfolio optimisation. The proposed method is composed of a customised population based incremental learning procedure and a mathematical programming application. It is based on the standard Markowitz model with additional practical constraints such as cardinality on the number of assets and quantity of the allocated capital. Computational experiments have been conducted and analysis has demonstrated the performance and effectiveness of the proposed approach. |
|---|---|
| AbstractList | This paper investigates a hybrid algorithm which utilizes exact and heuristic methods to optimise asset selection and capital allocation in portfolio optimisation. The proposed method is composed of a customised population based incremental learning procedure and a mathematical programming application. It is based on the standard Markowitz model with additional practical constraints such as cardinality on the number of assets and quantity of the allocated capital. Computational experiments have been conducted and analysis has demonstrated the performance and effectiveness of the proposed approach. |
| Author | Atkin, Jason Rong Qu Yan Jin |
| Author_xml | – sequence: 1 surname: Yan Jin fullname: Yan Jin email: ywj@cs.nott.ac.uk organization: ASAP Group, Univ. of Nottingham, Nottingham, UK – sequence: 2 surname: Rong Qu fullname: Rong Qu email: rxq@cs.nott.ac.uk organization: ASAP Group, Univ. of Nottingham, Nottingham, UK – sequence: 3 givenname: Jason surname: Atkin fullname: Atkin, Jason email: jaa@cs.nott.ac.uk organization: ASAP Group, Univ. of Nottingham, Nottingham, UK |
| BookMark | eNpVjDtPwzAYAI0ACShZWVj8B1L8fowh4lGp0EqlA1PlOF_AUmpXjhn49yBgYTqddLoLdBJTBISuKJlTSuzN5vW52bRzRqiYc3WEKqsNFdpaI4Qhx_9c8zNUTVPoCFNaCc7UOdo2eJ0OH6MrIcX61k3Q40X0GfYQixvxElyOIb7hJyjvqcdDyrhNcSrZhfjdrlMuQxpDwqtDCfsw_Ywu0engxgmqP87Q9v7upX2sl6uHRdss60C1LPUA3jGvu64z4LTSenDEW2VNT4g2XDJuPFghekqBsd5JxaQH31EprZOd5jN0_fsNALA75LB3-XOnCRfKKP4FIitVIg |
| CODEN | IEEPAD |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/SYNASC.2014.36 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Xplore url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Statistics |
| EISBN | 9781479984480 1479984485 |
| EndPage | 219 |
| ExternalDocumentID | 7034686 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IL ALMA_UNASSIGNED_HOLDINGS CBEJK RIB RIC RIE RIL |
| ID | FETCH-LOGICAL-i175t-feca2c7bbb8ea7677fa0c9698d007835238ce944d11e22da5625cecb1559a5b73 |
| IEDL.DBID | RIE |
| ISBN | 9781479984473 1479984477 |
| ISICitedReferencesCount | 7 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000366596600029&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Wed Jun 26 19:20:46 EDT 2024 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i175t-feca2c7bbb8ea7677fa0c9698d007835238ce944d11e22da5625cecb1559a5b73 |
| PageCount | 8 |
| ParticipantIDs | ieee_primary_7034686 |
| PublicationCentury | 2000 |
| PublicationDate | 2014-Sept. |
| PublicationDateYYYYMMDD | 2014-09-01 |
| PublicationDate_xml | – month: 09 year: 2014 text: 2014-Sept. |
| PublicationDecade | 2010 |
| PublicationTitle | 2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing |
| PublicationTitleAbbrev | SYNASC |
| PublicationYear | 2014 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssib026764326 |
| Score | 1.5990245 |
| Snippet | This paper investigates a hybrid algorithm which utilizes exact and heuristic methods to optimise asset selection and capital allocation in portfolio... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 212 |
| SubjectTerms | Heuristic algorithms Mathematical model Optimization Portfolios Sociology Statistics Vectors |
| Title | A Population-Based Incremental Learning Method for Constrained Portfolio Optimisation |
| URI | https://ieeexplore.ieee.org/document/7034686 |
| WOSCitedRecordID | wos000366596600029&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09b8IwED0B6sDUD6j6LQ8dayCxsZ2RVkVdSpEoEp2QPy4VEiUVgv7-2k6gHbp0S6Ioip4t3zv77j2A2xytS1O01PIkp9xYSzUTPnG1vM-RJYwrHc0m5GikZrNsXIO7fS8MIsbiM-yEy3iW7wq7DVtlXT87uVCiDnUpRdmrtZs7qZA-tqYi9m5Jn0NwLuVO0qm6Z5VoY9LLupO30WDyEEq7eCfIM_-yVomRZXj4v386gvZPix4Z74PPMdRwdQLNQB1L5eUWTAdkvHfnovc-WjniV4NyP1AvSaWs-k6eo4k08eyVBPvOaBrh3w01pnmxXBTkxS8rH1XZTxumw8fXhydamSjQhWcGG-oHQ6dWGmMUaimkzHXPZiJTLrADT7-Ysphx7pIE09TpkBBZtCYcV-q-kewUGqtihWdAnMo1s0obdB5T1zeJ0MZ5BuLCV1lyDq2Az_yz1MmYV9Bc_P34EpoB_bJe6woam_UWr-HAfnmc1jdxcL8BTgCkxQ |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LTwIxEJ4gmsjJBxjf9uDRBXbbbbtHJBKMsJIACZ5IX2tIEAwBf79td0EPXrxtN5tNM9N0vmlnvg_gPjNKR5FRgSJhFhCpVCAwtYmrIjExOMSECy82wdKUTybJoAQPu14YY4wvPjN19-jv8vVSbdxRWcOuTkI53YP9mJComXdrbVdPRJmNrhH13VvMZhGEMLYldSrGuKBtDJtJY_iWtoZtV9xF6o6g-Ze4io8tnaP_zeoYaj9NemiwCz8nUDKLU6g48JhzL1dh3EKDnT5X8GjjlUZ2P8hPBMUcFdyq76jvZaSRxa_ICXh62Qj7rasyzZbz2RK92o3loyj8qcG48zRqd4NCRiGYWWywDqw7RKSYlJIbwShjmWiqhCZcO3xgARjmyiSE6DA0UaSFS4mUUdJdWIpYMnwG5cVyYc4BaZ4JrLiQRlub6liGVEhtMYh2f8XhBVSdfaafOVPGtDDN5d-v7-CwO-r3pr3n9OUKKs4TefXWNZTXq425gQP1ZW22uvWO_gYQIKgM |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2014+16th+International+Symposium+on+Symbolic+and+Numeric+Algorithms+for+Scientific+Computing&rft.atitle=A+Population-Based+Incremental+Learning+Method+for+Constrained+Portfolio+Optimisation&rft.au=Yan+Jin&rft.au=Rong+Qu&rft.au=Atkin%2C+Jason&rft.date=2014-09-01&rft.pub=IEEE&rft.isbn=9781479984473&rft.spage=212&rft.epage=219&rft_id=info:doi/10.1109%2FSYNASC.2014.36&rft.externalDocID=7034686 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781479984473/lc.gif&client=summon&freeimage=true |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781479984473/mc.gif&client=summon&freeimage=true |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781479984473/sc.gif&client=summon&freeimage=true |