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...

Full description

Saved in:
Bibliographic Details
Published in:2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing pp. 212 - 219
Main Authors: Yan Jin, Rong Qu, Atkin, Jason
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2014
Subjects:
Heuristic algorithms
Mathematical model
Optimization
Portfolios
Sociology
Statistics
Vectors
ISBN:9781479984473, 1479984477
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
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 Proceedings Order Plan All Online (POP All Online) 1998-present by volume
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 Electronic Library (IEL)
  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 1.5991073
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/eLvHCXMwlV1LTwIxEJ4A8cDJBxjf6cGjC2xb-jiikXgRSZAET6Ttdg0JsoaAv99pd0EPXrxtN5tNM510vmlnvg_glhnraG7CCmiZcIPpjhaeoS-nSlrXz1XPRLEJORqp2UyPa3C374Xx3sfiM98Jj_EuPyvcNhyVddE7uVCiDnUpRdmrtfMdKiTGVipi75bEHIJzKXeUTtWYVaSNaU93J2-jweQhlHbxTqBn_iWtEiPL8PB_czqC9k-LHhnvg88x1PzqBJoBOpbMyy2YDsh4r86V3GO0ygjuBuV5oFmSiln1nTxHEWmC6JUE-c4oGoHfhhrTvFguCvKC28pHVfbThunw8fXhKalEFJIFIoNNkntnqJPWWuWNFFLmpue00CoL6ADhF1POa86zNPWUZiYkRM47G64rTd9KdgqNVbHyZ0AM4xaxuFNUOk49w19qg3YPMrqp19k5tIJ95p8lT8a8Ms3F368voRmsX9ZrXUFjs976azhwX2in9U1c3G9NlqMI
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NT8JAEJ0gmsjJDzB-uwePFuh26e4ekUgwQiUBEjyR7XZrSJAaAv5-Z7cFPXjx1jZN08xMdt7szrwHcB-oWNNUWQ9I7jGF5Y4MTYCx7Ase61YqmsqJTfAoEtOpHJbgYTcLY4xxzWembi_dWX6S6Y3dKmtgdLJQhHuw32KMNvNprW300JBjdqWhm97iWEUwxvmW1Km4DwraRr8pG6O3qD3q2OYuVrcEzb_EVVxu6R7976-OofYzpEeGu_RzAiWzPIWKBY8593IVJm0y3OlzeY-YrxKC60G-I6gWpOBWfScDJyNNEL8SK-DpZCPwXdtlmmaLeUZecWH5KBp_ajDpPo07Pa-QUfDmiA3WXmq0oprHcSyM4iHnqWpqGUqRWHyAACwQ2kjGEt83lCbKlkTa6NgeWKpWzIMzKC-zpTkHogIWIxrXgnLNqAnwk1Kh3a2Qrm9kcgFVa5_ZZ86UMStMc_n34zs47I0H_Vn_OXq5gor1RN69dQ3l9WpjbuBAf6HNVrfO0d_5Z6ZP
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