Enhancing portfolio optimization with machine learning methods

Bibliographic Details
Main Author: Raimundo, Bernardo
Publication Date: 2024
Other Authors: Bravo, Jorge Miguel
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/175690
Summary: Raimundo, B., & Bravo, J. M. (2024). Enhancing portfolio optimization with machine learning methods: A comparative study using commodity markets data. In MCIS 2024 Proceedings Article 39 AISEL. https://aisel.aisnet.org/mcis2024/39/ --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project UIDB/04152/2020 (DOI: 10.54499/UIDB/04152/2020) - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.
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spelling Enhancing portfolio optimization with machine learning methodsA comparative study using commodity markets dataSDG 8 - Decent Work and Economic GrowthRaimundo, B., & Bravo, J. M. (2024). Enhancing portfolio optimization with machine learning methods: A comparative study using commodity markets data. In MCIS 2024 Proceedings Article 39 AISEL. https://aisel.aisnet.org/mcis2024/39/ --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project UIDB/04152/2020 (DOI: 10.54499/UIDB/04152/2020) - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.Portfolio optimization is the process of selecting an optimal portfolio of assets according to some objective, investor preferences, and constraints, typically optimizing the trade-off between risk and return. This study evaluates the effectiveness of traditional and novel machine learning portfolio optimization techniques by incorporating short selling, a design feature often overlooked in previous research. We employ historical commodity market data from seven commodity groups. The strategies investigated include Mean-Variance Optimization, Global Minimum Variance, Equal Weights, Maximum Diversification, Risk Parity, and Hierarchical Risk Parity. The findings suggest that allowing for short selling impacts the performance portfolio optimization strategies. Mean-Variance Optimization potentially increases returns but at the cost of greater volatility. Global Minimum Variance consistently exhibits stability and minimal risk, ideal for portfolio managers who adopt conservative investment strategies. Maximum Diversifying Portfolio and Risk Parity show moderate but resilient performance, and Hierarchical Risk Parity, despite its innovation, tends to be more volatile. Surprisingly, the Equal Weighted strategy holds its ground against more complex approaches, providing a viable option for those who value simplicity. This analysis highlights the importance of matching portfolio strategies with investor risk preferences, especially when integrating techniques like short selling.AISELInformation Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNRaimundo, BernardoBravo, Jorge Miguel2024-11-22T22:21:41Z2024-10-032024-10-03T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion16application/pdfhttp://hdl.handle.net/10362/175690eng978-989-33-6886-2PURE: 102558742info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-12-02T01:35:44Zoai:run.unl.pt:10362/175690Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T19:15:59.488916Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Enhancing portfolio optimization with machine learning methods
A comparative study using commodity markets data
title Enhancing portfolio optimization with machine learning methods
spellingShingle Enhancing portfolio optimization with machine learning methods
Raimundo, Bernardo
SDG 8 - Decent Work and Economic Growth
title_short Enhancing portfolio optimization with machine learning methods
title_full Enhancing portfolio optimization with machine learning methods
title_fullStr Enhancing portfolio optimization with machine learning methods
title_full_unstemmed Enhancing portfolio optimization with machine learning methods
title_sort Enhancing portfolio optimization with machine learning methods
author Raimundo, Bernardo
author_facet Raimundo, Bernardo
Bravo, Jorge Miguel
author_role author
author2 Bravo, Jorge Miguel
author2_role author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Raimundo, Bernardo
Bravo, Jorge Miguel
dc.subject.por.fl_str_mv SDG 8 - Decent Work and Economic Growth
topic SDG 8 - Decent Work and Economic Growth
description Raimundo, B., & Bravo, J. M. (2024). Enhancing portfolio optimization with machine learning methods: A comparative study using commodity markets data. In MCIS 2024 Proceedings Article 39 AISEL. https://aisel.aisnet.org/mcis2024/39/ --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project UIDB/04152/2020 (DOI: 10.54499/UIDB/04152/2020) - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.
publishDate 2024
dc.date.none.fl_str_mv 2024-11-22T22:21:41Z
2024-10-03
2024-10-03T00:00:00Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/175690
url http://hdl.handle.net/10362/175690
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-989-33-6886-2
PURE: 102558742
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 16
application/pdf
dc.publisher.none.fl_str_mv AISEL
publisher.none.fl_str_mv AISEL
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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