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Forecasting meets Portfolio Theory

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Resumo:Decision-making under uncertainty is central in fields such as economics, operations research, and engineering. Real-world choices rely on incomplete, noisy, and evolving information, creating challenges for both predictive accuracy and decision quality. Even with advances in data and modelling, human judgement remains affected by biases, heuristics, and limited awareness of uncertainty. Over the past two decades, research in forecasting and portfolio theory has developed tools such as shrinkage, model averaging, adaptive weighting, and robust optimisation to address estimation error, model misspecification, and structural instability. Yet insights across these areas remain scattered (e.g. [3- 4]). This review analyses 503 publications (2000–2025) to map thematic intersections and identify shared strategies for more resilient, uncertainty-aware decision-making.
Autores principais:Raimundo, Bernardo
Assunto:SDG 8 - Decent Work and Economic Growth SDG 9 - Industry, Innovation, and Infrastructure
Ano:2025
País:Portugal
Tipo de documento:póster em conferência
Tipo de acesso:acesso aberto
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL
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author Raimundo, Bernardo
author_facet Raimundo, Bernardo
Raimundo, Bernardo
author_role author
contributor_name_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
country_str PT
creators_json_str [{\"Person.name\":\"Raimundo, Bernardo\"}]
datacite.contributors.contributor.contributorName.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
datacite.creators.creator.creatorName.fl_str_mv Raimundo, Bernardo
datacite.date.Accepted.fl_str_mv 2025-12-18T00:00:00Z
datacite.date.available.fl_str_mv 2026-01-20T14:23:10Z
datacite.date.embargoed.fl_str_mv 2026-01-20T14:23:10Z
datacite.rights.fl_str_mv http://purl.org/coar/access_right/c_abf2
datacite.subjects.subject.fl_str_mv SDG 8 - Decent Work and Economic Growth
SDG 9 - Industry, Innovation, and Infrastructure
datacite.titles.title.fl_str_mv Forecasting meets Portfolio Theory
A Bibliometric Approach to Decision-Making under Uncertainty [poster]
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.creator.none.fl_str_mv Raimundo, Bernardo
dc.date.Accepted.fl_str_mv 2025-12-18T00:00:00Z
dc.date.available.fl_str_mv 2026-01-20T14:23:10Z
dc.date.embargoed.fl_str_mv 2026-01-20T14:23:10Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv http://hdl.handle.net/10362/199557
dc.language.none.fl_str_mv eng
dc.rights.none.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.subject.none.fl_str_mv SDG 8 - Decent Work and Economic Growth
SDG 9 - Industry, Innovation, and Infrastructure
dc.title.fl_str_mv Forecasting meets Portfolio Theory
A Bibliometric Approach to Decision-Making under Uncertainty [poster]
dc.type.none.fl_str_mv http://purl.org/coar/resource_type/c_6670
description Decision-making under uncertainty is central in fields such as economics, operations research, and engineering. Real-world choices rely on incomplete, noisy, and evolving information, creating challenges for both predictive accuracy and decision quality. Even with advances in data and modelling, human judgement remains affected by biases, heuristics, and limited awareness of uncertainty. Over the past two decades, research in forecasting and portfolio theory has developed tools such as shrinkage, model averaging, adaptive weighting, and robust optimisation to address estimation error, model misspecification, and structural instability. Yet insights across these areas remain scattered (e.g. [3- 4]). This review analyses 503 publications (2000–2025) to map thematic intersections and identify shared strategies for more resilient, uncertainty-aware decision-making.
dirty 0
eu_rights_str_mv openAccess
format conferencePoster
fulltext.url.fl_str_mv https://run.unl.pt/bitstreams/14f22f93-8792-4eda-8372-63e7412fe4a8/download
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identifier.url.fl_str_mv http://hdl.handle.net/10362/199557
instacron_str unl
institution Universidade Nova de Lisboa
instname_str Universidade Nova de Lisboa
language eng
network_acronym_str run
network_name_str Repositório Institucional da UNL
oai_identifier_str oai:run.unl.pt:10362/199557
organization_str_mv urn:organizationAcronym:unl
person_str_mv Raimundo, Bernardo
publishDate 2025
reponame_str Repositório Institucional da UNL
repository_id_str urn:repositoryAcronym:run
service_str_mv urn:repositoryAcronym:run
spelling engenDecision-making under uncertainty is central in fields such as economics, operations research, and engineering. Real-world choices rely on incomplete, noisy, and evolving information, creating challenges for both predictive accuracy and decision quality. Even with advances in data and modelling, human judgement remains affected by biases, heuristics, and limited awareness of uncertainty. Over the past two decades, research in forecasting and portfolio theory has developed tools such as shrinkage, model averaging, adaptive weighting, and robust optimisation to address estimation error, model misspecification, and structural instability. Yet insights across these areas remain scattered (e.g. [3- 4]). This review analyses 503 publications (2000–2025) to map thematic intersections and identify shared strategies for more resilient, uncertainty-aware decision-making.application/pdfenForecasting meets Portfolio TheorySubtitleenA Bibliometric Approach to Decision-Making under Uncertainty [poster]Raimundo, BernardoInformation Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)HostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptURNIsPartOfPURE: 150676259URNIsPartOfPURE UUID: a9f08d11-98fe-4e83-b158-5c1bf603bf3c2026-01-20T14:23:10Z2025-12-182025-12-18T00:00:00ZHandlehttp://hdl.handle.net/10362/199557http://purl.org/coar/access_right/c_abf2open accessSDG 8 - Decent Work and Economic GrowthSDG 9 - Industry, Innovation, and Infrastructure1117013 bytesother research producthttp://purl.org/coar/resource_type/c_6670conference posterhttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/14f22f93-8792-4eda-8372-63e7412fe4a8/download
spellingShingle Forecasting meets Portfolio Theory
Forecasting meets Portfolio Theory
Raimundo, Bernardo
SDG 8 - Decent Work and Economic Growth
SDG 9 - Industry, Innovation, and Infrastructure
Raimundo, Bernardo
SDG 8 - Decent Work and Economic Growth
SDG 9 - Industry, Innovation, and Infrastructure
status NEW
subject.fl_str_mv SDG 8 - Decent Work and Economic Growth
SDG 9 - Industry, Innovation, and Infrastructure
title Forecasting meets Portfolio Theory
title_full Forecasting meets Portfolio Theory
title_fullStr Forecasting meets Portfolio Theory
Forecasting meets Portfolio Theory
title_full_unstemmed Forecasting meets Portfolio Theory
Forecasting meets Portfolio Theory
title_short Forecasting meets Portfolio Theory
title_sort Forecasting meets Portfolio Theory
topic SDG 8 - Decent Work and Economic Growth
SDG 9 - Industry, Innovation, and Infrastructure
topic_facet SDG 8 - Decent Work and Economic Growth
SDG 9 - Industry, Innovation, and Infrastructure
url http://hdl.handle.net/10362/199557
visible 1