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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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