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Dynamic factor models and deep learning architectures in GDP nowcasting: comparative evidence and macro momentum applications

datacite.subject.fosCiências Sociais::Economia e Gestão
dc.contributor.advisorJanuário, Afonso Varatojo
dc.contributor.authorVordermark, Timm
dc.date.accessioned2026-05-20T08:12:56Z
dc.date.available2026-05-20T08:12:56Z
dc.date.issued2026-01-23
dc.date.submitted2025-10-03
dc.description.abstractThis thesis compares Dynamic Factor Models and deep learning architectures (LSTM, MLP) for GDP nowcasting and tests their influence in a macro momentum strategy using U.S. macroeconomic data from 2016-2025. While deep learning models achieved comparable accuracy in stable periods, DFMs proved more robust during volatile phases where deep learning models struggled to adapt. Strategies on equities showed weak Sharpe Ratios with severe drawdowns, while strategies on treasuries were more stable yet inconclusive. In a macro momentum framework, GDP-based signals alone do not deliver abnormal returns and higher forecast accuracy does not necessarily lead to better trading performance.eng
dc.identifier.tid204241146
dc.identifier.urihttp://hdl.handle.net/10362/203222
dc.language.isoeng
dc.relationUID/ECO/00124/2013
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectGDP nowcasting
dc.subjectDynamic factor model
dc.subjectLong short-term memory network
dc.subjectMulti layer perceptron
dc.subjectDeep learning
dc.subjectMacro momentum
dc.subjectTrading strategies
dc.titleDynamic factor models and deep learning architectures in GDP nowcasting: comparative evidence and macro momentum applicationseng
dc.typemaster thesis
dspace.entity.typePublication
thesis.degree.nameA Work Project, presented as part of the requirements for the Award of a Master’s degree in Finance from the Nova School of Business and Economics

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