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Dynamic factor models and deep learning architectures in GDP nowcasting: comparative evidence and macro momentum applications
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This 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.
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Palavras-chave
GDP nowcasting Dynamic factor model Long short-term memory network Multi layer perceptron Deep learning Macro momentum Trading strategies