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The newest deep learning methods work best when the data contain rich, repeating structures.
Holding the data fixed and evaluating roughly a dozen univariate methods without exogenous regressors, it is found that asset returns remain near unforecastable across every method family and that hybrid and machine learning methods exhibit additional forecasting power on basis spreads and bank indicators.
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