We diversify an investment portfolio across macroeconomic factors that are mimicked by investable asset classes and style factors. Using a century of global data we analyze the resulting macro factor portfolio's sensitivities to different macroeconomic scenarios and highlight the relevance of navigating time variation in macroeconomic risk premia. Specifically, we adapt the portfolio allocation to align with the identified macro environment as predicted by a forward-looking business cycle model. A Black-Litterman framework is used to thus improve upon a diversified macro factor allocation and to further tap into predictive asset class and style factor signals.
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Swade et al. (2024) studied this question.
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