Trading Macro Narratives

Abstract

We study whether directional macro-narrative scores extracted from news can be used to form cross-sectional signals for U.S. equity and factor portfolios. Using 65 evergreen narratives over 2004–2025, we estimate expanding-window narrative betas and combine them with weekly changes in narrative scores to construct two related strategies: a characteristics-weighted portfolio that ranks assets by exposure to current narrative shifts, and a narrative momentum portfolio that ranks narratives by their recent score trends. We compare a prompted large language model (LLM) sentiment score with a simpler bag-of-words (BoW) attention measure and with a non-text benchmark based on principal components from the FRED-MD macro panel. Across both asset universes considered, the LLM-based signals generate positive returns in both portfolio constructions, while the BoW baseline is generally weaker and the macro benchmark remains competitive. The evidence is most naturally interpreted as showing that directional narrative measures extracted from text can complement traditional macro signals, rather than as a stand-alone replacement. Overall, the results are encouraging but still provisional: economic magnitudes are moderate, performance is not uniform across specifications, and questions of turnover, trading costs, and broader implementability remain open.

Keywords: Narrative economics, macro themes, cross-sectional allocation, narrative betas, large language models, characteristics-weighted portfolios.