  This study empirically tests the $\textit{Narrative Economics}$ hypothesis,
which posits that narratives (ideas that are spread virally and affect public
beliefs) can influence economic fluctuations. We introduce two curated datasets
containing posts from X (formerly Twitter) which capture economy-related
narratives (Data will be shared upon paper acceptance). Employing Natural
Language Processing (NLP) methods, we extract and summarize narratives from the
tweets. We test their predictive power for $\textit{macroeconomic}$ forecasting
by incorporating the tweets' or the extracted narratives' representations in
downstream financial prediction tasks. Our work highlights the challenges in
improving macroeconomic models with narrative data, paving the way for the
research community to realistically address this important challenge. From a
scientific perspective, our investigation offers valuable insights and NLP
tools for narrative extraction and summarization using Large Language Models
(LLMs), contributing to future research on the role of narratives in economics.
