Can LLMs Learn Macroeconomic Narratives from Social Media?

TL;DR

This paper empirically investigates whether large language models can extract and utilize social media narratives to improve macroeconomic forecasting, highlighting challenges and providing NLP tools for narrative analysis.

Contribution

It introduces curated Twitter datasets and NLP methods for narrative extraction, testing their predictive power for macroeconomic forecasting, and discusses challenges in integrating narrative data into economic models.

Findings

Narratives from social media can influence macroeconomic predictions.

NLP tools can effectively extract and summarize economic narratives.

Challenges remain in integrating narrative data into macroeconomic models.

Abstract

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.