Parsing the pulse: decomposing macroeconomic sentiment with LLMs

Byeungchun Kwon, Taejin Park, Phurichai Rungcharoenkitkul and Frank Smets

No 1294, BIS Working Papers from Bank for International Settlements

Abstract: Macroeconomic indicators provide quantitative signals that must be pieced together and interpreted by economists. We propose a reversed approach of parsing press narratives directly using Large Language Models (LLM) to recover growth and inflation sentiment indices. A key advantage of this LLM-based approach is the ability to decompose aggregate sentiment into its drivers, readily enabling an interpretation of macroeconomic dynamics. Our sentiment indices track hard-data counterparts closely, providing an accurate, near real-time picture of the macroeconomy. Their components–demand, supply, and deeper structural forces–are intuitive and consistent with prior model-based studies. Incorporating sentiment indices improves the forecasting performance of simple statistical models, pointing to information unspanned by traditional data.

Keywords: macroeconomic sentiment; growth; inflation; monetary policy; fiscal policy; LLMs; machine learning (search for similar items in EconPapers)
JEL-codes: C55 C82 E30 E44 E60 (search for similar items in EconPapers)
Date: 2025-10
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