Template-Type: ReDIF-Paper 1.0
Title: From Tweets to Transactions: High-Frequency Inflation Expectations, Consumption, and Stock Returns
Author-Name:Benjamin Born
Author-Email: b.born@uni-bonn.de
Author-Name: Nora Lamersdorf
Author-Email: nora.lamersdorf@bi.no
Author-Name: Jana-Lynn Schuster
Author-Email: j.schuster@fs.de
Author-Name: Sascha Steffen
Author-Email: s.steffen@fs.de

Classification-JEL: E31, D84, E58, C45, C81



Keywords: Inflation expectations, social media (Twitter/X), large language models (LLMs),
NLP, household consumption, stock returns, monetary policy

Abstract: Using modern natural language processing, we construct a high-frequency inflation expectations index from German-language tweets. 
This index closely tracks realized inflation and aligns even more closely with household survey expectations. It also improves short-run forecasts
relative to standard benchmarks. In response to monetary policy tightening, the index declines
within about a week, with the effects concentrated in tweets by private individuals and during
the recent period of elevated inflation. Using 117 million online transactions from German
retailers, we show that higher inflation expectations are followed by lower household spending on
discretionary goods. By linking these shifts in demand to stock returns, we find that, during
periods of elevated inflation, firms operating in discretionary sectors experience significantly
lower stock returns when inflation expectations rise. Thus, our Twitter-based index provides
market participants and policymakers with a timely tool to monitor inflation sentiment and its
economic consequences.

Note: 
Length: 61
Creation-Date: 2026-01
Revision-Date: 
File-URL: https://www.crctr224.de/research/discussion-papers/archive/dp724
File-Format: application/pdf
Handle: RePEc:bon:boncrc:CRCTR224_2025_724