From Tweets to Transactions: High-Frequency Inflation Expectations, Consumption, and Stock Returns - Benjamin Born; Nora Lamersdorf; Jana-Lynn Schuster; Sascha Steffen (Munich Society for the Promotion of Economic Research - CESifo GmbH, 2025)
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.
Keywords: E31; D84; E58; C45; C81; 330; inflation expectations; social media (Twitter/X); large language models (LLMs); NLP; household consumption; stock returns; monetary policy