From Text to Quantified Insights

This paper introduces a classification framework to analyze central bank communications across four dimensions: topic, communication stance, sentiment, and audience. Using a fine-tuned large language model trained on central bank documents, we classify individual sentences to transform policy language into systematic and quantifiable metrics on how central banks convey information to diverse stakeholders. Applied to a multilingual dataset of 74,882 documents from 169 central banks spanning 1884 to 2025, this study delivers the most comprehensive empirical analysis of central bank communication to date. Monetary policy communication changes significantly with inflation targeting, as backward-looking exchange rate discussions give way to forward-looking statements on inflation, interest rates, and economic conditions. We develop a directional communication index that captures signals about future policy rate changes and unconventional measures, including forward guidance and balance sheet operations. This unified signal helps explain future movements in market rates. While tailoring messages to audiences is often asserted, we offer the first systematic quantification of this practice. Audience-specific risk communication has remained stable for decades, suggesting a structural and deliberate tone. Central banks adopt neutral, fact-based language with financial markets, build confidence with the public, and highlight risks to governments. During crises, however, this pattern shifts remarkably: confidence-building rises in communication to the financial sector and government, while risk signaling increases for other audiences. Forward-looking risk communication also predicts future market volatility, demonstrating that central bank language plays a dual role across monetary and financial stability channels. Together, these findings provide novel evidence that communication is an active policy tool for steering expectations and shaping economic and financial conditions.