When Does Central Bank Communication Matter? Textual Information, Dynamics, and Regularization

This paper examines when textual information from central bank communication improves forecasts of policy rate changes. Using the minutes of the Brazilian Central Bank’s Monetary Policy Committee (Copom), we study whether textual content helps predict changes in the Selic target rate between consecutive meetings. The minutes are encoded using dense sentence-level embeddings, and low-dimensional textual factors are extracted via principal component analysis estimated exclusively on the training sample to prevent look-ahead bias. Predictive performance is assessed out of sample using an expanding-window backtesting framework and compared against standard forecasting benchmarks, including persistence and random-walk specifications, linear autoregressive models, regularized regressions, and state-space models estimated via the Kalman filter. We find that text-based predictors perform poorly when used in isolation but deliver meaningful forecast improvements when combined with short-run dynamics and regularization. These gains are economically relevant and arise primarily in episodes associated with policy rate adjustments, whereas simple persistence-based forecasts remain difficult to outperform during rate-hold periods. Overall, the results indicate that central bank communication contains forward-looking information that is valuable for forecasting policy changes, but that this information is sparse, episodic, and best extracted through disciplined regularization and dynamic modeling rather than purely cross-sectional textual signals.