Do Risk-Related Words Predict Financial Market Responses? Evidence from Federal Reserve Press Conferences
Abstract
Federal Reserve press conferences convey policy path information and uncertainty beyond formal decisions. This study tests whether transcript-derived risk language predicts the magnitude of financial market responses after policy surprises and event characteristics enter the model. The dataset contains 93 official press conference transcripts from April 2011 to June 2026, with 90 scheduled events in the primary sample. A Q&A lexical risk index combines standardised frequencies of negative, uncertainty, and weak modal terms from the Loughran–McDonald dictionary. The outcome is an equal weight composite of absolute S&P 500 returns, two- and ten-year Treasury yield changes, and US dollar returns during a 70 min press conference window. OLS models use HC3 standard errors, asset-specific regressions, influence analysis, permutation testing, and leave-one-out cross-validation. Q&A lexical risk does not predict larger responses. The full-model coefficient equals −0.063 (p = 0.444), incremental R2 equals 0.0034, and prediction error rises by 0.60% after adding the index. Policy surprise magnitude remains the strongest predictor. The 90-event sample limits precision for small effects, with an approximate 80% minimum detectable effect of 0.230 response index units. The estimates describe conditional association and incremental predictive content. They do not test acoustic delivery, realised intraday volatility, or a causal communication effect.
