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Working Paper
Mind Your Language: Market Responses to Central Bank Speeches
Researchers have carefully studied post-meeting central bank communication and have found that it often moves markets, but they have paid less attention to the more frequent central bankers’ speeches. We create a novel dataset of US Federal Reserve speeches and use supervised multimodal natural language processing methods to identify how monetary policy news affect financial volatility and tail risk through implied changes in forecasts of GDP, inflation, and unemployment. We find that news in central bankers’ speeches can help explain volatility and tail risk in both equity and bond ...
Working Paper
Mind Your Language: Market Responses to Central Bank Speeches
Researchers have carefully studied post-meeting central bank communication and have found that it often moves markets, but they have paid less attention to the more frequent central bankers’ speeches. We create a novel dataset of US Federal Reserve speeches and develop supervised multimodal natural language processing methods to identify how monetary policy news affect financial volatility and tail risk through implied changes in forecasts of GDP, inflation, and unemployment. We find that news in central bankers’ speeches can help explain volatility and tail risk in both equity and bond ...
Working Paper
FOMC Responses to Calls for Transparency
I apply latent semantic analysis to Federal Open Market Committee (FOMC) transcripts and minutes from 1976 to 2008 in order to analyze the Fed's responses to calls for transparency. Using a newly constructed measure of the transparency of deliberations, I study two events that define markedly different periods of transparency over this 32-year period. First, the 1978 Humphrey-Hawkins Act increased the degree to which the FOMC used meeting minutes to convey the content of its meetings. Historical evidence suggests that this increased transparency reflected a response to the Act's requirement ...
Working Paper
Regional Economic Sentiment: Constructing Quantitative Estimates from the Beige Book and Testing Their Ability to Forecast Recessions
We use natural language processing methods to quantify the sentiment expressed in the Federal Reserve's anecdotal summaries of current economic conditions in the national and 12 Federal Reserve District-level economies as published eight times per year in the Beige Book since 1970. We document that both national and District-level economic sentiment tend to rise and fall with the US business cycle. But economic sentiment is extremely heterogeneous across Districts, and we find that national economic sentiment is not always the simple aggregation of District-level sentiment. We show that the ...