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Keywords:Text analysis 

Working Paper
Sentiment in Central Banks' Financial Stability Reports

Using the text of financial stability reports (FSRs) published by central banks, we analyze the relation between the financial cycle and the sentiment conveyed in these official communications. To do so, we construct a dictionary tailored specifically to a financial stability context, which assigns positive and negative connotations based on the sentiment conveyed by words in FSRs. With this dictionary, we construct a financial stability sentiment (FSS) index. Using a panel of 35 countries for the sample period between 2005 and 2015, we find that central banks' FSS indexes are mostly driven ...
International Finance Discussion Papers , Paper 1203

Working Paper
Deciphering Federal Reserve Communication via Text Analysis of Alternative FOMC Statements

We apply a natural language processing algorithm to FOMC statements to construct a new measure of monetary policy stance, including the tone and novelty of a policy statement. We exploit cross-sectional variations across alternative FOMC statements to identify the tone (for example, dovish or hawkish), and contrast the current and previous FOMC statements released after Committee meetings to identify the novelty of the announcement. We then use high-frequency bond prices to compute the surprise component of the monetary policy stance. Our text-based estimates of monetary policy surprises are ...
Research Working Paper , Paper RWP 20-14

Working Paper
The Power of Narratives in Economic Forecasts

We apply textual analysis tools to the narratives that accompany Federal Reserve Board economic forecasts to measure the degree of optimism versus pessimism expressed in those narratives. Text sentiment is strongly correlated with the accompanying economic point forecasts, positively for GDP forecasts and negatively for unemployment and inflation forecasts. Moreover, our sentiment measure predicts errors in FRB and private forecasts for GDP growth and unemployment up to four quarters out. Furthermore, stronger sentiment predicts tighter than expected monetary policy and higher future stock ...
Finance and Economics Discussion Series , Paper 2020-001

Working Paper
Deciphering Federal Reserve Communication via Text Analysis of Alternative FOMC Statements

We propose a text-based measure of monetary policy stance that models FOMC statements as convex combinations of dovish and hawkish alternatives, providing a tractable representation of the Committee's position along the policy spectrum. Leveraging staff-drafted alternative statements, we fine-tune a pre-trained language model to capture both quantitative precision and semantic tone. Stance is defined as the product of tone and novelty, and decomposed into expected and surprise components using high-frequency financial data. Surprises arise from shifts in tone relative to expectations or from ...
Research Working Paper , Paper RWP 20-14

Working Paper
Deciphering Federal Reserve Communication via Text Analysis of Alternative FOMC Statements*

We present a text-based metric for monetary policy stance using official and alternative Federal Open Market Committee statements. Our advanced natural language processing, with numeric property detection, jointly evaluates quantitative decisions like interest rates and qualitative explanations for these choices from texts. Monetary policy stance is decomposed into expected stance and surprise components by leveraging high-frequency bond futures data around FOMC announcements. We examine responses of stock returns to counterfactual (more dovish or hawkish) policy surprises through alternative ...
Research Working Paper , Paper RWP 20-14

Working Paper
Deciphering Federal Reserve Communication via Text Analysis of Alternative FOMC Statements

We propose a text-based measure of monetary policy stance that models FOMC statements as convex combinations of dovish and hawkish alternatives, providing a tractable representation of the Committee's position along the policy spectrum. Leveraging staff-drafted alternative statements, we fine-tune a pre-trained language model to capture both quantitative precision and semantic tone. Stance is defined as the product of tone and novelty, and decomposed into expected and surprise components using high-frequency financial data. Surprises arise from shifts in tone relative to expectations or from ...
Research Working Paper , Paper RWP 20-14

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