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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 ...
Working Paper
Central Bank Communication about Climate Change
This paper applies natural language processing to a large corpus of central bank speeches to identify those related to climate change. We analyze these speeches to better understand how central banks communicate about climate change. By all accounts, communication about climate change has accelerated sharply in recent years. The breadth of topics covered is wide, ranging from the impact of climate change on the economy to financial innovation, sustainable finance, monetary policy, and the central bank mandate. Financial stability concerns are touched upon, but macroprudential policy is rarely ...
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
Sentiment in Bank Examination Reports and Bank Outcomes
We investigate whether the bank examination process provides useful insight into bank future outcomes. We do this by conducting textual analysis on about 5,500 small to medium-sized commercial bank examination reports from 2004 to 2016. These confidential examination reports provide textual context to the components of supervisory ratings: capital adequacy, asset quality, management, earnings, and liquidity. Each component is given a categorical rating, and each bank is assigned an overall composite rating, which are used to determine the safety and soundness of banks. We find that, ...
Working Paper
Sentiment in Bank Examination Reports and Bank Outcomes
We investigate whether the bank examination process provides useful insight into bank future outcomes. We do this by conducting textual analysis on about 5,500 small to medium-sized commercial bank examination reports from 2004 to 2016. These confidential examination reports provide textual context to the components of supervisory ratings: capital adequacy, asset quality, management, earnings, and liquidity. Each component is given a categorical rating, and each bank is assigned an overall composite rating, which are used to determine the safety and soundness of banks. We find that, ...
Journal Article
How You Say It Matters: Text Analysis of FOMC Statements Using Natural Language Processing
The Federal Reserve has increasingly used public statements to shape expectations about future policy actions. After the Great Recession, when the nominal short-term interest rate reached its effective lower bound, the Federal Open Market Committee turned toward explicit forward guidance about the future path of the policy rate as well as the amount and composition of large-scale asset purchases in their post-meeting statements. Although these statements sometimes included quantitative information, they also included more nuanced, qualitative descriptions of economic conditions. However, ...
Working Paper
Identifying Financial Crises Using Machine Learning on Textual Data
We use machine learning techniques on textual data to identify financial crises. The onset of a crisis and its duration have implications for real economic activity, and as such can be valuable inputs into macroprudential, monetary, and fiscal policy. The academic literature and the policy realm rely mostly on expert judgment to determine crises, often with a lag. Consequently, crisis durations and the buildup phases of vulnerabilities are usually determined only with the benefit of hindsight. Although we can identify and forecast a portion of crises worldwide to various degrees with ...
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 ...