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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
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
Tracking Real Time Layoffs with SEC Filings: A Preliminary Investigation
We explore a new source of data on layoffs: timely 8-K filings with the Securities and and Exchange Commission. We develop measures of both the number of reported layoff events and the number of affected workers. These series are highly correlated with the business cycle and other layoff indicators. Linking firm-level reported layoff events with WARN notices suggests that 8-K filings are sometimes available before WARN notices, and preliminary regression results suggest our layoff series are useful for forecasting. We also document the industry composition of the data and specific areas ...