Board of Governors
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Measuring AI Uptake in the Workplace
Leland Crane, Michael Green, Paul Soto
Artificial Intelligence (AI) may be poised to raise productivity across various domains, including writing (Noy and Zhang 2023), programming (Peng et al. 2023), and research and development (Toner-Rodgers 2024; Korinek 2023). However, understanding the extent to which AI—and generative AI in particular—has been adopted as part of the production process remains an open question. This […]
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Using Generative AI Models to Understand FOMC Monetary Policy Discussions
Wendy Dunn, Ellen E. Meade, Nitish Ranjan Sinha, Raakin Kabir
In an era increasingly shaped by artificial intelligence (AI), the public’s understanding of economic policy may be filtered through the lens of generative AI models (also called large language models or LLMs). Generative AI models offer the promise of quickly ingesting and interpreting large amounts of textual information. Thus far, however, little is known about […]
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Reasons Behind Words: OPEC Narratives and the Oil Market
Celso Brunetti, Marc Joets, Valerie Mignon
We analyze the content of the Organization of the Petroleum Exporting Countries (OPEC) communications and whether it provides information to the crude oil market. To this end, we derive an empirical strategy which allows us to measure OPEC’s public signal and test whether market participants find it credible. Using Structural Topic Models, we analyze OPEC […]
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Manufacturing Sentiment: Forecasting Industrial Production with Text Analysis
Tomaz Cajner, Leland D. Crane, Christopher Kurz, Norman Morin, Paul E. Soto, Betsy Vrankovich
This paper examines the link between industrial production and the sentiment expressed in natural language survey responses from U.S. manufacturing firms. We compare several natural language processing (NLP) techniques for classifying sentiment, ranging from dictionary-based methods to modern deep learning methods. Using a manually labeled sample as ground truth, we find that deep learning models—partially […]
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Tracking Real Time Layoffs with SEC Filings: A Preliminary Investigation
Leland D. Crane, Emily Green, Molly Harnish, Will McClennan, Paul E. Soto, Betsy Vrankovich, Jacob Williams
We explore a new source of data on layoffs: timely 8-K filings with the Securities 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 […]
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Sentiment in Bank Examination Reports and Bank Outcomes
Maureen Cowhey, Seung Jung Lee, Thomas Popeck Spiller, Cindy M. Vojtech
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, controlling for a variety of factors, including the ratings themselves, the sentiment supervisors express in describing most of the components predict relevant future bank outcomes. The sentiment conveyed in the asset quality, management, and earnings sections provides significant information in predicting future outcomes for problem loans, supervisory actions, and profitability, respectively, for all banks. Sentiment conveyed in the capital adequacy section appears to be predictive of future capital ratios for weak banks. These relationships suggest that bank supervisors play a meaningful role in the surveillance of the banking system.
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High Tech Business Entry in the Pandemic Era
Ryan Decker, John Haltiwanger
The COVID-19 pandemic and its aftermath have featured a surge in business entry (Decker and Haltiwanger 2024). A natural question is whether the elevated entry seen in recent years will have positive implications for aggregate productivity growth given the historically important role of business entry for productivity dynamics (Decker et al. 2014, Alon et al. […]
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Fed Communication, News, Twitter, and Echo Chambers
Bennett Schmanski, Chiara Scotti, Clara Vega, Hedi Benamar
We estimate monetary policy surprises (sentiment) from the perspective of three different textual sources: direct central bank communication (FOMC statements and press conferences), news articles, and Twitter posts during FOMC announcement days. Textual sentiment across sources is highly correlated, but there are times when news and Twitter sentiment substantially disagree with the sentiment conveyed by […]
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More than Words: Twitter Chatter and Financial Market Sentiment
Travis Adams, Andrea Ajello, Diego Silva, Francisco Vazquez-Grande
We build a new measure of credit and financial market sentiment using Natural Language Processing on Twitter data. We find that the Twitter Financial Sentiment Index (TFSI) correlates highly with corporate bond spreads and other price- and survey-based measures of financial conditions. We document that overnight Twitter financial sentiment helps predict next day stock market […]