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Can Earnings Calls Be Used to Gauge Labor Market Tightness?
Dueholm, Mick; Kalyani, Aakash; Ozkan, Serdar (2024-06-18)
An index that uses textual analysis of earnings calls to track labor issues appears to be highly correlated to one measure of labor market tightness.
On the Economy

Working Paper
Measuring Job Loss during the Pandemic Recession in Real Time with Twitter Data
Aizenman, Anbar; Cajner, Tomaz; Doniger, Cynthia L.; Williams, Jacob; Brennan, Connor M. (2023-05-23)
We present an indicator of job loss derived from Twitter data, based on a fine-tuned neural network with transfer learning to classify if a tweet is job-loss related or not. We show that our Twitter-based measure of job loss is well-correlated with and predictive of other measures of unemployment available in the official statistics and with the added benefits of real-time availability and daily frequency. These findings are especially strong for the period of the Pandemic Recession, when our Twitter indicator continues to track job loss well but where other real-time measures like ...
Finance and Economics Discussion Series , Paper 2023-035

Working Paper
Identifying Financial Crises Using Machine Learning on Textual Data
Chen, Mary; DeHaven, Matthew; Kitschelt, Isabel; Lee, Seung Jung; Sicilian, Martin (2023-03-31)
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 ...
International Finance Discussion Papers , Paper 1374

Working Paper
Deciphering Federal Reserve Communication via Text Analysis of Alternative FOMC Statements
Yang, Shu-Kuei X.; Song, Dongho; Doh, Taeyoung (2020-10-06)
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
FOMC Responses to Calls for Transparency
Acosta, Miguel (2015-07-10)
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 ...
Finance and Economics Discussion Series , Paper 2015-60

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