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Sentiment in Central Banks’ Financial Stability Reports*
Author(s) -
Ricardo Correa,
Keshav Garud,
Juan M. Londoño,
Nathan Mislang
Publication year - 2020
Publication title -
review of finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.933
H-Index - 61
eISSN - 1875-824X
pISSN - 1572-3097
DOI - 10.1093/rof/rfaa014
Subject(s) - construct (python library) , financial stability , index (typography) , stability (learning theory) , point (geometry) , financial crisis , asset (computer security) , context (archaeology) , central bank , financial system , business , monetary policy , economics , finance , monetary economics , computer science , macroeconomics , mathematics , machine learning , paleontology , geometry , computer security , biology , world wide web , programming language
We use the text of financial stability reports (FSRs) published by central banks to analyze the relation between the sentiment they convey and the financial cycle. We construct a dictionary tailored specifically to a financial stability context, which classifies words as positive or negative based on the sentiment they convey in FSRs. With this dictionary, we construct financial stability sentiment (FSS) indexes for thirty countries between 2005 and 2017. We find that central banks’ financial stability communications are mostly driven by developments in the banking sector. Moreover, the sentiment captured by the FSS index explains movements in financial cycle indicators related to credit, asset prices, systemic risk, and monetary policy rates. Finally, our results show that the sentiment in central banks’ communications is a useful predictor of banking crises—a one percentage point increase in FSS is followed by a twenty-nine percentage point increase in the probability of a crisis.

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