Measuring Central Bank Communication: An Automated Approach with Application to FOMC Statements

Abstract

for their comments and suggestions, as well as participants at various seminars and Ken Kuttner, our discussant at the meeting of the NBER Monetary Economics Program. Isaac Laughlin provided outstanding research assistance. Hal Varian and the Google University Research Program provided generous access to the Google Search technology. The views and analysis set forth are solely those of the authors and do not indicate concurrence by other members of the Board of Governors of the Federal Reserve System. Francesco Trebbi gratefully acknowledges support by the Initiative on Global Markets and the Mutch Family Faculty Research Fund at the Booth School of Business, the University of Chicago. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

Central Bank Communication on Financial Stability

“…An alternative approach is used byLucca and Trebbi (2009), where FOMC statements are cut down into small segments of text, the semantic orientation of which is then calculated by checking how often these text segments appear in conjunction with the words dovish or hawkish in a large body of text.4 The scores are computed using scores from six subcategories, by adding the standardised word frequencies of the subcategories labelled as optimism increasing by DICTION (praise, satisfaction and inspiration), while our pessimism score is computed by adding the standardised word frequencies of the subcategories labelled optimism decreasing by DICTION (blame, hardship and denial). The Praise score, for example, includes words that isolate social qualities (witty), physical qualities (strong), intellectual qualities (reasonable), entrepreneurial qualities (successful) and moral qualities (good).…”

How Do Member Countries Receive IMF Policy Advice

“…The method used most in the field is (refined) dictionary approach. Lucca and Trebbi (2009) construct sentiment score using the content of FOMC announcements to predict fluctuations in treasury securities. To do this, they use dictionarybased methods: Google and Factiva semantic orientation scores.…”

Analysing Monetary Policy Statements of the Reserve Bank of India

“…This seems to be a good starting point for our technical analysis, especially for India where English is not the native language. 5 3 Other papers that study the tone of CB statements include Lucca and Trebbi (2009) for the US, and Galardo and Guerrieri (2017);Tobback et al (2017) for the ECB.…”