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Federal Reserve Bank of San Francisco
Working Paper Series
Measuring News Sentiment
Adam Hale Shapiro
Moritz Sudhof
Daniel J. Wilson
Abstract

We develop and assess new time series measures of economic sentiment based on computational text analysis of economic and financial newspaper articles from January 1980 to April 2015. The text analysis is based on predictive models estimated using machine learning techniques from Kanjoya. We analyze four alternative news sentiment indexes. We find that the news sentiment indexes correlate strongly with contemporaneous business cycle indicators. We also find that innovations to news sentiment predict future economic activity. Furthermore, in most cases, the news sentiment measures outperform the University of Michigan and Conference board measures in predicting the federal funds rate, consumption, employment, inflation, industrial production, and the S&P500. For some of these economic outcomes, there is evidence that the news sentiment measures have significant predictive power even after conditioning on these survey-based measures.


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Adam Hale Shapiro & Moritz Sudhof & Daniel J. Wilson, Measuring News Sentiment, Federal Reserve Bank of San Francisco, Working Paper Series 2017-1, 05 Jan 2017.
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DOI: 10.24148/wp2017-01
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