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   <subfield code="a">Huang, Chengyu.</subfield>
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   <subfield code="a">News-based Sentiment Indicators /</subfield>
   <subfield code="c">Chengyu Huang, Sean Simpson, Daria Ulybina, Agustin Roitman.</subfield>
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   <subfield code="a">Washington, D.C. :</subfield>
   <subfield code="b">International Monetary Fund,</subfield>
   <subfield code="c">2019.</subfield>
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   <subfield code="a">1 online resource (56 pages)</subfield>
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   <subfield code="a">Electronic access restricted to authorized BRAC University faculty, staff and students</subfield>
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   <subfield code="a">We construct sentiment indices for 20 countries from 1980 to 2019. Relying on computational text analysis, we capture specific language like 'fear', 'risk', 'hedging', 'opinion', and, 'crisis', as well as 'positive' and 'negative' sentiments, in news articles from the Financial Times. We assess the performance of our sentiment indices as 'news-based' early warning indicators (EWIs) for financial crises. We find that sentiment indices spike and/or trend up ahead of financial crises.</subfield>
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   <subfield code="a">Simpson, Sean.</subfield>
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   <subfield code="a">Ulybina, Daria.</subfield>
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   <subfield code="a">IMF Working Papers; Working Paper ;</subfield>
   <subfield code="v">No. 2019/273</subfield>
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