What matters to firms? New insights from survey text comments

Ivan Yotzov, Nick Bloom, Philip Bunn, Paul Mizen, Pawel Smietanka and Greg Thwaites

Text data is often raw and unstructured, and yet it is the key means of human communication. Textual analysis techniques are increasingly being used in economic and financial research in a variety of different ways. In this post we apply these techniques to a new setting: the text comments left by respondents to the Decision Maker Panel (DMP) Survey, a UK-wide monthly business survey. Using over 20,000 comments, we show that: (i) these comments are a rich and unexplored data source, (ii) Brexit has been the dominant topic of comments since 2016, (iii) text-based indices match existing uncertainty measures from the DMP at both the aggregate and firm level, and (iii) sentiment among UK firms has been declining since 2016.

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Machine learning the news for better macroeconomic forecasting

Arthur Turrell, Eleni Kalamara, Chris Redl, George Kapetanios and Sujit Kapadia

Every day, journalists collate information about the world and, with nimble keystrokes, re-express it succinctly as newspaper copy. Events about the macroeconomy are no exception. So could there be additional valuable information about the economy contained in the news? In a recent research paper, we ask whether newspaper stories could help to predict future macroeconomic developments. We find that news can be used to enhance statistical economic forecasts of growth, inflation and unemployment — but only by using supervised machine learning techniques. We also find that the biggest forecast improvements occur when it matters most — during stressed periods.

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