Making text count: Economic forecasting using newspaper text

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

This paper examines several ways to extract timely economic signals from newspaper text and shows that such information can materially improve forecasts of macroeconomic variables including GDP, inflation and unemployment. Our text is drawn from three popular UK newspapers that collectively represent UK newspaper readership in terms of political perspective and editorial style. Exploiting newspaper text can improve economic forecasts both unconditionally and when conditioning on other relevant information, but the performance of the latter varies according to the method used. Incorporating text into forecasts by combining counts of terms with supervised machine learning delivers the highest forecast improvements relative to existing text-based methods. These improvements are most pronounced during periods of economic stress when, arguably, forecasts matter most.

Original languageEnglish
Pages (from-to)896-919
Number of pages24
JournalJOURNAL OF APPLIED ECONOMETRICS
Volume37
Issue number5
Early online date11 May 2022
DOIs
Publication statusPublished - Aug 2022

Keywords

  • forecasting
  • machine learning
  • text

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