Staff Working Paper No. 865
By Eleni Kalamara, Arthur Turrell, Chris Redl, George Kapetanios and Sujit Kapadia
This paper considers the best ways to extract timely economic signals from newspaper text, showing 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 in absolute and marginal terms, but this varies according to the method used. Incorporating text into forecasts by combining counts of terms with supervised machine learning delivers the best forecast improvements both in marginal terms and relative to existing text-based methods. These improvements are most pronounced during periods of economic stress when, arguably, forecasts matter most.
Making text count: economic forecasting using newspaper text
