Making text count: economic forecasting using newspaper text

Staff working papers set out research in progress by our staff, with the aim of encouraging comments and debate.
Published on 22 May 2020

Staff Working Paper No. 865

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

We consider the best way to extract timely signals from newspaper text and use them to forecast macroeconomic variables using three popular UK newspapers that collectively represent UK newspaper readership in terms of political perspective and editorial style. We find that newspaper text can improve economic forecasts both in absolute and marginal terms. We introduce a powerful new method of incorporating text information in forecasts that combines counts of terms with supervised machine learning techniques. This method improves forecasts of macroeconomic variables including GDP, inflation, and unemployment, including relative to existing text-based methods. Forecast improvements occur when it matters most, during stressed periods.

PDFMaking text count: economic forecasting using newspaper text

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