Making Text Count: Economic Forecasting Using Newspaper Text

49 Pages Posted: 1 Jun 2020

See all articles by Eleni Kalamara

Eleni Kalamara

affiliation not provided to SSRN

Arthur Turrell

Bank of England

Chris Redl

International Monetary Fund

George Kapetanios

King's College, London

Sujit Kapadia

European Central Bank (ECB); Bank of England

Date Written: May 22, 2020

Abstract

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.

Keywords: Text, forecasting, machine learning

JEL Classification: J42, C55, J6

Suggested Citation

Kalamara, Eleni and Turrell, Arthur and Redl, Chris and Kapetanios, George and Kapadia, Sujit, Making Text Count: Economic Forecasting Using Newspaper Text (May 22, 2020). Bank of England Working Paper No. 865, Available at SSRN: https://ssrn.com/abstract=3610770 or http://dx.doi.org/10.2139/ssrn.3610770

Eleni Kalamara

affiliation not provided to SSRN

Arthur Turrell (Contact Author)

Bank of England ( email )

Threadneedle Street
London, EC2R 8AH
United Kingdom

Chris Redl

International Monetary Fund ( email )

Kuwait

George Kapetanios

King's College, London ( email )

30 Aldwych
London, WC2B 4BG
United Kingdom
+44 20 78484951 (Phone)

Sujit Kapadia

European Central Bank (ECB) ( email )

Sonnemannstrasse 22
Frankfurt am Main, 60314
Germany

Bank of England ( email )

Threadneedle Street
London, EC2R 8AH
United Kingdom
020-7601-5507 (Phone)

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