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Nowcasting using news topics. Big Data versus big bank

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Abstract

The agents in the economy use a plethora of high frequency information, including news media, to guide their actions and thereby shape aggregate economic fluctuations. Traditional nowcasting approches have to a relatively little degree made use of such information. In this paper, I show how unstructured textual information in a business newspaper can be decomposed into daily news topics and used to nowcast quarterly GDP growth. Compared with a big bank of experts, here represented by official central bank nowcasts and a state-of-the-art forecast combination system, the proposed methodology performs at times up to 15 percent better, and is especially competitive around important business cycle turning points. Moreover, if the statistical agency producing the GDP statistics itself had used the news-based methodology, it would have resulted in a less noisy revision process. Thus, news reduces noise.

Suggested Citation

  • Leif Anders Thorsrud, 2016. "Nowcasting using news topics. Big Data versus big bank," Working Paper 2016/20, Norges Bank.
    • Handle: RePEc:bno:worpap:2016_20
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    Cited by:

    1. Aguilar, Pablo & Ghirelli, Corinna & Pacce, Matías & Urtasun, Alberto, 2021. "Can news help measure economic sentiment? An application in COVID-19 times," Economics Letters, Elsevier, vol. 199(C).
    2. Nuttanan Wichitaksorn, 2020. "Analyzing and Forecasting Thai Macroeconomic Data using Mixed-Frequency Approach," PIER Discussion Papers 146, Puey Ungphakorn Institute for Economic Research.
    3. Shapiro, Adam Hale & Sudhof, Moritz & Wilson, Daniel J., 2022. "Measuring news sentiment," Journal of Econometrics, Elsevier, vol. 228(2), pages 221-243.
    4. Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.
    5. Dooruj Rambaccussing & Craig Menzies & Andrzej Kwiatkowski, 2022. "Look who’s Talking: Individual Committee members’ impact on inflation expectations," Dundee Discussion Papers in Economics 305, Economic Studies, University of Dundee.
    6. Lino Wehrheim, 2017. "Economic History Goes Digital: Topic Modeling the Journal of Economic History," Working Papers 177, Bavarian Graduate Program in Economics (BGPE).
    7. Ardia, David & Bluteau, Keven & Boudt, Kris, 2019. "Questioning the news about economic growth: Sparse forecasting using thousands of news-based sentiment values," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1370-1386.
    8. Charles W. Calomiris & Harry Mamaysky, 2018. "How News and Its Context Drive Risk and Returns Around the World," NBER Working Papers 24430, National Bureau of Economic Research, Inc.
    9. Ulrich Fritsche & Johannes Puckelwald, 2018. "Deciphering Professional Forecasters’ Stories - Analyzing a Corpus of Textual Predictions for the German Economy," Macroeconomics and Finance Series 201804, University of Hamburg, Department of Socioeconomics.
    10. Dorinth van Dijk & Jasper de Winter, 2023. "Nowcasting GDP using tone-adjusted time varying news topics: Evidence from the financial press," Working Papers 766, DNB.
    11. Beomseok Seo & Younghwan Lee & Hyungbae Cho, 2024. "Measuring News Sentiment of Korea Using Transformer," Korean Economic Review, Korean Economic Association, vol. 40, pages 149-176.
    12. Tino Berger & Simon Wintter, 2025. "Economic Forecasting With German Newspaper Articles," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 497-512, March.
    13. Consoli, Sergio & Pezzoli, Luca Tiozzo & Tosetti, Elisa, 2021. "Emotions in macroeconomic news and their impact on the European bond market," Journal of International Money and Finance, Elsevier, vol. 118(C).
    14. Saiz, Lorena & Magro, Manuel Medina, 2025. "What can newspaper articles reveal about the euro area economy?," Working Paper Series 3122, European Central Bank.
    15. Lino Wehrheim, 2019. "Economic history goes digital: topic modeling the Journal of Economic History," Cliometrica, Springer;Cliometric Society (Association Francaise de Cliométrie), vol. 13(1), pages 83-125, January.
    16. Calomiris, Charles W. & Mamaysky, Harry, 2019. "How news and its context drive risk and returns around the world," Journal of Financial Economics, Elsevier, vol. 133(2), pages 299-336.
    17. Carlos Moreno Pérez & Marco Minozzo, 2022. "Monetary Policy Uncertainty in Mexico: An Unsupervised Approach," Working Papers 2229, Banco de España.
    18. Hanjo Odendaal & Monique Reid & Johann F. Kirsten, 2020. "Media‐Based Sentiment Indices as an Alternative Measure of Consumer Confidence," South African Journal of Economics, Economic Society of South Africa, vol. 88(4), pages 409-434, December.
    19. Olivier de Bandt & Jean-Charles Bricongne & Julien Denes & Alexandre Dhenin & Annabelle De Gaye & Pierre-Antoine Robert, 2023. "Using the Press to Construct a New Indicator of Inflation Perceptions in France," Working papers 921, Banque de France.
    20. Matthias Huber & Simone Schüller & Marc Stöckli & Klaus Wohlrabe, 2018. "Maschinelles Lernen in der ökonomischen Forschung," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 71(07), pages 50-53, April.
    21. Vegard Høghaug Larsen & Leif Anders Thorsrud, 2022. "Asset returns, news topics, and media effects," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(3), pages 838-868, July.
    22. Dorine Boumans & Henrik Müller & Stefan Sauer, 2022. "How Media Content Influences Economic Expectations: Evidence from a Global Expert Survey," ifo Working Paper Series 380, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    23. Christopher A. Hollrah & Steven A. Sharpe & Nitish R. Sinha, 2020. "The Power of Narratives in Economic Forecasts," Finance and Economics Discussion Series 2020-001, Board of Governors of the Federal Reserve System (U.S.).
    24. Vegard H. Larsen & Leif Anders Thorsrud, 2018. "Business cycle narratives," Working Paper 2018/3, Norges Bank.

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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