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Forecasting with Economic News

Author

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  • Luca Barbaglia
  • Sergio Consoli
  • Sebastiano Manzan

Abstract

The goal of this article is to evaluate the informational content of sentiment extracted from news articles about the state of the economy. We propose a fine-grained aspect-based sentiment analysis that has two main characteristics: (a) we consider only the text in the article that is semantically dependent on a term of interest (aspect-based) and, (b) assign a sentiment score to each word based on a dictionary that we develop for applications in economics and finance (fine-grained). Our dataset includes six large U.S. newspapers, for a total of over 6.6 million articles and 4.2 billion words. Our findings suggest that several measures of economic sentiment track closely business cycle fluctuations and that they are relevant predictors for four major macroeconomic variables. We find that there are significant improvements in forecasting when sentiment is considered along with macroeconomic factors. In addition, we also find that sentiment matters to explains the tails of the probability distribution across several macroeconomic variables.

Suggested Citation

  • Luca Barbaglia & Sergio Consoli & Sebastiano Manzan, 2023. "Forecasting with Economic News," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(3), pages 708-719, July.
    • Handle: RePEc:taf:jnlbes:v:41:y:2023:i:3:p:708-719
    DOI: 10.1080/07350015.2022.2060988
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    Citations

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    Cited by:

    1. Maria Saveria Mavillonio, 2024. "Natural Language Processing Techniques for Long Financial Document," Discussion Papers 2024/317, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    2. Alexandra Bozhechkova & Urmat Dzhunkeev, 2024. "CLARA and CARLSON: Combination of Ensemble and Neural Network Machine Learning Methods for GDP Forecasting," Russian Journal of Money and Finance, Bank of Russia, vol. 83(3), pages 45-69, September.
    3. Tiziana Assenza & Fabrice Collard & Patrick Fève & Stefanie Huber, 2024. "From Buzz to Bust: How Fake News Shapes the Business Cycle," ECONtribute Discussion Papers Series 287, University of Bonn and University of Cologne, Germany.
    4. José Francisco Lima & Fernanda Catarina Pereira & Arminda Manuela Gonçalves & Marco Costa, 2023. "Bootstrapping State-Space Models: Distribution-Free Estimation in View of Prediction and Forecasting," Forecasting, MDPI, vol. 6(1), pages 1-19, December.
    5. Luca Barbaglia & Sergio Consoli & Sebastiano Manzan, 2024. "Forecasting GDP in Europe with textual data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 338-355, March.
    6. Michael Bauer & Daniel Huber & Eric Offner & Marlene Renkel & Ole Wilms & Michael D. Bauer, 2024. "Corporate Green Pledges," CESifo Working Paper Series 11507, CESifo.
    7. Felix Drinkall & Janet B. Pierrehumbert & Stefan Zohren, 2024. "Forecasting Credit Ratings: A Case Study where Traditional Methods Outperform Generative LLMs," Papers 2407.17624, arXiv.org, revised Jan 2025.
    8. Michael D. Bauer & Daniel Huber & Eric Offner & Marlene Renkel & Ole Wilms, 2024. "Corporate Green Pledges," Working Paper Series 2024-36, Federal Reserve Bank of San Francisco.
    9. Bauer, Michael & Huber, Daniel & Offner, Eric & Renkel, Marlene & Wilms, Ole, 2024. "Corporate green pledges," IMFS Working Paper Series 214, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    10. Barbaglia, Luca & Bellia, Mario & Di Girolamo, Francesca & Rho, Caterina, 2024. "Crypto news and policy innovations: Are European markets affected?," JRC Working Papers in Economics and Finance 2024-07, Joint Research Centre, European Commission.
    11. Yang, Jinglan & Liu, Jianghuai & Yao, Zheng & Ma, Chaoqun, 2024. "Measuring digitalization capabilities using machine learning," Research in International Business and Finance, Elsevier, vol. 70(PB).
    12. Josué Thélissaint, 2024. "Assessing Cryptomarket Risks: Macroeconomic Forces, Market Shocks and Behavioural Dynamics," Economics Working Paper Archive (University of Rennes & University of Caen) 2024-14, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS.
    13. Julian Ashwin & Eleni Kalamara & Lorena Saiz, 2024. "Nowcasting Euro area GDP with news sentiment: A tale of two crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 887-905, August.

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