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The Structure of Economic News

Author

Listed:
  • Leland Bybee
  • Bryan T. Kelly
  • Asaf Manela
  • Dacheng Xiu

Abstract

We propose an approach to measuring the state of the economy via textual analysis of business news. From the full text content of 800,000 Wall Street Journal articles for 1984{2017, we estimate a topic model that summarizes business news as easily interpretable topical themes and quantifies the proportion of news attention allocated to each theme at each point in time. We then use our news attention estimates as inputs into statistical models of numerical economic time series. We demonstrate that these text-based inputs accurately track a wide range of economic activity measures and that they have incremental forecasting power for macroeconomic outcomes, above and beyond standard numerical predictors. Finally, we use our model to retrieve the news-based narratives that underly “shocks” in numerical economic data.

Suggested Citation

  • Leland Bybee & Bryan T. Kelly & Asaf Manela & Dacheng Xiu, 2020. "The Structure of Economic News," NBER Working Papers 26648, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:26648
    Note: AP EFG POL
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    References listed on IDEAS

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    Cited by:

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    3. J. Daniel Aromí, 2022. "Medición de Incertidumbre Económica en Redes Sociales en Base a Modelos de Procesamiento de Lenguaje Natural," Working Papers 179, Red Nacional de Investigadores en Economía (RedNIE).
    4. Aprigliano, Valentina & Emiliozzi, Simone & Guaitoli, Gabriele & Luciani, Andrea & Marcucci, Juri & Monteforte, Libero, 2023. "The power of text-based indicators in forecasting Italian economic activity," International Journal of Forecasting, Elsevier, vol. 39(2), pages 791-808.
    5. Eghbal Rahimikia & Stefan Zohren & Ser-Huang Poon, 2021. "Realised Volatility Forecasting: Machine Learning via Financial Word Embedding," Papers 2108.00480, arXiv.org, revised Mar 2023.
    6. Elliott Ash & Germain Gauthier & Philine Widmer, 2021. "RELATIO: Text Semantics Capture Political and Economic Narratives," Papers 2108.01720, arXiv.org, revised Apr 2022.
    7. Laura Battaglia & Timothy Christensen & Stephen Hansen & Szymon Sacher, 2024. "Inference for Regression with Variables Generated from Unstructured Data," Papers 2402.15585, arXiv.org, revised Mar 2024.
    8. Perico Ortiz, Daniel & Schnaubelt, Matthias & Seifert, Oleg, 2023. "A topic modeling perspective on investor uncertainty," FAU Discussion Papers in Economics 04/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    9. Szymon Sacher & Laura Battaglia & Stephen Hansen, 2021. "Hamiltonian Monte Carlo for Regression with High-Dimensional Categorical Data," Papers 2107.08112, arXiv.org, revised Feb 2024.
    10. Amarasinghe, Ashani, 2022. "Diverting domestic turmoil," Journal of Public Economics, Elsevier, vol. 208(C).
    11. Daniel Perico Ortiz, 2023. "Economic policy statements, social media, and stock market uncertainty: An analysis of Donald Trump’s tweets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(2), pages 333-367, June.
    12. Ristolainen, Kim & Roukka, Tomi & Nyberg, Henri, 2024. "A thousand words tell more than just numbers: Financial crises and historical headlines," Journal of Financial Stability, Elsevier, vol. 70(C).
    13. Jianhao Lin & Jiacheng Fan & Yifan Zhang & Liangyuan Chen, 2023. "Real‐time macroeconomic projection using narrative central bank communication," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 202-221, March.
    14. Geertsema, Paul & Lu, Helen, 2020. "The correlation structure of anomaly strategies," Journal of Banking & Finance, Elsevier, vol. 119(C).
    15. Łukasz Baszczak, 2023. "Ekonomia narracji – początki nowego nurtu," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 1, pages 66-81.
    16. Eleni Kalamara & Arthur Turrell & Chris Redl & George Kapetanios & Sujit Kapadia, 2022. "Making text count: Economic forecasting using newspaper text," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 896-919, August.
    17. Philine Widmer & Sergio Galletta & Elliott Ash, 2022. "Media Slant is Contagious," Papers 2202.07269, arXiv.org, revised Apr 2023.
    18. Venditti, Fabrizio & Veronese, Giovanni, 2020. "Global financial markets and oil price shocks in real time," Working Paper Series 2472, European Central Bank.
    19. Joelle Noailly; Laura Nowzohour; Matthias van den Heuvel, 2021. "Heard the News? Environmental Policy and Clean Investments," CIES Research Paper series 70-2021, Centre for International Environmental Studies, The Graduate Institute.
    20. Paul Geertsema & Helen Lu, 2023. "Relative Valuation with Machine Learning," Journal of Accounting Research, Wiley Blackwell, vol. 61(1), pages 329-376, March.
    21. Carlos Moreno Pérez & Marco Minozzo, 2022. "Monetary Policy Uncertainty in Mexico: An Unsupervised Approach," Working Papers 2229, Banco de España.
    22. Daniel Borup & Jorge Wolfgang Hansen & Benjamin Dybro Liengaard & Erik Christian Montes Schütte, 2023. "Quantifying investor narratives and their role during COVID‐19," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 512-532, June.
    23. Kumar, Alok & Lei, Zicheng & Zhang, Chendi, 2022. "Dividend sentiment, catering incentives, and return predictability," Journal of Corporate Finance, Elsevier, vol. 72(C).

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    More about this item

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E0 - Macroeconomics and Monetary Economics - - General
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G0 - Financial Economics - - General
    • G1 - Financial Economics - - General Financial Markets

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