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Narrative Fragmentation and the Business Cycle

Author

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  • Bertsch, Christoph

    (Research Department, Central Bank of Sweden)

  • Hull, Isaiah

    (Research Department, Central Bank of Sweden)

  • Zhang, Xin

    (Research Department, Central Bank of Sweden)

Abstract

According to Shiller (2017), economic and financial narratives often emerge as a con sequence of their virality, rather than their veracity, and constitute an important, but understudied driver of aggregate fluctuations. Using a unique dataset of newspaper articles over the 1950-2019 period and state-of-the-art methods from natural language processing, we characterize the properties of business cycle narratives. Our main finding is that narratives tend to consolidate around a dominant explanation during expansions and fragment into competing explanations during contractions. We also show that the existence of past reference events is strongly associated with increased narrative consolidation.

Suggested Citation

  • Bertsch, Christoph & Hull, Isaiah & Zhang, Xin, 2021. "Narrative Fragmentation and the Business Cycle," Working Paper Series 401, Sveriges Riksbank (Central Bank of Sweden).
    • Handle: RePEc:hhs:rbnkwp:0401
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    References listed on IDEAS

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    Cited by:

    1. Sergey Tsygankov & Vadim Syropyatov & Vyacheslav Volchik, 2021. "Institutional Governance of Innovations: Novel Insights of Leadership in Russian Public Procurement," Economies, MDPI, vol. 9(4), pages 1-16, December.
    2. Yuting Chen & Don Bredin & Valerio Potì & Roman Matkovskyy, 2022. "COVID risk narratives: a computational linguistic approach to the econometric identification of narrative risk during a pandemic," Digital Finance, Springer, vol. 4(1), pages 17-61, March.
    3. Fernando Delbianco & Andrés Fioriti & Fernando Tohmé & Federico Contiggiani, 2022. "A Tale of two narratives: assessing the sociological hypothesis of the appeal of the US dollar in Argentina," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(5), pages 3519-3537, October.
    4. Philippas, Dionisis & Dragomirescu-Gaina, Catalin & Goutte, Stéphane & Nguyen, Duc Khuong, 2021. "Investors’ attention and information losses under market stress," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 1112-1127.
    5. 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.
    6. Łukasz Baszczak, 2023. "Ekonomia narracji – początki nowego nurtu," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 1, pages 66-81.
    7. Kazım Berk Küçüklerli & Veysel Ulusoy, 2023. "The time-varying correlation between popular narratives and TRY/USD FX rate: Evidence from a DCC-GARCH model," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 13(4), pages 1-3.

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    More about this item

    Keywords

    Natural Language Processing; Machine Learning; Narrative Economics;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E70 - Macroeconomics and Monetary Economics - - Macro-Based Behavioral Economics - - - General

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