[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
IDEAS home Printed from https://ideas.repec.org/p/hhs/rbnkwp/0401.html
   My bibliography  Save this paper

Narrative Fragmentation and the Business Cycle

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.riksbank.se/globalassets/media/rapporter/working-papers/2019/no.-401-narrative-fragmentation-and-the-business-cycle.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Larsen, Vegard H. & Thorsrud, Leif Anders & Zhulanova, Julia, 2021. "News-driven inflation expectations and information rigidities," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 507-520.
    2. 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.
    3. Ryan Chahrour & Kristoffer Nimark & Stefan Pitschner, 2021. "Sectoral Media Focus and Aggregate Fluctuations," American Economic Review, American Economic Association, vol. 111(12), pages 3872-3922, December.
    4. Robert J. Shiller, 2017. "Narrative Economics," American Economic Review, American Economic Association, vol. 107(4), pages 967-1004, April.
    5. Summers, Lawrence H, 1986. "Does the Stock Market Rationally Reflect Fundamental Values?," Journal of Finance, American Finance Association, vol. 41(3), pages 591-601, July.
    6. De Bondt, Werner F M & Thaler, Richard, 1985. "Does the Stock Market Overreact?," Journal of Finance, American Finance Association, vol. 40(3), pages 793-805, July.
    7. Kfir Eliaz & Ran Spiegler, 2020. "A Model of Competing Narratives," American Economic Review, American Economic Association, vol. 110(12), pages 3786-3816, December.
    8. Nimark, Kristoffer P. & Pitschner, Stefan, 2019. "News media and delegated information choice," Journal of Economic Theory, Elsevier, vol. 181(C), pages 160-196.
    9. Matthew Rabin, 2013. "An Approach to Incorporating Psychology into Economics," American Economic Review, American Economic Association, vol. 103(3), pages 617-622, May.
    10. De Bondt, Werner F M & Thaler, Richard H, 1987. "Further Evidence on Investor Overreaction and Stock Market Seasonalit y," Journal of Finance, American Finance Association, vol. 42(3), pages 557-581, July.
    11. Jacob Boudoukh & Ronen Feldman & Shimon Kogan & Matthew Richardson, 2013. "Which News Moves Stock Prices? A Textual Analysis," NBER Working Papers 18725, National Bureau of Economic Research, Inc.
    12. William N. Goetzmann & Dasol Kim & Robert J. Shiller, 2016. "Crash Beliefs From Investor Surveys," NBER Working Papers 22143, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Walker, Clive B., 2024. "Going mainstream: Cryptocurrency narratives in newspapers," International Review of Financial Analysis, Elsevier, vol. 94(C).
    3. Lo, Andrew W & MacKinlay, A Craig, 1990. "When Are Contrarian Profits Due to Stock Market Overreaction?," The Review of Financial Studies, Society for Financial Studies, vol. 3(2), pages 175-205.
    4. Robert J. Shiller, 2017. "Narrative Economics," American Economic Review, American Economic Association, vol. 107(4), pages 967-1004, April.
    5. Macaulay, Alistair & Song, Wenting, 2022. "Narrative-Driven Fluctuations in Sentiment: Evidence Linking Traditional and Social Media," MPRA Paper 113620, University Library of Munich, Germany.
    6. Gunaratne, P. S. M. & Yonesawa, Y., 1997. "Return reversals in the Tokyo Stock Exchange: A test of stock market overreaction," Japan and the World Economy, Elsevier, vol. 9(3), pages 363-384, August.
    7. Dyl, Edward A. & Yuksel, H. Zafer & Zaynutdinova, Gulnara R., 2019. "Price reversals and price continuations following large price movements," Journal of Business Research, Elsevier, vol. 95(C), pages 1-12.
    8. Spierdijk, Laura & Bikker, Jacob A. & van den Hoek, Pieter, 2012. "Mean reversion in international stock markets: An empirical analysis of the 20th century," Journal of International Money and Finance, Elsevier, vol. 31(2), pages 228-249.
    9. Robert D. Arnott & Jason C. Hsu & Jun Liu & Harry Markowitz, 2015. "Can Noise Create the Size and Value Effects?," Management Science, INFORMS, vol. 61(11), pages 2569-2579, November.
    10. Felix Kapfhammer & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "Climate risk and commodity currencies," Working Paper 2020/18, Norges Bank.
    11. Michael Fung, 2013. "A trade-off between non-fundamental risk and incentives," Review of Quantitative Finance and Accounting, Springer, vol. 41(1), pages 29-51, July.
    12. Pedro Bordalo & Nicola Gennaioli & Rafael La Porta & Andrei Shleifer, 2019. "Diagnostic Expectations and Stock Returns," Journal of Finance, American Finance Association, vol. 74(6), pages 2839-2874, December.
    13. Arnswald, Torsten, 2001. "Investment Behaviour of German Equity Fund Managers - An Exploratory Analysis of Survey Data," Discussion Paper Series 1: Economic Studies 2001,08, Deutsche Bundesbank.
    14. Kwok-Wah Fung, Alexander & Lam, Kin, 2004. "Overreaction of index futures in Hong Kong," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 331-351, June.
    15. Juncal Cunado & Luis A. Gil-Alana & Fernando Pérez de Gracia, 2002. "Is the US Fiscal Deficit Sustainable? A Fractionally Integrated and Cointegrated Approach," Faculty Working Papers 02/02, School of Economics and Business Administration, University of Navarra.
    16. Andres Algaba & David Ardia & Keven Bluteau & Samuel Borms & Kris Boudt, 2020. "Econometrics Meets Sentiment: An Overview Of Methodology And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 34(3), pages 512-547, July.
    17. Ardia, David & Bluteau, Keven & Boudt, Kris, 2022. "Media abnormal tone, earnings announcements, and the stock market," Journal of Financial Markets, Elsevier, vol. 61(C).
    18. Barberis, Nicholas & Thaler, Richard, 2003. "A survey of behavioral finance," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, edition 1, volume 1, chapter 18, pages 1053-1128, Elsevier.
    19. John Hatgioannides & Spiros Mesomeris, 2005. "Mean Reversion in Equity Prices: the G-7 Evidence," Money Macro and Finance (MMF) Research Group Conference 2005 64, Money Macro and Finance Research Group.
    20. Kamal, Javed Bin & Wohar, Mark, 2023. "Heterogenous responses of stock markets to covid related news and sentiments: Evidence from the 1st year of pandemic," International Economics, Elsevier, vol. 173(C), pages 68-85.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hhs:rbnkwp:0401. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Lena Löfgren (email available below). General contact details of provider: https://edirc.repec.org/data/rbgovse.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.