IDEAS home Printed from https://ideas.repec.org/p/amz/wpaper/2021-04.html
   My bibliography  Save this paper

Reddit's self-organised bull runs: Social contagion and asset prices

Author

Listed:
  • Winkler, Julian
  • Semenova, Valentina

Abstract

This paper develops an empirical and theoretical case for how 'hype' among retail investors can drive large asset fluctuations. We use the dataset of discussions on WallStreetBets (WSB), an online investor forum with over nine million followers as of April 2021, to show how excitement about trading opportunities can ripple through an investor community with large market impacts. This paper finds empirical evidence of psychological contagion among retail investors by exploiting differences in stock price fluctuations and discussion intensity. We show that asset discussions on WSB are self-perpetuating: an initial set of investors attracts a larger and larger group of excited followers. Sentiments about future stock performance also spread from one individual to the next, net of any fundamental price movements. Leveraging these findings, we develop a model for how social contagion impacts prices. The proposed model and simulations show that social contagion has a destabilizing effect on markets. Finally, we establish a causal relationship between WSB activity and financial markets using an instrumental variable approach.

Suggested Citation

  • Winkler, Julian & Semenova, Valentina, 2021. "Reddit's self-organised bull runs: Social contagion and asset prices," INET Oxford Working Papers 2021-04, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, revised May 2021.
    • Handle: RePEc:amz:wpaper:2021-04
    as

    Download full text from publisher

    File URL: https://www.inet.ox.ac.uk/files/Reddit_draft_v2.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Barberis, Nicholas & Huang, Ming, 2009. "Preferences with frames: A new utility specification that allows for the framing of risks," Journal of Economic Dynamics and Control, Elsevier, vol. 33(8), pages 1555-1576, August.
    2. Robert J. Shiller, 1984. "Stock Prices and Social Dynamics," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 15(2), pages 457-510.
    3. Hommes,Cars, 2015. "Behavioral Rationality and Heterogeneous Expectations in Complex Economic Systems," Cambridge Books, Cambridge University Press, number 9781107564978.
    4. Diego García, 2013. "Sentiment during Recessions," Journal of Finance, American Finance Association, vol. 68(3), pages 1267-1300, June.
    5. Robert J. Shiller, 2017. "Narrative Economics," American Economic Review, American Economic Association, vol. 107(4), pages 967-1004, April.
    6. Barberis, Nicholas & Greenwood, Robin & Jin, Lawrence & Shleifer, Andrei, 2018. "Extrapolation and bubbles," Journal of Financial Economics, Elsevier, vol. 129(2), pages 203-227.
    7. Cars Hommes, 2021. "Behavioral and Experimental Macroeconomics and Policy Analysis: A Complex Systems Approach," Journal of Economic Literature, American Economic Association, vol. 59(1), pages 149-219, March.
    8. Alan Kirman, 1993. "Ants, Rationality, and Recruitment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 108(1), pages 137-156.
    9. Bouchaud,Jean-Philippe & Potters,Marc, 2003. "Theory of Financial Risk and Derivative Pricing," Cambridge Books, Cambridge University Press, number 9780521819169.
    10. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    11. Jiayi Liu & Anandhi Bharadwaj, 2020. "Drug Abuse and the Internet: Evidence from Craigslist," Management Science, INFORMS, vol. 66(5), pages 2040-2049, May.
    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. Ramit Sawhney & Shivam Agarwal & Vivek Mittal & Paolo Rosso & Vikram Nanda & Sudheer Chava, 2022. "Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models," Papers 2206.06320, arXiv.org.

    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. Dieci, Roberto & Schmitt, Noemi & Westerhoff, Frank H., 2022. "Boom-bust cycles and asset market participation waves: Momentum, value, risk and herding," BERG Working Paper Series 177, Bamberg University, Bamberg Economic Research Group.
    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. John Garcia, 2021. "Analyst herding and firm-level investor sentiment," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(4), pages 461-494, December.
    4. Francisco Gomes & Michael Haliassos & Tarun Ramadorai, 2021. "Household Finance," Journal of Economic Literature, American Economic Association, vol. 59(3), pages 919-1000, September.
    5. Schmitt, Noemi & Westerhoff, Frank, 2021. "Trend followers, contrarians and fundamentalists: Explaining the dynamics of financial markets," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 117-136.
    6. Szymon Lis, 2022. "Investor Sentiment in Asset Pricing Models: A Review," Working Papers 2022-14, Faculty of Economic Sciences, University of Warsaw.
    7. Guofu Zhou, 2018. "Measuring Investor Sentiment," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 239-259, November.
    8. Sharpe, Steven A. & Sinha, Nitish R. & Hollrah, Christopher A., 2023. "The power of narrative sentiment in economic forecasts," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1097-1121.
    9. Zhao, Dongxu & Li, Kai, 2022. "Bounded rationality, adaptive behaviour, and asset prices," International Review of Financial Analysis, Elsevier, vol. 80(C).
    10. Ardia, David & Bluteau, Keven & Boudt, Kris, 2022. "Media abnormal tone, earnings announcements, and the stock market," Journal of Financial Markets, Elsevier, vol. 61(C).
    11. 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.
    12. 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.
    13. Samuel M. Hartzmark & David H. Solomon, 2020. "Reconsidering Returns," NBER Working Papers 27380, National Bureau of Economic Research, Inc.
    14. Sun, Xiaojin & Tsang, Kwok Ping, 2019. "Large price movements in housing markets," Journal of Economic Behavior & Organization, Elsevier, vol. 163(C), pages 1-23.
    15. Alomari, Mohammad & Al Rababa’a, Abdel Razzaq & El-Nader, Ghaith & Alkhataybeh, Ahmad & Ur Rehman, Mobeen, 2021. "Examining the effects of news and media sentiments on volatility and correlation: Evidence from the UK," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 280-297.
    16. Kim Kaivanto & Peng Zhang, 2019. "Investor Sentiment as a Predictor of Market Returns," Working Papers 268005798, Lancaster University Management School, Economics Department.
    17. Yan Luo & Linying Zhou, 2020. "Textual tone in corporate financial disclosures: a survey of the literature," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 17(2), pages 101-110, September.
    18. Bennani, Hamza, 2018. "Media coverage and ECB policy-making: Evidence from an augmented Taylor rule," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 26-38.
    19. Klein, A. & Urbig, D. & Kirn, S., 2008. "Who Drives the Market? Estimating a Heterogeneous Agent-based Financial Market Model Using a Neural Network Approach," MPRA Paper 14433, University Library of Munich, Germany.
    20. repec:hal:spmain:info:hdl:2441/3mgbd73vkp9f9oje7utooe7vpg is not listed on IDEAS
    21. V. Alfi & L. Pietronero & A. Zaccaria, 2008. "Minimal Agent Based Model For The Origin And Self-Organization Of Stylized Facts In Financial Markets," Papers 0807.1888, arXiv.org.

    More about this item

    JEL classification:

    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

    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:amz:wpaper:2021-04. 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: INET Oxford admin team (email available below). General contact details of provider: https://edirc.repec.org/data/inoxfuk.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.