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Expectations Concordance and Stock Market Volatility: Knightian Uncertainty in the Year of the Pandemic

Author

Listed:
  • Roman Frydman

    (Department of Economics, New York University, New York, NY 10012, USA)

  • Nicholas Mangee

    (Department of Finance, Parker College of Business, Georgia Southern University, Savannah, GA 31419, USA)

Abstract

This study introduces a novel index based on expectations concordance for explaining stock-price volatility when novel events that are each somewhat unique cause unforeseeable change and Knightian uncertainty in the process driving outcomes. Expectations concordance measures the degree to which KU events are associated with directionally similar expectations of future returns. Narrative analytics of daily news reports allow for the assessment of bullish versus bearish views in the stock market. Increases in expectations concordance across all KU events results in reinforcing effects and an increase in stock market volatility. Lower expectations concordance produces a stabilizing effect wherein the offsetting views reduce market volatility. The empirical findings hold for ex post and ex ante measures of volatility and for OLS and GARCH estimates.

Suggested Citation

  • Roman Frydman & Nicholas Mangee, 2021. "Expectations Concordance and Stock Market Volatility: Knightian Uncertainty in the Year of the Pandemic," JRFM, MDPI, vol. 14(11), pages 1-13, November.
  • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:11:p:521-:d:669919
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    References listed on IDEAS

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    1. Campbell, John Y. & Giglio, Stefano & Polk, Christopher & Turley, Robert, 2018. "An intertemporal CAPM with stochastic volatility," Journal of Financial Economics, Elsevier, vol. 128(2), pages 207-233.
    2. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    3. Alexopoulos, Michelle & Cohen, Jon, 2015. "The power of print: Uncertainty shocks, markets, and the economy," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 8-28.
    4. Nicholas Mangee & Michael D. Goldberg, 2020. "A Cointegrated VAR Analysis of Stock Price Models: Fundamentals, Psychology and Structural Change," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 21(4), pages 352-368, October.
    5. Friberg, Richard & Seiler, Thomas, 2017. "Risk and ambiguity in 10-Ks: An examination of cash holding and derivatives use," Journal of Corporate Finance, Elsevier, vol. 45(C), pages 608-631.
    6. Frydman, Roman & Goldberg, Michael D. & Mangee, Nicholas, 2015. "Knightian uncertainty and stock-price movements: Why the REH present-value model failed empirically," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 9, pages 1-50.
    7. Liu, Li & Zhang, Tao, 2015. "Economic policy uncertainty and stock market volatility," Finance Research Letters, Elsevier, vol. 15(C), pages 99-105.
    8. Wiggins, James B., 1987. "Option values under stochastic volatility: Theory and empirical estimates," Journal of Financial Economics, Elsevier, vol. 19(2), pages 351-372, December.
    9. Manela, Asaf & Moreira, Alan, 2017. "News implied volatility and disaster concerns," Journal of Financial Economics, Elsevier, vol. 123(1), pages 137-162.
    10. Mangee,Nicholas, 2021. "How Novelty and Narratives Drive the Stock Market," Cambridge Books, Cambridge University Press, number 9781108838450.
    11. Harrison Hong & Jeremy C. Stein, 2007. "Disagreement and the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 109-128, Spring.
    12. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    13. Hull, John C & White, Alan D, 1987. "The Pricing of Options on Assets with Stochastic Volatilities," Journal of Finance, American Finance Association, vol. 42(2), pages 281-300, June.
    14. Hansen, Simon Lysbjerg, 2015. "Cross-sectional asset pricing with heterogeneous preferences and beliefs," Journal of Economic Dynamics and Control, Elsevier, vol. 58(C), pages 125-151.
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    More about this item

    Keywords

    expectations concordance; narrative analytics; volatility; Knightian uncertainty;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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