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Uncertainty and sentiments in asset prices

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  • Berardi, Michele

Abstract

In this paper, I propose a novel way to model sentiments in asset prices. Under this new representation, sentiments, or animal spirits, are sparked by exogenous shocks to beliefs, but feed on the uncertainty generated by imperfect information. Sentiments cause expectations to deviate from optimal, information-based estimates, with their magnitude depending on the amount of uncertainty: the higher the uncertainty, the larger the scope for psychological attitudes to affect expectations.

Suggested Citation

  • Berardi, Michele, 2022. "Uncertainty and sentiments in asset prices," Journal of Economic Behavior & Organization, Elsevier, vol. 202(C), pages 498-516.
    • Handle: RePEc:eee:jeborg:v:202:y:2022:i:c:p:498-516
    DOI: 10.1016/j.jebo.2022.08.023
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    1. Nicholas Bloom & Max Floetotto & Nir Jaimovich & Itay Saporta†Eksten & Stephen J. Terry, 2018. "Really Uncertain Business Cycles," Econometrica, Econometric Society, vol. 86(3), pages 1031-1065, May.
    2. Jess Benhabib & Pengfei Wang & Yi Wen, 2015. "Sentiments and Aggregate Demand Fluctuations," Econometrica, Econometric Society, vol. 83, pages 549-585, March.
    3. George‐Marios Angeletos & Fabrice Collard & Harris Dellas, 2018. "Quantifying Confidence," Econometrica, Econometric Society, vol. 86(5), pages 1689-1726, September.
    4. Baker, Malcolm & Stein, Jeremy C., 2004. "Market liquidity as a sentiment indicator," Journal of Financial Markets, Elsevier, vol. 7(3), pages 271-299, June.
    5. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    6. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    7. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    8. Roger E. A. Farmer, 2012. "Confidence, Crashes and Animal Spirits," Economic Journal, Royal Economic Society, vol. 122(559), pages 155-172, March.
    9. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    10. Mark J. Kamstra & Lisa A. Kramer & Maurice D. Levi, 2003. "Winter Blues: A SAD Stock Market Cycle," American Economic Review, American Economic Association, vol. 93(1), pages 324-343, March.
    11. Fabio Milani, 2011. "Expectation Shocks and Learning as Drivers of the Business Cycle," Economic Journal, Royal Economic Society, vol. 121(552), pages 379-401, May.
    12. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    13. Scotti, Chiara, 2016. "Surprise and uncertainty indexes: Real-time aggregation of real-activity macro-surprises," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 1-19.
    14. Milani, Fabio, 2017. "Sentiment and the U.S. business cycle," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 289-311.
    15. R?diger Bachmann & Steffen Elstner & Eric R. Sims, 2013. "Uncertainty and Economic Activity: Evidence from Business Survey Data," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(2), pages 217-249, April.
    16. Diamond, Douglas W. & Verrecchia, Robert E., 1981. "Information aggregation in a noisy rational expectations economy," Journal of Financial Economics, Elsevier, vol. 9(3), pages 221-235, September.
    17. Gregory W. Brown & Michael T. Cliff, 2005. "Investor Sentiment and Asset Valuation," The Journal of Business, University of Chicago Press, vol. 78(2), pages 405-440, March.
    18. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, May.
    19. Hellwig, Martin F., 1980. "On the aggregation of information in competitive markets," Journal of Economic Theory, Elsevier, vol. 22(3), pages 477-498, June.
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    Cited by:

    1. Berardi, Michele, 2021. "Uncertainty, sentiments and time-varying risk premia," MPRA Paper 106922, University Library of Munich, Germany.
    2. Carlos Giraldo & Iader Giraldo & Jose E. Gomez-Gonzalez & Jorge M. Uribe, 2023. ""US uncertainty shocks, credit, production, and prices: The case of fourteen Latin American countries"," IREA Working Papers 202302, University of Barcelona, Research Institute of Applied Economics, revised Feb 2023.

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    More about this item

    Keywords

    Information; Uncertainty; Sentiments; Bayesian learning; Financial markets;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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