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False News, Informational Efficiency, and Price Reversals

Author

Listed:
  • Jjrrme Dugast
  • Thierry Foucault

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique)

Abstract

Information processing filters out the noise in data but it takes time. Hence, low precision signals are available before high precision signals. We analyze how this feature affects asset price informativeness when investors can acquire signals of increasing precision over time about the payoff of an asset. As the cost of low precision signals declines, prices are more likely to reflect these signals before more precise signals become available. This effect can ultimately reduce price informativeness because it reduces the demand for more precise signals (e.g., fundamental analysis). We make additional predictions for trade and price patterns.

Suggested Citation

  • Jjrrme Dugast & Thierry Foucault, 2014. "False News, Informational Efficiency, and Price Reversals," Working Papers hal-02058260, HAL.
    • Handle: RePEc:hal:wpaper:hal-02058260
    DOI: 10.2139/ssrn.2398904
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    Citations

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    Cited by:

    1. Michael J. Aitken & Angelo Aspris & Sean Foley & Frederick H. de B. Harris, 2018. "Market Fairness: The Poor Country Cousin of Market Efficiency," Journal of Business Ethics, Springer, vol. 147(1), pages 5-23, January.
    2. Massa, Massimo & von Beschwitz, Bastian & Keim, Donald B, 2015. "First to ?Read? the News: News Analytics and Institutional Trading," CEPR Discussion Papers 10534, C.E.P.R. Discussion Papers.
    3. Jean-Edouard Colliard, 2017. "Catching Falling Knives: Speculating on Liquidity Shocks," Management Science, INFORMS, vol. 63(8), pages 2573-2591, August.
    4. Albert S Kyle & Anna A Obizhaeva & Yajun Wang, 2018. "Smooth Trading with Overconfidence and Market Power," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(1), pages 611-662.
    5. Bizzozero, Paolo & Flepp, Raphael & Franck, Egon, 2018. "The effect of fast trading on price discovery and efficiency: Evidence from a betting exchange," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 126-143.
    6. Alexandru-Ioan Stan, 2018. "Computational speed and high-frequency trading profitability: an ecological perspective," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(3), pages 381-395, August.
    7. Albert S. Kyle & Anna Obizhaeva & Yajun Wang, 2016. "Smooth Trading with Overconfidence and Market Power," Working Papers w0226, New Economic School (NES).
    8. Jun Aoyagi, 2019. "Strategic Speed Choice by High-Frequency Traders under Speed Bumps," ISER Discussion Paper 1050, Institute of Social and Economic Research, The University of Osaka.
    9. Md Miran Hossain & Babak Mammadov & Hamid Vakilzadeh, 2022. "Wisdom of the crowd and stock price crash risk: evidence from social media," Review of Quantitative Finance and Accounting, Springer, vol. 58(2), pages 709-742, February.
    10. Steffen, Viktoria, 2023. "A literature review on extreme price movements with reversal," Journal of Behavioral and Experimental Finance, Elsevier, vol. 38(C).
    11. Zachary S Levine & Scott A Hale & Luciano Floridi, 2017. "The October 2014 United States Treasury bond flash crash and the contributory effect of mini flash crashes," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-14, November.

    More about this item

    Keywords

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    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • 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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