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Noise traders and smart money: Evidence from online searches

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  • Fabrice Hervé

    (CREGO - Centre de Recherche en Gestion des Organisations (EA 7317) - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - UB - Université de Bourgogne - UBFC - Université Bourgogne Franche-Comté [COMUE] - UFC - Université de Franche-Comté - UBFC - Université Bourgogne Franche-Comté [COMUE])

  • Mohamed Zouaoui

    (CREGO - Centre de Recherche en Gestion des Organisations (EA 7317) - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - UB - Université de Bourgogne - UBFC - Université Bourgogne Franche-Comté [COMUE] - UFC - Université de Franche-Comté - UBFC - Université Bourgogne Franche-Comté [COMUE])

  • Bertrand Belvaux

    (CREGO - Centre de Recherche en Gestion des Organisations (EA 7317) - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - UB - Université de Bourgogne - UBFC - Université Bourgogne Franche-Comté [COMUE] - UFC - Université de Franche-Comté - UBFC - Université Bourgogne Franche-Comté [COMUE])

Abstract

Traditional finance theory considers that the impact of noise traders' attention on asset prices is offset by attention from smart investors. This paper uses online search data to study the influence of noise traders and smart investors on stock returns and volatility. Adopting an original approach, we construct a proxy for smart investor attention based on investors' online search behavior provided by Wikipedia Page Traffic. We combine this new measure with a standard measure of noise traders' attention as proxied by Google Search Volume Index. We show for a sample of 87 French firms over the period 2008–2018 that only noise traders' attention influences stock returns. Noise traders' attention increases volatility by creating an extra risk that is priced into the market. Conversely, smart investors' attention decreases volatility because their presence stabilizes stock prices by reducing uncertainty. Our empirical results support a behavioral explanation of stock prices.

Suggested Citation

  • Fabrice Hervé & Mohamed Zouaoui & Bertrand Belvaux, 2019. "Noise traders and smart money: Evidence from online searches," Post-Print hal-02065042, HAL.
    • Handle: RePEc:hal:journl:hal-02065042
    DOI: 10.1016/j.econmod.2019.02.005
    Note: View the original document on HAL open archive server: https://u-bourgogne.hal.science/hal-02065042
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    Cited by:

    1. Xun Zhang & Fengbin Lu & Rui Tao & Shouyang Wang, 2021. "The time-varying causal relationship between the Bitcoin market and internet attention," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-19, December.
    2. Chen, Xing & Diao, Xundi & Wu, Chongfeng, 2022. "Heterogeneous investor attention and post earnings announcement drift: Evidence from China," Economic Modelling, Elsevier, vol. 110(C).
    3. Thomas Boulton & Bill B. Francis & Thomas Shohfi & Daqi Xin, 2021. "Investor awareness or information asymmetry? Wikipedia and IPO underpricing," The Financial Review, Eastern Finance Association, vol. 56(3), pages 535-561, August.
    4. Chen, Xing & Wu, Chongfeng, 2022. "Retail investor attention and information asymmetry: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 75(C).
    5. Wei Zhang & Kai Yan & Dehua Shen, 2021. "Can the Baidu Index predict realized volatility in the Chinese stock market?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-31, December.
    6. Chen, Zhongdong & Craig, Karen Ann, 2023. "Active attention, retail investor base, and stock returns," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    7. Cheng, Feiyang & Chiao, Chaoshin & Wang, Chunfeng & Fang, Zhenming & Yao, Shouyu, 2021. "Does retail investor attention improve stock liquidity? A dynamic perspective," Economic Modelling, Elsevier, vol. 94(C), pages 170-183.

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    More about this item

    Keywords

    Behavioral finance; Attention measures; Smart investors; Noise traders; Price pressure hypothesis;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G4 - Financial Economics - - Behavioral Finance

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