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Estimating a Structural Model of Herd Behavior in Financial Markets

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  • Marco Cipriani
  • Antonio Guarino

Abstract

We develop a new methodology to estimate herd behavior in financial markets. We build a model of informational herding that can be estimated with financial transaction data. In the model, rational herding arises because of information-event uncertainty. We estimate the model using data on a NYSE stock (Ashland Inc.) during 1995. Herding occurs often and is particularly pervasive on some days. On average, the proportion of herd buyers is 2 percent; that of herd sellers is 4 percent. Herding also causes important informational inefficiencies in the market, amounting, on average, to 4 percent of the asset's expected value.

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  • Marco Cipriani & Antonio Guarino, 2014. "Estimating a Structural Model of Herd Behavior in Financial Markets," American Economic Review, American Economic Association, vol. 104(1), pages 224-251, January.
    • Handle: RePEc:aea:aecrev:v:104:y:2014:i:1:p:224-51
    Note: DOI: 10.1257/aer.104.1.224
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    2. Mathias Drehmann & Jörg Oechssler & Andreas Roider, 2005. "Herding and Contrarian Behavior in Financial Markets: An Internet Experiment," American Economic Review, American Economic Association, vol. 95(5), pages 1403-1426, December.
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    6. Marco Cipriani & Antonio Guarino, 2005. "Herd Behavior in a Laboratory Financial Market," American Economic Review, American Economic Association, vol. 95(5), pages 1427-1443, December.
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    Cited by:

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    2. Marco Cipriani & Antonio Guarino, 2009. "Herd Behavior in Financial Markets: An Experiment with Financial Market Professionals," Journal of the European Economic Association, MIT Press, vol. 7(1), pages 206-233, March.
    3. Al-Jarhi, Mabid Ali M. M., 2016. "An Economic Theory of Islamic Finance Regulation," Islamic Economic Studies, The Islamic Research and Training Institute (IRTI), vol. 24, pages 1-44.
    4. Kendall, Chad, 2018. "The time cost of information in financial markets," Journal of Economic Theory, Elsevier, vol. 176(C), pages 118-157.
    5. Xiong, Hang & Payne, Diane & Kinsella, Stephen, 2016. "Peer effects in the diffusion of innovations: Theory and simulation," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 63(C), pages 1-13.
    6. Cai, Fang & Han, Song & Li, Dan & Li, Yi, 2019. "Institutional herding and its price impact: Evidence from the corporate bond market," Journal of Financial Economics, Elsevier, vol. 131(1), pages 139-167.
    7. Duygun, Meryem & Tunaru, Radu & Vioto, Davide, 2021. "Herding by corporates in the US and the Eurozone through different market conditions," Journal of International Money and Finance, Elsevier, vol. 110(C).
    8. da Gama Batista, João & Massaro, Domenico & Bouchaud, Jean-Philippe & Challet, Damien & Hommes, Cars, 2017. "Do investors trade too much? A laboratory experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 140(C), pages 18-34.
    9. Noemi Schmitt & Frank Westerhoff, 2017. "Herding behaviour and volatility clustering in financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 17(8), pages 1187-1203, August.
    10. March, Christoph & Ziegelmeyer, Anthony, 2020. "Altruistic observational learning," Journal of Economic Theory, Elsevier, vol. 190(C).
    11. Frey, Stefan & Herbst, Patrick & Walter, Andreas, 2014. "Measuring mutual fund herding – A structural approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 32(C), pages 219-239.
    12. Marius Popescu & Zhaojin Xu, 2018. "Mutual fund herding and reputational concerns," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 42(3), pages 550-565, July.
    13. Duarte, Jefferson & Hu, Edwin & Young, Lance, 2020. "A comparison of some structural models of private information arrival," Journal of Financial Economics, Elsevier, vol. 135(3), pages 795-815.
    14. Ramos-Francia, Manuel & Garcia-Verdu, Santiago, 2018. "Is trouble brewing for emerging market economies? An empirical analysis of emerging market economies’ bond flows," Journal of Financial Stability, Elsevier, vol. 35(C), pages 172-191.
    15. Zhong, Guang-Yan & Li, Jiang-Cheng & Jiang, George J. & Li, Hai-Feng & Tao, Hui-Ming, 2018. "The time delay restraining the herd behavior with Bayesian approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 335-346.
    16. Hwang, Soosung & Rubesam, Alexandre & Salmon, Mark, 2021. "Beta herding through overconfidence: A behavioral explanation of the low-beta anomaly," Journal of International Money and Finance, Elsevier, vol. 111(C).

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    More about this item

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • 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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