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What drives the herding behavior of individual investors?

Author

Listed:
  • Maxime Merli

    (LARGE - Laboratoire de recherche en gestion et économie)

  • Tristan Roger

    ((Axe de recherche : Finance) - CERAG - Centre d'études et de recherches appliquées à la gestion - UPMF - Université Pierre Mendès France - Grenoble 2 - CNRS - Centre National de la Recherche Scientifique)

Abstract

This article intends to provide answers concerning what drives individual investor herding behavior. Our empirical study uses transaction records of 87,373 French individual investors for the period 1999-2006. In a Örst part, we show - using both the traditional Lakonishok et al. (1992) and the more recent Frey et al. (2007) measures - that herding is prevalent and strong among French individual investors. We then show that herding is persistent: stocks on which investors concentrate their trades at time t are more likely to be the stocks on which investors herd at time t+1. In a second part, we focus on the motivations of individual herding behavior. We introduce an investor speciÖc measure of herding which allows us to track the persistence in herding of individual investors. Our results highlight that this behavior is ináuenced by investor-speciÖc characteristics. We also reveal the fact that individual herding behavior is strongly and negatively linked with investorsíown past performance.

Suggested Citation

  • Maxime Merli & Tristan Roger, 2011. "What drives the herding behavior of individual investors?," Post-Print halshs-00658723, HAL.
  • Handle: RePEc:hal:journl:halshs-00658723 Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-00658723v2
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    References listed on IDEAS

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    1. Timotheos Angelidis & Stavros Degiannakis, 2007. "Backtesting VaR Models: An Expected Shortfall Approach," Working Papers 0701, University of Crete, Department of Economics.
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    3. Gencay, Ramazan & Selcuk, Faruk, 2004. "Extreme value theory and Value-at-Risk: Relative performance in emerging markets," International Journal of Forecasting, Elsevier, vol. 20(2), pages 287-303.
    4. Marimoutou, Velayoudoum & Raggad, Bechir & Trabelsi, Abdelwahed, 2009. "Extreme Value Theory and Value at Risk: Application to oil market," Energy Economics, Elsevier, vol. 31(4), pages 519-530, July.
    5. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    6. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    7. Basak, Suleyman & Shapiro, Alexander, 2001. "Value-at-Risk-Based Risk Management: Optimal Policies and Asset Prices," Review of Financial Studies, Society for Financial Studies, vol. 14(2), pages 371-405.
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    9. Viviana Fernandez, 2003. "Extreme Value Theory and Value at Risk," Revista de Analisis Economico – Economic Analysis Review, Ilades-Georgetown University, Universidad Alberto Hurtado/School of Economics and Bussines, vol. 18(1), pages 57-85, June.
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    Cited by:

    1. 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.
    2. repec:eee:pacfin:v:45:y:2017:i:c:p:174-185 is not listed on IDEAS
    3. Bar-Gill, Sagit & Gandal, Neil, 2017. "Online Exploration, Content Choice & Echo Chambers: An Experiment," CEPR Discussion Papers 11909, C.E.P.R. Discussion Papers.

    More about this item

    Keywords

    Herding behavior; investor;

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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