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Herding effects in order driven markets: The rise and fall of gurus

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  • Tedeschi, Gabriele
  • Iori, Giulia
  • Gallegati, Mauro

Abstract

We introduce an order driver market model with heterogeneous traders that imitate each other on a dynamic network structure. The communication structure evolves endogenously via a fitness mechanism based on agents performance. We assess under which assumptions imitation, among noise traders, can give rise to the emergence of gurus and their rise and fall in popularity over time. We study the wealth distribution of gurus, followers and non followers and show that traders have an incentive to imitate and a desire to be imitated since herding turns out to be profitable. The model is then used to study the effect that different competitive strategies (i.e. chartist & fundamentalist) have on agents performance. Our findings show that positive intelligence agents cannot invade a market populated by noise traders when herding is high.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Economic Behavior & Organization.

Volume (Year): 81 (2012)
Issue (Month): 1 ()
Pages: 82-96

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Handle: RePEc:eee:jeborg:v:81:y:2012:i:1:p:82-96

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Web page: http://www.elsevier.com/locate/jebo

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Keywords: Dynamic network; Herding; Guru; Order driver market;

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References

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Citations

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Cited by:
  1. Iori, G. & Porter, J., 2012. "Agent-Based Modelling for Financial Markets," Working Papers 12/08, Department of Economics, City University London.
  2. Vivien Lespagnol & Juliette Rouchier, 2014. "Trading volume and market efficiency: an Agent Based Model with heterogenous knowledge about fundamentals," AMSE Working Papers 1419, Aix-Marseille School of Economics, Marseille, France, revised May 2014.
  3. Vivien Lespagnol & Juliette Rouchier, 2014. "Trading Volume and Market Efficiency: An Agent Based Model with Heterogenous Knowledge about Fundamentals," Working Papers halshs-00997573, HAL.
  4. Bargigli, Leonardo & Tedeschi, Gabriele, 2014. "Interaction in agent-based economics: A survey on the network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 1-15.
  5. Lenzu, Simone & Tedeschi, Gabriele, 2012. "Systemic risk on different interbank network topologies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(18), pages 4331-4341.
  6. Mark Setterfield & Bill Gibson, 2013. "Real and financial crises," Working Papers 1309, Trinity College, Department of Economics, revised Sep 2013.
  7. Liudmila G. Egorova, 2014. "The Effectiveness Of Different Trading Strategies For Price-Takers," HSE Working papers WP BRP 29/FE/2014, National Research University Higher School of Economics.
  8. A. E. Biondo & A. Pluchino & A. Rapisarda & D. Helbing, 2013. "Are random trading strategies more successful than technical ones?," Papers 1303.4351, arXiv.org, revised Jul 2013.

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