Advanced Search
MyIDEAS: Login to save this paper or follow this series

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

Contents:

Author Info

  • Iori, G.
  • Tedeschi, G.

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 otherway 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 to be imitated since herding turns out to be profitable.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://openaccess.city.ac.uk/1487/1/Herding_Effects_in_Order_Driven_Markets.pdf
Download Restriction: no

Bibliographic Info

Paper provided by Department of Economics, City University London in its series Working Papers with number 10/05.

as in new window
Length:
Date of creation: 2010
Date of revision:
Handle: RePEc:cty:dpaper:10/05

Contact details of provider:
Postal: Department of Economics, Social Sciences Building, City University London, Whiskin Street, London, EC1R 0JD, United Kingdom,
Phone: +44 (0)20 7040 8500
Web page: http://www.city.ac.uk
More information through EDIRC

Related research

Keywords: dynamic network; herding; guru; order driver market;

Other versions of this item:

Find related papers by JEL classification:

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  1. Benabou, R. & Laroque, G., 1989. "Using Privileged Information To Manipulate Markets: Insiders, Gurus, And Credibility," Working papers 513, Massachusetts Institute of Technology (MIT), Department of Economics.
  2. Heemeijer, Peter & Hommes, Cars & Sonnemans, Joep & Tuinstra, Jan, 2009. "Price stability and volatility in markets with positive and negative expectations feedback: An experimental investigation," Journal of Economic Dynamics and Control, Elsevier, vol. 33(5), pages 1052-1072, May.
  3. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-35, November.
  4. Carl Chiarella & Giulia Iori, 2005. "The Impact of Heterogeneous Trading Rules on the Limit Order Book and Order Flows," Research Paper Series 152, Quantitative Finance Research Centre, University of Technology, Sydney.
  5. Vriend, Nicolaas J., 2000. "An illustration of the essential difference between individual and social learning, and its consequences for computational analyses," Journal of Economic Dynamics and Control, Elsevier, vol. 24(1), pages 1-19, January.
  6. John Haltiwanger & Michael Waldman, 1983. "Rational Expectations and the Limits of Rationality: An Analysis of Heterogeneity," UCLA Economics Working Papers 303, UCLA Department of Economics.
  7. Ramon Marimon & Shyam Sunder, 1993. "Indeterminacy of equilibria in a hyperinflationary world: Experimental evidence," Economics Working Papers 25, Department of Economics and Business, Universitat Pompeu Fabra.
  8. Black, Fischer, 1986. " Noise," Journal of Finance, American Finance Association, vol. 41(3), pages 529-43, July.
  9. Xavier Gabaix & Parameswaran Gopikrishnan & Vasiliki Plerou & H. Eugene Stanley, 2005. "Institutional Investors and Stock Market Volatility," NBER Working Papers 11722, National Bureau of Economic Research, Inc.
  10. Cars Hommes & Joep Sonnemans & Jan Tuinstra & Henk van de Velden, 2005. "Coordination of Expectations in Asset Pricing Experiments," Review of Financial Studies, Society for Financial Studies, vol. 18(3), pages 955-980.
  11. J. Bradford De Long & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, . "Noise Trader Risk in Financial Markets," J. Bradford De Long's Working Papers _124, University of California at Berkeley, Economics Department.
  12. Sutan, Angela & Willinger, Marc, 2009. "Guessing with negative feedback: An experiment," Journal of Economic Dynamics and Control, Elsevier, vol. 33(5), pages 1123-1133, May.
  13. LeBaron, Blake & Arthur, W. Brian & Palmer, Richard, 1999. "Time series properties of an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1487-1516, September.
  14. Marco Licalzi & Paolo Pellizzari, 2003. "Fundamentalists clashing over the book: a study of order-driven stock markets," Quantitative Finance, Taylor & Francis Journals, vol. 3(6), pages 470-480.
  15. repec:att:wimass:9621 is not listed on IDEAS
  16. KIRMAN, Alan & TEYSSIÈRE, Gilles, 2002. "Microeconomic models for long-memory in the volatility of financial time series," CORE Discussion Papers 2002056, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  17. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186 Elsevier.
  18. Robert J. Shiller, 1984. "Stock Prices and Social Dynamics," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 15(2), pages 457-510.
  19. Pushkin, Dmitri O & Aref, Hassan, 2004. "Bank mergers as scale-free coagulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 571-584.
  20. Orlean, Andre, 1995. "Bayesian interactions and collective dynamics of opinion: Herd behavior and mimetic contagion," Journal of Economic Behavior & Organization, Elsevier, vol. 28(2), pages 257-274, October.
  21. LeBaron, Blake & Yamamoto, Ryuichi, 2007. "Long-memory in an order-driven market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(1), pages 85-89.
  22. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
  23. Gode, Dhananjay K & Sunder, Shyam, 1997. "What Makes Markets Allocationally Efficient?," The Quarterly Journal of Economics, MIT Press, vol. 112(2), pages 603-30, May.
  24. Klaus Adam, 2007. "Experimental Evidence on the Persistence of Output and Inflation," Economic Journal, Royal Economic Society, vol. 117(520), pages 603-636, 04.
  25. Giulia Iori, 2000. "A microsimulation of traders activity in the stock market: the role of heterogeneity, agents' interactions and trade frictions," Finance 0004007, EconWPA.
  26. C.H. Hommes & J.H. Sonnemans & J. Tuinstra & H. van de Velde, 2003. "Learning in Cobweb Experiments," Tinbergen Institute Discussion Papers 03-020/1, Tinbergen Institute.
  27. De Long, J Bradford, et al, 1990. " Positive Feedback Investment Strategies and Destabilizing Rational Speculation," Journal of Finance, American Finance Association, vol. 45(2), pages 379-95, June.
  28. G. Tedeschi & G. Iori & M. Gallegati, 2009. "The role of communication and imitation in limit order markets," The European Physical Journal B - Condensed Matter and Complex Systems, Springer, vol. 71(4), pages 489-497, October.
  29. Blake LeBaron & Ryuichi Yamamoto, 2008. "The Impact of Imitation on Long Memory in an Order-Driven Market," Eastern Economic Journal, Palgrave Macmillan, vol. 34(4), pages 504-517.
  30. Carl Chiarella & Giulia Iori, 2002. "A simulation analysis of the microstructure of double auction markets," Quantitative Finance, Taylor & Francis Journals, vol. 2(5), pages 346-353.
  31. Robert J. Shiller, 1984. "Stock Prices and Social Dynamics," Cowles Foundation Discussion Papers 719R, Cowles Foundation for Research in Economics, Yale University.
  32. Gaunersdorfer, A. & Hommes, C.H. & Wagener, F.O.O., 2003. "Bifurcation Routes to Volatility Clustering under Evolutionary Learning," CeNDEF Working Papers 03-03, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  33. Stauffer, Dietrich & Sornette, Didier, 1999. "Self-organized percolation model for stock market fluctuations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 271(3), pages 496-506.
  34. Sheri Markose & Amadeo Alentorn & Andreas Krause, 2004. "Dynamic Learning, Herding and Guru Effects in Networks," Economics Discussion Papers 582, University of Essex, Department of Economics.
  35. Cont, Rama & Bouchaud, Jean-Philipe, 2000. "Herd Behavior And Aggregate Fluctuations In Financial Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 4(02), pages 170-196, June.
  36. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-37, February.
  37. Archishman Chakraborty & Bilge Yilmaz, 2008. "Microstructure Bluffing with Nested Information," American Economic Review, American Economic Association, vol. 98(2), pages 280-84, May.
  38. Gerasymchuk, S. & Pavlov, O.V., 2010. "Asset Price Dynamics with Local Interactions under Heterogeneous Beliefs," CeNDEF Working Papers 10-02, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  39. Figlewski, Stephen, 1979. "Subjective Information and Market Efficiency in a Betting Market," Journal of Political Economy, University of Chicago Press, vol. 87(1), pages 75-88, February.
Full references (including those not matched with items on IDEAS)

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. Mark Setterfield & Bill Gibson, 2013. "Real and financial crises: A multi-agent approach," Working Papers 1309, Trinity College, Department of Economics, revised Jul 2014.
  2. Simone LENZU & Gabriele TEDESCHI, 2012. "Systemic risk on different interbank network topologies," Working Papers 375, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
  3. Iori, G. & Porter, J., 2012. "Agent-Based Modelling for Financial Markets," Working Papers 12/08, Department of Economics, City University London.
  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. 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.
  6. 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.
  7. 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.
  8. Vivien Lespagnol & Juliette Rouchier, 2014. "Trading Volume and Market Efficiency: An Agent Based Model with Heterogenous Knowledge about Fundamentals," Working Papers halshs-00997573, HAL.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:cty:dpaper:10/05. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Research Publications Librarian).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.