Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Studies in Nonlinear Dynamics & Econometrics

Ed. by Mizrach, Bruce

5 Issues per year

IMPACT FACTOR 2017: 0.855

CiteScore 2017: 0.76

SCImago Journal Rank (SJR) 2017: 0.668
Source Normalized Impact per Paper (SNIP) 2017: 0.894

Mathematical Citation Quotient (MCQ) 2016: 0.03

See all formats and pricing
More options …
Volume 18, Issue 1


Herd behavior, bubbles and social interactions in financial markets

Sheng-Kai Chang
Published Online: 2013-08-13 | DOI: https://doi.org/10.1515/snde-2013-0024


This paper studies herd behavior, bubbles and social interactions in financial markets through the asset pricing models with heterogeneous interacting agents. The relationship between social interactions, herd behavior and bubbles is examined. It is found that herd behavior arises naturally when there are strong enough social interactions among individual investors. In addition, an extremely small bubble may cause a sufficiently large number of traders to engage in herd behavior when the social interactions among traders are strong.

Keywords: herd behavior; heterogeneous beliefs; social interactions


  • Alfarano, S., and M. Milakovic. 2009. “Network Structure and N-Dependence in Agent-Based Herding Models.” Journal of Economic Dynamics and Control 33: 78–92.Web of ScienceGoogle Scholar

  • Anderson, P., A. Palma, and J. Thisse. 1996. Discrete Choice Theory of Product Differentiation. 13–62. Cambridge, Massachusetts, USA: MIT press.Google Scholar

  • Blume, L., and D. Easley. 1992. “Evolution and Market Behavior.” Journal of Economic Theory 58: 9–40.CrossrefGoogle Scholar

  • Blume, L., and D. Easley. 1993. “Economic Natural Selection.” Economics Letters 42: 281–289.CrossrefGoogle Scholar

  • Brock, W., and S. Durlauf. 2001a. “The Econometrics of Interactions-Based Models.” In Handbook of Econometrics, Vol. V, edited by J. Heckman and E. Leamer, Amsterdam: North-Holland.Google Scholar

  • Brock, W., and S. Durlauf. 2001b. “Discrete Choice with Social Interactions.” Review of Economic Studies 68: 235–260.CrossrefGoogle Scholar

  • Brock, W., and C. Hommes. 1997. “Rational Routes to Randomness.” Econometrica 65: 1059–1096.CrossrefGoogle Scholar

  • Brock, W., and C. Hommes. 1998. “Heterogeneous Beliefs and Routes to Chaos in a Simple Asset Pricing Model.” Journal of Economic Dynamics and Control 22: 1237–1274.Google Scholar

  • Brock, W., J. Lakonishok, and B. LaBaron. 1992. “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance 47: 1731–1764.CrossrefGoogle Scholar

  • Chang, S.-K. 2007. “A Simple Asset Pricing Model with Social Interactions and Heterogeneous Beliefs.” Journal of Economic Dynamics and Control 31: 1300–1325.Web of ScienceGoogle Scholar

  • Chiarella, C., and X. He. 2002. “Heterogeneous Beliefs, Risk and Learning in A Simple Asset Pricing Model.” Computational Economics 19: 95–132.Web of ScienceCrossrefGoogle Scholar

  • Chiarella, C., M. Gallegati, R. Leombruni, and A. Palestrini. 2003. “Asset Price Dynamics Among Heterogeneous Interacting Agents.” Computational Economics 22: 213–223.CrossrefGoogle Scholar

  • De Bond, F., and R. Thaler. 1985. “Does the Stock Market Overreact?” Journal of Finance 40: 793–808.CrossrefGoogle Scholar

  • DeLong, J., A. Shleifer, L. Summers, and R. Waldmann. 1990. “Noise trader risk in financial markets.” Journal of Political Economy 98: 703–738.Google Scholar

  • Friedman, M. 1953. “The Case for Flexible Exchange Rates.” In Essays in Positive Economics. Chicago, USA: University of Chicago Press.Google Scholar

  • Gerasymchuk, S., V. Panchenko, and O. Pavlov. 2010. “Asset Price Dynamics with Local Interactions Under Heterogeneous Beliefs.” CeNDEF Working paper 10-02, University of Amsterdam.Google Scholar

  • Hommes, C. 2001. “Financial Markets as Nonlinear Adaptive Evolutionary Systems.” Quantitative Finance 1: 149–167.CrossrefGoogle Scholar

  • Hommes, C. 2006. “Heterogeneous Agent Models in Economics and Finance.” In Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, edited by L. Tesfatsion and K. L. Judd, 1109–1186. Amsterdam, North-Holland: Elsevier Science B.V.Google Scholar

  • Hong, H., J. Kubik, and J. Stein. 2004. “Social Interaction and Stock-Market Participation.” Journal of Finance LIX: 137–163.Google Scholar

  • Hull, J. 2011. Options, Futures, and Other Derivatives. New Jersey, USA: Prentice Hall.Google Scholar

  • Kaizoji, T. 2000. “Speculative Bubbles and Crashes in Stock Markets: an Interacting-Agent Model of Speculative Activity.” Physica A 287: 493–506.Google Scholar

  • LeBaron, B. 2006. “Agent-Based Computational Finance.” In Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, edited by L. Tesfatsion and K. L. Judd, 1187–1233. Amsterdam, North-Holland: Elsevier Science B.V.Google Scholar

  • Leombruni, R., A. Palestrini, and M. Gallegati. 2003. “Mean Field Effects and Interaction Cycles in Financial Markets.” In Heterogeneous Agents, Interactions and Economic Performance, edited by R. Cowan and N. Jonard. Berlin, Heidelberg: Springer.Google Scholar

  • Lucas, R. 1978. “Asset Prices in An Exchange Economy.” Econometrica, 46: 1426–1446.Google Scholar

  • Lux, T. 1998. “The Socio-Economic Dynamics of Speculative Markets: Interacting Agents, Chaos, and the Fat Tails of Return Distributions.” Journal of Economic Behavior and Organization 33: 143–165.Google Scholar

  • Scharfstein, D., and J. Stein. 1990. “Herding Behavior and Investment.” American Economic Review 80: 465–479.Web of ScienceGoogle Scholar

  • Shiller, R., and J. Pound. 1989. “Survey Evidence on The Diffusion of Interest and Information Among Investors.” Journal of Economic Behavior and Organization 12: 47–66.Google Scholar

  • Tedeschi, G., G. Iori, and M. Gallegati. 2012. “Herding Effects in Order Driven Markets: The Rise and Fall of Gurus.” Journal of Economic Behavior and Organization 81: 82–96.Web of ScienceGoogle Scholar

  • Vaglica, G., F. Lillo, E. Moro, and R. Mantegna. 2008. “Scaling Laws of Strategic Behavior and Size Heterogeneity in Agent Dynamics.” Physical Review E 77: 036110.CrossrefGoogle Scholar

  • Welch, I. 2000. “Herding Among Security Analysts.” Journal of Financial Economics 58: 369–396.CrossrefGoogle Scholar

About the article

Corresponding author: Sheng-Kai Chang, Department of Economics, National Taiwan University, 21 Hsu-Chow Road, Taipei 100, Taiwan, e-mail:

Published Online: 2013-08-13

Published in Print: 2014-02-01

See also Blume and Easley (1992, 1993).

It is possible that rational arbitrageurs are risk-averse, and noise traders are risk-loving. Therefore, as pointed out by one of the referees, the assumption of homogeneous risk-aversion is unreasonable in the current paper. By relaxing the assumptions of homogeneous degrees of risk-aversion and homogeneous expected conditional variance, Chiarella and He (2002) test the robustness of the results of Brock and Hommes (1998). They also investigate the effects of different memory lengths on the dynamics. Chiarella and He (2002) found that Brock and Hommes’s (1998) results are robust to this generalization. However, they do point out that the resulting dynamic behavior with this generalization is considerably enriched and exhibits some significant differences. Thus, how to incorporate the heterogeneous beliefs and risk in a simple asset pricing model with social interactions in order to investigate the resulting herd behavior and bubbles in financial markets will be an important extension of the current paper.

The belief type of the noise trader used in this paper is equivalent to the term “trend chaser” with a belief bias in Brock and Hommes (1998). Notice that the term “noise trader” may have a different scope than a “trend chaser” with a belief bias in Brock and Hommes (1998). See for example, DeLong et al. (1990).

The belief type of arbitrageurs used in this paper is equivalent to the term “contrarian” with a belief bias in Brock and Hommes (1998). Notice that the term “arbitrageurs” may be used differently in the finance literature. For example, in Hull (2011), “arbitrageurs” is used to denote the traders who are involved in “locking in a risk-less profit by simultaneously entering into transactions in two or more markets.”

Here, S(‧) represents proportional spillover-type social interactions; see Brock and Durlauf (2001a,b).

See Brock and Durlauf (2001a,b) and Anderson, Palma and Thisse (1996) for a derivation.

Notice that the sufficient condition for |km*|<R is k<R.

Friedman (1953, p. 175) argues, “People who argue that speculation is generally destabilizing seldom realize that this is largely equivalent to saying that speculators lose money, since speculation can be destabilizing in general only if speculators on average sell when currency is low and buy when it is high.”

That is,

Blume and Easley (1992, 1993) find that the most fit behavior in the financial market is that which maximizes the expected growth rate of wealth share accumulation.

Citation Information: Studies in Nonlinear Dynamics and Econometrics, Volume 18, Issue 1, Pages 89–101, ISSN (Online) 1558-3708, ISSN (Print) 1081-1826, DOI: https://doi.org/10.1515/snde-2013-0024.

Export Citation

©2014 by Walter de Gruyter Berlin Boston.Get Permission

Comments (0)

Please log in or register to comment.
Log in