Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-02T21:14:16.946Z Has data issue: false hasContentIssue false

HERD BEHAVIOR AND NONFUNDAMENTAL ASSET PRICE FLUCTUATIONS IN FINANCIAL MARKETS

Published online by Cambridge University Press:  23 August 2006

GIAN-ITALO BISCHI
Affiliation:
University of Urbino
MAURO GALLEGATI
Affiliation:
University of Ancona
LAURA GARDINI
Affiliation:
University of Urbino
ROBERTO LEOMBRUNI
Affiliation:
Laboratorio R. Revelli
ANTONIO PALESTRINI
Affiliation:
University of Teramo

Abstract

In this paper we investigate the effects of herding on asset price dynamics during continuous trading. We focus on the role of interaction among traders, and we investigate the dynamics emerging when we allow for a tendency to mimic the actions of other investors, that is, to engage in herd behavior. The model, built as a mean field in a binary setting (buy/sell decisions of a risky asset), is expressed by a three-dimensional discrete dynamical system describing the evolution of the asset price, its expected price, and its excess demand. We show that such dynamical system can be reduced to a unidirectionally coupled system. In line with the rational herd behavior literature [Bikhchandani, S., Sharma, S. (2000), Herd Behavior in Financial Markets: A Review. Working paper, IMF, WP/00/48], situations of multistability are observed, characterized by strong path dependence; that is, the dynamics of the system are strongly influenced by historical accidents. We describe the different kinds of dynamic behavior observed, and we characterize the bifurcations that mark the transitions between qualitatively different time evolutions. Some situations give rise to high sensitivity with respect to small changes of the parameters and/or initial conditions, including the possibility of invest or reject cascades (i.e., sudden uncontrolled increases or crashes of the prices).

Type
ARTICLES
Copyright
© 2006 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Avery C., and Zemsky P. 1998 Multidimensional uncertainty and herd behavior in financial markets. American Economic Review 88, 724748.Google Scholar
Banerjee A.V. 1992 A simple model of herd behavior. Quarterly Journal of Economics 107 (3), 797818.Google Scholar
Bikhchandani S., Hirshleifer D., and Welch I. 1992 A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy 100, 9921026.Google Scholar
Bikhchandani S., and Sharma S. 2000 Herd behavior in financial markets: A review. Working paper, IMF, WP/00/48.Google Scholar
Bischi G.I., Dieci R., Rodano G., and Saltari E. 2001 Multiple attractors and global bifurcations in a Kaldor-type business cycle model. Journal of Evolutionary Economics 11, 527554.Google Scholar
Black F. 1986 Noise. Journal of Finance 41, 529544.Google Scholar
Brock W.A., and Durlauf S.N. 2001 Interaction-based models. Handbook of Econometrics, vol. 5. Amsterdam: North Holland.
Brock W.A., and Hommes C. 1997a A rational route to randomness. Econometrica 65, 10591096.Google Scholar
Brock W.A., and Hommes C. 1997b Models of complexity in economics and finance. In C. Heij, J.M. Schumacher, B. Hanzon, and C. Praagman (eds.), Systems Dynamics in Economics and Finance Models, pp. 341. New York: Wiley.
Brock W.A., and Hommes C. 1998 Heterogeneous beliefs and route to chaos in a simple asset pricing model. Journal of Economic Dynamics and Control 22, 12351274.Google Scholar
Brock W.A., and Hommes C. 1999 Rational animal spirits. In P.J.L. Herings, G. van der Laan, and A.J.J. Talman (eds.), The Theory of Markets, pp. 109137. Amsterdam: North-Holland.
Chiarella C. 1992 The dynamics of speculative markets. Annals of Operation Research 37, 101123.Google Scholar
Chiarella C., and He X.Z. 2002 Heterogeneous beliefs, risk and learning in a simple asset pricing model. Computational Economics 19 (1), 95132.Google Scholar
Chiarella C., Dieci R., and Gardini L. 2002 Speculative behaviour and complex asset price dynamics. Journal of Economic Behaviour and Organization 49 (1), 173197.Google Scholar
Chiarella C., Dieci R., and Gardini L. 2001 Asset price dynamics in a financial market with fundamentalists and chartists. Discrete Dynamics in Nature and Society 6, 6999.Google Scholar
Christie W.G., and Huang R.D. 1995 Following the pied piper: Do individual returns herd around the market? Financial Analysts Journal 51 (4), 3137.Google Scholar
Cont R., and Bouchaud J.P. 2000 Herd behavior and aggregate fluctuations in financial markets. Macroeconomic Dynamics 4, 170196.Google Scholar
Cooper R., and John A. 1988 Coordinating coordination failures in Keynesian models. Quarterly Journal of Economics 103 (3) Aug. 441464.Google Scholar
DeLong J.B., Schleifer A., Summers L., and Waldman R. 1990 Positive feedback investment strategies and destabilizing rational speculation. Journal of Finance 45, 379395.Google Scholar
Dieci R., Bischi G.I., and Gardini L. 2001 Multistability and role of noninvertibility in a discrete-time business cycle model. Central European Journal of Operation Research 9, 7196.Google Scholar
Eguíluz V.M., and Zimmermann M.G. 2000 Transmission of information and herd behavior: An application to financial markets. Physical Review Letters 85 (26), 56595662.Google Scholar
Engle R. 2001 GARCH 101: The use of ARCH/GARCH models in applied econometrics. Journal of Economic Perspectives 15, 157168.Google Scholar
Föllmer H. 1974 Random economies with many interacting agents. Journal of Mathematical Economics 1, 5262.Google Scholar
Forni M., and Lippi M. 1997 Aggregation and the Microfoundations of Dynamic Macroeconomics. Oxford: Clarendon.
Friedman M. 1953 The methodology of positive economics. In M. Friedman, Essays in positive economics. Chicago: University of Chicago Press.
Gandolfo G. 1997 Economic Dynamics. Berlin–Heidelberg: Springer-Verlag.
Gleason K.C., Mathur I., and Peterson M.A. 2004 Analysis of Intraday Herding Behavior Among the Sector ETFs. Journal of Empirical Finance 11, 681694.Google Scholar
Guckenheimer J., and Holmes P. 1983 Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields. Berlin–Heidelberg: Springer-Verlag.
Gumowski I., and Mira C. 1980 Dynamique Chaotique. Toulouse: Cepadues Editions.
Haltiwanger J.C., and Waldmann M. 1985 Rational expectations and the limits of rationality: An analysis of heterogeneity. American Economic Review 75, 326340.Google Scholar
Kaizoji T. 2000 Speculative bubbles and crashes in stock markets: An interacting agent model of speculative activity. Physica A 287 (3–4), 493506.Google Scholar
Kirman A. 1992 Whom or what does the representative individual represent? Journal of Economic Perspectives 6, 117136.Google Scholar
Kirman A.P., and Teyssière G. 2002 Microeconomic models for long-memory in the volatility of financial time series. Studies in Nonlinear Dynamics and Econometrics 5, 281302.Google Scholar
LeBaron B. 2000 Agent based computational finance: Suggested readings and early research. Journal of Economic Dynamics and Control 24, 679702.Google Scholar
Leombruni R., Palestrini A., and Gallegati M. 2002 Mean fields effects and interaction cycles in financial markets. In R. Cowan and N. Jonard (eds.), Heterogeneous Agents, Interactions and Economic Performance, Lecture Notes in Economics and Mathematical Systems, vol. 521, pp. 259276. Berlin–Heidelberg: Springer-Verlag.
Levy M., and Levy H. 1996 The danger of assuming homogeneous expectations. Financial Analyst Journal 52 (3), 6570.Google Scholar
Liera M., and Beltratti A. 2000 Capire la borsa: Guida all'investimento azionario globale nell'era di Internet. Milan: Il sole 24 ore.
Lorenz H.W. 1993 Nonlinear Dynamical Economics and Chaotic Motion, 2nd ed. Berlin–Heidelberg: Springer-Verlag.
Lucas R. 1978 On the size distribution of business firms. Bell Journal of Economics 2, 508523.Google Scholar
Lux T. 1998 The socio-economic dynamics of speculative markets: Interacting agents, chaos, and the fat tail of return distribution. Journal of Economic Behavior and Organization 33, 143165.Google Scholar
Lux T., and Marchesi M. 2001 Volatility clustering in financial markets: A micro-simulation of interacting agents. Journal of Theoretical and Applied Finance 3, 675702.Google Scholar
Mantegna R.N., and Stanley E.H. 2000 An Introduction to Econophysics. Cambridge, UK: Cambridge University Press.
Medio A., and Lines M. 2001 Nonlinear Dynamics. Cambridge, UK: Cambridge University Press.
Orléan A. 1995 Bayesian interactions and collective dynamics of opinion. Journal of Economic Behavior and Organisation 28, 257274.Google Scholar
Routledge B.R. 1999 Adaptive learning in financial markets. Review of Financial Studies 12 (5), 11651202.Google Scholar
Stark J. 1997 Invariant graphs for forced systems. Physica D 109, 163179.Google Scholar
Terna P. 2000 Mind no-mind dilemma in agents for social science simulations. In G. Ballot and G. Weisbuch (eds.), Applications of Simulation to Social Sciences, pp. 257271. Oxford: Hermes Science Publishing.
Welch I. 1992 Sequential sales, learning and cascades. Journal of Finance 47, 695732.Google Scholar