Herd Behavior and Nonfundamental Asset Price Fluctuations in Financial Markets

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).

How to grow a bubble: A model of myopic adapting agents

“…This is a standard assumption in the social interaction literature (Bischi et al, 2006;Brock and Durlauf, 2001) and decision making theory (see for instance Körding, 2006). To take into account the heterogeneity in trading style and preferences of traders, we assume that each agent i is characterized by a triplet of fixed traits, in the form of the weights (c 1i , c 2i , c 3i ) she attributes to each of the three pieces of information (social network, news and idiosyncratic).…”

Market Dynamics When Agents Anticipate Correlation Breakdown

“…where χ 1 ∈ R−{0} is a sensitivity parameter. Linking current excess demand to its expectation is a classical way to model a contagion mechanism e.g., 18 . We do not specify how uninformed agents obtain an estimate of w e t it can be argued that some popular methods based on the observation of past prices such as chart or technical analysis are adopted to this purpose , nor dos we give details on the mechanism translating those estimates into an investment decision.…”

Market entry waves and volatility outbursts in stock markets

“…Cont and Bouchaud (2000) and Stauffer et al (1999) assume that speculators' herding behavior influences whether they are optimistic or pessimistic. Bischi et al (2006) show that complex asset price dynamics may emerge if speculators mimic the buying and selling behavior of other speculators. LeBaron and Yamamoto (2008) study imitation behavior which results from social learning and show that it may be responsible for long memory effects in trading volume and volatility.…”