Transmission of Information and Herd Behavior: An Application to Financial Markets

We propose a model for stochastic formation of opinion clusters, modeled by an evolving network, and herd behavior to account for the observed fat-tail distribution in returns of financial-price data. The only parameter of the model is $h$, the rate of information dispersion per trade, which is a measure of herding behavior. For $h$ below a critical $h^{*}$ the system displays a power-law distribution of the returns with exponential cutoff. However, for $h>\mathrm{}{h}^{*}$ an increase in the probability of large returns is found and may be associated with the occurrence of large crashes.