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

TL;DR

This paper introduces a model for financial market herd behavior based on opinion clusters and network evolution, explaining fat-tail return distributions and crash probabilities through a herding parameter.

Contribution

It presents a novel stochastic opinion formation model linking herd behavior to fat-tail distributions and market crashes in financial data.

Findings

For low herding rate h, returns follow a power-law distribution with exponential cutoff.

For high herding rate h, probability of large returns increases, indicating potential crashes.

The model captures the transition from normal to crash-prone market behavior.

Abstract

We propose a model for stochastic formation of opinion clusters, modelled by an evolving network, and herd behaviour 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 cut-off. However for h>h* an increase in the probability of large returns is found, and may be associated to the occurrence of large crashes.