A particle model for the herding phenomena induced by dynamic market signals

TL;DR

This paper introduces a particle-based model to analyze herding behavior in financial markets driven by collective interactions and dynamic signals, providing theoretical insights and numerical validation.

Contribution

It develops a novel agent-based particle model capturing herding phenomena induced by market signals and interactions, with analytical and numerical analysis.

Findings

Herding behavior can be characterized by specific functionals satisfying invariance properties.

The model predicts the emergence of herding phenomena under certain conditions.

Numerical tests confirm the theoretical predictions of herding dynamics.

Abstract

In this paper, we study the herding phenomena in financial markets arising from the combined effect of (1) non-coordinated collective interactions between the market players and (2) concurrent reactions of market players to dynamic market signals. By interpreting the expected rate of return of an asset and the favorability on that asset as position and velocity in phase space, we construct an agent-based particle model for herding behavior in finance. We then define two types of herding functionals using this model, and show that they satisfy a Gronwall type estimate and a LaSalle type invariance property respectively, leading to the herding behavior of the market players. Various numerical tests are presented to numerically verify these results.