Behavioural breaks in the heterogeneous agent model: the impact of herding, overconfidence, and market sentiment

TL;DR

This paper extends a Heterogeneous Agent Model with behavioural finance elements like herding, overconfidence, and sentiment, to analyze their impact on asset prices during turbulent market periods, using numerical simulations and empirical data.

Contribution

It introduces behavioural breaks into the Heterogeneous Agent Model framework, enhancing its ability to replicate turbulent market dynamics.

Findings

Behavioural breaks significantly affect asset price dynamics.

The extended model can partially replicate real market turbulence.

Different behavioural modifications lead to varied model outcomes.

Abstract

The main aim of this work is to incorporate selected findings from behavioural finance into a Heterogeneous Agent Model using the Brock and Hommes (1998) framework. Behavioural patterns are injected into an asset pricing framework through the so-called `Break Point Date', which allows us to examine their direct impact. In particular, we analyse the dynamics of the model around the behavioural break. Price behaviour of 30 Dow Jones Industrial Average constituents covering five particularly turbulent U.S. stock market periods reveals interesting pattern in this aspect. To replicate it, we apply numerical analysis using the Heterogeneous Agent Model extended with the selected findings from behavioural finance: herding, overconfidence, and market sentiment. We show that these behavioural breaks can be well modelled via the Heterogeneous Agent Model framework and they extend the original model considerably. Various modifications lead to significantly different results and model with behavioural breaks is also able to partially replicate price behaviour found in the data during turbulent stock market periods.