Opinion Dynamics in Financial Markets via Random Networks

TL;DR

This paper presents an agent-based opinion formation model for financial markets, capturing key features like fat-tailed returns and volatility clustering by simulating heterogeneous trader interactions on a random network.

Contribution

It introduces a novel heterogeneous agent model with a noise parameter and network structure, linking local and global influences to market dynamics.

Findings

Model reproduces fat-tailed return distributions

Captures volatility clustering and long-term correlations

Shows shift from leptokurtic to mesokurtic regimes with agent composition

Abstract

We investigate the financial market dynamics by introducing a heterogeneous agent-based opinion formation model. In this work, we organize the individuals in a financial market by their trading strategy, namely noise traders and fundamentalists. The opinion of a local majority compels the market exchanging behavior of noise traders, whereas the global behavior of the market influences the fundamentalist agents' decisions. We introduce a noise parameter $q$ to represent a level of anxiety and perceived uncertainty regarding the market behavior, enabling the possibility for an adrift financial action. We place the individuals as nodes in an Erd\"os-R\'enyi random graph, where the links represent their social interaction. At a given time, they assume one of two possible opinion states $\pm 1$ regarding buying or selling an asset. The model exhibits such fundamental qualitative and quantitative real-world market features as the distribution of logarithmic returns with fat-tails, clustered volatility, and long-term correlation of returns. We use Student's t distributions to fit the histograms of logarithmic returns, showing the gradual shift from a leptokurtic to a mesokurtic regime, depending on the fraction of fundamentalist agents. We also compare our results with the distribution of logarithmic returns of several real-world financial indices.