Three-State Opinion Dynamics for Financial Markets on Complex Networks

The recession due to the COVID-19 pandemic and the rise in inflation rates owing to the Ukraine war are current economic scenarios that demonstrate the fundamental impacts of collective behavior in the evolution of economic systems and financial markets. In this work, we propose to investigate the rich dynamics of financial markets by a heterogeneous agent-based opinion formation model. We divide the network of economic interactions into two sets of trader strategies, noise traders and fundamentalists. The former invests based on its local majority, whereas the latter acts based on the market index. Each agent is represented as a node in a complex network and may assume at a given time three distinct option states regarding buying, selling, or holding an asset. We investigate the model in several complex networks: random graphs, scale-free, and small-world networks. The model presents 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 fit the histograms of logarithmic returns by using Student's t distributions, showing the gradual shift from a leptokurtic to a mesokurtic regime, depending on the fraction of fundamentalist agents. We also qualitatively compare our results with the distribution of logarithmic returns of several real-world financial indices.