What is it about?

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

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Why is it important?

Financial markets reflect the economic activity of nations, industries, companies, and societies. The recent housing market crash, COVID-19 pandemic, high inflation indices, and cryptocurrency frenzy have illustrated the decisive influence of such systems in modern culture. This work uses an agent-based model within the framework of a complex network to describe asset price dynamics in stock markets. We assume that a financial market comprises two main categories of investors—noise traders and fundamentalists—with the former investing based on its acquaintances and the latter acting based on the market index. Our model reproduces real-world market features for a finite level of social anxiety, denoted by a social temperature, and for a small number of interacting fundamentalist agents in a random network.


The model contributes to our understanding of the rich and complex dynamics of financial markets, driven by the emotional and rational decisions of its agents, via a straightforward description. We reduce the complexities of such systems by assuming that financial markets consist of a random network of interacting agents who follow either the opinion of most of their peers or strategies of their own. Furthermore, our model outputs are comparable to real-world markets in terms of a finite socioeconomic temperature, with the most significant fraction of agents interacting with a small number of neighbors. In contrast, the remaining fraction follows the market index. We remark that this framework allows us to enrich the knowledge of such complex economic systems without using an extensive number of variables.

André L. M. Vilela
Universidade de Pernambuco

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This page is a summary of: Opinion dynamics in financial markets via random networks, Proceedings of the National Academy of Sciences, November 2022, Proceedings of the National Academy of Sciences, DOI: 10.1073/pnas.2201573119.
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