Computational modeling of collective human behavior: Example of financial markets

TL;DR

This paper presents a minimal financial market model combining global information and social network dynamics, resulting in more accurate price return distributions that resemble real-world data.

Contribution

It introduces a novel hybrid model that integrates global decision-making with social network influences in financial markets.

Findings

Enhanced price return distribution matching stylized facts

Combining global and local information improves model realism

Applicable to various collective human activity systems

Abstract

We discuss how minimal financial market models can be constructed by bridging the gap between two existing, but incomplete, market models: a model in which a population of virtual traders make decisions based on common global information but lack local information from their social network, and a model in which the traders form a dynamically evolving social network but lack any decision-making based on global information. We show that a suitable combination of these two models -- in particular, a population of virtual traders with access to both global and local information -- produces results for the price return distribution which are closer to the reported stylized facts. We believe that this type of model can be applied across a wide range of systems in which collective human activity is observed.