Stochastic Cellular Automata Model for Stock Market Dynamics

TL;DR

This paper introduces a stochastic cellular automata model that simulates stock market dynamics by modeling traders as active or inactive cells on a grid, capturing key features of financial time series.

Contribution

It presents a novel cellular automata framework using percolation to model trader interactions and reproduce market stylized facts.

Findings

Reproduces key stylized facts of financial markets

Models trader clusters with percolation-based hierarchy

Simulates buy/sell decision dynamics effectively

Abstract

In the present work we introduce a stochastic cellular automata model in order to simulate the dynamics of the stock market. A direct percolation method is used to create a hierarchy of clusters of active traders on a two dimensional grid. Active traders are characterised by the decision to buy, (+1), or sell, (-1), a stock at a certain discrete time step. The remaining cells are inactive,(0). The trading dynamics is then determined by the stochastic interaction between traders belonging to the same cluster. Most of the stylized aspects of the financial market time series are reproduced by the model.