Modeling Stock Market Based on Genetic Cellular Automata

TL;DR

This paper introduces a cellular automata-based model of the stock market where agents learn and interact, capturing complex fluctuations and return distributions similar to real markets.

Contribution

It presents a novel artificial stock market model using cellular automata with self-learning and neighbor influence, simulating realistic market phenomena.

Findings

Large market events occur frequently in the model

Stock price fluctuations follow a Levy distribution centrally

Returns exhibit exponential truncation in distribution

Abstract

An artificial stock market is established with the modeling method and ideas of cellular automata. Cells are used to represent stockholders, who have the capability of self-teaching and are affected by the investing history of the neighboring ones. The neighborhood relationship among the stockholders is the expanded Von Neumann relationship, and the interaction among them is realized through selection operator and crossover operator. Experiment shows that the large events are frequent in the fluctuations of the stock price generated by the artificial stock market when compared with a normal process and the price returns distribution is a Levy distribution in the central part followed by an approximately exponential truncation.