Self-Organized Percolation Model for Stock Market Fluctuations

TL;DR

This paper introduces a dynamic percolation model for stock market fluctuations where clusters of investors continuously form and break, leading to realistic power-law distributions of price changes and capturing key market behaviors.

Contribution

It proposes a novel self-organized percolation model with evolving clusters, improving upon fixed-parameter models to better reflect market dynamics.

Findings

The model reproduces power-law distributions of price changes.

It captures stylized facts of stock market fluctuations.

Clusters dynamically form and break, reflecting market mood changes.

Abstract

In the Cont-Bouchaud model [cond-mat/9712318] of stock markets, percolation clusters act as buying or selling investors and their statistics controls that of the price variations. Rather than fixing the concentration controlling each cluster connectivity artificially at or close to the critical value, we propose that clusters shatter and aggregate continuously as the concentration evolves randomly, reflecting the incessant time evolution of groups of opinions and market moods. By the mechanism of ``sweeping of an instability'' [D. Sornette, Journal de Physique I 4, 209 (1994)], this market model spontaneously exhibits reasonable power law statistics for the distribution of price changes and accounts for the other important stylized facts of stock market price fluctuations.