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Type: Journal article
Title: Stochastic cellular automata model for stock market dynamics
Author: Bartolozzi, M.
Thomas, A.
Citation: Physical Review E, 2004; 69(4):046112-1-046112-7
Publisher: American Physical Society
Issue Date: 2004
ISSN: 1539-3755
Statement of
M. Bartolozzi and A. W. Thomas
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 characterized by the decision to buy, σi(t)=+1, or sell, σi(t)=−1, a stock at a certain discrete time step. The remaining cells are inactive, σi(t)=0. The trading dynamics is then determined by the stochastic interaction between traders belonging to the same cluster. Extreme, intermittent events, such as crashes or bubbles, are triggered by a phase transition in the state of the bigger clusters present on the grid, where almost all the active traders come to share the same spin orientation. Most of the stylized aspects of the financial market time series, including multifractal proprieties, are reproduced by the model. A direct comparison is made with the daily closures of the S&P500 index.
Keywords: Complex Systems; Percolation; Stochastic Processes; Econophysics
Description: An erratum exists for this article. Please see the DOI link below for details.
Rights: ©2004 The American Physical Society
RMID: 0020040403
DOI: 10.1103/PhysRevE.69.046112
Appears in Collections:Physics publications

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