Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/18059
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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: Statistical, Nonlinear, and Soft Matter Physics, 2004; 69(4):046112-1-046112-7 |
Publisher: | American Physical Society |
Issue Date: | 2004 |
ISSN: | 1539-3755 1063-651X |
Statement of Responsibility: | 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 |
DOI: | 10.1103/PhysRevE.69.046112 |
Published version: | http://dx.doi.org/10.1103/physreve.69.046112 |
Appears in Collections: | Aurora harvest 2 Physics publications |
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hdl_18059.pdf | Published version | 483.91 kB | Adobe PDF | View/Open |
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