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