  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 characterised by the decision to buy,
(+1), or sell, (-1), a stock at a certain discrete time step. The remaining
cells are inactive,(0). The trading dynamics is then determined by the
stochastic interaction between traders belonging to the same cluster. Most of
the stylized aspects of the financial market time series are reproduced by the
model.
