Self-Organized Criticality and Stock Market Dynamics: an Empirical Study

The Stock Market is a complex self-interacting system, characterized by an intermittent behavior. Periods of high activity alternate with periods of relative calm. In the present work we investigate empirically about the possibility that the market is in a self-organized critical state (SOC). A wavelet transform method is used in order to separate high activity periods, related to the avalanches of sandpile models, from quiescent. A statistical analysis of the filtered data show a power law behavior in the avalanche size, duration and laminar times. The memory process, implied by the power law distribution, of the laminar times is not consistent with classical conservative models for self-organized criticality. We argue that a ''near-SOC'' state or a time dependence in the driver, which may be chaotic, can explain this behavior.