Financial time series are approached from a systemic perspective, looking for evidence of self-organization. A methodology was developed to identify as units of study, each fall from a given maximum price level. A range, within the space of states, in which price falls could be explained as a process that follows a power law was explored. A critical level in the depth of price falls was found to separate a segment operating under a random walk regime, from a segment operating under a power law. This level was interpreted as a point of phase transition in a self-organized system. Evidence of self-organization was found in all stock market indices studied but in none of the control synthetic random series. Findings partially explain the fractal structure characteristic of financial time series and suggests that price fluctuations adopt two different operating regimes. We propose to identify downward movements larger than the critical level, explainable as subject to the power law, as self-organized states, making allowance to explaining price descends smaller than the critical level, as a random walk with the Markov property.
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