  A new approach to the understanding of complex behavior of financial markets
index using tools from thermodynamics and statistical physics is developed.
Physical complexity, a magnitude rooted in Kolmogorov-Chaitin theory is applied
to binary sequences built up from real time series of financial markets
indexes. The study is based on NASDAQ and Mexican IPC data. Different behaviors
of this magnitude are shown when applied to the intervals of series placed
before crashes and to intervals when no financial turbulence is observed. The
connection between our results and The Efficient Market Hypothesis is
discussed.
