  By analyzing a large data set of daily returns with data clustering
technique, we identify economic sectors as clusters of assets with a similar
economic dynamics. The sector size distribution follows Zipf's law. Secondly,
we find that patterns of daily market-wide economic activity cluster into
classes that can be identified with market states. The distribution of
frequencies of market states shows scale-free properties and the memory of the
market state process extends to long times ($\sim 50$ days). Assets in the same
sector behave similarly across states. We characterize market efficiency by
analyzing market's predictability and find that indeed the market is close to
being efficient. We find evidence of the existence of a dynamic pattern after
market's crashes.
