  This paper presents an exclusive classification of the largest crashes in Dow
Jones Industrial Average (DJIA), SP500 and NASDAQ in the past century. Crashes
are objectively defined as the top-rank filtered drawdowns (loss from the last
local maximum to the next local minimum disregarding noise fluctuations), where
the size of the filter is determined by the historical volatility of the index.
It is shown that {\it all} crashes can be linked to either an external shock,
{\it e.g.}, outbreak of war, {\it or} a log-periodic power law (LPPL) bubble
with an empirically well-defined complex value of the exponent. Conversely,
with one sole exception {\it all} previously identified LPPL bubbles are
followed by a top-rank drawdown. As a consequence, the analysis presented
suggest a one-to-one correspondence between market crashes defined as top-rank
filtered drawdowns on one hand and surprising news and LPPL bubbles on the
other. We attribute this correspondence to the Efficient Market Hypothesis
effective on two quite different time scales depending on whether the market
instability the crash represent is internally or externally generated.
