  Financial markets are well known for their dramatic dynamics and consequences
that affect much of the world's population. Consequently, much research has
aimed at understanding, identifying and forecasting crashes and rebounds in
financial markets. The Johansen-Ledoit-Sornette (JLS) model provides an
operational framework to understand and diagnose financial bubbles from
rational expectations and was recently extended to negative bubbles and
rebounds. Using the JLS model, we develop an alarm index based on an advanced
pattern recognition method with the aim of detecting bubbles and performing
forecasts of market crashes and rebounds. Testing our methodology on 10 major
global equity markets, we show quantitatively that our developed alarm performs
much better than chance in forecasting market crashes and rebounds. We use the
derived signal to develop elementary trading strategies that produce
statistically better performances than a simple buy and hold strategy.
