Detection of Crashes and Rebounds in Major Equity Markets
AbstractFinancial 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.
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Bibliographic InfoPaper provided by ETH Zurich, Chair of Systems Design in its series Working Papers with number ETH-RC-11-001.
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JLS model; financial bubbles; crashes; rebounds; log-periodic power law; pattern recognition method; alarm index; prediction; error diagram; trading strategy.;
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