Detection of Crashes and Rebounds in Major Equity Markets

TL;DR

This paper introduces an advanced pattern recognition-based alarm index using the JLS model to detect and forecast crashes and rebounds in major global equity markets, outperforming chance and simple strategies.

Contribution

It extends the JLS model with a new alarm index and demonstrates its effectiveness in predicting market crashes and rebounds across multiple markets.

Findings

Alarm index outperforms chance in forecasts

Trading strategies based on alarm yield better returns

Method effective across 10 global markets

Abstract

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.