An integrated early warning system for stock market turbulence

Abstract

This study constructs an integrated early warning system (EWS) that identifies and predicts stock market turbulence. Based on switching ARCH (SWARCH) filtering probabilities of the high volatility regime, the proposed EWS first classifies stock market crises according to an indicator function with thresholds dynamically selected by the two-peak method. An hybrid algorithm is then developed in the framework of a long short-term memory (LSTM) network to make daily predictions that alert turmoils. In the empirical evaluation based on ten-year Chinese stock data, the proposed EWS yields satisfying results with test-set accuracy of 96.4% and an average of 2.8 days forewarned period. The model’s stability and practical value in the real-time decision-making are also proven by the cross-validation, back-testing and reality check.