  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. A 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 the test-set accuracy of $96.6\%$ and on average $2.4$ days of the
forewarned period. The model's stability and practical value in real-time
decision-making are also proven by the cross-validation and back-testing.

Econometrics;Mathematical Finance