An Integrated Early Warning System for Stock Market Turbulence

TL;DR

This paper develops an integrated early warning system combining switching ARCH filtering, dynamic thresholding, and LSTM predictions to effectively forecast stock market turbulence with high accuracy and practical utility.

Contribution

It introduces a novel hybrid framework that combines regime-switching volatility modeling with deep learning for real-time stock market crisis prediction.

Findings

Achieved 96.6% accuracy in predicting stock market turbulence.

Provided an average of 2.4 days of advance warning.

Demonstrated stability and practical value through cross-validation and back-testing.

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. 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.