2019/11/28 by Peiwan Wang, Wang, Peiwan, Lu Zong +3 · 5 citations
Decision Sciences · Economics, Econometrics and Finance · Engineering · #Complex Systems and Time Series Analysis #Computer science #Econometrics #Econometrics (econ.EM) #Economics #Engineering #FOS: Economics and business #Financial Risk and Volatility Modeling #Geography #Mathematical Finance (q-fin.MF) #Stock (firearms) #Stock Market Forecasting Methods #Stock market #Telecommunications #Volatility (finance) #Warning system #econ.EM #q-fin.MF
paper · pdf · doi:10.48550/arxiv.1911.12596
published in arXiv (Cornell University) (Cornell University)
arxiv created 2019/11/28 · openalex publication_date 2019/11/28 · arxiv updated 2019/12/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
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.