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Activity Number:
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321
- Machine Learning and Variable Selection
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Type:
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Contributed
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Date/Time:
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Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
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Sponsor:
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Section on Statistical Computing
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Abstract #318006
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Title:
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The 2015 Chinese Stock Market Bubble and Crash
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Author(s):
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Min Shu and Wei Zhu
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Companies:
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University of Wisconsin Stout and Stony Brook University
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Keywords:
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Chinese Stock Market;
Covariance matrix adaptation evolution strategy;
Financial bubble;
Log-periodic power law singularity model (LPPLS);
Market crash
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Abstract:
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We perform a novel analysis of the 2015 Chinese stock market crash by calibrating the Log Periodic Power Law Singularity (LPPLS) model to two important Chinese stock indices, SSEC and SZSC, from early 2014 to June 2015. Our analysis indicates that the LPPLS model can readily detect the bubble behavior of the faster-than-exponential increase corrected by the accelerating logarithm-periodic oscillations in the crash. The existence of the log-periodicity is identified by applying the Lomb spectral analysis on the detrended residuals. The Ornstein-Uhlenbeck property and the stationarity of the LPPLS fitting residuals are confirmed by the Unit-root test. We find that the actual critical day tc can be well predicted by the LPPLS model as far back as two months before the actual bubble crash. We have shown that the covariance matrix adaptation evolution strategy (CMA-ES) can be used as an alternative optimization algorithm for LPPLS model fit. Our methodology can be readily applied to detect newly emerging bubbles in any stock market or even other financial market such as that of the crypto-currency.
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Authors who are presenting talks have a * after their name.
