Bubble Diagnosis and Prediction of the 2005-2007 and 2008-2009 Chinese Stock Market Bubbles

By Zhi-Qiang Jiang, Wei‐Xing Zhou, Didier Sornette, Ryan Woodard, Ken Bastiaensen, P. Cauwels. 2009

243 citations recorded by the source corpus.

By combining (i) the economic theory of rational expectation bubbles, (ii) behavioral finance on imitation and herding of investors and traders and (iii) the mathematical and statistical physics of bifurcations and phase transitions, the log-periodic power law model has been developed as a flexible tool to detect bubbles. The LPPL model considers the faster-than-exponential (power law with finite-time singularity) increase in asset prices decorated by accelerating oscillations as the main diagnostic of bubbles. It embodies a positive feedback loop of higher return anticipations competing with negative feedback spirals of crash expectations. We use the LPPL model in one of its incarnations to analyze two bubbles and subsequent market crashes in two important indexes in the Chinese stock markets between May 2005 and July 2009. Both the Shanghai Stock Exchange Composite and Shenzhen Stock Exchange Component indexes exhibited such behavior in two distinct time periods: 1) from mid-2005, bursting in Oct. 2007 and 2) from Nov. 2008, bursting in the beginning of Aug. 2009. We successfully predicted time windows for both crashes in advance with the same methods used to successfully predict the peak in mid-2006 of the US housing bubble and the peak in July 2008 of the global oil bubble. The more recent bubble in the Chinese indexes was detected and its end or change of regime was predicted independently by two groups with similar results, showing that the model has been well-documented and can be replicated by industrial practitioners. Here we present more detailed analysis of the individual Chinese index predictions and of the methods used to make and test them.

Subfields: Computer Networks and Communications, Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/18d1ebfcc5b908594867bf57200da2c3da961e54

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Citations in the corpus

Clarifications to questions and criticisms on the Johansen–Ledoit–Sornette financial bubble model (2011, methodology): “See for instance Ref.[18] where this is made as clear as possible.”
A Stable and Robust Calibration Scheme of the Log-Periodic Power Law Model (2011, methodology): “Applied to previous calibrations using the procedures described in this section [5– 8, 10, 17, 26], we are able to conﬁrm the essential goodness of ﬁts of the obtained calibrations on empirical ﬁnancial time series.”
Dragon-kings: Mechanisms, statistical methods and empirical evidence (2012, background): “onential acceleration before collapse have been documented for many systems, from material rupture and earthquakes (Sammis and Sornette, 2002), to financial bubbles ending in crashes (Sornette, 2003; Jiang et al., 2010; Johansen and Sornette, 2010). Akaev et al. (2011) apply mathematical models with”
Stock Market Embedding and Prediction: A Deep Learning Method (2018, background): “[1] conducted a detailed study of diagnosis and prediction of Chinese stock market bubbles occurred in 2005-2007 and 2008-2009.”