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


Zhi-Qiang Jiang


East China University of Science and Technology (ECUST)

Wei-Xing Zhou


East China University of Science and Technology - School of Business

Didier Sornette


Swiss Finance Institute; ETH Zurich

Ryan Woodard


ETH Zurich

Ken Bastiaensen


BNP Paribas

Peter Paul Cauwels


BNP Paribas Fortis

September 7, 2009


Abstract:     
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 logperiodic power law (LPPL) 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 index (US ticker symbol SSEC) and Shenzhen Stock Exchange Component index (SZSC) exhibited such behavior in two distinct time periods: 1) from mid-2005, bursting in October 2007 and 2) from November 2008, bursting in the beginning of August 2009. We successfully predicted time windows for both crashes in advance [24, 1] with the same methods used to successfully predict the peak in mid-2006 of the US housing bubble [37] and the peak in July 2008 of the global oil bubble [26]. 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. We complement the detection of log-periodic behavior with Lomb spectral analysis of detrended residuals and (H, q)-derivative of logarithmic indexes for both bubbles. We perform unit-root tests on the residuals from the log-periodic power law model to confirm the Ornstein-Uhlenbeck property of bounded residuals, in agreement with the consistent model of ‘explosive’ financial bubbles [16].

Number of Pages in PDF File: 28

Keywords: stock market crash, financial bubble, Chinese markets, rational expectation bubble, herding, log-periodic power law, Lomb spectral analysis, unit-root test

JEL Classification: G01, G17, O16

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Date posted: September 28, 2009  

Suggested Citation

Jiang, Zhi-Qiang, Zhou, Wei-Xing, Sornette, Didier , Woodard, Ryan, Bastiaensen, Ken and Cauwels, Peter Paul, Bubble Diagnosis and Prediction of the 2005-2007 and 2008-2009 Chinese Stock Market Bubbles (September 7, 2009). Available at SSRN: http://ssrn.com/abstract=1479479 or http://dx.doi.org/10.2139/ssrn.1479479

Contact Information

Zhi-Qiang Jiang
East China University of Science and Technology (ECUST) ( email )
Shanghai
China
Wei-Xing Zhou
East China University of Science and Technology - School of Business ( email )
130 Meilong Road
Shanghai, 200237
China
Didier Sornette (Contact Author)
Swiss Finance Institute ( email )
c/o University of Geneve
40, Bd du Pont-d'Arve
1211 Geneva, CH-6900
Switzerland
ETH Zurich ( email )
Department of Management, Technology and Economics
Scheuchzerstrasse 7
8092 Zurich
Switzerland
41446328917 (Phone)
41446321914 (Fax)
HOME PAGE: http://www.er.ethz.ch/
Ryan Woodard
ETH Zurich ( email )
Department of Management, Technology and Economics
Kreuzplatz 5
8032 Zurich, CH-1015
Switzerland
+41 44 632 83 79 (Phone)
+41 44 632 19 14 (Fax)
HOME PAGE: http://www.er.ethz.ch/
Ken Bastiaensen
BNP Paribas ( email )
Brussels
Belgium
Peter Paul Cauwels
BNP Paribas Fortis ( email )
Brussels
Belgium
Feedback to SSRN (Beta)


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