Bubble diagnosis and prediction of the 2005–2007 and 2008–2009 Chinese stock market bubbles

Jiang, Zhi‐Qiang; Zhou, Wei‐Xing; Sornette, Didier; Woodard, Ryan; Bastiaensen, Ken; Cauwels, Peter

doi:10.1016/j.jebo.2010.02.007

Cited by 181 publications

(47 citation statements)

References 37 publications

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“…Compared with the whole series of To further check the conjecture that our trading rules can only earn money in the market with prominent trends, we perform the trading rule tests on both indexes with a five-year moving window. As shown in Tables 1 and 2 [20]. For the other periods, no such obviously rising trends are observed, which gives rise to the fact that not all p-values for the different values of q are less than 0.1.…”

Section: Empirical Testmentioning

confidence: 82%

Testing the performance of technical trading rules in the Chinese markets based on superior predictive test

Wang

Jiang

et al. 2015

Physica A: Statistical Mechanics and its Applications

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Technical trading rules have a long history of being used by practitioners in financial markets. Their profitable ability and efficiency of technical trading rules are yet controversial. In this paper, we test the performance of more than seven thousands traditional technical trading rules on the Shanghai Securities Composite Index (SSCI) from May 21, 1992 through June 30, 2013 and Shanghai Shenzhen 300 Index (SHSZ 300) from April 8, 2005 through June 30, 2013 to check whether an effective trading strategy could be found by using the performance measurements based on the return and Sharpe ratio. To correct for the influence of the data-snooping effect, we adopt the Superior Predictive Ability test to evaluate if there exists a trading rule that can significantly outperform the benchmark. The result shows that for SSCI, technical trading rules offer significant profitability, while for SHSZ 300, this ability is lost. We further partition the SSCI into two sub-series and find that the efficiency of technical trading in sub-series, which have exactly the same spanning period as that of SHSZ 300, is severely weakened. By testing the trading rules on both indexes with a five-year moving window, we find that the financial bubble from 2005 to 2007 greatly improve the effectiveness of technical trading rules. This is consistent with the predictive ability of technical trading rules which appears when the market is less efficient.

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Section: Empirical Testmentioning

confidence: 82%

Testing the performance of technical trading rules in the Chinese markets based on superior predictive test

Wang

Jiang

et al. 2015

Physica A: Statistical Mechanics and its Applications

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“…The critical time t c is searched in the interval [t end − ηdt, t end + ηdt], with η is typically equal to 0.20. Previous calibrations of the LPPLS specification Eq (3) to the log-price development during a number of historical financial bubbles have suggested to qualify fits based on the parameters of the LPPLS model belonging to the following intervals [5,35,36] The optimization problem using the standard Ordinary Least Squares (OLS) method Filimonov and Sornette [36] suggested to expand the cosine term of Eq (3) with C 1 = C cos ϕ, C 2 = −C sin ϕ to obtain a representation with 4 linear and 3 nonlinear parameters, providing a substantial gain in efficiency and stability of the calibration. This leads to rewrite Eq (3) as…”

Section: Log-periodic Power Law Singularity (Lppls) Modelmentioning

confidence: 99%

“…The first one is S&P 500 2007, which was studied in [6,8]. The second and third one are SSEC 2007 and SSEC 2009, discussed in details in [35]. For each bubble, we picked one value of t end , spanning from one to three months before the crash that terminated the bubble at T c , as given in Table 1.…”

Section: Applications To the Prediction Of The End Of Four Historicalmentioning

confidence: 99%

“…While informative, the effective use of so many fluctuating and often conflicting signals to inform on the danger for a bubble burst and to trigger an actionable decision remains a challenge. To address this, we propose two indicators that aggregate these signals, inspired from previous works on historic bubbles [6,35,38] via the implementation of pattern recognition of LPPLS structures and filtering, as suggested in Fig 5. These two indicators have been briefly discussed to present the ex-ante forecast of the Chinese bubble and its burst that started in June 2015 [39].…”

Section: Consolidated Ds Lppls™ Indicatorsmentioning

confidence: 99%

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Early Warning Signals of Financial Crises with Multi-Scale Quantile Regressions of Log-Periodic Power Law Singularities

Zhang

Sornette

2015

SSRN Journal

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We augment the existing literature using the Log-Periodic Power Law Singular (LPPLS) structures in the log-price dynamics to diagnose financial bubbles by providing three main innovations. First, we introduce the quantile regression to the LPPLS detection problem. This allows us to disentangle (at least partially) the genuine LPPLS signal and the a priori unknown complicated residuals. Second, we propose to combine the many quantile regressions with a multi-scale analysis, which aggregates and consolidates the obtained ensembles of scenarios. Third, we define and implement the so-called DS LPPLS Confidence™ and Trust™ indicators that enrich considerably the diagnostic of bubbles. Using a detailed study of the "S&P 500 1987" bubble and presenting analyses of 16 historical bubbles, we show that the quantile regression of LPPLS signals contributes useful early warning signals. The comparison between the constructed signals and the price development in these 16 historical bubbles demonstrates their significant predictive ability around the real critical time when the burst/rally occurs.

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“…Equations (15)(16)(17)(18) lead to the following estimate of the speculative bubble component defined as the average distance between fundamental and bubble prices:…”

Section: Assumption 2 (Intrinsic Level Of Risk)mentioning

confidence: 99%

Multivariate bubbles and antibubbles

Fry

2014

Eur. Phys. J. B

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In this paper we develop models for multivariate financial bubbles and antibubbles based on statistical physics. In particular, we extend a rich set of univariate models to higher dimensions. Changes in market regime can be explicitly shown to represent a phase transition from random to deterministic behaviour in prices. Moreover, our multivariate models are able to capture some of the contagious effects that occur during such episodes. We are able to show that declining lending quality helped fuel a bubble in the US stock market prior to 2008. Further, our approach offers interesting insights into the spatial development of UK house prices.

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Bubble diagnosis and prediction of the 2005–2007 and 2008–2009 Chinese stock market bubbles

Cited by 181 publications

References 37 publications

Testing the performance of technical trading rules in the Chinese markets based on superior predictive test

Testing the performance of technical trading rules in the Chinese markets based on superior predictive test

Early Warning Signals of Financial Crises with Multi-Scale Quantile Regressions of Log-Periodic Power Law Singularities

Multivariate bubbles and antibubbles

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