Real-Time Prediction and Post-Mortem Analysis of the Shanghai 2015 Stock Market Bubble and Crash

Sornette, Didier; Demos, Guilherme; Zhang, Qun; Cauwels, Peter; Filimonov, V. A.; Zhang, Qunzhi

doi:10.2139/ssrn.2693634

Cited by 14 publications

(6 citation statements)

References 16 publications

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“…When employing the LPPLS model, commonly, conditions for nonlinear parameters (m, ω, t c ) are based on empirical evidence from past bubbles [12]. These conditions are defined in the literature and build reliability and validity of the model itself (see [34,59,60]).…”

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

confidence: 99%

“…The 24 outcomes now classified into B < 0, B > 0 are filtered, based on empirical evidence of previous bubble investigations [34,60]. The filter in Equation (8) ensures that the time it takes for the oscillation to complete is big enough.…”

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

confidence: 99%

“…Moreover, as you approach the critical time (t c ), the frequency of oscillations (ω) increases. The filter in Equation ( 9), as derived by [60,61], is set by definition.…”

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

confidence: 99%

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Is the Metaverse Dead? Insights from Financial Bubble Analysis

Frank,

Rudolf

2024

FinTech

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This paper explores the economic trends and identifies speculative bubbles within the emerging metaverse, based on the specific example of Decentraland, which is represented by its underlying native token MANA.For comparability, we consider three further tokens: SAND, ETH, and BTC.The study shows price prediction and provides further insight into bubble behavior to provide a deeper insight into the real trend and situation of the metaverse. When comparing all considered tokens, evidence of comovement and positive as well as negative bubbles is identified. This paper makes use of proven modeling techniques, such as SARIMA, for price prediction and LPPLS for financial bubble identification, visualization, and time stamping.

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

confidence: 99%

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

confidence: 99%

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Is the Metaverse Dead? Insights from Financial Bubble Analysis

Frank,

Rudolf

2024

FinTech

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“…Since the current situation of peaking indices is too new to expect extensive research on this topic, there are currently only a few similar articles available to the knowledge of the author [7,8]. Their research, however, is either limited to a single stock, market or pursues another approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Selected Market Indicators During the Dot-Com Bubble

Schubert¹,

Gavurová²,

Kováč³

et al. 2018

Financial Management From an Emerging Market Perspective

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Since the outbreak of the recent inancial crisis in 2007, central banks around the world have lowered interest rates while stock markets soared. On the example of North America, Europe, and Asia and in particular the United States, Germany, and China, the situation as of December 2015 is compared on the basis of economic theory and selected key performance indicators to the United States dot-com bubble in the nineties years of the twentieth century. Literature review ofers a complex general view on the issue of market bubbles with a historical review of the situation in 2007 and 2008. The only indication of a bubble can be found in the China Securities Index 300, more speciically in the technology sector. The further aim of the paper is related to analyse and compare returns of the explored indices among the regions and the sectors. On a broader level, the study inds that even though there are similarities, the current rise in indices does not qualify for an asset price bubble. Conclusion sums all the observed indings on both the levelsregional and national. Also, it ofers suggestions for discussion about the situation on the markets after the inancial crisis.

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“…The concept of financial log-periodicity [1][2][3][4][5][6] often termed as Log-Periodic Power-Law (LPPL) model, has widely been used for detecting bubbles and subsequent crashes already for almost two decades. In spite of rising some controversies [7][8][9], many successful attempts to describe [10][11][12][13][14][15][16][17][18][19][20][21][22] and even to detect bubbles and their subsequent bursts by using this technique [23][24][25][26] have been reported. One of the most spectacular such examples is ex-ante exceptionally precise prediction of Brent Crude Oil bubble bursting time in early July 2008, delivered three months ahead as described in ref.…”

Section: Introductionmentioning

confidence: 99%

World Financial 2014-2016 Market Bubbles: Oil Negative - US Dollar Positive

2016

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Based on the log-periodic power law methodology, with the universal preferred scaling factor λ ≈ 2, the negative bubble on the oil market in 2014-2016 has been detected. Over the same period a positive bubble on the so-called commodity currencies expressed in terms of the US dollar appears to take place with the oscillation pattern which largely is mirror reflected relative to oil price oscillation pattern. It documents recent strong anticorrelation between the dynamics of the oil price and of the USD. A related forecast made at the time of FENS 2015 conference (beginning of November) turned out to be quite satisfactory. These findings provide also further indication that such a log-periodically accelerating down-trend signals termination of the corresponding decreases.

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Real-Time Prediction and Post-Mortem Analysis of the Shanghai 2015 Stock Market Bubble and Crash

Cited by 14 publications

References 16 publications

Is the Metaverse Dead? Insights from Financial Bubble Analysis

Is the Metaverse Dead? Insights from Financial Bubble Analysis

Comparison of Selected Market Indicators During the Dot-Com Bubble

World Financial 2014-2016 Market Bubbles: Oil Negative - US Dollar Positive

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