Jan-Christian Gerlach, Guilherme Demos, Didier Sornette
arXiv 17 Apr 2018 · Econometrics · publishedRoyal Society Open Science (2019) · 4 citations (OpenAlex)
arXiv:1804.06261 · PDF · DOI · OpenAlex · Extracted main text
We present a detailed bubble analysis of the Bitcoin to US Dollar price dynamics from January 2012 to February 2018. We introduce a robust automatic peak detection method that classifies price time series into periods of uninterrupted market growth (drawups) and regimes of uninterrupted market decrease (drawdowns). In combination with the Lagrange Regularisation Method for detecting the beginning of a new market regime, we identify 3 major peaks and 10 additional smaller peaks, that have punctuated the dynamics of Bitcoin price during the analyzed time period. We explain this classification of long and short bubbles by a number of quantitative metrics and graphs to understand the main socio-economic drivers behind the ascent of Bitcoin over this period. Then, a detailed analysis of the growing risks associated with the three long bubbles using the Log-Periodic Power Law Singularity (LPPLS) model is based on the LPPLS Confidence Indicators, defined as the fraction of qualified fits of the LPPLS model over multiple time windows. Furthermore, for various fictitious 'present' times $t_2$ before the crashes, we employ a clustering method to group the predicted critical times $t_c$ of the LPPLS fits over different time scales, where $t_c$ is the most probable time for the ending of the bubble. Each cluster is proposed as a plausible scenario for the subsequent Bitcoin price evolution. We present these predictions for the three long bubbles and the four short bubbles that our time scale of analysis was able to resolve. Overall, our predictive scheme provides useful information to warn of an imminent crash risk.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Johansen A, Sornette D, Ledoit O (1999) Predicting Financial Crashes Using Discrete Scale Invariance | 0.843 | 4 | 3 | 75% |
| 2 | Johansen A, Ledoit O, Sornette D (2000) Crashes as critical points | 0.843 | 5 | 3 | 60% |
| 3 | Sornette D, Demos G, Zhang Q, Cauwels P, Filimonov V, Zhang Q (2015) Real-Time Prediction and Post-Mortem Analysis of the Shanghai 2015 Stock Market Bubble and Crash | 0.737 | 4 | 3 | 50% |
| 4 | Zhang Q, Zhang Q, Sornette D (2016) Early warning signals of financial crises with multi-scale quantile regressions of Log-Periodic Power Law Singularities | 0.737 | 3 | 3 | 67% |
| 5 | Datastream (2018) Thomson Reuters Datastream. 2018; [Online] Available at: Subscription Service (Accessed February 2018) | 0.737 | 3 | 2 | 100% |
| 6 | Demos G, Sornette D (2017) Lagrange Regularisation Approach to Compare Nested Data Sets and Determine Objectively Financial Bubbles' Inceptions | 0.644 | 3 | 2 | 67% |
| 7 | Sornette D (2003) Why stock markets crash: Critical events in complex financial systems | 0.644 | 3 | 2 | 67% |
| 8 | Garcia D, Tessone CJ, Mavrodiev P, Perony N (2014) The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy | 0.644 | 2 | 2 | 100% |
| 9 | Kristoufek L (2013) BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era | 0.644 | 2 | 2 | 100% |
| 10 | Nakamoto S. Bitcoin: A Peer-to-Peer Electronic Cash System (2008) Available from: https://bitcoin.org/bitcoin.pdf | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 87 scored citations.