Dissection of Bitcoin’s multiscale bubble history from January 2012 to February 2018
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 decre...
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doaj-e170b0711cdc4308ac50cea4bca3bff62020-11-25T04:06:40ZengThe Royal SocietyRoyal Society Open Science2054-57032019-07-016710.1098/rsos.180643180643Dissection of Bitcoin’s multiscale bubble history from January 2012 to February 2018J. C. GerlachG. DemosD. SornetteWe 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 Regularization Method for detecting the beginning of a new market regime, we identify three major peaks and 10 additional smaller peaks, that have punctuated the dynamics of Bitcoin price during the analysed 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 t2 before the crashes, we employ a clustering method to group the predicted critical times tc of the LPPLS fits over different time scales, where tc 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.https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.180643bitcoink-means clusteringmultiscale bubble indicatorlog-periodic power-law singularity analysisforecastingmarket crashes |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
J. C. Gerlach G. Demos D. Sornette |
spellingShingle |
J. C. Gerlach G. Demos D. Sornette Dissection of Bitcoin’s multiscale bubble history from January 2012 to February 2018 Royal Society Open Science bitcoin k-means clustering multiscale bubble indicator log-periodic power-law singularity analysis forecasting market crashes |
author_facet |
J. C. Gerlach G. Demos D. Sornette |
author_sort |
J. C. Gerlach |
title |
Dissection of Bitcoin’s multiscale bubble history from January 2012 to February 2018 |
title_short |
Dissection of Bitcoin’s multiscale bubble history from January 2012 to February 2018 |
title_full |
Dissection of Bitcoin’s multiscale bubble history from January 2012 to February 2018 |
title_fullStr |
Dissection of Bitcoin’s multiscale bubble history from January 2012 to February 2018 |
title_full_unstemmed |
Dissection of Bitcoin’s multiscale bubble history from January 2012 to February 2018 |
title_sort |
dissection of bitcoin’s multiscale bubble history from january 2012 to february 2018 |
publisher |
The Royal Society |
series |
Royal Society Open Science |
issn |
2054-5703 |
publishDate |
2019-07-01 |
description |
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 Regularization Method for detecting the beginning of a new market regime, we identify three major peaks and 10 additional smaller peaks, that have punctuated the dynamics of Bitcoin price during the analysed 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 t2 before the crashes, we employ a clustering method to group the predicted critical times tc of the LPPLS fits over different time scales, where tc 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. |
topic |
bitcoin k-means clustering multiscale bubble indicator log-periodic power-law singularity analysis forecasting market crashes |
url |
https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.180643 |
work_keys_str_mv |
AT jcgerlach dissectionofbitcoinsmultiscalebubblehistoryfromjanuary2012tofebruary2018 AT gdemos dissectionofbitcoinsmultiscalebubblehistoryfromjanuary2012tofebruary2018 AT dsornette dissectionofbitcoinsmultiscalebubblehistoryfromjanuary2012tofebruary2018 |
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