Bitcoin Bubble Trouble

Kreuser, Jerome; Sornette, Didier

doi:10.1002/wilm.10672

Cited by 9 publications

(2 citation statements)

References 5 publications

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“…A prevalent and common choice is the Geometric Brownian Motion (GBM) which has been used for modeling stocks (Merton 1973;Black and Scholes 1973;Wilmott et al 1995;Reddy and Clinton 2016;Øksendal 2003) and more recently also for cryptocurrencies Tarnopolski 2017;Kreuser and Sornette 2018;Wu et al 2018). There is, however, debate on the extent these assets follow GBM dynamics.…”

Section: Introductionmentioning

confidence: 99%

“…While GBM operates using Gaussian noise, there is evidence that the noise distribution of stocks such as the S&P 500 has curvature different than a normal distribution (Mantegna and Stanly 2000). Implication of this difference is most prominent in the frequency of rare events as a result of heavy-tailed noise, also possibly present for cryptocurrencies (Kreuser and Sornette 2018;Wu et al 2018;Fry 2018). For small sample sizes, this difference is small enough that many continue to use GBM and other variants for modeling.…”

Section: Introductionmentioning

confidence: 99%

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Using correlated stochastic differential equations to forecast cryptocurrency rates and social media activities

et al. 2020

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The growing interconnectivity of socioeconomic systems requires one to treat multiple relevant social and economic variables simultaneously as parts of a strongly interacting complex system. Here, we analyze and exploit correlations between the price fluctuations of selected cryptocurrencies and social media activities, and develop a predictive framework using noise-correlated stochastic differential equations. We employ the standard Geometric Brownian Motion to model cryptocurrency rates, while for social media activities and trading volume of cryptocurrencies we use the Geometric Ornstein-Uhlenbeck process. In our model, correlations between the different stochastic variables are introduced through the noise in the respective stochastic differential equation. Using a Maximum Likelihood Estimation on historical data of the corresponding cryptocurrencies and social media activities we estimate parameters, and using the observed correlations, forecast selected time series. We successfully analyze and predict cryptocurrency related social media and the cryptocurrency market itself with a reasonable degree of accuracy. In particular, we show that our method has impressive accuracy in predicting whether a cryptocurrency market will increase or decrease a day in the future, a significant result with regards to investing and trading cryptocurrencies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using correlated stochastic differential equations to forecast cryptocurrency rates and social media activities

et al. 2020

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Deep Recurrent Modelling of Stationary Bitcoin Price Formation Using the Order Flow

Lim

Gorse

2020

Artificial Intelligence and Soft Computing

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Bitcoin’s bubbly behaviors: does it resemble other financial bubbles of the past?

Náñez Alonso,

Jorge-Vázquez,

Echarte Fernández

et al. 2024

Humanit Soc Sci Commun

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A number of financial bubbles have occurred throughout history. The objective of this study was to identify the main similarities between Bitcoin price behavior during bubble periods and a number of historical bubbles. Once this had been carried out, we aimed to determine whether the solutions adopted in the past would be effective in the present to reduce investors’ risk in this digital asset. This study brings a new approach, as studies have previously been conducted analyzing the similarity of Bitcoin bubbles to other bubbles individually, but these were not conducted in such a broad manner, addressing different types of bubbles, and over such a broad time period. Starting from a dataset with 9967 records, a combined methodology was used. This consisted of an analysis of the standard deviations, the growth rates of the prices of the assets involved, the percentage increase in asset prices from the origin of the bubble to its peak and its fundamental value, and, finally, the bubble index. Lastly, correlation statistical analysis was performed. The results obtained from the combination of the above methods reveal the existence of certain similarities between the Bitcoin bubbles (2011, 2013, 2017, and 2021) and the tulip bubble (1634–1637) and the Mississippi bubble (1719–1720). We find that the vast majority of the measures taken to avoid past bubbles will not be effective now; this is due to the digital and decentralized nature of Bitcoin. A limitation of the study is the difficulty in making a comparison between bubbles that occurred at different historical points in time. However, the results obtained shed light and provide guidance on the actions to be taken by regulators to ensure the protection of investors in this digital asset.

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Bitcoin Bubble Trouble

Abstract: Jérôme Kreuser and Didier Sornette introduce a new bubble theory.

Cited by 9 publications

References 5 publications

Using correlated stochastic differential equations to forecast cryptocurrency rates and social media activities

Using correlated stochastic differential equations to forecast cryptocurrency rates and social media activities

Deep Recurrent Modelling of Stationary Bitcoin Price Formation Using the Order Flow

Bitcoin’s bubbly behaviors: does it resemble other financial bubbles of the past?

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