Bitcoin Bubble Research Articles

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

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.

Bubbles in Bitcoin and Ethereum: The role of halving in the formation of super cycles

This study examines the price dynamics of Bitcoin and Ethereum between 2013 and 2022 using two distinct approaches: financial market technical analysis and econometric analysis. Financial market technical analysis employs indicators such as the Relative Strength Index (RSI) and the Hull moving average, while econometric analysis involves the Hodrick-Prescott filter and an Autoregressive Distributed Lag (ARDL) model. The study shows that Bitcoin and Ethereum experienced supercycle years in 2013, 2017, and 2021. The Bitcoin cycle, which averages 3.5 years, was particularly emphasized. The impact of the Bitcoin halving is also noteworthy, especially in the formation of supercycle bubbles in 2021, which affected altcoins such as Ethereum. The implications of this extend to portfolio management advice. It is recommended to carefully evaluate portfolio diversification and adopt a proactive regulatory approach, especially during the Bitcoin halving period.

Livestreamed land: Scams and certainty in Myanmar’s digital land market

Scams are endemic to digital capitalism, whether they manifest as bitcoin bubbles or bullshit jobs. Drawing on two years of digital ethnography in Myanmar’s Facebook land markets, this article explains what happens when the land scam migrates online. By unraveling warnings of trickery, interviewing wary participants, and inhabiting Facebook Live real estate tours, we argue that the scam is a vocation born of hope and desperation that targets land as the most-stable asset amidst crisis, one which operates through the networked and affective affordances of social media sites. Specifically, we highlight how Facebook enables brokers to ‘crowd’ transactions and amplify hype around sought-after plots, obscuring risk and responsibility while generating excitement and competition. Live video formats enable brokers to cultivate digital intimacy and authenticity from afar, creating a collective emotional investment in what we call the “virtual reality of land.” Bringing together critical geography and media studies, our analysis situates the scam in particular histories of inequality while explaining how these relations are reformulated through social media sites' sensory, affective, and connective affordances.

Oil price and the Bitcoin market

Motivated by the significant role oil plays in the production of Bitcoin, we test whether its price can influence the realized volatility of Bitcoin returns. Using data over the period of January 27, 2017 (coinciding with the emergence of Bitcoin bubbles) to June 3, 2022, we conduct some predictability analyses and establish the following outcomes. First, we find that higher oil prices tend to raise the cost of producing Bitcoins, therefore lowering its returns and by extension its trading and volatility. Second, we find improved forecast performance of oil price for the realized volatility of Bitcoin as our proposed model that accounts for oil price consistently outperforms the benchmark (random walk) model, regardless of the oil price variant and forecast horizon. Third, investors in the Bitcoin market that observe oil price movements when making investment decisions are more likely to derive higher economic gains than their counterparts that ignore it.

Do collective emotions drive bitcoin volatility? A triple regime-switching vector approach

In this paper, we build an empirical specification that helps to explain bitcoin volatility and to characterize phases of the bitcoin bubble using information derived from investors’ emotions and sentiment that captures investment intentions and investors’ aversion to risk. To this end, we investigated the bilateral relations between bitcoin volatility and investor emotions between 2018 and 2021, a period characterized by significant changes in bitcoin prices as well as wide disparities in investor emotions, especially in the context of the ongoing COVID-19 pandemic. The study was based on a linear and nonlinear Vector Autoregressive (VAR) model that we applied to data related to bitcoin prices and market sentiment as expressed by the Fear and Greed index. Overall, our results evince the key role played by collective emotions in the formation and collapse of the bitcoin bubble. Two findings in particular stand out. First, our model shows significant time-varying lead-lag effects between bitcoin volatility and investor sentiment that come into play bilaterally and help to characterize the dynamics of bitcoin volatility. Second, these interactions exhibit asymmetry and nonlinearity as the sign and size of collective emotions (resp. bitcoin volatility) vary with the regime and market state under consideration (calm state versus period of bubble formation, etc.). In other words, the power of sentiment has a time-varying effect on the market. Indeed, in the first regime (“calm state”), where bitcoin volatility is relatively low and the market shows evidence of stability, collective emotions have a negative impact on bitcoin volatility, prompting a stabilizing strength. However, in the second regime (“bubble formation”), the effect of emotions turns significantly positive as investors gradually become less fearful and more reassured, which can simultaneously increase volatility and destabilize the market. Finally, in the third regime (“bubble collapse”), when bitcoin reaches a high level of value and experiences impressive volatility excess, the effect of emotions again turns negative, resulting in further switching behavior that pushes investor action to provoke a bitcoin price correction, moving it toward a new state of stability. Our conclusion helps improve predictions of bitcoin price dynamics informed by the information provided by investor emotions.

The 2021 Bitcoin Bubbles and Crashes—Detection and Classification

In this study, the Log-Periodic Power Law Singularity (LPPLS) model is adopted for real-time identification and monitoring of Bitcoin bubbles and crashes using different time scale data, and the modified Lagrange regularization method is proposed to alleviate the impact of potential LPPLS model over-fitting to better estimate bubble start time and market regime change. The goal here is to determine the nature of the bubbles and crashes (i.e., whether they are endogenous due to their own price evolution or exogenous due to external market and/or policy influences). A systematic market event analysis is performed and correlated to the Bitcoin bubbles detected. Based on the daily LPPLS confidence indictor from 1 December 2019 to 24 June 2021, this analysis has disclosed that the Bitcoin boom from November 2020 to mid-January 2021 is an endogenous bubble, stemming from the self-reinforcement of cooperative herding and imitative behaviors of market players, while the price spike from mid-January 2021 to mid-April 2021 is likely an exogenous bubble driven by extrinsic events including a series of large-scale acquisitions and adoptions by well-known institutions such as Visa and Tesla. Finally, the utilities of multi-resolution LPPLS analysis in revealing both short-term changes and long-term states have also been demonstrated in this study.

The time-varying causal relationship between the Bitcoin market and internet attention

The increasing attention on Bitcoin since 2013 prompts the issue of possible evidence for a causal relationship between the Bitcoin market and internet attention. Taking the Google search volume index as the measure of internet attention, time-varying Granger causality between the global Bitcoin market and internet attention is examined. Empirical results show a strong Granger causal relationship between internet attention and trading volume. Moreover, they indicate, beginning in early 2018, an even stronger impact of trading volume on internet attention, which is consistent with the rapid increase in Bitcoin users following the 2017 Bitcoin bubble. Although Bitcoin returns are found to strongly affect internet attention, internet attention only occasionally affects Bitcoin returns. Further investigation reveals that interactions between internet attention and returns can be amplified by extreme changes in prices, and internet attention is more likely to lead to returns during Bitcoin bubbles. These empirical findings shed light on cryptocurrency investor attention theory and imply trading strategy in Bitcoin markets.

Time-Varying Nexus between Investor Sentiment and Cryptocurrency Market: New Insights from a Wavelet Coherence Framework

This study attempts to investigate the nexus between investor sentiment and cryptocurrencies prices. Our empirical investigation merges bivariate and multivariate wavelet tools to examine the investor sentiment nexus to inter-cryptocurrencies prices. The study outcomes show that the Sentix Investor Confidence index provides significant information in explaining long-term changes in Bitcoin and Litecoin prices. Moreover, the findings generated from the multiple wavelet coherence illustrate the simultaneous contribution of cryptocurrencies and the Sentix Investor Confidence index in explaining the Bitcoin index movement across frequencies and over horizons, especially during bubble burst periods. The study also suggests a time-dependent relationship of Bitcoin prices with alternative cryptocurrencies and the Sentix Investor Confidence index, mostly pronounced during the Bitcoin bubble. We discuss our results using GSV-based investor sentiment. Our findings remain robust and confirm the strong predictive power of investor sentiment in cryptocurrencies price movements over time and across scales.

The rise and fall of cryptocurrency coins and tokens

Since Bitcoin’s introduction in 2009, interest in cryptocurrencies has soared. One manifestation of this interest has been the explosion of newly created coins and tokens. In this paper, we analyze the dynamics of this burgeoning industry. We consider both cryptocurrency coins and tokens. The paper examines the dynamics of coin and token creation, competition and destruction in the cryptocurrency industry. In order to conduct the analysis, we develop a methodology to identify peaks in prices and trade volume, as well as when coins and tokens are abandoned and subsequently “resurrected”. We also study trading activity. Our data spans more than 4 years: there are 1082 coins and 725 tokens in the data. While there are some similarities between coins and tokens regarding dynamics, there are some striking differences as well. Overall, we find that 44% of publicly-traded coins are abandoned, at least temporarily. 71% of abandoned coins are later resurrected, leaving 18% of coins to fail permanently. Tokens experience abandonment less frequently, with only 7% abandonment and 5% permanent token abandonment at the end of the data. Using linear regressions, we find that market variables such as the bitcoin price are not associated with the rate of introducing new coins, though they are positively associated with issuing new tokens. We find that for both coins and tokens, market variables are positively associated with resurrection. We then examine the effect that the bursting of the Bitcoin bubble in December 2017 had on the dynamics in the industry. Unlike the end of the 2013 bubble, some alternative cryptocurrencies continue to flourish after the bursting of this bubble.

Bitcoin: Bubble that bursts or Gold that glitters?

This paper aims to shed light on the 2017 Bitcoin bubble. Firstly, by applying the dynamic time warping algorithm, we identify among several financial instruments a subsample of five assets with similar characteristics to the cryptocurrency bubble. Interestingly, among the fluctuations characterizing these assets, the algorithm shows a close affinity between the Bitcoin bubble and the 2000 NASDAQ Dotcom one. Once the subsample is identified, we study the (de)synchronization among these assets via the wavelet coherence approach. Although Bitcoin is poorly correlated with the other indices, given its scarce connection with the real economy, we observe switching phenomena among these instruments. A more careful study on these portfolio reallocations, conducted via an event study analysis, reveals that traders seem to redirect capital from stock markets and gold to Bitcoin in case of positive events of the cryptocurrency.

Herding in the crypto market: a diagnosis of heavy distribution tails

PurposeWith the unprecedented growth of digitalization across the globe, a new asset class, that is cryptocurrency, has emerged to attract investors of all stripe. The novelty of this newly emerged asset class has led researchers to gauge anomalous trade patterns and behavioural fallacies in the crypto market. Therefore, the present study aims to examine the herd behaviour in a newly evolved cryptocurrency market during normal, skewed, Bitcoin bubble and COVID-19 phases. It, then, investigates the significance of Bitcoin in driving herding bias in the market. Finally, the study gauges herding contagion between the crypto market and stock markets.Design/methodology/approachThe study employs daily closing prices of cryptocurrencies and relevant stocks of S&P 500 (USA), S&P BSE Sensex (Index) and MERVAL (Argentina) indices for a period spanning from June 2015 to May 2020. Quantile regression specifications of Chang et al.’s (2000) absolute deviation method have been used to locate herding bias. Dummy regression models have also been deployed to examine herd activity during skewed, crises and COVID-19 phases.FindingsThe descriptive statistics reveal that the relevant distributions are leptokurtic, justifying the selection of quantile regression to diagnose tails for herding bias. The empirical results provide robust evidence of crypto herd activity during normal, bullish and high volatility periods. Next, the authors find that the assumptions of traditional financial doctrines hold during the Bitcoin bubble. Further, the study reveals that the recent outbreak of COVID-19 subjects the crypto market to herding activity at quantile (t) = 0.60. Finally, no contagion is observed between cryptocurrency and stock market herding.Practical implicationsDrawing on the empirical findings, it is believed that in this age of digitalization and technological escalation, this new asset class can offer diversification benefits to the investors. Also, the crypto market seems quite immune to behavioural idiosyncrasies during turbulence. This may relieve regulators of the possible instability this market may pose to the entire financial system.Originality/valueThe present study appears to be the first attempt to diagnose leptokurtic tails of relevant distribution for crypto herding in the wake of two remarkable events: the crypto asset bubble (2016–2017) and the outbreak of coronavirus (early 2020).

Identifying price bubble periods in the Bitcoin market-based on GSADF model

The advert of cryptocurrency era is signified by the luanch of Bitcoin, which has been considered as a milestone event. Ever since its emergence, Bitcoin has experienced rapid price increase along with great price fluctuations. This paper aims to explore the beginning and the culmination of possible speculative bubbles the Bitcoin market. Based on the daily closing price of Bitstamp, a major Bitcoin exchange platform, from September 13, 2011 to October 12, 2020, this paper adopts GSADF (generalized sup augmented Dickey–Fuller) to explore the existence of the bubble in the Bitcoin market, estimate the time of emergence and collapse of the bubble, investigate the causes of the bubbles based on its time order, and attempts to put forward some policy suggestions. Through this study, we have found that: (1) There are a lot of speculative bubbles. During the research period, there were a total of four long bubbles (i.e. 2013.01.25–2013.04.05; 2013.11.05–2013.12.10; 2017.04.27–2017.07.05; 2017.07.15 –2018.01.09) and four short bubbles (2016.06.08–2016.06.18; 2016.12.30–2017.01.15; 2017.02.22 –2017.03.07; 2019.06.19 –2019.06.25). (2) The emergence and burst of bubbles were mostly related to the occurrence of relevant events and policies. (3) Bitcoin market is far from mature. A key policy risk triggering the speculative behavior is policy announcement. The prominent innovation of this paper is that the latest developed bubble detection method GSADF has been employed in the Bitcoin market. This method can detect the existence of multiple Bitcoin bubbles and their durations.

The 2021 Bitcoin Bubbles and Crashes – Detection and Classification

In this study, we adopted the Log-Periodic Power Law Singularity (LPPLS) model for real-time identification and monitoring of Bitcoin bubbles and crashes using different time scale data and proposed the modified Lagrange regularization method to alleviate the impact of potential LPPLS model over-fitting to better estimate bubble start time and market regime change. We also aimed to determine the natures of the bubbles and crashes – be it endogenous due to its own price evolution or exogenous due to external market and/or policy influences. We performed a systematic market event analysis and correlated which to Bitcoin bubbles detected. Based on the daily LPPLS confidence indictor from December 1, 2019 to June 24, 2021, we found that the Bitcoin boom from November 2020 to mid-January 2021 is an endogenous bubble, stemming from the self-reinforcement of cooperative herding and imitative behaviors of market players, while the price spike from mid-January 2021 to mid-April 2021 is likely an exogenous bubble driven by extrinsic events including a series of large-scale acquisitions and adoptions by well-known institutions such as Visa and Tesla. We have also demonstrated the utilities of multi-resolution LPPLS analysis in revealing both short-term changes and long-term states.

Bitcoin price manipulation: evidence from intraday orders and trades

ABSTRACT We analyse 519.4 million Bitcoin orders placed on Gemini Exchange during January 2016-August 2019 and find limit orders dominate at 99.92%. We document order-based evidence of price manipulation during the Bitcoin bubble in late 2017, when the daily number of market orders during the bubble period more than triples the overall daily average. The changes in both prices and liquidity satisfy two criteria specified in Kyle and Viswanathan (2008) for the price manipulation definition. Moreover, we find a significant increase in market order imbalance associated with price manipulations modelled in Jarrow, Protter and Roch (2012).

Bubbles on Altcoins: Rush versus Manipulation

The aim of this research is to explore the bubble effects on different crypto-currencies. Bubbles are generated by investors’ urge to step-in a promising market and by price pumping trades. The main goal of the paper is to assess the presence of bubble effects in this market with customized tests able to detect the timing of various bubbles. We analyze the evolution of a representative sample crypto-currencies over time encompassing both high and low liquidity coins. The results show that several crypto-currencies prices had episodes of rapid inflation in 2017 related to the Bitcoin bubble and a few emerging coins saw their prices pumped by speculative actions.

A study on the bursting point of Bitcoin based on the BSADF and LPPLS methods

We aim to reveal the characteristics and mechanism of the Bitcoin bubble in 2019. First, we identify the period during which two important Bitcoin bubbles occurred based on the generalized supremum augmented Dickey-Fuller (GSADF) method. There are two significant bubble cycles. The first bubble lasted approximately 26 days from November 25, 2017, to December 21, 2017, while the second bubble lasted approximately one week from June 22 to June 29, 2019. The occurrence of the first bubble was related to the considerable expansion of initial coin offerings (ICOs) in 2017, while the formation of the second bubble was affected by the release of Libra. Second, as the GSADF method cannot be used to accurately infer the time at which a bubble bursts, we employ the log-periodic power law singularity (LPPLS) model for this purpose. We verify that the LPPLS method can not only infer the timing of a bubble burst but also shows stable results. Finally, we demonstrate the implications of the 2019 bubble. During the 2019 bubble, due to the increased supervision of European and American governments and the impact of hedging assets, the bubble’s duration was shorter, and the positive feedback mechanism was not as strong as that of the 2017 bubble. In addition, the oscillating frequency of the bubble in 2019 was low and unstable, which means that it would be more beneficial for investors to hold the currency for a long time.

Financial implications of fourth industrial revolution: Can bitcoin improve prospects of energy investment?

Bitcoin and the blockchain technology on which it is based are the key drivers behind the accelerated pace of Fourth Industrial Revolution in the domain of Finance. The offshoots of this technology however are not limited and are rapidly spreading in other domains such as oil market. This paper investigates the causality or influences that both markets, Bitcoin price (BP) and oil price (OP) have on each other by applying the bootstrap Granger causal relationship tests considering full as well as sub-samples. Our analysis reveals that shocks originated in OP and transmitted towards BP can be both positive or negative. The positive impact indicates that Bitcoin can be viewed as an asset helpful in avoiding the risks of the high OP, which also indicates that Bitcoin and oil are in the same boat, however, the negative effects cannot support this view. The negative influence of OP on BP can be explained by the burst of the Bitcoin bubble which has weakened its hedging ability. In turn, there is also a negative influence or reverse causality running from BP to OP, highlighting that the demand for oil by investors can be threatened by the increasing BP. Keeping in view the more integrated and complexed financial dynamics which are the results of Fourth Industrial Revolution, investors can benefit from this interrelationship to diversify the risks and optimize their investment by building a more balanced portfolio. Also, governments could promote and protect the healthy development of the Bitcoin and energy market by preventing the Bitcoin bubbles and understanding the reasons of oil price volatility.

Kimchi premium and speculative trading in bitcoin

This paper examines whether cryptocurrency bubbles are loud. We focus on the relation between bitcoin prices and trading volume. Using bitcoin data from Korea and the US, we document that the elevated trading volume and price volatility can explain the ‘Kimchi premium’. In particular, as the bitcoin bubble grows, the relationship between trading volume and premium becomes clearer. This is consistent with the speculative bubble literature, which shows that the size of a bubble is positively related to trading volume. Our findings imply that fundamental uncertainty generates more dispersion in heterogeneous beliefs among investors and leads to high trading and to speculative bubbles.

Does investor sentiment on social media provide robust information for Bitcoin returns predictability?

We use a dataset of approximately one million messages sent on StockTwits to explore the relationship between investor sentiment on social media and intraday Bitcoin returns. We find a statistically significant relationship between investor sentiment and Bitcoin returns for frequencies of up to 15 minutes. For lower frequencies, the relation disappears. We also find that the impact of sentiment on returns is concentrated on the period around the Bitcoin bubble. However, the magnitude of the effect is rather small making it impossible for a trader to make economic profits by trading on the information published on social media.

Real-time prediction of Bitcoin bubble crashes

In the past decade, Bitcoin as an emerging asset class has gained widespread public attention because of their extraordinary returns in phases of extreme price growth and their unpredictable massive crashes. We apply the log-periodic power law singularity (LPPLS) confidence indicator as a diagnostic tool for identifying bubbles using the daily data on Bitcoin price in the past two years. We find that the LPPLS confidence indicator based on the daily Bitcoin price data fails to provide effective warnings for detecting the bubbles when the Bitcoin price suffers from a large fluctuation in a short time, especially for positive bubbles. In order to diagnose the existence of bubbles and accurately predict the bubble crashes in the cryptocurrency market, this study proposes an adaptive multilevel time series detection methodology based on the LPPLS model and a finer (than daily) timescale for the Bitcoin price data. We adopt two levels of time series, 1 h and 30 min, to demonstrate the adaptive multilevel time series detection methodology. The results show that the LPPLS confidence indicator based on this new method is an outstanding instrument to effectively detect the bubbles and accurately forecast the bubble crashes, even if a bubble exists in a short time. In addition, we discover that the short-term LPPLS confidence indicator being highly sensitive to the extreme fluctuations of Bitcoin price can provide some useful insights into the bubble status on a shorter time scale – on a day to week scale, while the long-term LPPLS confidence indicator has a stable performance in terms of effectively monitoring the bubble status on a longer time scale – on a week to month scale. The adaptive multilevel time series detection methodology can provide real-time detection of bubbles and advanced forecast of crashes to warn of the imminent risk in not only the cryptocurrency market but also other financial markets.