The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy

By David García, Claudio Juan Tessone, Pavlin Mavrodiev, Nicolas Perony. Journal of the Royal Society Interface, 2014

439 citations recorded by the source corpus.

What is the role of social interactions in the creation of price bubbles? Answering this question requires obtaining collective behavioural traces generated by the activity of a large number of actors. Digital currencies offer a unique possibility to measure socio-economic signals from such digital traces. Here, we focus on Bitcoin, the most popular cryptocurrency. Bitcoin has experienced periods of rapid increase in exchange rates (price) followed by sharp decline; we hypothesize that these fluctuations are largely driven by the interplay between different social phenomena. We thus quantify four socio-economic signals about Bitcoin from large datasets: price on online exchanges, volume of word-of-mouth communication in online social media, volume of information search and user base growth. By using vector autoregression, we identify two positive feedback loops that lead to price bubbles in the absence of exogenous stimuli: one driven by word of mouth, and the other by new Bitcoin adopters. We also observe that spikes in information search, presumably linked to external events, precede drastic price declines. Understanding the interplay between the socio-economic signals we measured can lead to applications beyond cryptocurrencies to other phenomena that leave digital footprints, such as online social network usage.

Subfields: Economics and Econometrics, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/7328d31f9b5078658718ada22e94088c5a094291

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Citations in the corpus

Datestamping the Bitcoin and Ethereum bubbles (2017, neutral): “Garcia et al. [2014] examines feedback loops in social media to Bitcoin price, but presupposes the existence of bubbles.”
Social signals and algorithmic trading of Bitcoin (2015, neutral): “05 and does not pass the KPSS test, giving a p-value above 0 .”
Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies (2016, background): “Most researchers analyze user sentiments related to cryptocurrencies on social media, e.g., Twitter, or quantified Web search queries on search engines, such as Google, as well as fluctuations in price and trade volume to determine any relation [8 – 12].”
Can volume predict Bitcoin returns and volatility? A quantiles-based approach (2017, neutral): “Garcia et al. (2014) and Hayes (2016) show that the cost of producing a Bitcoin via mining adds some fundamental value to Bitcoins.”
Bitcoin price and its marginal cost of production: support for a fundamental value (2018, background): “Bubbles in the Bitcoin market have been explored in-depth elsewhere (e.g. Garcia et al. 2014; Cheah and Fry 2015; Li et al. 2018; Hafner 2018).”
Price Fluctuations and the Use of Bitcoin: An Empirical Inquiry (2015, neutral): “[62] that examined how the price of Bitcoin was related to general interest in it, measured by an indicator such as Google searches (a variable that we also use here).”
Negative bubbles and shocks in cryptocurrency markets (2016, background): “Neither the events of October 18th 2011 (identified in Garcia et al., 2014) nor of April 13th 2013 (identified in Rogojanu and Badea, 2014) seem to have a lasting impact.”
Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions (2016, background): “Similar results are reported by Glaser et al. (2014) and Polasik et al. (2014). Garcia et al.”
Bitcoin for energy commodities before and after the December 2013 crash: diversifier, hedge or safe haven? (2017, neutral): “Garcia et al. (2014) use the cost of the energy involved in Bitcoin mining to indicate a lower-bound estimate of Bitcoin’s fundamental value.”
On the inefficiency of Bitcoin (2017, background): “Garcia et al. (2014) studied the links between social signals and Bitcoin price through a social feedback cycle.”
On the return-volatility relationship in the Bitcoin market around the price crash of 2013 (2017, positive): “These factors include internet search (see, among others, Kristoufek, 2013), word-of-mouth information on social media, and information on google trends (Garcia et al., 2014).”