Are Bitcoin Bubbles Predictable? Combining a Generalized Metcalfe's Law and the LPPLS Model

Abstract

We develop a strong diagnostic for bubbles and crashes in bitcoin, by analyzing the coincidence (and its absence) of fundamental and technical indicators. Using a generalized Metcalfe's law based on network properties, a fundamental value is quantified and shown to be heavily exceeded, on at least four occasions, by bubbles that grow and burst. In these bubbles, we detect a universal super-exponential unsustainable growth. We model this universal pattern with the Log-Periodic Power Law Singularity (LPPLS) model, which parsimoniously captures diverse positive feedback phenomena, such as herding and imitation. The LPPLS model is shown to provide an ex-ante warning of market instabilities, quantifying a high crash hazard and probabilistic bracket of the crash time consistent with the actual corrections; although, as always, the precise time and trigger (which straw breaks the camel's back) being exogenous and unpredictable. Looking forward, our analysis identifies a substantial but not unprecedented overvaluation in the price of bitcoin, suggesting many months of volatile sideways bitcoin prices ahead (from the time of writing, March 2018).

Herding behaviour in digital currency markets: An integrated survey and empirical estimation

“…For the purposes of estimations, data are employed spanning two separate sub periods. The first sub period covers from 1 January 2017 until 18 December 2017 when the Bitcoin bubble when the bubble of this market is considered to have burst ( Wheatley et al., 2018 ). This represents the bull period in markets of digital currencies.…”

Bitcoin Transaction Networks: An Overview of Recent Results

“…, i.e., the percentage of links having a "partner" pointing in the opposite direction. Figure 4 plots the evolution of the temporal z-scores for N core and r. Overall, the two trends show some similarities, being characterized by peaks that correspond to so-called bubbles, i.e., periods of "unsustainable" price growth [36]; interestingly, such periods are characterized by values of the inspected topological quantities which are significant also in a statistical sense, as demonstrated by the values of the corresponding temporal z-scores (in fact, z (t) ≥ 2 in both cases). Moreover, peaks are also revealed in 2014-2016, thus signaling some kind of "activity" missed by purely financial indicators (e.g., the RPMA).…”

Order flow analysis of cryptocurrency markets

“…Current studies on price dynamics are mostly interested in predicting price series of cryptocurrency assets and try to understand how to value cryptocurrency objectively (Pagnottoni et al 2018). Wheatley et al (2018) are able to model Bitcoin's market cap via application of Metcalfe's law to network size and show that Bitcoin price behaviour breaks its "fundamental" value on at least four occasions. Their analysis shows that such behaviour is well modelled by Log-Periodic Power Law Singularity (LPPLS) model, which parsimoniously captures diverse positive feedback phenomena, such as herding and imitation.…”