Wheatley Spencer, Sornette Didier, Huber Tobias, Reppen Max, Gantner Robert N
Department of Management, Technology and Economics, ETH Zurich, Zürich Switzerland.
Swiss Finance Institute, c/o University of Geneva, Geneva Switzerland.
R Soc Open Sci. 2019 Jun 5;6(6):180538. doi: 10.1098/rsos.180538. eCollection 2019 Jun.
We develop a strong diagnostic for bubbles and crashes in Bitcoin, by analysing 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) is 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).
我们通过分析基本面指标和技术指标的吻合情况（以及两者的背离情况），对比特币的泡沫和崩盘现象进行了有力的诊断。基于网络属性使用广义梅特卡夫定律，我们量化了比特币的基本价值，并发现至少有四次泡沫的增长和破裂使其基本价值被大幅超越。在这些泡沫中，我们检测到一种普遍的超指数不可持续增长模式。我们用对数周期幂律奇异性（LPPLS）模型对这种普遍模式进行建模，该模型简洁地捕捉了各种正反馈现象，如羊群效应和模仿行为。结果表明，LPPLS模型能够对市场不稳定提供事前预警，量化高崩盘风险以及与实际回调一致的崩盘时间概率区间；不过，和以往一样，精确的崩盘时间和触发因素（即压垮骆驼的最后一根稻草）是外生且不可预测的。展望未来，我们的分析表明比特币价格存在大幅高估，但并非前所未有的高估，这意味着在（从撰写本文时的2018年3月起）未来数月比特币价格将出现波动的横向走势。
