We further explore the significance of cryptocurrency fundamentals at the aggregate market level using traditional asset pricing tests. For this analysis, we construct risk factors that are based on aggregate values of computing power and network. We denote the aggregate computing power factor with ACP and aggregate network factor with ANET. The innovation in constructing these factors is that we express them in cryptocurrency return units following the factor mimicking portfolio approach (Knez et al. 1994, Lamont 2001, Vassalou 2003).
About 15 years later, an accomplished software engineer named Wei Dai published a white paper on b-money, a virtual currency architecture that included many of the basic components of modern cryptocurrencies, such as complex anonymity protections and decentralization.

As noted elsewhere by a prominent author in the area [25], there is not a widely accepted or consistent definition of the term “bubble”. The GDASF test used here assumes a bubble is any time series interval which deviates from a random walk to become explosive.
So what happened? And is there any hope for a recovery? To answer both, you have to look at quite a few factors.