The day after April 20, a day celebrated by marijuana enthusiasts, the SafeMoon team hosted an AMA with two of its members who joined by live video from a crypto conference in Miami. Instead of just providing updates on the token, Thomas Smith, SafeMoon’s chief technical officer, wandered around the conference floor in an unfocused way to capture attendee shoutouts and show off a painted Porsche.
— “Those are among the more visible recent fruits of a well-funded campaign by Mr. Orban in the United States that stretches back a decade and now stands as a case study in how governments around the world seek to shape policies and debates in Washington, sometimes raising concerns about improper foreign influence in U.S. politics.”

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).
Although analysis of a single factor and cryptocurrency combination, as above, is of interest, more general findings across multiple cryptocurrency/factor combinations can also be pursued. Fig 7 shows, for each cryptocurrency and factor combination, the mean coherence values during the bubble and non-bubble regimes. Each horizontal subplot shows a different coherence period band, from the lowest period band (2–4 days) at the top to the highest period band (256–512 days) at the bottom. As the duration of data for each cryptocurrency varies, certain ranges are left blank when that cryptocurrency does not have enough data to produce values for such bands.