At each point information from neighbouring data is used. As the time series considered are finite, the areas at the start and end of the data (especially at higher period bands) will not have all the data required. One solution to make computation possible, chosen here, is to pad the time series with zeros where required. However, the zero padding will impact the reliability of the results. It is standard to use a cone of influence to represent this difference in reliability of results. Pale colours represent those areas outside the cone of influence with less reliable results (as seen on Fig 3). Higher period bands require more data for computation resulting in the cone shape.
We also consider two additional factors that account for the finding that in some periods prices drift away from fundamentals. The first factor is the return of Bitcoin. We argue that Bitcoin, being the largest and most traded cryptocurrency, is the most perceptible to sentiment and speculative trading by investors. Thus, as a factor, it can capture periods when trading forces unrelated to fundamentals are the strongest. The second factor is a cryptocurrency price momentum factor that we construct following the seminal work of Jegadeesh and Titman (1993). Momentum effects have been linked to investor psychology (e.g. Barberis et al. 1998).

One key point to note about Safemoon crypto is that the developers haven’t really described any real-world use cases yet. The Safemoon protocol was hyped hugely by celebrities back in May, causing retail investors to jump on the bandwagon and push price higher. Following this, the price fell dramatically, leading many to claim that Safemoon was a ‘pump and dump’ scheme designed to make certain investors rich.
Fast fact, altcoins games and metaverse tokens are showing user-driven, huge popularity, and their continued presence in the crypto industry these days. In fact, their presence has been well-felt since the beginning of this year.