The strengthening of coherence in bubble regimes is much less prominent in the short and long term. In the short term, the effect of bubbles may be hidden by the effects of daily news items and intraday trading activity. It is also seen that in the short term the relationship between online factors and cryptocurrency prices are erratic and generally weak; there is little consistency as to whether the price or factors are leading, though slightly more negative relationships exist in this period band. The erratic relationships over the short term suggest online factors may not be best predictor in the shorter term.
We’ll go through the several factors that influence demand for a particular crypto, but before we do that, it’s important to note that cryptocurrencies don’t fit comfortably into our existing asset categories.

It’s certainly fascinating to watch Binance, which has historically been one of the most aggressive crypto companies, transition into a more regulatory-compliant business. At the same time, those who have been cautious, such as Coinbase, are beginning to add new assets.
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.