It can be seen from Fig 6 that coherence in the short run is erratic throughout the time interval analysed, and that there is little appreciable difference between the bubble and non-bubble regimes. However in the medium term (8–16 and 16–32 days), coherence generally peaks around areas where bubbles have been identified in the price series. The longer term relationship, though, is less dependent on whether the price is in a bubble phase.
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You can't ever determine how long or how far up a speculative investment can go, and you tend to be very shocked at how much it falls
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Steep rises and unexpected dives in the price of cryptocurrencies are relatively common. As the market stabilises, the volatility has slowly decreased, but irrationality will always be part of the trading game.
Google Trends returns data with different granularity depending on the historical time interval queried: daily search volumes are returned for queries under 90 days and weekly search volumes for queries of length over 90 days. However it is possible to reconstruct daily data for long time intervals using a combination of daily and weekly data and the method described and validated by [4]: Daily data is retrieved in buckets of under 90 days, and weekly data is also retrieved for the complete time interval of interest. Then using the daily data, the percentage change of each day in a week from the first day of the week is calculated; these percentage changes are then applied to the weekly data to build a daily time series over a longer period.
WARREN DAVIDSON: The industry is basically pleading, give us some regulatory clarity.
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Is the Subject Area "Coherence" applicable to this article? Yes No
In the short term, situations occur where the factors lead the price and where the factors lag the price. However in most cases, the factor lags the price in the short term (seen by upward facing arrows near the top of each scalogram). This is understandable given short term changes appear likely to be the result of particular events, as discussed above. It is likely the market price will reflect the event quicker than social media; social media may experience a longer interval of discussion and activity relating to the original event and resulting price change.
Enforcement Director Joe Rotunda discusses fraud in crypto-related securities offerings:
Is the Subject Area "Coherence" applicable to this article? Yes No
More importantly, we find that the ACP and ANET factors can price cryptocurrency returns. In particular, all of our five cryptocurrencies have positive and significant exposures to the fundamental factors. The explanatory power of ACP and ANET is over and above any effects related to the Bitcoin and momentum factors. This finding is non-trivial given that the five cryptocurrencies in our sample have consistently accounted for between 80% to 95% of the total cryptocurrency market capitalisation. Furthermore, we extend our factor analysis to an out-of-sample set of 33 cryptocurrencies obtained from the Bittrex exchange. We find that the aggregate computing power and network factors also price cryptocurrencies in this large out-of-sample data set.
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