Bartram, S. M. and M. Grinblatt (2018), “Agnostic fundamental analysis works”, Journal of Financial Economics 128 (1), 125–147.
We set the stage for our formal econometric analysis by providing some graphical evidence. For example, in Figure 1, we see a close mapping of the prices of Bitcoin and Ethereum with their respective computing power values. Moreover, in Figure 2, we observe an even stronger mapping of the prices of Bitcoin and Ethereum with their respective network values. Interestingly, these graphs also show that when prices significantly exceed the trend in computing power or network (as was the case in late 2017), prices eventually fall and trace the trend in fundamentals. The convergence of prices to the trend with fundamentals suggests that fundamentals are the ultimate, long-run determinants of prices.

“I’ve been through the 2017 cycle, too,” Yang says, referencing the ‘crypto crash’ of 2017 that saw many major cryptocurrencies, including Bitcoin, lose major value. “I know that these things are super volatile, like some days they can go down 80%.”
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.