In this eventuality, BTC could drop toward the 100 or 200 four-hour moving average. These demand zones sit at $59,000 and $52,000 respectively.
One example is the negative correlation that occurs between Ethereum and its associated factors around June 2016 (left facing arrows at the top and just left of the horizontal middle of the Ethereum scalograms). During this time interval, one of the most well-known applications at the time, the DAO, built on top of the Ethereum environment, was hacked. It can be seen that all factors are negatively correlated in the short term with the price during this time interval. As a result of the uncertainty generated by the hack, price dropped sharply, but activity on social media and interest increased (causing the negative correlation). The negative relationship can be seen during the 2–4 day band for all factors.

Identifying the determinants of asset prices is one of the most important questions in finance. Traditional asset pricing theories are founded on the idea that equity prices should be determined by fundamentals such as earnings (Gordon 1959, Campbell and Shiller 1988). In contrast, the behavioral finance literature posits that prices do not always follow fundamentals due to investor sentiment trading (Shiller 1981, Baker and Wurgler 2006, Stambaugh et al. 2012). Eventually however, equity prices seem to trace their fundamentals (Pa´stor and Veronesi, 2003, 2006, Bartram and Grinblatt 2018).
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