On the fifth of February, 2018, the Dow Jones Industrial Average dropped 1,175.21 points, the largest single-day fall in history in raw point terms. This followed a 666-point loss on the second, and another drop of over a thousand points occurred three days later. It is natural to ask whether these events indicate a transition to a new regime of market behavior, particularly given the dramatic fluctuations --- both gains and losses --- in the weeks since. To illuminate this matter, we can apply a model grounded in the science of complex systems, a model that demonstrated considerable success at unraveling the stock-market dynamics from the 1980s through the 2000s. By using large-scale comovement of stock prices as an early indicator of unhealthy market dynamics, this work found that abrupt drops in a certain parameter $U$ provide an early warning of single-day panics and economic crises. Decreases in $U$ indicate regimes of "high co-movement", a market behavior that is not the same as volatility, though market volatility can be a component of co-movement. Applying the same analysis to stock-price data from the beginning of 2016 until now, we find that the $U$ value for the period since 5 February is significantly lower than for the period before. This decrease entered the "danger zone" in the last week of May, 2018.
Eq. (14) defines the sum negativity as $\sum_u |W_u| - 1$, but there should be an overall factor of $1/2$ (see arXiv:1307.7171, definition 10). For both the Strange states and the Norrell states, the sum negativity should be $1/3$: The Strange states (a.k.a., Hesse SIC vectors) have one negative entry in their Wigner representation, while the Norrell states each have two negative entries of value $-1/6$. This makes the greater robustness of the Strange states under incoherent noise easy to see, because mixing in the garbage state hits the Norrell states twice as hard.