Market dislocations (aka crashes) are among those things that are not well defined and difficult to understand, especially for those who have never experienced them directly. But like true love, you know it when it happens and don't tend to forget it.
Simply put, a market dislocation is when a sustained bubble (eg. Ponzi scheme) begins to wobble and fall apart, and the realization comes generally that it is collapsing, with all participants remaining invested heading for the exits in a mass panic. It can be in real estate, financial assets, shares in a venture or cash flows from a project, stamps, matches, and even tulip bulbs.
These patterns of collapse tend to have a common framework. The challenge is separating a market dislocation from an ordinary correction. In our work, we have arrived at some hallmarks that characterize a market dislocation, which as you know is always a low probability event.
The setup for a market dislocation begins with a sustained increase in price (the Ramp) to a significant new high (the Top) over a period of time which is multiples of the subsequent decline. US equity markets saw such a top late last year in October. From there the first assault in confidence occurs as profit taking, creating a decline more significant than the declines serving as corrections up to the Top. It is usually an initial decline of ten percent or greater. Often we get an uncharacteristic decline
The rally back from this first low not exceed the Top (obviously) and is referred to as the Second High (with the TOP being the first or highest high). It can be equal to the TOP.
The next low must set a lower low, ruling out a double bottom. It is preferable but not necessary that the Lows be noticeably lower than the Highs. The lower the lows, the more likely that the dislocation will mark the start of a bear market rather than just a market clearing event like the Crash of 1987.
It is not uncommon to reach this point, and the vast majority of times will merely be an A-B-C correction. The next step is a critical differentiator, the Failed Rally. If there is a bounce of 2 to 5 percent that fails to gain momentum, and drops back to a lower low, and fails to rally again from there, it sets up a higher than normal probability of a market dislocation which we define for our purposes as a market decline of 30 percent or greater within a one year period.
If you have been paying attention, you may have noticed that we never spoke about timeframes. How many days from the first Top to the first low, and then to the second high, and so forth? We have this information in our database, but we do not use it in generating the CrashTrak model per se. We consider market time to be highly relative and variable. What it may have taken a day to be communicated to the broader market participants in 1929 may have taken only an hour in 1987, and only minutes or even seconds in 2008. So we have seen a significant increase in the reaction time of markets which would almost certainly affect timeframes.
Second, and mostly overlooked, the responses of market regulators and the financial authority have increased in power and sophistication, and in some ways remarkably so. This is not your grandfather's Fed or market anymore. They are smarter and more capable of distorting and forestalling events. We believe that they cannot stop a primary trend, but they can delay or defer it, and sometimes significantly so.
Lastly, we are seeking to apply this model to a wider variety of market dislocations. Everyone knows about 1929 and 1987, but what about 1932, 1937, and a slow grind like 1973-74? We don't think ignoring the time periods is all that revolutionary, especially if you consider the long standing use of Point and Figure charts, which are also based on patterns of price action. Still, we have to admit we do keep an eye of sorts on the dates, and map out what we call windows of opportunity. We're in one now, and will be so until about mid-February.
Even in its most high probability moments relatively speaking, a market dislocation is still a very low probability event. We want to make this very clear.
Let's see what happens.