Disequilibrium (DQ) Analysis is an attempt to quantify the same kind of edge that has been used for years by the great investors of all time such as Warren Buffett or George Soros.
The most successful investors of the last century have been able to do well by identifying markets that are undervalued or overvalued due to some emotional reaction that has created a temporary inefficiency that is large and liquid enough to allow for the positioning of sizable amounts of investment capital before it closes. Soros wrote about this by labeling it as his theory of “reflexivity” and has been able to profit in a wide variety of markets around the world with this same concept going long and short. Buffett referred to it as the “manic-depressive Mr. Market”.
What DQ Analysis attempts to do is to precisely quantify not only what constitutes these market inefficiencies but to do so on a multitude of time frames, from the very short term to the very long term, in a way that allows for positions to be taken with a minimum of risk and with a high degree of overall accuracy. When certain predefined conditions are met, testing on both a historical and real time basis has shown that there is over a 70% probability that a market inefficiency is present in current prices and that a trend reversal should occur. The other 30% of the time what you will typically get is a brief counter-trend reaction followed by acceleration back in the direction of the prevailing trend.
In other words, what we believe we have done is to precisely quantify what Soros would refer to as points of “reflexivity” or the conditions that normally precipitate the appearance of a “catalyst” that causes reversion both to the mean and, potentially, to a point of over or under valuation from an opposing point of under or overvaluation.
DQ analysis, at its core, is built upon the idea that there is a constant feedback process between perception and reality in financial markets because human beings are active participants in that process. It then holds that “perfect knowledge” is not possible due to this feedback process whereby perception and reality constantly exert influence on one another. Thus at any time, the market participants’ perception of reality is actually false, because their perceptions are actually changing the state of that reality. Instead, markets actually operate in a state of dynamic disequilibria with market participants always operating in a state of relative uncertainty with varying degrees of imperfect knowledge and information available to them.
Nobel Prize winner Dr. Ilya Prigogine described an example of this process when he tried to describe how this same type of feedback process could affect driving conditions. In Dr. Prigogine’s words:
“When you drive on the highway you have your own program, your own speed. When other people drive at the same time, competition begins. This competition brings about a change in your behavior. This is feedback. Feedback is a situation that involves non-linearities. It is far from equilibrium in the sense that as more and more people drive, the situation becomes more and more distorted.”
“First you drive as you want to. Then you take into account the other drivers, but you still drive as you want to. That is what I call the individual regime. Then you go beyond the critical concentration of cars and come into a new organization in which you force the other drivers to drive as you drive. I call that the collective regime. It’s a very good example of bifurcation, a phase change to a coherent structure…the highway as a whole. Now, this is not necessarily beneficial. You are embedded in something that does not depend on you and in which you are a part. You contribute to it but can’t escape. You are now embedded in this process. You drive in a way that influences others, and other people influence you. You can no longer say that you have free will. You are part of a collectivity in which you contribute, even in a sense against your will. And data on highway driving show there really is a transition to a different phase when the critical concentration is reached.”
Likewise, the same analogy can be made for financial markets. When you are simply trading with one person or entity you would also trade according to your own “speed” or knowledge. This is your “individual regime”. It is the kind of trading that you might do with a local merchant or in a retail store. Forecasting emotional behavior patterns in individual regimes can indeed be done but it requires variations to the overall DQ model.
When more participants enter the market, liquidity begins to deepen, and greater and greater competition begins which brings about changes in behavior and a similar phase change. Now each individual trader is embedded in the larger process that is now the “market.” Each individual is a part of the market but the market is not dependent on any one individual. Instead the market is now a collective mentality or “mind” that, while perhaps better able to process the large amounts of complex and information that determine fair value, is also subject to the same emotional extremes of fear and greed that individuals are subject to, only to a much larger degree. In many ways it is similar to the kind of flocking behavior that you see with birds and insects.
It is these collective emotional extremes or collective emotional overreactions to uncertainty that create market inefficiencies that can be exploited for profit, and this is what DQ analysis first tries to identify through monitoring shifts in emotion and volatility patterns much as a doctor might monitor a patient’s heart rate for signs of anxiety, calm, or excitement.
But identifying potential inefficiencies is only part of what the model tries to accomplish. Within complex systems there usually exists a defined order of some sort, which, in chaos theory, is referred to as a “strange attractor.” This “order” either attracts bodies in motion toward it or repels them away from it. In chaotic systems we have the “Feigenbaum Constant” which tends to define the order that we see in these systems. In “living forms” systems, such as financial markets, we tend to find this order expressed in the form of “the Golden Mean” or Fibonacci ratios.
What DQ analysis does in the second part of its process is once a potential inefficiency is identified it will then apply a methodology using these two techniques for price projection to identify likely “attractor”, or support and resistance levels, or zones that are nearby at the same point in time that a potential inefficiency is occurring. The model will then give “Buy” signals in projected support zones and “Sell” signals in projected resistance zones. These signals are further confirmed by subtle shifts in market momentum in the direction of the trade signal given, thus generally making for a system that often gives close to optimal entries and exits on its trade signals and thereby produces a high rate of profitable trades resulting in a model that should be able to consistently produce directional style returns but with non-directional style risk.
DQ analysis is then able to generally stay with trend reversals or accelerations for the bulk of their move by recognizing that once such a trend reversal does occur it is reinforced by periodic tests of that trend which should fail at other projected support and resistance zones and generally be accompanied by diminishing volatility. When one of these tests fails to hold properly a trade is then exited.
By applying the DQ model we are then generally able to isolate market inefficiencies that permit the deployment of capital at what are often very close to optimal price levels. It does not do this by trying to “predict” the market in any way. Instead it focuses on trying to identify the conditions necessary for a trend change or acceleration and then give itself sufficient time to get a position on that can take full advantage of that trend shift. In short, it does not try and predict the market but instead patiently waits, like a surfer on the ocean, for the right conditions that will allow it to take advantage of the best wave available.
For example, let’s take a quick look at what is considered one of the greatest financial coups of all time, George Soros’s profit from shorting the British Pound in 1992 and let us see how DQ analysis would have enabled anyone with knowledge of it the ability to profit in precisely the same way. (I recommend that you try printing out the following comments or viewing them in a different window so you can have them handy when you click on the chart to see it in full size)
George Soros’s 1992 British Pound Trade
Above we have a weekly chart of the British Pound. Notice in the middle of the chart is the time period in which George Soros shorted the British Pound in the summer of 1992.
That top was preceded by a DQ signal (the red up arrow shown) warning of an impending peak (DQ signals by themselves are not “buy” or “sell” signals but simply indications of a potential “mispricing” of the market by traders and investors), and a particular pattern that we call a “BX cluster”, the downward pointing blue triangles shown, that often occurs when trends are peaking and running out of steam.
Notice also that as the British Pound was approaching that high it was also entering into long term resistance zones indicated by the Magister trading bands shown (a variation on Bollinger Bands we have developed) as well as a set of resistance projections directly overhead in the form of the yellow dashed horizontal lines shown just above the price peak.
When market momentum turned negative, indicated by the bars turning from green to red, at the end of the first week of September, 1992, the trade was confirmed (although a trader wanting to be more aggressive could easily have been building a position beforehand based on the indicators shown. For that we would go to a daily chart in order to build the position.
George Soros’s 1992 British Pound trade: Daily Chart
Above we have a daily chart of the British Pound for the same time frame in the summer of 1992.
Notice the pattern formed right at the top by the yellow zigzag lines shown. The peak on the K line of the stochastic oscillator shown below during the first swing peak on September 2 was 93.68. The K line peak on the second swing peak when prices peaked at 16297 on September 8 was 91.43. This is how we define a bearish pivot divergence and is a trigger indication that we always look for after a DQ signal is given such as the one shown on the chart on July 21 (the red up arrow).
When the price bars turned red at the close of the next day, indicating downward momentum was now taking hold, it was then time to aggressively sell the Pound.
The result was arguably one of the greatest trades of all time. And, if we are diligent and disciplined, DQ analysis should allow us to identify similar trades like this in the future on a recurring basis.