Theory and reality are not the same thing. One of the pervasive problems of economic modeling and financial market modeling is that it does not account for the really wide range of outcomes that occur but, in theory, shouldn’t.
The Psy-Fi blog had a recent article that archly discusses this problem:
By the late 1980′s there was a growing recognition that the existing understanding of financial systems was flawed. Not only did markets not behave as the economic theories predicted but they often exhibited behaviour that didn’t seem to have any pattern or cause at all.
In response to this a number of economists began looking at some of the research emerging from physics, biology and computer science in the area of complex adaptive systems and this led, in 1987, to a group of economists and scientists getting together at the Santa Fe Institute. The program of work that came out of this seminal event is still unfolding today, but suggests why academics and traders have had such different views on markets: one set lives in the real world, and the other doesn’t.
That’s a good introduction to the problem. Markets don’t behave the way traditional economic theories suggest. A good bit of the problem is in some of the assumptions that theory makes:
To the physicists involved in the original Santa Fe seminars it was obvious that the basis of economic quantification was highly suspect: indeed it was perfectly clear that the assumptions behind the models – stuff like people being perfectly rational – had been introduced to make the maths work, rather than being realistic assumptions.
At the heart of the problem with economics was the focus on equilibrium, the idea that there’s some perfect steady state of the economy in which everything is in balance (see Exit the Walras, Followed by Equilibrium). Essentially equilibrium economics defines the desirable outcome as a static position, then asks what conditions can give rise to this, then assumes that these conditions are present. This is a bit like deciding that riding a bicycle is a good thing, identifying the balance of forces needed to achieve this successfully and then declaring that this balance is the natural order of things.
And then ignoring what happens if you try to go cycling in a typhoon.
Beyond the problem with faulty assumptions, physicists were no strangers to the observer problem in science. They quickly recognized something that the economists did not. Economic systems themselves are reflexive; the actions of the participants can change the course of events:
Equilibrium economics is designed to ignore psychology, because it assumes that a person never changes their mind: they’ve already considered all possible outcomes and rejected all but the most economically rational. In the new economics we can’t assume this: we must work on the basis that if the world changes then a person’s expectations also change. This is a reflexive world, not a static one.
Suddenly, human behaviour matters in economic models in a way it never did before. Psychology, having been banished out of the front door, has crept in the back and has started cooking itself a three course dinner with all the trimmings.
The outcome of the Santa Fe meeting was a focus on non-equilibrium approaches to economics. In fact non-equilibrium economics doesn’t claim that equilibrium conditions don’t occur, only that they’re not guaranteed. The reason for this is human behaviour: we’re not external to the economic machinery, we’re part of it, and our expectations about the future have a critical impact on how it develops. All of which means that the future is not predictable and models that assume it is so are just wrong.
The reality is that people are reflexive and markets are adaptive. One begets the other and in a world where traders are forever trying to anticipate what other traders are going to do the last thing markets will ever be, in the short-term, is efficient and in equilibrium. But, of course, the trick to riding a bike is not falling off, no matter what the weather throws at you.
Source: Elite Bicycles
If the future is not predictable because equilibrium will change each time expectations change, it is essential to have a model that adapts to markets. Relative strength provides just such an adaptive framework. As expectations change, different stocks or asset classes become strong or weak. Relative strength drives the portfolio toward the strongest areas, where the most positive changes in expectations are occurring. Unlike classical models of market or economic behavior, relative strength models do not make any assumptions about potential returns, about correlations between different assets, or about volatility of different assets. Indeed, in the real world these things change all the time–as the expectations of participants change. Behavior that economic models find aberrational, like periods of stability interrupted by intense bouts of volatility, are, in fact, the norm.
The problem with Modern Portfolio Theory and strategic asset allocation is that they are based on the optimization of equilibrium models. If equilibrium is not guaranteed, you can assume the model will fail every time equilibrium conditions don’t exist—which is much of the time. (The fact that these models have failed bear market stress tests repeatedly somehow has not convinced their supporters that they are fatally flawed. Imagine if NASA had insisted on repeatedly using O-rings that cracked in cold weather like the ones that brought down the Challenger. )
Tactical asset allocation driven by relative strength can sometimes be buffeted by rapidly changing expectations, but the underlying drive is always toward adapting to the current environment. Since we cannot predict what will happen going forward, a systematic relative strength process seems like a more robust solution to the asset allocation problem.
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