Chapter 1: Financial markets as complex adaptive systems
Using the right tactics at the right time
As most modern-day traders will attest, there is an overwhelming array of choices when it comes to the different trading techniques and tactics that one can employ when dealing with financial markets. In fact this ācompilationā book is testament to this recent trend with about 20 acknowledged experts all offering up their different strategies on how to take profits from the markets. And indeed I make my own contribution to this wide array of choices by offering Australian equity traders and investors three different investment newsletter services.
Some traders will search through this plethora of trading systems and philosophies trying to filter out what works as opposed to what doesnāt work. But alas, this approach will only lead to more frustration as you will inevitably discover that there is some validity to just about every technique ever invented. The reality is that success or failure of virtually all systems is largely dependent on the prevailing broad market conditions. And these conditions change over time, therefore the effectiveness of any trading system will fluctuate accordingly.
So what we really need to do is take a step back for a moment and try to get a handle on how the broad market behaves. Because if we can better understand how the broader market works then it logically follows that we should know which are the most suitable trading and investing tactics to apply at what time. Simple ā¦ well, not quite.
Now weāre striving to comprehend the real nature of financial markets, and this is the crux of this discussion. And while Iād like to say that it is a simple matter to understand the underlying nature of financial markets, unfortunately we are about to enter the world of chaos theory and complex adaptive systems. But fear not, as I will make every effort to maintain clarity when dealing with these somewhat esoteric topics. And so letās start at the simplest point: the beginning.
Straight lines and curvy bits
The word linear essentially means straight line or straight line progression, and in order to simplify everything we see and observe mankind has a profound tendency to view the world from a linear perspective. The main reason we want everything to be linear, or to progress in a straight line, is so we can both easily understand it and predict what it is likely to do in the future.
In more recent times, thanks largely to the computational power of modern computers, we have also pretty much mastered the ability to get our heads around curvy things as well. Of course, this is largely on the proviso that they are either constantly curvy or consistently changing, such as the case of an exponential curve like the one pictured in figure 1.1.
We can even project lines and curvy things into the future with a reasonably high degree of accuracy and determine if, when and where theyāre likely to intersect. Although there is one proviso: that there arenāt too many variables to consider.
Figure 1.1: exponential curve
But thereās another problem that even the scientific community doesnāt like to talk about and thatās the possibility of things changing but not doing so in a consistent way. In other words, the rate of change is not constant. Itās bad enough that something can be ādynamicā rather than āstaticā (thus rendering statistical analysis and the bell curve largely useless), but when the rate of change itself isnāt linear or at least constant then everyone starts to get really scared. This is known as non-periodic behaviour, as shown in figure 1.2.
Figure 1.2: non-periodic behaviour
Source: Does God Play Dice? The New Mathematics of Chaos, Ian Stewart, Blackwell UK, 1989, p. 141.
But letās sidetrack for a moment and look at the idea of a system being dynamic as opposed to static. Take the average life expectancy of the Australian population, for example. If you wanted to know the average number of years weāre all expected to live then you would most likely use data available from the past 10 years or so:
But what about using recorded deaths from the last 100 years instead of just the past 10? Surely this larger sample of data will give us a more accurate and reliable answer:
Put simply, no ā¦ because over this expanse of time factors that impact our lifespan such as our diet and medical advances have changed significantly, making this sample period non-static and invalidating any averages taken. So letās go to the other extreme now and just use some very recent data. This should definitely give us the most up-to-date and accurate answer possible:
But unfortunately we now have the problem of insufficient data to work with. Thus any sample of data that we subject to statistical analysis must be from a static system or a representative snapshot that allows for the dynamic nature of a system. Hence using the average lifespan of Australians over the past 10 years to reflect todayās average is in fact a snapshot approach and a compromise of sorts.
This is a pity because everyone held out so much hope that statistical analysis was a universal solution for problems of randomisation. So the stock market, like other irregular phenomena, gets labelled as being unpredictable and thatās that. Just like weather patterns and the human heart, the stock market has too many variables and is a dynamic system thatās not always linear by nature. Hence figure 1.3 shows how the Australian stock market index, the All Ords, is forever changing its behaviour.
Figure 1.3: the All Ords
Source: Created with TradeStation. Ā© TradeStation Technologies, Inc. All rights reserved.
Thus if we canāt get our heads around it then itās random or so close to random it doesnāt matter. Another neat way of dismissing things we canāt fully comprehend and/or predict is by calling it noise, interference or turbulence. Thus an engineer working in fluid dynamics will most likely attempt to eliminate turbulent flow rather than try to understand it.
Introducing chaos theory
So you can imagine ever...