Financial Markets as Complex Adaptive Systems Part 2

Chaos and Financial Markets

Of course any chart that shows the change in price with respect to time is in fact a
2 dimensional map and if the Stockmarket is a chaotic system of sorts then anyone
looking at price charts should observe nearly identical repetitive patterns…
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Such as Benoit Mandelbrot, a mathematician in the employ of IBM in the 1960s,
did when studying self-similarity. One of the areas he was studying was cotton
price fluctuations. No matter how the data on cotton prices was analyzed, the
results did not fit the normal distribution curve. Mandelbrot eventually obtained all
of the available data on cotton prices, dating back to 1900. But when he analyzed
the data with IBM's computers, he noticed an astonishing fact:

The numbers that produced aberrations from the point of view of normal
distribution produced symmetry from the point of view of scaling. Each
particular price change was random and unpredictable. But the sequence of
changes was independent of scale: curves for daily price changes and
monthly price changes matched perfectly. Incredibly, analysed Mandelbrot's
way, the degree of variation had remained constant over a tumultuous sixty-
year period that saw two World Wars and a depression.

Thus Mandelbrot both identified a repeating pattern in the price activity and also
observed that it was nested, thus occurring at different levels of scaling.
Furthermore he confirmed the hopelessness of employing statistical analysis to
study non-linear dynamical systems such that financial markets are…
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Benoit Mandelbrot had largely rediscovered what Ralph Elliott had been jumping
up and down about decades earlier. The quintessential difference being that Benoit
Mandelbrot was a world renowned Mathematician while poor old Ralph was just
an Accountant. Of course its hard enough to get people to listen to their accountant
when its time to lodge their tax returns, let alone the rest of the year…
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What's more, it didn’t help matters when Ralph tried to tell everyone that Elliott
Waves were the ultimate explanation to life, the universe and everything else…
Benoit on the other hand was a little more measured in his interpretation of what
he observed by simply suggesting that financial markets, given their fractal nature,
appear to be chaotic, to some degree….
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Hence when the Stockmarket is predominantly behaving in a chaotic manner then
we can observe patterns (Elliott Waves) that occur both repeatedly and at different
levels of scaling…where said patterns are very similar but never identical…
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But (and this is the part that Ralph vehemently disagrees with) at any fixed scale of
observation, the market does not always behave in a predominantly chaotic state.
Sometimes, like after a catastrophic event, markets will trade in a clearly defined
trading range advancing in any given direction, in a straight line progression.

So whilst we may take a moment to pause and pat ourselves on the back for being
so clever in understanding the nature of the curvy bits, we still have to explain why
the market behaves in a chaotic state and why (some of the time) it doesn’t….
The best scientific research and development has only just been able to answer this
question in the last decade or so with the discovery of what is commonly referred
to as, ‘Complex Adaptive Systems’.
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Put simply, a Complex Adaptive System is one that is made up of independent yet
freely interacting agents that react and adapt either to external information and/or
information feeding back from the system itself. Well I suppose it’s not really all
that simple so maybe it would be more helpful if we explain it with a diagram.

Thus we can view the Stockmarket as a type of Complex Adaptive System where;

External information
=>
Company information, macro
economic factors, external events, etc

System outcomes
=>
Price movements are caused by the
the interaction of investors/traders

Freely Interacting Agents =>
Market participants, investors/traders

(Note that this is a highly simplistic representation of the Stockmarket as a CAS
and could include many more relevant influences and external variables, etc)
And transposing into our diagram we get;

Market participants react to a combination of both external stimuli, such as
Company information, etc and feedback from the market itself, via the price
activity. Hence when market participants are being strongly influenced by price
activity, ie. internal feedback, the market is sentiment driven and behaves largely
in a chaotic manner. But when external influences are the principle motivating
force then the market moves away from this excited state, near the edge of chaos.
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Furthermore CASs have several additional properties worth noting…namely that
the boundaries of a CAS are determined by the observer and that CASs interact
with each other as agents of larger CASs. Mind boggling stuff…

Thus our Stockmarket is part of a larger global financial system and it reacts to
changes in the larger system as well as causing changes in the larger system. In
other words the age old idea of cause and effect becomes largely obsolete in the
face of feedback loops such as we have in Complex Adaptive Systems. Cause
produces an effect which alters the cause which changes the effect and so on.

So in understanding the overall nature of the Stockmarket as a CAS we come to
the harsh realization that it is very much a puzzle that is constantly changing and
evolving, thus avoiding any permanent solution…it’s all a bit depressing really.
Emergence and Adaptation

But now we get to the really fun stuff when it comes to dealing with complex
adaptive systems…emergent properties and behavior. This is where we must be
prepared to part company with reductionist thinking because what we are about to
explore is what happens when you get a really large number of independent yet
freely interacting agents together, ie. sharemarket investors and traders.
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But before we look at something as complex as human behavior let’s start with a
slightly less complicated agent…ants. If you’ve ever watched an ant running
around by itself you may have marveled at its apparent random behavior patterns.
It will probe around then suddenly dart one way before stopping and probing
around again. Sometimes it will stop near a food source and sometimes it
won’t…or it may suddenly return to the food, pickup some up and then scuttle off.


But when you observe a group of ants the picture is remarkably different with the
ants acting in what appears to be a cooperative manner, ie. colony-like behavior.
Well you’re not imagining things because when a group of ants reaches a certain
population their behavior patterns switch from being disorganized and random to
being periodic and predictable. And we know this thanks to the efforts of Blaine
Cole, a research scientist from Houston, Texas.
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Blaine measured the rest/activity cycle of both individual ants and ants in groups
of different populations and discovered that this cycle went from being chaotic
(not disorganized but specifically chaotic as we have discussed) to being periodic.
Thus the rest/activity cycle of ants in a colony is approximately 25 minutes (see
the well defined spike in the chart at top right) which you can compare to the far
more random cycles of individual ants (chart at bottom right)…

This is quite amazing research and the emergent behavior (or self organization) of
colony ants goes considerably further than just their rest/activity cycles. For
example if you put together a group of any animals, insects or even human beings,
inevitable outcomes will result. For instance a group of people will generate
rubbish and ants are no different. But an ant colony will purposefully situate its
rubbish (dead ant carcasses, food scraps, etc) well away from its food storage.
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Hence emergent properties are not just logical outcomes of mechanical processes
that can be predicted by breaking down and studying them on a step by step basis.
This is the reductionist approach to the problem and it has already been applied to
the understanding of ants. But alas, no one has ever managed to teach an ant to
take out the rubbish and therefore this behavior is not inherent in each ant.
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So from the example of an ant colony as a complex adaptive system we can begin
to understand the importance of viewing complex systems as a whole rather than
being waylaid by trying to break them down into their component parts. So if
Benoit Mandelbrot (a Mathematician working for IBM to solve the problem of line
noise) observed that price data in financial markets was predominantly chaotic,
like individual ant behavior, then are financial markets complex adaptive systems
which exhibit emergent properties that can be exploited for profit?
Well if a financial market’s emergent properties are obvious and practical (like the
location of ant rubbish) then we should be able to look at it and the answer should
probably just jump out at us….
Well maybe some trendlines will help…

Now ask yourself two simple questions;

1. Do you want the value of your shares to rise over time?
2. Do you want the value of your portfolio to remain within reasonable limits?
If you answered yes to both these questions then what you want (but may not be
consciously aware of it) is for your shares to rise up over time in some sort of price
channel. But here’s the kicker;

3. Do you want the boundaries of these channels to be straight lines or curves?
(Remember what we said at the start about preferring straight lines)

Obvious and practical…thus the fact that we can apply a couple of trendlines to a
chart and capture 98% or more of the price activity is truly amazing and yet so
obvious that no one really pays it much attention. But what makes it even more
amazing is that the vast majority of market participants don’t even use charts.

Thus we want the value of our investments to be contained by boundaries, whilst
still wanting the value over time to increase. So even if we’re not consciously
aware of it, we actually want to have price activity move up in some type of
channel. Furthermore the boundaries of these channels are linear because we
want them to be linear…once again, even if we’re not consciously aware of it.
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But as global circumstances shift with the passage of time (ie. technological
developments, etc) markets must evolve and adapt as change is forced upon them.
However, given time, they will always re-establish their linear boundaries…

So financial markets are a product of both internal & external forces and must
constantly adapt to satisfy both. Hence they are a type of complex adaptive system.
Chaos and Self Organization
It is well worth noting at this juncture that at any given level of magnification a
market will behave in an apparently chaotic manner inside the extremes of price
activity, yet demonstrate self organization by forming linear boundaries. Thus we
can observe fractal patterns (elliott waves) occurring inside most price channels…

Hence this phenomena is nested and we see the same behavior occur at lower and
lower levels of magnification…channels within elliott waves within channels, etc.

Market Developments

Looking towards the future; technological advances and globalization means that
investors will generally be better informed and able to communicate with each
other more readily. And this means that financial markets will operate more and
more effectively as complex adaptive systems…
Thus the links connecting each component of the above complex adaptive system
will become stronger with the passage of time.
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Of course, these communication improvements will also mean that the interaction
between different financial markets will also become more efficient over time,
inevitably making cross market arbitrage trading less and less feasible.
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Even changes to trading conditions, such as the introduction of 24 hour continuous
trading, will have no ill affect on interpreting financial markets as complex
adaptive systems. Hence this understanding of how financial markets operate
should only prove more and more useful as time progresses.
Swarm Behavior

Another very important feature of complex adaptive systems is the fact that they
are driven by swarm behavior and not top down control. Thus in our earlier
example of ants, the queen does not dictate the layout of the nest and where she
would like her rubbish to be placed. Nor does she have a global perspective of the
colony anyway, which would be required for such macro decision making.


In a similar vein, key investors like Warren Buffet don’t look at a chart of the SP-
500 and say, ‘look we’ve reached a trendline so we had better turn this thing
around’. As mentioned earlier, very few market participants actually use charts and
this is particularly the case with major institutional investors who largely invest on
the basis of the underlying businesses, such is Warren Buffet’s approach.
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This isn’t to say of course that market regulators, including central banks, don’t try
to exert top down control over supposedly free market systems. For instance the
Bank of Japan (BOJ) has for a long time had a policy of keeping their currency
(the Yen) above a ratio of 100:1 against the US dollar. In other words if the Yen
gets too strong with respect to foreign currencies, thus hurting Japanese exports,
then the BOJ will print and distribute more yen…thus watering it down.
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However, when regulators and central banks do attempt to exert control over
financial markets, they will often just postpone the inevitable rather than have any
real overriding control. This doesn’t mean that their efforts should be ignored but
that we should recognize that the primary force at work in any complex adaptive
system is the bottom up influence of the swarm.

To be Continued