Financial Markets as Complex Adaptive Systems

Financial Markets as Complex Adaptive Systems
by Alan Hull www.alanhull.com
More is Better Part 3
Harping back to our little friends once again; when is a group of ants a colony?
Apparently, critical mass is achieved at about 30 to 40 ants with an efficient
colony having a population of at least 100 or more. The key point here is that there
is a minimum requirement and the general rule of thumb is…the more, the merrier.
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And so it is with financial markets and individual shares. Recognizing of course
that the investors in any individual share are the independent yet freely interacting
agents whereas in the case of an Index, the shares themselves are the independent
yet freely interacting agents.

Thus an example of an inefficient CAS would be the Dow Jones Industrial
Average which is made up of only 30 constituent stocks which represents about
20% of the NYSE by market capitalization. (note that I am not saying that the
NYSE is an inefficient CAS but that the Dow Jones is a poor representative of it)
The above chart of the Dow Jones has two key features. Firstly, where the index
rises above 10,000 points, note the degree of indecision as indicated by the
sustained sideways movement and the magnitude of the candlestick shadows, ie.
the candlesticks have this sort of fuzzy, out of focus appearance. Secondly, during
2002/2003, the Dow dropped away but didn’t manage to reach its historical norm.
Now take a look at the following chart of the SP-500 Index, which is made up of
500 constituent stocks, and you’ll see that it tells a different story…
Firstly, the pullback in 2002/2003 does manage to touch down on the historical
norm and whilst the SP-500 includes constituent stocks from the NASDAQ as well
as the NYSE, it is primarily created from NYSE stocks.
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Secondly, but no less important, where the SP-500 forms a peak at about 1,500 at
the turn of the millennium, there is virtually no sideways consolidation and far less
candlestick shadow occurring, indicating much more decisive market behavior.
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So whilst the Dow Jones clearly displays chaotic and fractal behavior (see the Dow
from 1994 to 2003), it makes for a poor CAS as it displays indecision at the market
extremes and seems reluctant to form linear boundaries, even over the long term.
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Hence the more agents within a CAS the better…30 is poor, 100 is OK and 200 or
more is good to excellent. Thus understanding and interpreting financial markets
as complex adaptive systems is definitely a technique suitable for dealing with
large capitalization, liquid instruments and not for trading penny dreadful shares.
Appropriate Trading Tactics
As you’ve probably already surmised, linear trading tactics are best used to trade
the boundary effect and non-linear techniques for trading non-adaptive chaotic
behavior (which principally occurs when there are an insufficient number of agents
to support self organization and/or the market is in a transitional phase)

Non-linear trading tactics include…

•
Elliott Wave Theory
• Gann
•
•
•
Fibonacci retracements and price projections
Volatility based techniques including price projections & turning points
Volume based techniques including continuation and turning points
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Linear trading tactics include…

• Trendlines
•
•
MACD, moving average convergence divergence indicator
Rate of Return measurement
• Fundamental analysis
• Momentum oscillators including indicator/price divergence
• Range or channel trading tactics
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And of course, there are some universal indicators and trading tactics as well;

•
Moving averages and applications, ie. crossovers
• Multiple moving averages
• Support/resistance levels
• Range Indicator
• Count Back line
• Swing trading
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Thus everything works and everything doesn’t work. Therefore viewing financial
markets as complex adaptive systems doesn’t in any way invalidate any pre-
existing concepts in technical analysis.
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What this new found understanding does do however, is tell us which technique is
the most appropriate for any given circumstance. Thus the MACD which is usually
notorious for giving false entry/exit signals (whipsaws) is far more effective if
used in conjunction with the identification of linear boundaries….

Further Reading

CHAOS, the amazing science of the unpredictable by James Gleick
COMPLEXITY, life at the edge of chaos by Roger Lewin
EMERGENCE by Steven Johnson
DEEP SIMPLICITY, bringing order to chaos and complexity by John Gribbin
HOW THE LEOPARD CHANGED ITS SPOTS by Brian Goodwin

For information on Alan’s home study course (which delves much deeper into the
subject of financial markets as complex adaptive systems) please send your
enquiry direct to Alan at alanhull@bigpond.net.au

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

Financial Markets as Complex Adaptive Systems Part 1

Financial Markets as Complex Adaptive Systems
by Alan Hull www.alanhull.com

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 progress in a straight line, is so we can both easily understand it and
predict what it is likely to do in the future.
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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 below…

We can even project lines and curvy things into the future and with a reasonably
high degree of accuracy, determine if, when and where they’re likely to intersect.
Although there is one proviso…that there aren’t too many variables to consider.
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But there’s another problem that even the scientific community don’t like to talk
about and that’s the possibility of things changing but not doing it in a consistent
way. In others words the rate of change is not consistent….its bad enough that
something can be ‘Dynamic’ rather than ‘Static’ (thus rendering statistical analysis
and the bell curve useless) but when the rate of change itself isn’t linear either then
everyone starts to get really scared. This is known as non-periodic behavior…


But let’s digress for a second 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 we’re all expected to
live then you would most likely use data available from the past 10 years or so…
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But what about using recorded deaths from the last 100 years instead of just the
past 10…wouldn’t this give us a more accurate answer? Put simply, no…because
over this time span factors that impact our lifespan have changed significantly
making this sample period non-static and invalidating any averages taken…
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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
today’s 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
would solve what appeared to be problems of randomization. So the Stockmarket
like other supposedly irregular phenomenon gets labeled as being unpredictable
and that’s that. Just like weather patterns and the human heart, the Stockmarket has
too many variables and is a dynamic system that’s not always linear by nature…
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Thus if we can’t get our heads around it then its random or so close to random it
doesn’t matter. Another neat way of dismissing things we can’t get our heads
around is by calling it noise, interference or turbulence. Thus an engineer working
in fluid dynamics works to eliminate turbulent flow rather than try to understand it.
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So you can imagine everyone’s excitement about Chaos theory when it first
appeared back in the early ‘60s because it went a long way towards understanding
what had previously appeared to be random phenomena. (Well actually it was
largely dismissed by the broader scientific community as a stream of pure
mathematics without any real world application and just a good excuse not to
work on more practical stuff like how to eliminate turbulence)
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A brief history lesson (Reprinted with the kind permission of Greg Rae www.imho.com)

The first true experimenter in chaos was a meteorologist, named Edward Lorenz.
In 1960, he was working on the problem of weather prediction. He had a computer
set up, with a set of twelve equations to model the weather. It didn't predict the
weather itself. However this computer program did theoretically predict what the
weather might be, a weather simulator of sorts…
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One day in 1961, he wanted to see a particular sequence again. To save time, he
started in the middle of the sequence, instead of the beginning. He entered the
number off his printout and left to let it run.
When he came back an hour later, the sequence had evolved differently. Instead of
the same pattern as before, it diverged from the pattern, ending up wildly different
from the original. Eventually he figured out what happened. The computer stored
the numbers to six decimal places in its memory. To save paper, he only had it
print out three decimal places. In the original sequence, the number was .506127,
and he had only typed the first three digits, .506.
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By all conventional ideas of the time, it should have worked. He should have
gotten a sequence very close to the original sequence. A scientist considers himself
lucky if he can get measurements with accuracy to three decimal places. Surely the
fourth and fifth, impossible to measure using reasonable methods, can't have a
huge effect on the outcome of the experiment… Lorenz proved this idea wrong.
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This effect came to be known as the butterfly effect. The amount of difference in
the starting points of two variables is so small that it is comparable to the minute
forces created when a butterfly flaps its wings…

The flapping of a single butterfly's wing today produces a tiny change in the
state of the atmosphere. But over a period of time, the atmosphere actually
does diverge from what it previously would have done. So, in a month's time,
a tornado that would have devastated the Indonesian coast doesn't happen.
Or maybe one that wasn't going to happen, does.

This phenomenon, inherent to chaos theory, is also known as sensitive dependence
on initial conditions. Just a small change in the initial conditions can drastically
change the long-term behavior of a system. Such a small amount of difference in a
measurement might be considered experimental noise, background noise, or an
inaccuracy of the equipment.
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Such things are impossible to avoid in even the most isolated lab. With a starting
number of 2, the final result can be entirely different from the same system with a
starting value of 2.000001. It’s simply impossible to achieve this level of accuracy.
For instance just try and measure something to the nearest millionth of an inch!
From this idea, Lorenz stated that it is impossible to predict the weather with any
accuracy…particularly a long way into the future. However, this discovery led
Lorenz on to other aspects of what has become known today as chaos theory.
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Whilst there is no commonly acknowledged fixed definition of what constitutes a
chaotic system, it is generally accepted that the following conditions must be met;

1. The system must be highly dependent on initial conditions
2. The system must employ at least 2 or more interacting variables
3. The initial conditions must be at least partially dependent on output

A good example of a chaotic system is the operation of a roulette wheel which is
probably best understood by analyzing the process step by step…

• An operator picks up a ball from a roulette wheel which he then spins
(the starting position of the wheel is dependent on where the ball landed after
the previous operation…initial condition is dependent on the previous outcome)
• He or she then sets the ball rotating in the opposite direction
(the wheel is the first variable whilst the ball represents the second variable)
• The ball eventually loses enough energy to drop into the spinning wheel
(The outcome is extremely sensitive to the interaction of the 2 variables)

Roulette is an excellent example of a 2 variable chaotic system which would in
fact be predictable to a degree if a machine was used as the operator. The human
operator introduces the random factor but because the system is chaotic it can’t be
manipulated to any practical degree…
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One of the other principle discoveries that Lorenz went on to make was that
systems or models of systems behaving in a chaotic state produced repeating
patterns that could be observed if the outputs were mapped in 2 dimensions. Note
that these repeating patterns were similar in form but never precisely identical…
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Oil…..running scared!!

Oil…..running scared!!
At the moment oil appears to be that commodity where
traders are just running scared. It doesn’t really matter
what bearish views you put to the market traders are
just content with taken it higher. We have just fallen
short of the US120.00 level and with the geopolitical
picture heating up in Nigeria/Iran; strikes in Scotland
and inventory draws in the US, traders are expecting
prices to remain firm. So given these latest develop-
ments we feel it is appropriate for us to question Oils
ability to head upwards of US120.00.
In past reports we have been suggesting that the fun-
damental picture just does not add up to prices being
above US100.00. Why, we are about to enter into a re-
cession; we have oil inventories at levels which are not
critical; gasoline inventories at 15 year highs and sea-
sonally at a period of shoulder demand. At a time when
prices should be leveling off they are firm. Why? So
after much discussion and contemplation we have ar-
rived at two main reasons we feel why sentiment is so
bullish;
1. Governments around the world stockpiling
2. Profitability on refining oil into petroleum frac-
tions.
Governments Stockpiling:
Over the last couple of years we have seen the emer-
gence/growth of Sovereign Wealth Funds (SWF) and
we see these funds becoming more influential in the
markets in particular in the commodities. A SWF is a
state owned fund purely initiated by Governments to
ensure economic stability and to seek return. Of note
has been the emergence of these funds in the com-
modity markets helping to insuring economies against
price shocks such as what is occurring in Oil.
As we are aware the demand/supply picture for Oil is
balanced at about 86 million bpd. We have seen this
balance upset several times on the back of Geopolitical
events namely in the Middle East and Nigeria with the
resulting consequences well known. Given this poten-
tial maelstrom and our dependence on oil, any major
occurrence particular in the Middle East would have
devastating effects. Therefore there is a growing need
for Governments to orchestrate supplies for there
economies. Stockpiling the commodity provides a
breathing space if disruptions happen to occur and this
is fast becoming common practice among those na-
tions
which rely on imports of Oil to meet demands.
In the US for example the Strategic Petroleum Reserve
(SPR) is the key reserve the US has. It stands at 701
million barrels or 33 days of supply to meet current
consumption patterns. Due to the instability of the bal-
ance of supply and demand the Government has man-
dated to increase the SPR by 100% there by pushing
out the days of dependency to 66 days. Although this
increased demand equates to only about 70,000 bar-
rels or .33% of daily consumption in the US it is the
sentiment created that is driving consumers and other
Governments to act on initiating or increasing stock-
piles. If the US relaxed their purchases, which is being
argued in the Senate at the moment, for the SPR then
we think this would send a clear message to all that we
should not be as concerned and this should see prices
retreat.
Profits on Refining:
It is interesting to note that Gasoline supplies have
been retreating from 15 year highs and the market is
taking this as a sign that we will not have enough sup-
ply to meet Summer Drive time demand in the US. In
addition to these inventory draws we see refinery utili-
sation rates low which can present an ominous sign for
supply in the future. This rhetoric has been keeping
prices well bid however; looking between the lines we
can see that there is a good reason why the draws are
occurring and why utilization rates are low. It all comes
down to economics, in fact the profit from refining
crude ie the “crack spread” which is the differential be-
tween the price of crude oil and the petroleum products
extracted from it is historical low and at levels which
encourage refiners not to refine or wait until the spread
Phone 1300 660 734
www.commoditybroking.com.au
28th April 2008
is more profitable.
Valero Energy Corp. the largest US refinery is one such
example where low refining margins have resulted in a
poor Q1 performance. It issued a statement recently
suggesting that profits for Q1 have tumbled by 77% due
to the profitability of the crack spread. This is in stark
contrast to other Oil companies such as Conoco-
Phillps , Royal Dutch and BP who have all reported
sharply higher results for the quarter. Valero noted that
although conditions are improving they had been deal-
ing with a spread that was US3.00 to US6.50 a barrel
well down from the 5 yr average of US8.50. The spread
only recently has been as high as US 37.00 a barrel.
So with profitability so low isn’t any wonder that you
would put the breaks on production until prices firmed.
This result is once again forcing sentiment as people
fail to realise the true meaning
behind the draws and refining rates. Once profitability
returns to the Refiners they will step up production and
this should see inventories increase which should see
sentiment ease and prices retreat.
So are we forming a bubble like we have seen so many
other commodities?
Most bubbles occur when prices over shoot their mark
beyond the fundamentals and panic sets in. These
cause irrational movements in the prices and as fast as
it moves higher it moves lower there by balancing the
move. The bubble is traditionally characterised by a
“blow off” in prices. Gauging whether or not we have a
bubble in oil is a little harder as the increase has been
solid and we have not seen a “blow off” yet. Recent ex-
amples of this can be seen in the Wheat markets where
we have seen a 30% correct on the price of the com-
modity within a month. We have not yet seen this occur
in oil, however, we are monitoring the price rise to
US120.00 to see if prices sharply come off.
It is interesting to note that up until the US and other
countries change their rhetoric towards the SPR or
stockpiles and the crack spread widens then sentiment
towards oil will remain strong. Any geopolitical concern
will only add weight to the underlying story concerning
supply tightness. So we can suggest that dips will be
well supported until these issues change.
What does the technical picture look like?
Technically, we can see that we have a double top at
US120.00 so traders will be working off this level to im-
plement trading strategies either to sell into or to place
a stop loss /entry. Momentum indicators are over
bought and this should see some profit taking enter the
market. This profit taking should be limited to support
at US110.00 then US100.00 The bull trend of the move
from US50.00 is still
with us as long as US95.00 stays in tack. We would be
looking for a move back to US110.00 possible over the
coming
week. A break above US120.00 can be seen as an op-
portunity to add to long positions or go long. Only a
break below US95.00 will see a reversal of the trend.
Yours Faithfully,
Jonathan Barratt


Commodity Broking Services Pty Ltd AFSL 280 372 FICS 4312
Commodity Broking Services, it officers, employees, representatives and agents expressly advise that they shall not be liable in any way what so ever for
any loss or damage, whether direct, indirect, consequently or otherwise how so ever a rising (whether in negligence or otherwise) out of, or in connection
with the contents of and/or any omissions from this communication
Please do not hesitate to call concerning any
of the above. I can be reached on direct call
1300 660 734.
Jonathan Barratt

A Professional's Guide to Not Losing Your Mind Or Your Profits

This is a very interesting look at the amateur traders mindset, and what you shouldn't do as opposed to what you should do to make money

written by DC Bonta:Forex Amnesia:A Professional's Guide to Not Losing Your Mind Or Your ProfitsToday, we're going to do something a little different.I'm just going to ramble.I'm just going to take you through a handful of scenarios and paint a picture for you about how critical it is for you to meticulously plan your trade… and then forget about it.I call it Forex amnesia.Ready? Then let's get started…The Planning PhaseThe most critical aspect of trading is planning the trade.This means looking at the charts, knowing where support and resistance levels are and targeting potential trades from those levels. Having a clear view of the market before you enter a trade is key.This eliminates emotion and allows you to stay focused even in times of extreme volatility (like during a news release) when everyone else is going crazy. You should be able to see a trade set up long before price gets to a point where you pull the trigger.The Trigger PhaseAnd once you pull the trigger and enter the market with your pre-determined profit target and stop loss your work is done!The Amnesia PhaseNow, your job is to totally and completely forget about what you just did. Erase it from your brain. This is a hard concept to grasp because I know you are still staring at the screen as every pip goes up and down…Now, you're getting nervous because it's not going quick enough in your favor…It almost hit your stop loss, maybe you should move that stop up just in case…Whew, it didn't hit your stop loss and now you're up a few pips…Now you're thinking, "I should just close this out at a small profit, I almost got stopped out before and it will never hit my profit target… I'm getting too greedy."This is what goes through the heads of LOSING traders.If you start changing the terms of the trade in the middle, you seriously hurt your long-term success. Let the trade complete itself and live with the results, good or bad.A Look Inside The Trades Of A Pro… And How He Uses Forex AmnesiaI'll give you an example of how this works with a pro trader that isn't even average. In fact, he stinks… he's only right 40% of the time.Here's the set up…* 10 trades* Stop loss at -10 pips* Profit target at +20 pips* $10,000 initial balance* Trading 1 standard lot per trade.And here are his results…* 4 winners x 20 pips x $10 per pip x 1 lot = +$800* 6 losers x 10 pips x $10 per pip x 1 lot = -$600The net gain from our pro trader (only being correct 40% of the time) with solid money management is +$200.He was able to do that because he did his homework and planned well, pulled the trigger on his 10 trades, and then forgot about them, letting them ride with the market.An Inside Look At An Amateur… And How He Micromanages Every PipHere's what an amateur might do with those same 10 trades…* Trade 1-He saw it was moving against him and moved the stop to -20, which was also hit* Trade 2-It moved in his favor and he closed it out +10, price eventually hit the original target of +20* Trade 3-It moved so fast, he couldn't close out early and it hit his target of +20* Trade 4-He was convinced he was right this time and moved the stop twice to -30, which was hit* Trade 5-It moved against him fast and stopped out at -10* Trade 6-It moved against him and he held steady at -10* Trade 7-An agonizing trade, it almost stopped him out, then went in his favor slowly. It made it to +13 and started stalling. Fear set in that is was going back down and he closed out +11* Trade 8-He's learning, stopped out -10* Trade 9-The best thing that could happen, he had a meeting and could not watch the computer. When he got back, the trade hit the profit target of +20* Trade 10-He's really feeling confident, so as price moves near his stop, he moves it again and loses -20Now, let's add up the damage. Mark this point very carefully… he had the same winning percentage of 40%. But look at what a difference it makes when you don't have Forex Amnesia…* 4 winners (61 pips total x $10 per pip x 1 lot) = +$610* 6 losers (100 pips total x $10 per pip x 1 lot) = -$1000The net gain from our amateur trader (still correct 40% of the time) with horrible money management is -$510

Ten Ways to Improve Your Market Timing

Ten Ways to Improve Your Market Timing

It's December and getting close to the time when traders need to wrap things up and evaluate their profit-and-loss performance for the year. Unfortunately, most folks speculating on the financial markets in 2007 face major disappointment when they look at their results and realize how tough it's been to make money.
Sadly, most traders follow the same worn-out strategy over and over again, regardless of market conditions. They just buy upside momentum and hang on, hoping the bottom doesn't drop out of their positions. But as we know in this volatile trading year, almost every sharp move higher or lower has been followed by a vicious counterswing.
It's easy to dismiss this ragged price action, believing it's just an aberration in an otherwise perfect bull market, but nothing could be further from the truth. In reality, a choppy and dangerous tape is the most common environment in which traders need to risk their capital and book results.
That's why the concept of market timing is so important. Regardless of short-term conditions, every position is forced to negotiate a minefield of conflicting time elements in order to book profits. Simply stated, it's the gateway through which you take on monetary and emotional risk.
So what's the best timing strategy for your next trade? Unfortunately, the correct answer changes over time. As a result, market players must plan each trade within the context of the current environment, reward-to-risk profile and pre-chosen holding period. The good news: This extra effort pays off handsomely on their bottom lines.
Had a tough year picking your fights and choosing your exits in 2007? Well, it's time to shake it off and get ready for the new year. To help you get things started on the right foot, here are 10 things you can do to improve your market timing.

1. Sell Rallies: Stop selling short into selloffs. Instead, wait for weak rallies to fail at resistance. Then use the breakdown of a two- or three-day topping pattern to enter your position.

2. Play Pullbacks: Pullbacks work in all kinds of market conditions, so use them to take on all kinds of exposure. Stand aside when a new trend gets underway and stalk the chart until a counterswing forces price back to the level where you wanted to play it in the first place.

3. Enter in Quiet Times: The best time to enter a position is just before a breakout or breakdown. That way you can sell your position for a nice profit after other traders trip over themselves to get on board. Find these setup points using narrow range and volatility contraction patterns.

4. Follow the VIX: The most profitable trades show up when the crowd is leaning the wrong way. How can you see this happen in advance? Become a student of sentiment and track the Market Volatility Index (VIX) for reversals after sharp peaks and valleys.

5. Keep Sector Lists: A rising market floats all boats, even the leakiest ones. But in tough times, it's wise to play the strongest stocks in the strongest market groups. To this end, keep sector lists that show relative performance on a weekly basis, and then limit your trade search to the cream of the crop.

6. Mark the Gaps: Watch the gaps on the major indices and assume every one will fill, sooner or later. Avoid aggressive trade entry after a gap unless the market is in a running trend. Expect indices to turn on a dime as soon as a gap gets filled because smart traders use these pivot points to take profits and establish contrary positions.

7. Match Time to Opportunity: Decide your holding period before you enter the trade, and then stick to it. Are you scalping, daytrading, swing trading or picking up an investment for the grandkids? Keep separate trading accounts if you want to do all of the above.

8. Exit in Wild Times: Take profits in high volatility, whenever possible. Prices move through relatively narrow boundaries most of the time. Wide swings, triggered by greed or fear, open the floodgates and let the market move very big distances over short timeframes. Use these magic moments to book your profits and jump back to the sidelines.

9. Track the Pivot Points: Focus your attention on prior highs and lows, whether they're two days old or printed in the last decade. Traders use these focal points to make the majority of their entry and exit decisions. Learn to wait for the second test of a high or low, rather than jumping in too soon and getting stuck in a double-top or bottom reversal.

10. Read the Tape: The numbers on your trading screen are far more important than the pretty pictures they draw on the charts. Memorize key levels on your favorite stocks and then watch what happens whenever price approaches one of these inflection points. Yes, tape reading takes years to learn but it gives you a lifetime edge, so it's worth the effort.

Matching Your Mood to the Mood of the Market

The first and simplest emotion which we discover in the human mind, is curiosity.
- Edmund Burke -

Matching Your Mood to the Mood of the Market

It is Tuesday morning. Tommy woke up early and had a good run. He drank four cups of coffee before the open to get psyched up. He is energized and ready to trade the markets. He looks forward to a winning day. But how will he do? At first glance, one might think that Tommy has a winning mental edge today. There's no one single, proper way to trade, however. What's a good day for some people may not be a good day for others. Take Tommy, for example. When he is in a great mood, he is a little overconfident. He can't wait to get started, and in his zeal, he may take trades he should have stayed away from. Today the markets are generally bullish. (As you may recall, the Dow, NASDAQ, and S&P 500 were all up last Tuesday.) But what will happen in the long run? Will the market optimism last, or is Tommy jumping in too soon? He may have bought stocks at a top, and when he tries to sell off his positions in a few days, he may not find enough buyers. On this particular day, Tommy may allow his optimistic mood to get the better of him. Today is a day when Tommy would be wise to trade with caution or just stand aside.
When trading the markets, it's vital to have a good match between your personal mood, your trading style, and the mood of the markets. Again, there is no one right way to trade. It's a matter of personal preference, but a critical issue is whether your current mood matches the market mood. People differ in the kinds of personal moods that are optimal for trading. Ideally, you want to trade in the zone. You want to be alert, focused, and ready for the challenge the markets place before you. Obviously, if you are extremely tired or depressed, you will not be alert. You'll be easily distracted and annoyed by minor setbacks. So being rested, relaxed and ready for the action is essential for profitable trading. But how alert do you need to be? Many profitable traders, for example, develop detailed trading plans during off hours and merely execute (rather then re-think) their plans during the trading day. Some trading experts argue that one doesn't want to be too alert. They argue that if you are too mentally active, you may unnecessarily question your trading plan rather than flawlessly execute it. The ideal approach is to merely follow your plan and execute it without much thought, sort of how an assembly line worker might methodically get the job done. A sense of alert indifference is the preferred mode of thinking for some traders. While in this indifferent state of mind, everything just seems to click and they enjoy themselves. Other traders, in contrast, feel they need to be extremely energized while trading. They need energy in order to seek out trading opportunities during the trading day. They need a vast supply of energy to scan through charts, to discover what is happening, and to take action. Whatever mood you trade in, however, it must suit your trading style. Some people can't trade in a bearish market no matter what their mood. That's all right. Again, there is no one right to trade. Some people like bull markets while others like bear markets. The main thing you need to do is trade in the market mood you prefer and make sure that your personal mood is conducive to your style of trading. The better the match between your personal mood and the current market mood, the more profitable you will trade.