Issue 2 - The Technical Analyst

Transcription

march 2004
www.technicalanalyst.co.uk
Genetically
Engineered
Trading
Decision time for
German bunds
Gann
untangled
Beware of the
cycle theory
Advertisements - STA and Paritech
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A s a r e s p o n s e t o s o m e o f t h e s e c o m m e n ts , w e h a v e e x pa n d ed the Subject Matters section to include recent academic
research that is relevant to technical analysis. Such
r e s e a r c h o ft e n d o e s n ' t r e a c h t h e t e c h n i c a l a n a l y s t c o m m u n i t y, w i t h p r a c t i t i o n e r s l e ft t o r e l y o n a f e w t r i e d - a n d - t e s t e d
m e t h o d s . H o w e v e r, t h e r e ’s a v a s t a m o u n t o f a n a l y s i s , n o t
only in economics and finance but also in other disciplines
s u c h a s c o m p u t e r s c i e n c e , m a t h e m a t i c s a n d ps y c h o l o g y,
which is pushing the subject forward. Genetic programming
and time-series modelling are just two examples and both
a r e f e a t u r e d i n t h i s i s s u e . I t ' s i m p o r ta n t t h a t t h e s e f i n d i n g s
are presented to a wider audience so that they can be contested, developed or incorporated into everyday use.
I f y o u h a v e a n y c o m m e n ts t h a t y o u w o u l d l i k e t o s e e p u b l i s h e d i n a f u t u r e i s s u e o f T h e Te c h n i c a l A n a l y s t , p l e a s e
email them to me at [email protected]. I would
very much like to see our publication evolve into an open
forum for the exchange of ideas and a place in which technic a l a n a l y s i s c a n p r o g r e s s i n a r i g o r o u s a n d c r i t i c a l w a y.
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Analyst. No statement in this publication is
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Readers should be aware that this publication is not intended to replace the need to
obtain professional advice in relation to
any topic discussed.
Matthew Clements
Editor
March 2004
THE TECHNICAL ANALYST
1
CONTENTS
04
28
42
44
48
2
Product News
The Technical Analyst Talks To...
Robin Griffiths, chief technical strategist,
HSBC Corporate & Investment Banking Division
Book Review
The Investor’s Guide To Technical Analysis
by Curt Renz
Commitments of Traders Report
Training & Events Diary
March 2004
MARCH 2004
07
14
30
Market Views
07
Decision time for German bunds
08
Should gold bulls be running for cover?
10
Outlook for the US technology market
12
Plateau of stability for the US dollar
Techniques
14
The fall and rise of the Advance-Decline Line
16
Using neural networks for the FX markets
18
The Type 1 trade
21
Gann untangled
24
Intraday trading: Revisiting the 1-box reversal
26
Predicting option volatility with
point-and-figure charts
Subject Matters
30
Genetically engineered trading
34
Beware of the cycle theory
36
Central bank interventions, chartists & the
FX markets
38
South-East Asian stock markets follow a
non-random walk
40
Research roundup
March 2004
3
Product News
e-yield launches Pairs System
e-yield has launched Pairs
System, a new low-risk trading
strategy that uses pairs trading
to reduce risk by simultaneously buying and selling two
stocks with similar characteristics.
money simply by buying and
holding stocks for the long
term. For this reason we have
developed a low-risk trading
strategy to take advantage of
minor to intermediate trends in
stock prices.”
Thierry Laduguie of e-yield told
The
Technical
Analyst,
“Because of the excesses of
the last decade it will be practically impossible to make
The Pairs System will initially
be available to learn online at
www.eyield.co.uk and a series
of seminars are also being
planned.
MTPREDICTOR JOINS UP WITH
NEW DATA PROVIDERS
MTPredictor has released a
major upgrade to its End-ofDay trading program and
announced partnerships with
two data companies, Primate
Software of the US and Q-Data
of the UK.
According
to
MTPredictor,
End-of-Day 4.0 takes the trader
through the complete trading
process – identifying set-ups,
evaluating
the
risk/reward
outlook, determining trade size
and managing exit-stop strategies on-screen.
Partnering with Primate Software offers customers a
datafeed for US futures and
stocks,
which
is
directly
integrated into End-of-Day 4.0
for $24.95 a month. MTPredictor also told The Technical
Analyst that the launch of the
new Real-Time 4.0 software,
with an integrated eSignal
datafeed, is imminent.
John Bollinger
BOLLINGER BANDS
RELEASE NEW SEMINAR
ON DVD
Bollinger Bands have launched
a two-volume DVD. The DVDs
are an extension of John
Bollinger’s book “Bollinger on
Bollinger Bands” and was
produced from a two-day seminar held in Los Angeles.
The DVDs, which contain more
than nine hours of teaching,
cover advanced trading topics
that are based on Bollinger
Bands as well as the basics of
technical
analysis.
Each
volume of the DVD is accompanied by a book which contains
relevant charts and formulas.
eSignal launches information
service for mobiles
eSignal has announced the
availability of QuoTrek, a new
wireless quote and market
information service for mobile
phones. Chuck Thompson,
president of eSignal told The
Technical Analyst, “With the
new
QuoTrek,
service,
subscribers can access their
portfolios and comprehensive
market information virtually
anywhere and at anytime.”
4
March 2004
QuoTrek offers real-time information on international stocks,
indices, futures, options and
forex data. Information can be
displayed in line, bar or candlestick charts. The service is
available
immediately
for
$49.95 per month for new
clients and $25.00 per month
for existing eSignal customers.
Product News
FutureSource unveils new software
FutureSource has announced
the release of FutureSource
Workstation 1.1, their new
real-time charting and technical
analysis software.
According to FutureSource,
Workstation 1.1 is designed for
the serious investor and
professional
trader.
The
advanced quote component
includes support for expres-
sions, Greeks, copy-and-paste
DDE and over 50 customizable
headings. The advanced charting
component
includes
support for overlays, underlays, chartable expressions,
studies on studies and annotations. Tick, intra-day, daily,
weekly, monthly and continuation charts are included along
with over 32 technical indicators.
BLOOMBERG ADDS PRONET’S
REAL-TIME FX RESEARCH
Pronet Analytics will soon be
available on the Bloomberg
Professional platform. Shane
Smith of Pronet says, "Our
collaboration with Bloomberg
comes at a time when foreign
exchange, which is the core
market on which we provide
intelligence, has also become a
strategic focus for Bloomberg.”
Bloomberg recently partnered
with EBS Dealing Resources
International to provide an
enhanced spot interbank dealing facility.
Testers required for
SnapDragon RT
SnapDragon Systems will soon
be releasing a new version of
its real-time charting and technical analysis software, SnapDragon RT, for beta testing.
SnapDragon RT is designed to
work with a variety of datafeeds
and also directly with trading
platforms, thereby eliminating
the need for a datafeed.
The aim is to increase the
product’s flexibility and ease of
use.
If you are interested in becoming a beta tester please email
Adam Hartley at:
[email protected]
Product News
PARITECH ENHANCE EOD DATA SERVICE
Paritech has announced a
new look London Stock
Exchange data service covering end-of-day data for the
LSE, Alternative Investment
Market and major world indices. The data, which is triple
checked to ensure quality, is
now sourced via Reuters
and can be downloaded in
MetaStock or text formats.
Paritech have also added
US and Australian equity
datafeeds, which are downloadable using their Data
Director 2 software.
Pfscan adds Fast Track
Pfscan 2 has been updated to
V2.93.13 and now includes the
Fast Track datafeed.
FastTrack is a United States
charting/investment
program
that specialises in dividend
adjustment for stock prices and
mutual funds.
Updata Technical Analyst
now Bloomberg
compatible
Updata has launched a new
version of its Technical Analyst
software to run as an add-on to
the Bloomberg Terminal. This
gives Bloomberg users a
windows analysis system
which operates with a host of
technical analysis tools and
indicators, such as INDEXIA
proprietary indicators, not
otherwise found on the Bloomberg system.
Market Views
DECISION TIME FOR GERMAN BUNDS:
WILL WE GET A HEAD-AND-SHOULDERS?
by Clive Lambert
uropean bond market futures have been flirting with
head-and-shoulders formations for some time. But how
likely is it that a head-and-shoulders formation will complete
and in turn signal a significant reversal?
E
Figure 1 is a weekly candle chart for bund futures from May
2002 to the present showing three peaks, akin to a headand-shoulders formation. In November 2003 the neckline was
broken but there wasn't a big downside push on the back of
the completed pattern. Instead, bunds bounced back
and have held above this line since, but without retesting the old highs. So what we had was a failed pattern.
The start of 2004 has seen the market steadily move
higher up to the 61.8% Fibonacci retracement (of the
move from the all-time-high to the November 2003
low). But the volume on the latest up-move has been
very low. Whether the head-and-shoulders completes
(with the recent highs being the new right shoulder)
will depend on selling from here to test the green line
on the chart - the new neckline. It should be stressed
that a head-and-shoulders doesn't exist until this new
neckline is broken, i.e. unless we move below the
110.90 region. At that point a measured move to
around 103.00 would be expected.
Figure 1.
A look at the 5-year bobl could explain why the
November 2003 break of the neckline didn't produce
the desired effect. The bund future is the long end of
the European futures curve, based on 10-year bonds.
A move down the curve to the 5-year bobl future gives
a weekly candle chart as seen in Figure 2.
Again, there is the classic left shoulder, followed by a
head, and then a sell-off until early September when a
hammer candlestick pattern was posted. This was followed by a bounce that gave a potential head-andshoulders formation and, in turn, a neckline. However,
the November weakness didn't reach the neckline
(unlike the 10-year) and as of late February the neckline is still in place. So is a new right shoulder currently
forming for the 5-year future? Or are we going on to
make new highs by trading through 113.67?
Something that will give us an early warning on direction is the uptrend line that has defined the recent
strength. If this holds we can expect that move to
113.67. If it doesn't we can look for a test of the neckline at 108.40, with a break here suggesting a move to
around 103. The recent volume has been very light, (a
typical right shoulder characteristic), which favours the
bear argument.
Figure 2.
Clive Lambert is director of FuturesTechs. He
writes daily analysis on the key European and US
financial futures markets.
March 2004
7
Market Views
SHOULD GOLD BULLS BE RUNNING
FOR COVER? by Gerry Celaya
he gold market has been on a bull run over the last few
years buoyed by a weak US dollar and low US interest
rates. Medium-term trend following tools have been useful in
trying to take advantage of the uptrend from the $253.85 low
of 2001. One of the simplest techniques is to use moving
averages to smooth out market noise, making it easier to
trade with the trend. Figure 1 shows a weekly bar chart of
spot gold with 13 and 50-week moving averages. Once the
uptrend began in 2001, the 13-week moving average never
crossed below the 50-week moving average thereby providing a strong trend signal.
T
Chart patterns were dominated by the double bottom (Figure
2) which had a base near $250 and a midpoint near $340,
targeting $430. This was met on January 6th, leaving gold
bulls waiting for the next uptrend. The monthly futures chart
(Figure 3) shows how important the $418/430 area is. If this
is recovered on a sustained basis then chartists will be looking for confirmation of a larger basing pattern. Near-term targets are at the $450 and $480 levels, while longer-term
extensions would point towards the $600 area. The 13 and
50-week moving averages remain bullish and argue in favour
of a continued uptrend, but the profit taking at $430 was quite
impressive and a near-term chart risk seems to be building
for a pullback to the 50-week moving average.
The pullback story is a cautious one given the strong uptrend
and a US dollar that remains vulnerable to further weakness.
Figure 2.
However, Figure 4 shows that the weekly RSI has been turning lower for a few weeks now and that the peaks in this indicator have been at lower and lower levels. Not a clear-cut
sell signal in its own right but this sort of action is associated
with a mature trend, which gold now qualifies for. Figure 5
shows the bullish resistance line from the $340.50 and $389
highs, which was broken on the last uptrend in December
2003. Price action has returned to this trendline and if this
line is penetrated on a sustained basis, another negative signal will have been generated, arguing for a shift back to the
lower support line near $327 on a range trade move.
Fibonacci retracements can also be used to gauge pullback
potential as Figure 6 shows. If a correction to the entire
move up from the 2001 low occurs, then the 23.6% retracement at $389 and the 38.2% retracement at $363 are the
near-term targets to watch for. Resistance can be expected
at the highs of the last few weeks just below $418 (the 1996
high).
The ability to regain this level would put gold bulls back in the
driving seat. However, as long as $418 holds intact above
here, a deep pullback in the price of gold towards the $360
area, and potentially lower, can be expected.
Gerry Celaya is chief strategist at Redtower Research.
Figure 1.
8
March 2004
Market Views
"As long as $418 holds intact above here,
a deep pullback... can be expected."
Figure 3.
Figure 4.
Figure 5.
Figure 6.
March 2004
9
Market Views
OUTLOOK FOR THE
US TECHNOLOGY MARKET by Dane Halling
ince the US high-tech indices peaked in mid-January, following their surge in December, they have gone through
an extended period of poor action while remaining close to
recent peaks.
S
We use the QQQ Nasdaq-100 tracking index as our primary
bellweather for the NASDAQ. As can be seen from the 9month chart (Figure 1), we are still in the powerful uptrend
that began in March 2003. The end of February upward
move has re-established the QQQ's above this key mediumterm trendline. But having seen four closes recently below
this line, this trend has now been seriously tested.
Looking at the 3-month timeframe (Figure 2), the market drift
of the last month, after the sharp mid-January sell-off, is
essentially still with us.The QQQ remains locked between the
two green lines which mark the top and bottom of the current
boxed range, as investors remain undecided about whether
this year will bring accelerated IT spending, higher order
rates and faster revenue growth.
But there are positives in this shorter-term timeframe: the
holding of a key support at the lower level of the range where
we now have eight or nine touches at the 36.33 level. Also,
there was a high volume day on January 24th that held at
this key level and closed (fractionally) up on the day.
On the negative side, the QQQ has so far made no inroads
at all into the three major down candles of January 28th and
29th, and February 19th. It's looking more likely we will see
a test of these three important zones. This will determine
whether we go on to see a test of prior highs and a potential
breakout to new high ground. The level just under $37.50,
which is marked by the half-way mark up the most recent
long black candle, is likely to be a tough test. Just above this
level at $37.50 lies the midpoint of the second long black
candle of January 29th. These levels in the middle of major
tall candles, referred to as the stomach in candlestick charting, often offer excellent support/resistance levels, particularly
in the absence of convincing data elsewhere.
1 - ©2004 eSignal, Inc.
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V o lu me
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March 2004
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Market Views
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Figure 3.
Why the laboured performance from the QQQ's? Look no further than the semiconductor indices for some of the answer.
The semiconductor fund we track is the SMH (Figure 3).
Poor up-volume versus down- volume, lots of near-term overhead supply and multiple support/resistance lines, (starting
with $42), do not paint a picture of an index about to move
smoothly higher after a much needed period of consolidation.
But the SMH, as with the QQQ and indeed the NASDAQ
Composite, has not broken down technically in a major way,
and that is really key to understanding the market psycholo-
gy. The 200-day moving average is the gold standard for
measuring the medium-term health of any market. Taking this
broader perspective, the QQQ and the SMH have both
caught a particularly nasty strain of flu, but hospitalisation?
Forget it, for now at least.
Dane Halling is the founder of Synvestor Ltd, which publishes
US & UK equity market research & technical analysis.
March 2004
11
Market Views
PLATEAU OF STABILITY FOR THE US DOLLAR
by Robin Griffiths
he trade-weighted US dollar index is currently in a prime
downtrend although we are currently seeing a period of
relative stability that is likely to last for another four months.
Although it seems clear that being short the dollar is the right
strategy for the long-term, it is not the best trade for now. The
existing open short positions are so large that natural market
forces will produce a rally phase.
T
There is a well-known chart law being invoked here which
states that the shape of the counter trend correction will alternate. Note that in mid-2002 the dollar went into a sideways
range for six months. Then in mid-2003, it rallied strongly for
three months and died again. Now from the early January
top, a six-month sideways range is the most probable outcome. The implication is that the upside for the dollar is not
large, but it will not dump again until late June.
12
March 2004
For now, the trade-weighted index will probably move in the
range 93-97 and euro/dollar should stay in the 119-129
range. Dollar/yen is now in exactly the same range as it was
in 2000, 103-110.
From late June onwards, the dollar may well enter the next
period of weakness, targeting 80 and taking six months to get
there. In the long run, China will probably re-peg its currency
to a basket including the dollar, euro and yen. Until that happens, any renewed weakness in the dollar will impact on the
majors. This implies a sterling target at 2.05, yen at 85 and
the euro at 145. The existing dollar bears have got the right
story, but the timing is wrong for now.
Robin Griffiths is chief technical strategist within the corporate and investment banking division at HSBC Bank in
London.
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Techniques
THE FALL AND RISE OF THE
ADVANCE-DECLINE LINE by Paul F Desmond
A
popular tool for examining the internal
condition of the stock market has
gone awry. The Advance-Decline Line, the
standard measurement of market breadth
for more than 100 years, doesn't seem to
work any more. In recent years, at critical
turning points in the equity market, the
Advance-Decline Line has signaled a positive bias just before stocks turned decisively negative.
In simple terms, the Advance-Decline Line
shows whether more stocks are going up
than down. More importantly, it can show
whether buying enthusiasm is spread across
a broad number of stocks (positive), or
whether buying is narrowly focused (negative). It provides investors with a simple
way to adjust the level of diversification in
their portfolios. That is, as the AdvanceDecline Line expands, investors can expand
the number of stocks in their portfolios. As
the Advance-Decline Line contracts,
investors should become more selective by
excluding weak holdings.
Perhaps the most widely accepted value of
the Advance-Decline Line is in "divergences" - when the Advance-Decline Line
(market breadth) improves during a period
of market weakness (bullish signal), or
when it contracts during a period of market
advance (bearish signal). The AdvanceDecline Line has often turned down about
four to six months before many major market declines in the past. Because of its ele-
gant simplicity and the valuable insights it
has provided at market turning points, it has
been highly prized by analysts throughout
the decades.
But in recent years something seems to
have gone wrong. For example, in July and
August 2001 (Figure 1) when the Dow
Jones Industrial Average (DJIA) was moving in an indecisive pattern, the AdvanceDecline Line began to rise vigorously, leading investors to conclude that the internal
strength of the market was improving.
Many investors bought aggressively in
August 2001, based on the strength of the
Advance-Decline Line. But the market was
actually weakening. The Advance-Decline
Line was giving off a false signal. The
DJIA plunged 20.7%, exacerbated by the
tragedies of September 11th.
In early September 2002 (Figure 2), the
Industrial Average was recovering from
previous losses. The Advance-Decline Line
lead the recovery, rising to a new rally high,
creating the impression that buying enthusiasm was broadening. In fact, the market
was weakening and the DJIA dropped
15.3% to a market low in October 2002.
Again, in mid-January 2003 (Figure 2), the
Industrial Average appeared to be breaking
out to new rally highs. The AdvanceDecline Line added to the illusion by rising
to its highest level in six months, a seemingly bullish indication that investors were
pouring money into a broadening array of
stocks. Many investors rushed in to buy
only to find that the Advance-Decline Line
was once again providing misleading information. Over the next two months, the
DJIA plunged 14.9%.
How could the time-honoured AdvanceDecline Line give off such obviously false
signals? The answer is simple but not easily seen. Over the past decade, the New
Figure 1.
14
March 2004
Techniques
Companies-Only (OCO) Advance-Decline
Line, was compiled, which excludes all preferred issues, real estate partnerships, foreign issues and ADRs, and closed-end stock
and bond funds. The remaining issues are
simply domestic common stocks listed on
the NYSE.
Throughout its twelve year history, Lowry's
OCO Advance-Decline Line has provided a
far more accurate measurement of the
internal strength of the stock market, particularly at critical turning points, such as
those cited above. As shown in Figures 1
and 2, in each of the three cases Lowry's
OCO Advance-Decline Line was correctly
reflecting market weakness at exactly the
same time that the standard AdvanceDecline Line was showing misleading
strength.
Figure 2.
York Stock Exchange (NYSE) has allowed
trading in a growing number of issues that
are not, or do not trade like, domestic common stocks. For example, of the 3,500
issues listed on the exchange, approximately five hundred are closed-end bond funds.
Another six hundred issues are preferred
stocks that trade more like bonds than common stocks. In addition, there are more
than four hundred foreign stocks and
American Depositary Receipts (ADRs) that
may or may not reflect the trends of the
domestic stock market. Lastly, roughly three
hundred real estate limited partnerships are
listed on the NYSE that, like mutual funds,
are not operating companies. The bottom
line is that almost half (48%) of the issues
currently listed on the NYSE are not really
common stocks - at least not what investors
generally think of as stocks. These nonoperating companies have been the princi-
pal cause of the false signals given off by
the Advance-Decline Line in recent years.
In each of the three cases cited above, common stocks were in a generally sideways
trend, while the bond market was strongly
rising. Thus, the common stock components of the Advance-Decline Line offset
one another, while the bond related components were rising strongly, giving the
Advance-Decline Line an overall positive
bias. In other words, during each of those
periods, the Advance-Decline Line was, in
essence, measuring the strength of the
bond market, not the stock market. It's no
wonder that the signals were misleading.
This is not a recently discovered phenomenon. In 1990, Lowry's Reports became
concerned about the changing character of
the Advance-Decline statistics. As a result, a
new indicator, Lowry's Operating-
March 2004
New distortions to the accuracy of NYSE
trading data may be coming. The NYSE is
working aggressively to add Exchange
Traded Funds (ETFs) to its listed issues.
The trading of ETFs (which essentially will
double-count issues already listed) will play
havoc with not only the Advance-Decline
statistics, but more importantly with the
upside and downside volume statistics that
investors have relied on over the years.
Interestingly, Lowry's Reports originated
the compilation of the Upside and
Downside Volume statistics in 1938.
Lowry's has also been compiling Operating
-Companies-Only Upside Volume and
Downside Volume statistics since 1990. In
an ever changing world, traders, investors
and analysts must constantly re-examine the
accuracy of the data that plays such a vital
role in their trading and investment decisions.
Paul Desmond is president of Lowry's
Reports, Inc. He is a past president of
the Market Technicians Association
and was winner of the Charles H. Dow
Award in 2002.
15
Techniques
USING NEURAL NETWORKS
FOR THE FX MARKETS by Tim Finch
W
hat is the best combination of technical indicators and oscillators
required to predict short-term movements
in the foreign exchange markets?
Initial research at Nostradamus found that
by using neural networks, we could eliminate all human bias when selecting an indicator. To do this, we took 110 well-known
market indicators and oscillators (e.g. RSI
and moving averages) and applied them to
five years of historical data. The neural network then chose 40-50 indicators, weighted
into the best combination, to make predictions of high and lows for the next two and
eight hour periods. As a result, accurate predictions were achieved. For USD/DEM,
the average error was around 15 pips in any
given week (60 periods of two hours).
Similar accuracy was attained with the
inception of the euro (error rates are now
under 7 pips on average).
Further research created a proprietary indicator, now called COMPASS, which measures the direction and strength of momentum in market movement. Taking data from
Reuters, the indicator is calculated on a one
second basis, based on the current bid price
of the underlying currency pair.
The COMPASS dial frame shows two
extremes of upward or downward trending
momentum, edged with reversal/signal
areas to indicate the point where momentum is being lost (Figure 1). This is especially useful for options traders looking to
hedge their underlying spot position as it
gives turning points at which they should
decrease their hedging cover after a trend is
over.
The neural network updates its choice of
indicators and weighting at the end of each
period having learnt from the movement
and volatility of the previous two or eight
hours' data. This eliminates any human bias
in judging the market and leaves the trader
free to make his own decision on his exact
16
What are neural networks?
A neural network is a system of programs and data structures that approximates the operation of the human brain. It usually involves a large number of processors operating in parallel, each with its own area of knowledge and access to data in its memory. Typically, a neural network is initially trained or fed large amounts of data and rules about data relationships (for example, a grandfather is older than a person's father). A program can then tell the network how to behave in response to an external
stimulus.*
In technical analysis, neural networks refer to computer programs that are
able to identify price patterns, which are then interpreted to generate trading signals. For the end-user, they are a form of automatic trading system
in which the computer, rather than the trader or analyst, identifies the
most relevant patterns.
* Source: TechTarget
time of entry. A glance at the historical plot
of COMPASS over the previous 8 hours
(Figure 2) gives an appreciation of the next
possible signal to come and confirms or
denies his current or anticipated prediction.
powerful momentum and directional tool
with predicted future levels gives the trader
a real-time appreciation of when a level
might break due to the force of momentum
in the trend.
Neural networks take a more advanced
combination of indicators than any individual could hope to monitor and weigh in
terms of effectiveness. Combining this
Tim Finch is managing director of
Nostradamus Systems Ltd.
www.nostradamus.co.uk
Figure1.
March 2004
Techniques
“The neural network updates its choice of
indicators and weighting at the end of each
period having learnt from the movement and
volatility of the previous two or eight hours' data.”
Figure 2.
March 2004
17
Techniques
THE TYPE 1 TRADE
by Steve Griffiths
A
s explained in last month's issue of
The Technical Analyst, one lesson
learnt over years of trading is that the simple approach to analysis is often all that is
needed to uncover excellent trading opportunities. Arguably, one of the clearest trade
set-ups for a professional trader should be
the simple ABC correction.
The last article outlined not only how this
clean ABC correction can yield profitable
trade opportunities, but also how simple
and easy-to-recognise it could be. Taking
this a stage further, when the basic ABC
correction occurs at a certain juncture in a
market, it can produce one of the most
profitable trades available. This happens
when the simple ABC correction develops
as part of the first correction to the first
move off an important high or low. In
Elliott Wave terms, this is a Wave 2 or B
correction. When this correction is complete, it can lead into a Wave 3 type move,
which is usually the strongest and longest
swing in a typical 5-wave sequence. This
swing has the largest profit potential in any
Elliott Wave pattern.
Figure 1.
Figure 1 presents a simple ABC correction
on the UK equity ICI. The most important
point is that this ABC correction unfolded
as part of the first correction to the initial
swing off an important low.
ICI made a major low in March 2003, which
was followed by an initial rally off the low.
The simple ABC pattern then appeared
during the correction to this initial rally
(Figure 2). This is referred to as a Type 1
trade set-up in MTPredictor.
Why is this set-up so important? Very often
this initial correction is the springboard for
an extremely strong move. Moving forward
in time we can see how, once this particular
corrective ABC was complete, ICI proceeded to rally by over 75%, from a price of 124
to approximately 220 (Figure 3).
18
Figure 2.
Clearly, identifying these set-ups can result
in enormously profitable trades. This is why
the Type 1 trade is the principal trade set-up
sought out of the three ABC corrective
variants. More importantly, this set-up is
March 2004
straightforward to identify, with its mathematical simplicity - a simple ABC correction that unfolds as part of the first correction to the first move off an important high
or low.
Techniques
In the example shown in Figure 4, being
able to identify the end of the correction
enabled the trader to enter a very profitable
trade precisely at the start of the strong
move. This has the major advantage of
reducing the initial risk on the trade, particularly compared with the potential profit
from these Type 1 set-ups.
Expanding on this theme, Figure 4 shows
how the ABC correction developed exactly
at the Typical Wave C Wave Price Target
support zone. (This support/resistance
zone was covered in last month's article).
Anticipating that the market was reversing
at this zone enabled the trader to go long
from 124 (ignoring slippage and commission) with an initial protective stop loss at
118 - an initial risk of only 6 points.
Now compare this with the potential profit as ICI rallied to approximately 220, a
profit of 96 points. Given the initial risk of
only 6 points needed to take the trade, this
is a spectacular risk/reward ratio. Over
time, this can result in a track record where
the losses are kept small relative to the
profits - a prerequisite for low risk/high
return trading.
Figure 3.
This particular trade set-up can unfold in
any market and on any timeframe (from
weekly charts to five minute charts), so
whether you trade UK equities, US equities,
futures or currencies, the identification of
this set-up arguably should be a major part
of your trading plan.
Steve Griffiths is managing director of
MTPredictor Ltd and developer of the
MTPredictor software.
Figure 4.
March 2004
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Techniques
GANN UNTANGLED
by Fred Stafford
T
he use of percentage movements has
been far and away the most important of
Gann's techniques, enabling identification of
significant future price levels. Gann observed
that the level of future tops and bottoms has
a direct mathematical relationship to previous
tops and bottoms. Gann theory should therefore enable the analyst to project and forecast
price levels.
Like Fibonacci ratios, Gann percentages are
based on natural laws. In Gann's case this was
the observed mathematical relationship
between musical harmonics. When observing
the movement of shares and other investments, Gann noted that percentage movements from significant past levels would
determine the point where rises and falls were
likely to terminate. In other words, when a
market is in harmony, it reverses a previous
trend by an amount deduced through the use
of simple mathematical ratios.
Gann's original percentage levels were derived
by dividing price moves into eighths and
thirds. These have since been added to by
smaller, but related, percentages, such as sixteenths and sixths. Table 1 sets out the important percentage levels.
The next high or low of a price move should
be at a level related by a Gann percentage to a
previous low or high, with the primary percentage movement of 33% being the most
notable. Once the primary percentages have
been identified, secondary percentages can be
used to confirm the level. The 50% movement is the most significant of these.
If the price moves through a secondary percentage from a market top or bottom, then
the corresponding primary percentage will
Primary
Secondary
percentages percentages
4.165
8.33
16.66
33.33
66.66
100
3.125
6.25
12.5
25
50
75
give you the new price target of the current
trend. For instance, a move from a low or high
through 25% will almost certainly be curtailed
at 33.33%, at least temporarily, and perhaps
for many years. This explains the difference
between the two sets of numbers and the reason for the dominance of the primary set.
During 2003, 25 stock market buy signals
were triggered by the use of percentage movements. Taking a medium-term view there were
no losses, with the least successful trade providing a profit of 9% for Portugal and the
best return being 178% for Venezuela.
The following three examples illustrate the
use of these percentages in identifying percentage moves: (see pages 22-23)
Fred Stafford is chairman of Gann
Management.
"The next high or low
of a price move
should be at a level
related by a Gann
percentage to
a previous
low or high."
Table 1.
March 2004
21
Techniques
UK Gold Mines (Figure 1)
On 3 March 2003 we anticipated a low in the
1000/1030 region for UK Gold Mines. The reasons for this were:
1.
This area was the 50% retracement of
the November 2000 to May 2002 price
rise
2.
A fall of 33.33% from the May 2002
high
3.
A fall of 25% from the January 2003
high.
The market turned at 1030.
Figure 1.
UK Gold Mines (Figure 2)
UK Gold Mines recently been trading strictly within Gann's rules. The recent rise was terminated by:
1. A rise of 50% from the July low
2. A rise of 25% from the October low
3. A rise of 16.66% from the November 6th
low
4. A rise of 8.33% from the November 24th
The market then fell 16.66% from the January 5th
2004 high and then retraced 50% of the fall. 1600
is the next level of interest, this being 8.33% from
the previous high. This is close to the last 16.66%
fall at 1580 and the 50% retracement of the July to
December 2003 rise at 1556.
Figure 2.
22
March 2004
Techniques
The Dow Jones (Figure 3)
The flat performance of the Dow Jones Industrial
Average in 2004 can be explained by the small magnitude of the Gann retracement levels:
Figure 3.
1.
This area was the 50% retracement of the
November 2000 to May 2002 price rise
2.
A fall of 33.33% from the May 2002 high
3.
A fall of 25% from the January 2003 high.
Techniques
INTRADAY TRADING:
REVISITING THE 1-BOX REVERSAL
by Jeremy du Plessis
I
am often asked by experienced traders
whether point-and-figure charts can be
used for intraday real-time analysis. My
answer every time is yes.
Point-and-figure charts were devised by
traders and tape readers to record the tickby-tick prices from the floor or the ticker
machine. They were the first kind of realtime chart that technical analysts ever used
and, although they are currently out-offashion, they continue to offer a powerful
basis for short-term trading.
To draw intraday point-and-figure charts,
you need a real-time feed. If, however, tick
data is not available, then other intraday
time frames such as every 15 minutes, while
not ideal, may still yield meaningful results.
Even if there is insufficient data for day
trading, relatively low frequency intraday
data can still provide crucial information
for improving longer-term trading decisions.
The original point-and-figure charts were 1box reversal charts. These are rarely seen
today and when they are, they are often
constructed incorrectly. The reason for
using 1-box charts is that they have a
unique condition whereby X and O can
occupy the same column. This cannot
occur with any other point-and-figure chart.
An X and O in the same column is a powerful sign. For example, the presence of a
single O in an uptrend column of Xs indicates strength in the trend. It means the
sellers have managed to force the price
against the trend by the value of one O, but
the buyers have seen it as an opportunity to
buy again and have pushed it higher to
retake the X. I call this the 'one step back'.
It is a pause for breath, a sign of a resumption in the trend.
The circled areas in Figure 1 show this condition. The price is forced down by one box
24
Figure 1.
creating a O, but immediately the bulls take
charge again and push it back up by one
box resulting in an X above the O, so we
have X and O in the same column. Notice
that this happened three times during the
up trend. But notice also what happens at
the top of the trend in the rectangular section. As before, the sellers push the price
down by one O and the bulls push it back
up by one X, but this time they can't get a
second X above. They make four attempts
at doing this, with the sellers forcing it
down and the buyers forcing it up each
time. Finally the sellers push it down by one
O again but this time the buyers don't push
it back up by one X again. This shows
weakness on the buying side. When the sellers push it down by another O, a point-andfigure sell signal is given and a downtrend
starts.
Notice that the reverse happens in the
downtrend. We have the 'one step back' in
reverse. The three square sections on the
chart show it clearly. During the downtrend,
the buyers push the price up by one X and
March 2004
the sellers immediately push it back down
by one O, which then continues the downtrend. As with the uptrend, when this
occurs during a downtrend, it is a trend
enhancer. It is like a test by the bulls to see
whether the bears are serious.
Some will argue that the 1-box reversal
chart is too sensitive and can only be used
for day trading. This is not the case.
Changing the box size (the value of each X
& O) reduces the sensitivity. Figure 2 is a 5
x 1 chart (5 pence box size, 1-box reversal)
and shows the one step back during the X
column uptrends in the same way.
One of the problems in using tick data is
the shear amount of data that has to be
stored. A few months of a vigorously traded instrument runs into tens, maybe hundreds of thousands of ticks, which means it
is only practical to store a few months
worth of data. This however does not prevent you from using 1-box reversal charts.
They are not restricted to short-term
traders. Although true point-and-figure
Techniques
charts do require tick data, they can be used
with any time frame, even daily, to good
effect.
One of the most powerful advantages of
point-and-figure charts is the simple way in
which it portrays sideways congestion within a range. The 1-box reversal chart shows
this congestion better than any other chart
because every change in direction is recorded. This allows us to have a visual understanding of the contest between the buyers
and sellers, which helps us to predict the
extent of any move out of these sideways
patterns. This is done using what is called
the point-and-figure count, which is based
on the width of the pattern and projecting
it up or down from the point of breakout.
The logic being that the more vigorous the
contest, the further the price will move on
the breakout.
Figure 2.
Using a 1 x 1 tick point-and-figure chart of
Reuters again, you can see in Figure 3 how
accurate these counts have been in predicting Reuters' rise in the last 2 months.
Count A established in early January gives a
target of 313 which was achieved two
weeks later. Count B established in early
February gives a target of 402 which was
achieved two weeks later. Count C established in mid-February gives a target of 403
which matches count B and was achieved
on 17 February. Finally count D established
on 13 February gives a target of 450 which
was achieved on 18 February.
If you are a user of point-and-figure charts,
then spending time with the 1-box reversal
should enhance your trading.
Jeremy du Plessis is head of technical
analysis at Updata plc and is responsible for the design of the Updata
Technical Analyst software. He is a
Chartered Market Technician and a
Fellow of the Society of Technical
Analysts.
Figure 3.
March 2004
25
Techniques
PREDICTING OPTION VOLATILITY WITH
POINT-AND-FIGURE CHARTS
by Heinrich Weber and Kermit Zeig
T
he point-and-figure charting method is
excellent for analysing and predicting
price volatility, an essential part of options
trading. However, because volatility is
mean reverting (i.e. volatility tends to revert
to its long-term average), point-and-figure
charts need to be adapted before they can
be used for this purpose.
of volatility is referred to as volatility percent or V% to avoid confusion. In Figure
1, the long-term mean is 15.9 V%, with the
highest level around 40 V% and the lowest
around 8 V%. Applying this to the box
scale, if a box is set at 10 V%, the next
highest box is 5% higher, which is 10.50
V% and the box below is set at 9.50 V%.
Scaling the volatility chart
To generate the 5% logarithmic scale for
volatility point-and-figure charting, start at
a level of volatility that is lower than the alltime low for volatility. 5 V% is often an
appropriate starting point. So the first box
is 5 V%. Then calculate the second box
level by multiplying 5 V% by 1.05 to give
5.25 V%, the third box number by multiplying 5.25 V% by 1.05 to give 5.51 V% and so
on until the entire range from the all-time
low to the all-time high has been spanned.
First, an appropriate box size must be chosen. Based on the authors’optimisations, a
logarithmic scale of 5% is recommended as
a growth factor from one box to the next.
That is, a box is always 5% bigger than the
box immediately below.
Given both volatility and logarithmic box
sizes are measured in percent, the measure
Adapting point-and-figure for volatility
1. Use a 5% log scale 3-box reversal chart
2. Trendlines govern
3. Buy signals on high levels and sell signals on low levels are systematically
ignored
4. On long poles, i.e., long columns of Xs
or Os, positions are neutralised on a reversal (three boxes in the opposite direction)
Interpretation
In equity point-and-figure charts the signals
dominate, whereas in volatility point-andfigure the trendlines govern. This means
that volatility charts are primarily analysed
according to the channels given by the
trendlines and, secondly, according to the
bullish or bearish quality of the chart,
which simply refers to the last signal. Thus,
if the last signal was buy, then the chart is
bullish, if it was sell, then the chart is bearish.
Once these adaptations have been made
(see box), point-and-figure charts can provide clear trendlines from which to forecast
volatility. The chart of the Nasdaq volatility index in Figure 2 illustrates this clarity. It
is based on a 3-box reversal and a logarithmic 5% scale. The trendlines defining the
bearish channel are evident - (please refer to
last month's issue of The Technical Analyst
for details on how to draw trendlines). Two
instances where the bearish resistance line
was touched are indicated in green. The
second instance is quite recent, November
2003, but as in the first instance, December
2002, the trendline was not broken by the
bullish X-column.
Figure 1. The volatility of the S&P500 index. The red line marks the long-term average,
15.9%. Volatility is clearly attracted to this red line, a feature that differentiates volatility from
price.
26
March 2004
Therefore, the trendline is terminated and a
new one has to be drawn, shifted towards
the right. (Note: a bearish trendline has to
be broken by an X-column, a bullish trendline by an O-column). A break of the resist-
Techniques
“...in volatility
point-and-figure,
the trendlines
govern.”
Figure 2. The Nasdaq volatility index
ance line would normally provide a signal to
buy volatility (perhaps through a long straddle position).
Figure 3, a line chart for the same period of
the Nasdaq volatility index, shows the difficulty of working with trendlines on this
type of chart. Three possible trendlines
have been drawn, all of which are valid.
Point-and-figure, once adapted, is an excellent tool for the analysis of volatility timeseries. Trendlines are easier to draw in
point-and-figure and do not suffer from the
ambiguity often associated with other chart
types. As well as informing your trading
decisions, such as deciding whether to sell
options naked or take out covered calls,
they can form the basis of any analysis that
requires a view on future volatility.
Heinrich Weber is a partner and risk
manager of DeTraCo, an active Eurex
member firm. Kermit Zieg is a Full
Professor of Finance and Investments
at Florida Institute of Technology's
National Capital Region campus in
Alexandria, Virginia.
Figure 3. The Nasdaq volatility index
March 2004
27
Interview
THE TECHNICAL ANALYST TALKS TO….
Robin Griffiths
Robin Griffiths has more than 25 years experience
in technical analysis. He is chief technical strategist within the corporate and investment banking
division of HSBC Bank in London and previously
held technical analysis roles with HSBC Securities
in New York, W I Carr and Grieveson Grant. He is
also a past chairman of both the International
Federation of Technical Analysts and the Society of
Technical Analysts.
TTA: HSBC publishes a great deal of market research
that includes a lot of technical analysis. How many analysts do you have in your team in London?
RG: There are three analysts in the corporate and
investment banking division. Alan Johnson covers Asia
and Charles Morris is head of technical analysis within
investment management. Obviously, we also have analysts in our other banking divisions such as foreign
exchange and capital markets.
TTA: What software and charting packages do you use?
RG: I use Bloomberg for all my charting analysis
although we also have Thompson Financial Datastream
to construct historical charts and plot some moving averages. Once one becomes fully familiar and adept at
using one system the tendency is to stick with what you
know.
TTA: What markets do you look at?
28
March 2004
RG: We look at all the major markets from foreign
exchange to energy although the equity markets probably occupy most of our time. Our research is published
in a monthly global strategy report and we also produce
a fortnightly FTSE chartbook which includes more
detailed analysis of individual stocks.
TTA: Do you prefer to use any particular type of chart?
RG: I use bar charts almost exclusively for my analysis.
Despite the excellent software available for other chart
types such as candlesticks and point-and-figure, time
restrictions mean I tend to use only one type.
TTA: How do you assess the performance of your analysis?
RG: If our analysis is below par then we don't get the
orders. It's as simple as that. My clients expect more
than just an insight into technical analysis. If my forecasts impact on their portfolios and I'm wrong, then they
Interview
"I pay close attention to percentage
deviations from the moving averages.
These provide a more quantitative
method of using MAs."
lose money. Here technical analysis differs from fundamental analysis in that I'm expected to anticipate future
moves in the market, not merely explain what's happening. If you are consistently wrong then it will be noticed.
TTA: Are there any particular indicators that you rely
on?
RG: I use 25-day and 200-day moving averages for
short and long-term analysis. However, I also pay close
attention to percentage deviations from the moving
averages. These provide a more quantitative method of
using moving averages rather than relying upon a purely graphical interpretation.
TTA: What fundamentals, if any, do you look at?
RG: I attach a great deal of value to the analysis of
demographics for my longer-term views. This is a somewhat overlooked area of research that has significant
implications for economic fundamentals and market levels over a longer-term horizon.
TTA: What’s the importance of demographics to the
markets?
RG: Demographics and shifts in a country's age/population profile impact on future savings ratios which, in
turn, has implications for stock market levels.
TTA: What are your favourite patterns and techniques
that you use for forecasting?
RG: I'm a big fan of Elliott Waves and Gann analysis.
My Elliott Wave analysis has proved successful in anticipating movements in the Dow and euro/dollar over the
past two years. Gann retracements of 50% and 100%
have also proved reliable in estimating market corrections in certain stock and currency markets. You'll have
to become a client to find out more about my forecasts
for the year ahead!
March 2004
29
Subject Matters
GENETICALLY ENGINEERED TRADING
by Mukund Seshadri and Lee Becker
D
on't worry about finding the best technical trading strategy. Let your computer do it for you and it will formulate, backtest
and modify a strategy over and over again,
such that its development is akin to the evolution of a species through millions of years
of natural selection. This is the message
from the relatively new branch of computer
science, genetic programming, the principals
of which are rooted in evolutionary theory.
John Koza, a consulting professor at
Stanford University, explains the concept,
"One of the central challenges of computer
science is to get a computer to do what
needs to be done, without telling it how to
do it. Genetic programming addresses this
challenge by providing a method for automatically creating a working computer program from a high-level statement of the
problem. Genetic programming achieves this
goal of automatic programming by genetically breeding a population of computer programs using the principles of Darwinian natural selection and biologically inspired operations."
Our study
Genetic programming has recently been
applied to the financial markets and, specifically, technical analysis. In 2003, the authors
30
of this article showed that an evolved trading
rule could outperform a buy and hold strategy on the S&P500. To do this, we used several monthly technical data sets for the
S&P500 from 1960 to 1990 as the material
from which the trading rules could evolve
(see Box 1). We then let the computer make
ten evolutionary runs, with each run going
through 100 generational steps.
The 10 evolutionary runs were carried out
under three different environmental conditions, giving a total of 30 runs:
Mating
Figure 1 illustrates how two trading rules
might have been mated to produce two new
trading rules in one generational step. The
genomes (subtrees) to be exchanged are
marked by a dashed box.
The top left-hand trading rule in green
reads: Buy the S&P500 if a) the 3-month
moving average is less than the 6-month
moving average OR b) if the 12-month mov-
The technical datasets used
Experiment 1: 10 runs using the total portfolio value as the fitness function (i.e. the
measure of performance).
opening, closing, high, low of current
month
2, 3, 6 and 10-month moving averages
Experiment 2: 10 runs using the greatest
number of 12 month periods with well-performing returns as the fitness function.
Experiment 3: 10 runs with the aim of
producing separate trading rules for when to
buy and when to sell. This process, whereby
the two rules are considered separate species
and are therefore developed independently, is
known as Coevolution. The fitness function
used was the number of 12-month periods
with well performing returns as in
Experiment 2 above.
March 2004
3 and 12-month rates of change
price resistance markers - two previous 3month moving average minima and two
previous 3-month moving average maxima
trendline indicators - a lower resistance
line based on the slope of the two previous minima and an upper resistance line
based on the slope of the two previous
maxima
Box 1.
Subject Matters
OR
<
MA3
AND
OR
<
MA6
MA12
>
MA3
P
<
>
LT
MA6
P
MX1(MA3)
MN2(MA3)
CROSSOVER MATING
OR
OR
>
<
MA12
MA3
MX1(MA3)
AND
<
MA3
MN2(MA3)
MA6
LT
<
>
P
P
MA6
Figure 1. Genetic Trees - Crossover Mating
ing average is less than the 3-month moving
average AND the 6-month moving average is
greater than the month-end closing price. If
you are already long, then sell if both a) and
b) become false.
The top right-hand rule in blue reads: Buy
the S&P500 if a) the lower trendline is less
than the month-end closing price OR b) if
the first local maximum of the 3-month
moving average is greater than the second
preceding local minimum of the 3-month
moving average. If you are already long, then
sell if both a) and b) become false.
The new trading rules created by crossover
mating are then tested for fit against the
S&P500 data from 1960 to 1990, and the
process of mating and testing then continues
until the evolutionary run is complete.
Forecasting performance
The best performing rule from each run was
harvested and then used to make forecasts
for the out-of-sample period, 1991 to 2002,
allowing a comparison of their forecasting
performance (based on an initial investment
in the S&P500 of $1,000 at the beginning of
the period- see Table 1).
It is worth noting that the coevolved trading rules (which give separate rules for when
to buy and when to sell) gave the best average out-of-sample performance. The best
single trading rule, however, with a return of
$4,001 in the out-of-sample period, was
derived from the "consistency of performance" fitness function in experiment 2 (see
Box 2).
March 2004
Our use of genetic programming to derive
technical trading rules is by no means the
first of its kind and its application does not
as yet present a miracle answer to technical
trading. Previous attempts to use genetic programming for acquiring technical trading
rules had not been able to establish that GPevolved technical trading rules could outperform a buy-and-hold strategy if transaction
In-sample Out-of-sample
(1960-1990) (1991-2002)
Buy and hold
$5,457
$2,638
Ave performance in Experiment 1
$11,269
$2,812
$7,990
$3,014
$14,521
$3,142
Table 1.
31
Subject Matters
A NEW APPROACH
We incorporated a number of significant changes from that of previous researchers such as Allen & Karjalainen and Neely.
These include:
using monthly as opposed to daily data in order to reduce the number of transactions, with the result that there is only a
transaction, on average, once every two years (transaction costs are assumed to be 0.5% for each buy or sell)
reducing the operator set and increasing the number of technical indicators
using a complexity-penalizing factor in the fitness function to avoid overfitting as well as to improve comprehensibility. The
result being that the forecasting ability of the trading rules improved for the out-of-sample period (1991 to 2002)
using a fitness function which considers the number of periods a rule performs well, and not just its total return or average
excess return
using co-evolution of a specialized buy rule and a specialized sell rule.
Box 3.
costs were taken into consideration. Our
studies have since described an approach that
includes transaction costs which can outperform a buy-and-hold strategy, at least if dividends are excluded from stock returns (see
Box 3).
The findings of our research call into
question the Efficient Market Hypothesis
and provide strong evidence to suggest the
development of genetic programming may
soon be able to help traders exploit price patterns in a systematic way. More powerful
computing and/or the consideration of even
more technical datasets at the outset, should
lead to significant developments in this field.
32
References:
Koza, J. (1992) Genetic programming - On the programming of computers by means of natural selection. MIT
Press, Cambridge, MA.
Allen, F. and Karajalainen, R. (1993) Using Genetic
Algorithms to Find Technical Trading Rules, Rodney L.
White Center for Financial Research, The Wharton
School, Technical Report 20-93
Neely, C.J., Weller, P.A., and Dittmar, R. (1997) Is technical analysis in the foreign exchange market profitable? A
genetic programming approach, Journal of Financial and
Quantitative Analysis, 32, pp. 405-426.
Mukund Seshadri and Lee A. Becker
([email protected], [email protected])
Department of Computer Science,
Worcester Polytechnic Institute, United
States.
March 2004
Best performing trading rule
A
12-month rate of change
< 3-month rate of change
B
1st local maximum of the
3-month moving average
> 2nd preceding local
minimum of the 3-month
moving average.
If you are out of the market and either A
becomes true or B becomes true, you
should buy.
If you are in the market and both A and B
become false, then you should sell.
Box 2.
Subject Matters
BEWARE OF THE CYCLE THEORY
By Wing-Keung Wong and Chen Dujuan
I
s the Singapore stock market cyclical in
nature? If so, can these cycles be used
reliably as a basis for investment decisions?
The presence of cycles in Singapore's
Strait Times Index (STI) is a hot topic
among the Singapore financial community.
Many commentators believe the STI follows
a 14-year cycle, with alternate 7-year bull and
7-year bear runs. According to the 14-year
cycle theory, the STI is now in the third year
of a 7-year bull run, which will lead to a peak
for the STI in 2008. So will the STI peak in
2008?
Analysis of cycles
The most widely used method for the analysis of cyclical patterns is spectrum analysis.
A classic example of applying spectrum
analysis to cycle theory is the study of
sunspot activity, in which a cycle period of
around 11 years is found. Assuming sunspot
activity reaches its peak this year, one may
expect sunspot activity to peak 11 years later.
Equally, analysts frequently apply spectrum
analysis to measure cycles in stock markets.
The analysis reveals the periodicity as well as
the magnitudes of the cycle(s). Figures 1 and
2 illustrate the cyclical patterns in the
Singapore and US stock markets.
As shown in Figure 1, the STI possesses
many different cycles, including a 143-week
cycle (2.7 years) and a 350-week cycle (6.7
years), while Figure 2 shows that the US
stock market comprises cycles of 107 weeks
(2.06 years) and 195 weeks (3.75 years).
The 6.7 year cycle for the STI is of particular interest. If this holds true, and assuming the STI was at the bottom of the cycle in
2001, the next time the STI will be at the
bottom of the cycle will be in 2008
(2001+6.7). This prediction is the exact
opposite to that derived from the 14-year
cycle theory, which states that the STI is in
the third year of a bull run and will peak in
2008.
Wing-Keung Wong (Associate Professor)
and Chen Dujuan (Postgraduate
Student), Department of Economics,
National University of Singapore
Lessons from the US stock market
But do the cycles identified by spectrum
analysis remain constant over time?
Unfortunately, the Singapore index yields
insufficient data points to test this. Data
from the US stock market, however, can be
used as a proxy.
We divided 40 years of US stock market
data (from 1964 to 2003) into two equal subperiods: the first sub-period from 1964 to
1983 and the second sub-period from 1984
to 2003. Spectrum analysis was then applied
Figure 1. Spectrum for the Singapore Strait Times Index
from 1973 to 2004
34
to the two sub-periods, the results of which
can be found in Figures 3 and 4.
Figures 3 and 4 show that the cyclical patterns in the US stock market have changed
significantly. The cycle periods are around
200 weeks (3.8 years) and 55 weeks (about 1year) in the first sub-period, and around 115
weeks (2.2 years) and 70 weeks (1.3 years) for
the second sub-period. If investors had used
the data at the end of first sub-period to
determine their investments, their returns
would not have matched their expectations.
March 2004
Note on cycles:
The prices of many commodities such as wheat
and corn reflect seasonal cycles because of their
agricultural nature. In these cases, cycles are easily
explained and understood. For financial securities,
cycles are more difficult to identify and, once identified, difficult to explain. Theories to explain such
cycles range widely in their scope and include an
analysis of sunspots, planetary movements and
human psychology, which are not within the scope
of this study.
Figure 2. Spectrum for the US Standard & Poor's Index from
1964 to 2003
Subject Matters
Figure 3. Spectrum for the US Stock Market from 1964 to 1984
Figure 4. Spectrum for the US Stock Market from 1985 to 2003
WARNING - Using cycle theory for investment decisions …
Although there is a great deal of evidence for the presence of cyclical patterns in stock markets, some characteristics of cycles are worth
paying attention to if cycle theory is to be used as a basis for investment decisions.
1.
The cycle may be periodic, but the period does not repeat precisely. For example, a significant body of evidence suggests
that short- to medium-term cycles (less than or equal to four years) may vary in periodicity by about 10%. Long-term
cycles (more than four years) typically vary by 15% to 18%.
2.
Cycles are often not symmetrical in shape. For example, cycle bottoms may be periodic, but the cycle tops may not be and
vice versa.
3.
One cycle may be observed within other more powerful longer-term cycles, which will result in different interpretations
based on the individual's analysis about when tops or bottoms occur.
4.
Cycles can be distorted by market manipulations and irrational human logic.
5.
One cycle can be out of phase while others are not, leading to greater complexity.
6.
There may be many unobservable core cycles and the cycles that appear in the stock market may result from the combination of these core cycles. Therefore, even though the core cycles may remain unchanged, the cycles that appear in the stock
market may change dramatically. (Tai and Wong 2003)
March 2004
35
Subject Matters
CENTRAL BANK INTERVENTIONS, CHARTISTS &
THE FX MARKETS by Frank Westerhoff and Cristian Wieland
C
entral banks frequently intervene in foreign exchange markets to reduce volatility or to correct misalignments. Such operations may be successful if they drive away
destabilizing speculators. However, speculators do not simply vanish but may reappear
on other foreign exchange markets. Using a
model in which traders are able to switch
between foreign exchange markets, we
demonstrate that while a central bank indeed
has several means at hand to stabilize a specific market, the affect on the other markets
depends on how the interventions are implemented.
Interventions are motivated by the desire
to check short-run trends or to correct longterm deviations from fundamental values
(Neely 2001). Although central banks seem
to believe in the power of intervention operations, both the theoretical and the empirical
literature remains sceptical about its usefulness.
One noteworthy exception is Hung (1997)
who argues that central bank interventions
may be successful in the presence of trendextrapolating chartists. First, a central bank
may try to destroy technical trading signals
by breaking a price trend. Second, a central
bank may stimulate positive feedback trading
by inducing a price trend in order to guide
the exchange rate closer towards its fundamental value.
While chartists display bandwagon behaviour, fundamentalists expect the exchange
rate to converge towards its fundamental
value. However, the fundamentalists only
perceive the fundamental value on average.
When the exchange rate is equal to its fundamental value, half the fundamentalists view
the exchange rate as undervalued and half as
overvalued. Consequently, the net impact of
fundamentalists is zero. But as the distortion
in the market becomes larger, the influence
of fundamentalists increases. Due to this
nonlinear weighting scheme, the model generates interesting dynamics. Overall, this
chartist-fundamentalist approach has proven
to be quite successful in replicating the stylized facts of financial markets.
36
The model
We develop a model in the spirit of this
chartist-fundamentalist approach that allows
us to investigate the effectiveness of central
bank interventions. The model makes the
following key assumptions:
Fundamental analysis is time-consuming
and requires intensive research.
Fundamentalists are therefore regarded as
experts who specialize in one market and
thus remain in that market.
Since chartists use rather flexible extrapolative trading rules, they may easily wander between markets.
Fundamentalists bet on mean reversion,
whereas the philosophy of technical
analysis is to ride on a bubble (Murphy
1999).
To limit the risk of being caught in a
bursting bubble, chartists prefer markets
which are not too distorted. To be precise, chartists trade forcibly in those markets which display price trends but which
are not too misaligned.
The behaviour of fundamentalists tends
to stabilize markets whereas the activity of
chartists is typically destabilizing. If a
market attracts an increasing number of
chartists, the exchange rate is likely to be
driven away from fundamental values (and
vice-versa).
(TARGET), which means that the central
bank always trades in the direction of the
fundamental exchange rate. If the exchange
rate is below (above) its fundamental value,
the central bank submits buying (selling)
orders.
The two strategies, LAW and TARGET,
are in turn assessed under two different conditions, according to whether the interventions have been used to drive the rate closer
to the fundamental value (unbiased interventions) or away from it (biased interventions).
Thus, a total of four types of intervention
are considered in the model, the results of
which are summarised in Table 1.
Unbiased interventions
LAW interventions which are unbiased (i.e.
are not contrary to fundamental values) stabilize all markets. The reason is that this rule
destroys or at least weakens the trading signals of chartists. Since the intervention market is less distorted, it draws in chartists from
the other markets so that these markets also
benefit from the intervention operations.
TARGET interventions also calm down all
the markets because they effectively work like
an increase in the power of fundamentalists.
If more demand is based on mean reversion,
exchange rates are indeed driven closer
towards fundamentals. As in the previous
case, more chartists enter the intervention
market so that all markets profit from this
policy.
Biased interventions
Our model examines the consequences of
interventions by a single central bank within
a system of linked foreign exchange markets.
In particular, we study the effectiveness of
the two most common intervention strategies
(Neely 2001). First, the "leaning against the
wind" rule (LAW), which aims at reducing
positive feedback pressure. For instance, if
the price of a currency goes up, the central
bank takes a short position. Second, the
"targeting long-run fundamentals" rule
March 2004
Central banks sometimes attempt to shift the
exchange rate away from fundamentals in
order to boost the domestic economy.
According to the model, both LAW and
TARGET interventions successfully result in
moving the exchange rate away from the fundamentals. Moreover, exchange rate fluctuations are completely eliminated in the intervention market. Since the intervention market is now very unattractive for the chartists,
Subject Matters
Volatility in
Intervention
Market
Distortion in
Intervention
Market
Volatility in
Other Markets
Distortion in
Other Markets
LAW
Unbiased
Intervention
TARGET
LAW
Biased
Intervention
TARGET
Table 1. Summary of simulation results
they wander to the other markets. Volatility
therefore increases in these related markets.
Conclusions
We provide the first multi-foreign exchange
market framework based on a chartist-fundamentalist approach to evaluate central bank
operations. Simulation analysis reveals that
central bank interventions may succeed in
calming down markets. However, the market
in which intervention takes place either
attracts more or drives away some destabilizing chartists, depending on the nature of the
intervention, which may lead to the destabilisation of other markets.
References:
Hung, J. (1997) Intervention strategies and
exchange rate volatility: A noise trading perspective,
Journal of International Money and Finance, 16,
779-793.
Frank Westerhoff, Department of
Economics, University of Osnabrueck
and Cristian Wieland, RAAD Consult,
Munster.
© 2004, Frank Westerhoff and Cristian Wieland
Murphy, J. (1999) Technical Analysis of Financial
Markets, New York Institute of Finance, New
York.
Neely, C. (2001) The practice of central bank intervention: Looking under the hood, Federal Reserve
Bank of St. Louis Review, 83, 1-10.
March 2004
Further details of this study will be available in the
paper "Spill-over dynamics of central bank interventions", Frank Westerhoff and Cristian Wieland,
due for publication in the German Economic
Review at the end of 2004.
37
Subject Matters
SOUTH-EAST ASIAN STOCK MARKETS
FOLLOW A NON-RANDOM WALK
by Venus Khim-Sen Liew, Kian-Ping Lim and Chee-Keong Choong
A
re the returns of the major South-East
Asian stock markets forecastable? If so,
can those returns be forecast by models that
rely entirely on one variable - the stock price
itself ?
To seek answers to the above questions, we
resorted to time-series modelling, a methodology which is rooted in the same principles
as technical analysis. A time-series model
demands nothing more than the historical
records of the variable under investigation,
whereby the movements of the variable are
explained solely in terms of its own past.
Parallels have even been drawn between
the recent trend in non-linear time-series
modelling (where the output from a model is
not proportional to the sum of its input variables) and technical analysis. Clyde and
Osler (1997) argued that technical analysis
could be viewed as a simple way of exploring
the non-linear behaviour of financial timeseries. For example, patterns such as headand-shoulders are clearly attempting to find
some kind of non-linearity in the series.
In our study we looked at daily stock market indices from the five major South-East
Asian countries (ASEAN-5: Indonesia,
Malaysia, Philippines, Singapore and
Thailand) from January 1990 to October
2001. From this data, we computed the percentage daily returns (based on the price
move from the close of one trading day to
the next). Figure 1 provides an example of
the resulting time-series; a plot of the daily
returns from Singapore's Strait Times Index.
The data was divided into two periods.
Data from January 1990 to October 2000
was used to create six time-series models
(two linear and four non-linear) and a random walk model. The seven models were
then used to generate 1-day, 1-week, 1month, 3-month, 6-month, 9-month and 1year forecasts.
The forecasts from these models were then
compared with the actual data from
November 2000 to October 2001 and their
performance was measured using the root
mean squared error (RMSE) method.
Forecasting Performance
The forecasting performances of the seven
models are summarized in Table 1 (note that
models with better performance have smaller
average values). In addition, the average
ranking of the models (based on RMSE for
each forecast horizon) is given in Table 2.
On average, linear models are superior to
non-linear models for forecast horizons of
20
15
10
5
0
-5
-10
500
1000
1500
Figure 1. Daily returns of the Strait Times Index
38
March 2004
2000
2500
3000
Subject Matters
Forecast
Horizon
RMSE
Random Walk
Linear Models
Non-Linear Models
Jakarta Composite Index
1 Year
9 Months
6 Months
3 Months
1 Month
1 Week
1 Day
1.854
1.826
1.779
2.079
1.431
1.264
1.480
0.001 - 0.006
1.342
1.061 - 1.062
1.638 - 1.639
1.430 - 1.432
1.403
1.414
0.009 – 0.029
1.344 - 1.349
1.064 - 1.069
1.635 - 1.637
1.433 - 1.434
1.403
1.413
ASEAN-5
Average Random Walk
RMSE
1 Year
9 Months
6 Months
3 Months
1 Month
1 Week
1 Day
1.6 - 3.6
1.6 - 3.6
2.0 - 4.6
3.0 - 3.8
1.2 - 3.8
2.6 - 2.8
3.0 - 4.0
1.789
1.706
1.793
1.767
1.389
1.256
1.932
1.296 - 1.299
1.343 - 1.346
1.263 - 1.288
0.819 - 0.825
0.882 - 0.897
1.222 - 1.233
0.006 - 0.044
2.252
2.274
2.426
3.052
4.290
2.090
3.127
1.295 - 1.298
1.341 - 1.345
1.260 - 1.270
0.815 - 0.823
0.886 - 0.915
1.211 - 1.230
0.022 - 0.036
1.716 - 1.719
1.720 - 1.723
1.913 - 1.914
2.484 - 2.485
3.810 - 3.814
1.605 - 1.611
0.136 - 1.605
1.718 - 1.720
1.721 - 1.724
1.913 - 1.915
2.484 - 2.486
3.812 - 3.815
1.609 - 1.636
0.129 - 0.238
1.431 - 1.436
1.425 - 1.430
1.282 - 1.285
1.285 - 1.341
1.350 - 1.351
2.171 - 2.174
0.255 - 0.316
1.430 - 1.434
1.423 - 1.428
1.282 - 1.284
1.340 - 1.345
1.351 - 1.356
2.173 - 2.189
0.256 - 0.401
1.677
1.725
1.559 - 1.565
1.799
2.161 - 2.184
1.726 - 1.767
0.036 - 0.089
1.677 - 1.679
1.725 - 1.727
1.560 - 1.569
1.795 - 1.802
2.167 – 2.197
1.718 - 1.789
0.013 - 0.155
Strait Times Index
1 Year
9 Months
6 Months
3 Months
1 Month
1 Week
1 Day
2.062
2.035
1.955
2.040
1.828
2.172
1.598
Venus Khim-Sen Liew, Faculty of
Economics and Management, Universiti
Putra Malaysia.
Stock Exchange of Thai
1 Year
9 Months
6 Months
3 Months
1 Month
1 Week
1 Day
2.135
2.115
1.981
2.361
2.421
2.105
2.297
3.2 - 3.8
2.8 - 3.6
2.8 - 3.8
2.4 - 4.2
3.0 - 4.8
2.6 - 4.2
1.6 - 4.2
1-month and longer, whereas non-linear
models are superior for 1-day forecasts. For a
forecast horizon of 1-week, linear models are
at most comparable with non-linear models.
Thus, although there is evidence of non-linearity on stock returns (Tse, 2001), information on non-linearity seems to produce little
gain in the prediction of stock returns.
More significantly, Table 1 shows that the
RMSE values of the random walk models
are substantially greater than all the timeseries models considered, for nearly all the
forecasting horizons and across all five countries. In other words, the random walk model
ranked last in all cases, with the only exception being the 1-week horizon for the Strait
Times Index (in which the random walk
managed to rank second out of the seven
models under study). This suggests that the
returns of the ASEAN-5 stock markets do
not follow a random walk and are forecastable by time-series models, thus providing further justification for the work of technical analysts.
Philippines Composite Price
1 Year
9 Months
6 Months
3 Months
1 Month
1 Week
1 Day
Non-Linear Models
Table 2. Ranking of Forecasting Models by Forecast Horizon
Kuala Lumpur Composite Index
1 Year
9 Months
6 Months
3 Months
1 Month
1 Week
1 Day
7.0
7.0
7.0
7.0
7.0
6.0
7.0
Linear Models
Kian-Ping Lim, Labuan School of
International Business and Finance,
Universiti Malaysia Sabah.
Chee-Keong Choong, Faculty of
Accountancy and Management,
Universiti Tunku Abdul Rahman.
Table 1. Forecasting Performance by the RMSE
March 2004
39
Subject Matters
RESEARCH ROUNDUP
Local traders are less likely to recognise losses
than non-local traders.
NYMEX dominates the IPE in setting the price
of crude oil,
This is the finding from a behavioral study conducted by Alex Frino
and Hui Zheng of the University of Sydney and David Johnstone of
the University of Wollongong. Consistent with other studies, they
find evidence of a disposition effect (the propensity of traders to ride
losses yet realise gains) for both on-floor professional futures traders
(locals) and a matched sample of non-local traders. After controlling
for potential differences in trader characteristics, comparisons reveal a
stronger disposition effect among locals than non-local traders. The
authors hypothesise that because locals must trade profitably to survive, it is improbable that they are more irrational in their loss riding
than non-locals. To the contrary, they find evidence to show that
paper losses for local traders are more likely to be turned into paper
gains by the time of the next transaction, when compared to nonlocal traders. This result is consistent with the hypothesis that locals,
by their presence on the trading floor, have privileged albeit shortlived information on order flow that allows them to form relatively
accurate probability predictions of the direction and strength of
short-term market price shifts.
according to Lin and Tamvakis of CASS Business School in London.
The authors look at the interaction of the two main price setting energy markets (London's International Petroleum Exchange (IPE) and
New York's Mercantile Exchange (NYMEX)) when both of them are
open (synchronous trading) and when only London is open (asynchronous trading). Specifically, they test the hypothesis that London is
affected by New York by analysing the transaction duration of the IPE
Brent futures contract, both when the NYMEX WTI futures contract
is being traded and when NYMEX is closed. Using tick-by-tick data
obtained from IPE, transaction durations are found to form two distinctive and inverted U-shaped patterns. A statistical model is applied
to the data, which shows that the parameters of the IPE in the morning and the afternoon are significantly different from each other,
underlining the dominant effects of NYMEX on IPE trading. The
results from the current analysis reinforce previous results by the
authors, which indicate that NYMEX is a leading price setter in crude
oil futures prices and has a dominant effect on the IPE-traded contracts.
The propensity for local traders in futures markets to ride losses:
Evidence of irrational or rational behavior? Alex Frino, David Johnstone
and Hui Zheng, Journal of Banking & Finance, Volume 28, Issue 2,
February 2004, Pages 353-372
Effects of NYMEX trading on IPE Brent Crude futures markets: a duration analysis. Sharon Xiaowen Lin and Michael N. Tamvakis, Energy
Policy, Volume 32, Issue 1, January 2004, Pages 77-82
Abstracts reprinted with permission from Elsevier.
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Subject Matters
Moving average trading rules in the currency
markets are unprofitable,
Key price levels play a strong role in determining
stock market activity.
according to Dennis Olson of the American University of Sharjah,
United Arab Emirates. In his paper, the author analyses 18 exchange
rate series from 1971 to 2000. The moving average trading rules were
optimised for successive five-year sample periods from 1971 to 1995
and tested over the subsequent out-of-sample period 1995 to 2000.
His results show that risk-adjusted moving average trading rule profits
have declined from an average of over 3% in the late 1970s and early
1980s to about zero in the 1990s. The author hypothesises that earlier
profits resulted from inefficiencies that have since been eliminated.
This is the conclusion from the Helsinki School of Economics'
Markku Kaustia, whose study looks at the reluctance of investors to
realise losses (disposition). To do this, the author examines IPO trading volume, in which case all initial investors have a common purchase
price and the disposition effect should be easier to detect. His findings
show that turnover is significantly lower for negative initial return IPOs
when the stock trades below the offer price, and increases significantly
on the day the price surpasses the offer price for the first time. The
increase in volume lasts for two weeks. On a daily level, attaining new
maximum and minimum stock prices also produces a strong increase in
volume.
Have trading rule profits in the currency markets declined over time?
Dennis Olson, Journal of Banking & Finance, Volume 28, Issue 1,
January 2004, Pages 85-105
Market-wide impact of the disposition effect: evidence from IPO trading
volume. Markku Kaustia, Journal of Financial Markets, Volume 7, Issue
2, February 2004, Pages 207-235.
Finnish investors realise losses more than gains towards the end of the tax year (31 December),
according to Finnish researchers Mark Grinblatt of The Anderson School at UCLA and NBER and Matti Keloharju of the Helsinki School of
Economics. Moreover, the authors continue, Finnish investors repurchase the same stocks recently sold. The repurchase rate depends on the
magnitude of loss, firm size and how late in the year the sale takes place.
Tax-loss trading and wash sales. Mark Grinblatt and Matti Keloharju, Journal of Financial Economics, Volume 71, Issue 1, January 2004, Pages 51-76
Book review
THE INVESTOR'S GUIDE TO TECHNICAL ANALYSIS
umerous books now exist providing introductions to
technical analysis and charting, many of which
claim to give the private investor an indispensable guide
to making profits on the stock market. Well established
text books such as those by John Murphy and Cornelius
Luca may be targeted at the more experienced trader
and would-be professional analyst, but should not be
beyond the scope of the novice investor. Nevertheless,
Curt Renz's book, while covering little new ground, provides a useful guide to individuals who have neither the
time nor inclination to delve more deeply into the subject.
N
While there is nothing new to say on the subject of introductory level technical analysis, starter-level books on
the subject continue to emerge as the profile of technical analysis rises. This may be due to the failure of fundamental analysis to adequately explain and anticipate
the stock market crash of 2001. Furthermore, ambiguous fundamental news continues to make profit-making
opportunities on the stock markets hard to identify.
The core of Renz's book looks at bar-chart analysis in
the context of various US stock market indices.
Reiterating that "the trend is your friend" and presenting
an overview of basic chart patterns such as flags, pennants and head-and-shoulders will not inspire experienced investors, but then the book doesn't claim to be
more than an introduction for those new to the subject.
Everything the casual investor needs to know about
charting is included and no space is wasted on more
complicated patterns, indicators and oscillators.
By Curt Renz
Published by McGraw Hill
148 pages, £12.99
ISBN 0-07-138998-9
The author claims the basic chart formations included in
his book should cover around 95% of situations the private investor is likely to encounter. The remaining 5%
of situations may offer significant profit opportunities,
but their exploitation demands the time and experience
only professional traders or analysts possess. He then
goes on to discuss the use of 10, 20 and 200-day moving averages and cross-over methods as tools of confirmation. He concludes with a set of thirteen problems
where the reader can test his or her interpretation of different patterns and check his answers against the solutions provided.
Carl Renz's book is short, well laid-out and written in an
upbeat North American investor style. It is also reasonably priced for the retail market and should prove an
attractive and user-friendly introduction to technical
analysis for the non-professional.
42
March 2004
March 2004
43
COMMITMENTS OF TRADERS REPORT
16 December 2003 – 24 February 2004
Non-commercial net long positions and spot rates
10-year US Treasury
5-year US Treasury
10-yr Treasury
Spot
5-yr Treasury
-100,000
4.35
Spot
300,000
3.30
3.25
4.30
-80,000
250,000
3.20
4.25
-60,000
3.15
4.20
200,000
-40,000
3.10
4.15
150,000
-20,000
3.05
4.10
3.00
0
4.05
100,000
2.95
20,000
4.00
2.90
50,000
40,000
3.95
60,000
3.90
Dec-16
Dec-22
Dec-30
Jan-06
Jan-13
Jan-20
Jan-27
Feb-03
Feb-10
Feb-17
2.85
0
2.80
Dec-16
Feb-24
Source: CBOT
Dec-22
Dec-30
Jan-06
Jan-13
Jan-20
Jan-27
Feb-03
Feb-10
Feb-17
Feb-24
Source: CBOT
Dow Jones Industrial Average
DJIA
Swiss franc
Spot
Swiss franc
-6,000
10800
10700
-5,000
Spot
18,000
1.27
16,000
1.26
10600
14,000
-4,000
1.25
10500
12,000
10400
-3,000
1.24
10,000
10300
8,000
-2,000
1.23
10200
6,000
10100
-1,000
10000
1.22
4,000
0
1.21
1,000
Dec-16
Dec-22
Dec-30
Jan-06
Jan-13
Jan-20
Jan-27
Feb-03
Feb-10
Feb-17
9900
2,000
9800
0
Feb-24
Source: CBOT
44
1.2
Dec-16
Source: CME
March 2004
Dec-22
Dec-30
Jan-06
Jan-13
Jan-20
Jan-27
Feb-03
Feb-10
Feb-17
Feb-24
Pound sterling
Yen
Pound sterling
Japanese yen
Spot
25,000
1.95
Spot
70,000
108.5
60,000
108
1.90
20,000
107.5
50,000
1.85
107
40,000
15,000
106.5
30,000
1.80
106
20,000
10,000
105.5
1.75
10,000
105
5,000
1.70
0
0
Dec-16
Dec-22
Dec-30
Jan-06
Jan-13
Jan-20
Jan-27
Feb-03
Feb-10
Feb-17
Feb-24
104.5
-10,000
104
-20,000
103.5
1.65
Dec-16
Dec-22
Dec-30
Jan-06
Jan-13
Jan-20
Jan-27
Feb-03
Feb-10
Feb-17
Feb-24
Source: CME
Source: CME
Euro
3-month eurodollar
Euro
Spot
3-month eurodollar
40,000
35,000
1.29
450,000
1.28
400,000
1.27
350,000
1.26
300,000
1.25
250,000
1.24
200,000
1.23
150,000
1.22
100,000
1.21
50,000
Spot
1.14
1.12
30,000
1.10
25,000
1.08
20,000
1.06
15,000
1.04
10,000
1.02
5,000
0
1.2
Dec-16
Source: CME
Dec-22
Dec-30
Jan-06
Jan-13
Jan-20
Jan-27
Feb-03
Feb-10
Feb-17
Feb-24
1.00
0
0.98
Dec-16
Dec-22
Dec-30
Jan-06
Jan-13
Jan-20
Jan-27
Feb-03
Feb-10
Feb-17
Feb-24
Source: CME
March 2004
45
Nasdaq
Nikkei
Nasdaq
Spot
Nikkei
6,000
Spot
2200
11200
2,000
4,000
2150
1,500
11000
2,000
1,000
2100
0
500
10800
2050
-2,000
0
Dec-16
2000
-4,000
Dec-22
Dec-30
Jan-06
Jan-13
Jan-20
Jan-27
Feb-03
Feb-10
Feb-17
Feb-24
10600
-500
-1,000
-6,000
1950
10400
-1,500
-8,000
1900
10200
-2,000
-10,000
1850
-12,000
-2,500
10000
-14,000
1800
Dec-16
Dec-22
Dec-30
Jan-06
Jan-13
Jan-20
Jan-27
Feb-03
Feb-10
Feb-17
-3,000
Feb-24
9800
-3,500
Source: CME
Source: CME
Gold
US dollar index
Gold
US dollar index
Spot
140,000
430
Spot
-12000
115
114.5
425
120,000
-10000
114
420
113.5
100,000
415
-8000
113
80,000
410
112.5
-6000
405
60,000
112
400
-4000
111.5
40,000
395
111
-2000
20,000
110.5
390
0
385
Dec-16
Dec-22
Dec-30
Jan-06
Jan-13
Jan-20
Jan-27
Feb-03
Feb-10
Feb-17
Feb-24
Source: CEI
46
0
110
Dec-16
Dec-22
Source: NYBOT
March 2004
Dec-30
Jan-06
Jan-13
Jan-20
Jan-27
Feb-03
Feb-10
Feb-17
Feb-24
Non-commercial
Feb 3
Feb 10
Feb 17
Feb 24
10yr Treasury
-49,823
-53,187
-12,128
52,831
5yr Treasury
176,465
211,342
230,387
250,599
DJIA
-1,385
-1,837
-2,689
-4,848
Swiss franc
9443
11,003
11,479
5,374
Pound Sterling
20,893
19,906
20,159
21,076
Japanese yen
64,499
58,630
55,198
-12,181
Euro
20,446
26,242
28,409
26,367
3m eurodollar
174,972
45,682
147,772
194,247
Nasdaq
2,663
-855
-3,011
-11,892
Nikkei
-420
-617
-1,604
-2,884
Gold
67,564
60,286
70,305
63,058
US$ index
-7,259
-6,641
-9,617
-6,137
Feb 3
Feb 10
Feb 17
Feb 24
10yr Treasury
166,521
157,614
122,121
69,476
5yr Treasury
-123,236
-155,529
-172,435
-200,139
8,146
9,790
11,544
13,258
Swiss franc
-13,696
-17,364
-20,434
-10,109
Pound Sterling
-34,669
-36,627
-33,868
-35,363
Japanese yen
-85,882
-75,786
-75,498
4,417
Euro
-36,721
-45,368
-48,060
-39,744
3m eurodollar
-85,788
42,544
-31,505
-69,723
Nasdaq
2,159
3,967
5,719
6,814
Nikkei
-419
-262
110
1,124
-109,445
-106,391
-119,597
-104,499
8,435
8,591
12,336
7,318
Commercial
DJIA
Gold
US$ index
Charts and tables: Open interest (futures only)
All data provided by the Commodity Futures Trading Commission (CFTC) with permission
March 2004
47
TRAINING AND EVENTS DIARY
23/24 March
23/24 March
24 March
Course:
Technical analysis & charting
Organiser:
ChartWatch
Contact:
Market Directional Analysis
Tel: 020 7723 0684
Course:
Advanced technical analysis
Organiser:
ChartWatch
Contact:
Tel: 020 7723 0684
Course:
Introduction to technical analysis
Organiser:
Quorum Training
Contact:
[email protected]
6 April
17 April
22 April
Course:
STA revision day
Organiser:
Society of Technical Analysts
Contact:
[email protected]
Course:
One day technical analysis
Organiser:
The Technical Analysis Workshop Co.
Contact:
[email protected]
Course:
Alpesh Patel Live! secrets of
an active trader
Organiser:
The Information Exchange
Contact:
[email protected]
4 June
22/23 June
23 June
Course:
Organiser:
7City
Contact:
[email protected]
Course:
An introduction to charting and
technical analysis
Organiser:
IPE
Contact:
[email protected]
Course:
Advanced technical analysis
Organiser:
The Oxford Princeton Programme
Contact:
[email protected]
19 July
23 August
Late October
Course:
Organiser:
Quorum Training
Contact:
[email protected]
Course:
Organiser:
7City
Contact:
[email protected]
Course:
Technical analysis & charting
Organiser:
ChartWatch
Contact:
Tel: 020 7723 0684
Late October
10 November
15 November
Course:
Advanced t echnical analysis
Organiser:
ChartWatch
Contact:
Tel: 020 7723 0684
Course:
Organiser:
7City
Contact:
[email protected]
Course:
Organiser:
Quorum Training
Contact:
[email protected]
For training and events diary submissions please email us at: [email protected]
All venues are in London
48
March 2004

Issue 2 - The Technical Analyst

Transcription

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