market technician no 54

The Society’s Dinner held at the National Liberal Club was rated a greatsuccess. Many of the attendees said on the night how much they hadenjoyed it and we have subsequently had a number of emails saying thesame thing. After pre-dinner drinks in the library, dinner was served in theVictorian splendour of the David Lloyd George Room. Before introducingthe after-dinner speaker, the Society’s Chairman, Adam Sorab, said a fewwords about the importance of technical analysis in deciphering the ficklemarkets of 2005. He also thanked the membership of the STA for theircontinued support throughout the year and the many people who workbehind the scenes to ensure the STA’s success. The guest speaker of theevening was Martin Mallett, Chief Dealer of the Bank of England. He wasquick to admit that he was a supporter – though not a practitioner – oftechnical analysis and acknowledged its usefulness. The Bank studiestechnical conditions and also, where appropriate, employs a technicalapproach to enhance its trading activities. During his address Martinspoke broadly about his role as a Central Banker, peppering his talk withvery amusing asides. He also addressed some serious issues regarding thecurrency markets in general and liquidity conditions in particular.

Inspired by Clive Lambert, we then played a variation of the game 'Standup, sit down', to decide a charity to which the Society should give £500.The eventual winner was Neil Smith of RBS, who nominated CancerResearch to receive the money. After the dinner the night owls adjournedto the Club bar and other nearby watering holes. The general view wasthat it had been a good evening, allowing members to get to know eachother and exchange market views in a very relaxed atmosphere.

The Society will be participating in two more technical analysis events. Wewill be taking a stand at the Investors Chronicle's IX Investor Conferenceat the London Olympia Conference Centre on 21 and 22nd October. A fewdays later on 25th October, Robin Griffiths and David Sneddon will bespeaking at The Technical Analyst’s seminar, ‘Technical Analysis in theCommodity and FX Markets’ (for further details seewww.technicalanalyst.co.uk ).

The Canadian Society of Technical Analysts (CSTA) will be hosting the 18thAnnual IFTA Conference in Vancouver. The theme of the conference is‘Digging for Gold’, and the events programme includes a Gold Rush GalaDinner! The CSTA have put together a distinguished panel of speakers andfor those unable to attend, there will be a summary of the proceedings inthe next issue of the Journal.

Led by the fashion industry, autumn is the time of the year for makeovers

and David Watts has decided that it is time to give our website anoverhaul. He is looking for someone with website experience to assist inthis exercise and so if any member feels they can help or knows someonewho can, could they please contact David ([email protected]).

MONTHLY MEETING DATESFOR 2006

11 January

8 February

15 March

12 April

10 May

14 June

5 July

13 September

11 October

8 November

6 December

IN THIS ISSUE

D. Watts Bytes and Pieces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

D. Linton Ichimoku Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

W-K Wong and L. ChanLucky 13?– Does the Singapore Equities

market move in 13-year cycles? . . . . . . . . . . . . . . 4

T. Plummer A theoretical basis of technical analysis . . . . . 6

COPY DEADLINE FOR THE NEXT ISSUE 31st January 2006

PUBLICATION OF THE NEXT ISSUE March 2006

FOR YOUR DIARY

Wednesday, 9th November (AGM) David SneddonDirector, Fixed Income Research

Technical Analysis, CSFB

Wednesday, 6th December Christmas Party

N.B. Unless otherwise stated, the monthly meetings will take

place at the Institute of Marine Engineering, Science and

Technology, 80 Coleman Street, London EC2 at 6.00 p.m.

October 2005 The Journal of the STAIssue No. 54 www.sta-uk.org

TECHNICIANMARKET

MARKET TECHNICIAN Issue 54 – October 20052

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NetworkingWHO TO CONTACT ON YOUR COMMITTEE

Bytes and Pieces

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David Watts

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Issue 54 – October 2005 MARKET TECHNICIAN 3

A cursory observation of the Singapore equities market based on the SESAll-Share index suggests that the market possibly moves in 13-year cycleswith the first cycle being between January 1975 to December 1987 andthe second cycle being between January 1988 to December 2000. SeeFigure 1.

Figure 1: 13-year cycle pattern for the SES All-Share Index

The 13-year cycle is evident when the two 13-year cycles are juxtaposedas seen in Figure 2. The interesting observation is that the two 13-yearcycles are almost mirror images of each other over their own 13-yearperiod, particularly from the 46th month onwards to the end of the cycle.

Figure 2: Juxtaposistion of the two 13-year cycles

Based on the anecdotal evidence regarding the previous two 13-yearcycles, it appears that the Singapore equities market could be in a third13-year cycle which began in January 2001. The SES All-Share index hasbeen chosen as the benchmark indicator over the ST index as the ST indexhas been subjected to changes in component stocks over the years and,as such, may not be representative of the overall market direction.Moreover, the ST Index also consists mainly of blue chips while smallcapitalisation stock are not included, further limiting its use here as anoverall market indicator.

Double Vision?

From a purely technical analysis viewpoint, arbitrary market movementpatterns from January 2001 to December 2004 lend credence to our viewthat the Singapore equities market is possibly on the third 13-year cyclesince the market is currently displaying similar arbitrary movementpatterns over the first 45 months to those observed in the first two 13-year cycles as can be seen in Figure 3.

On the assumption that the market is currently experiencing a third 13-year cycle, volatile range-bound trading is expected to dominate overthe next 12 months on the basis that the market was trapped in asideways band during the 46th month to 57th month of the first two 13-year cycles.

Figure 3: First 45 months market movement patterns for the three 13-year cycles

Analysis of the Cycle

There are few techniques which enable us to turn cycle analysis intopractical trading strategies. The most widely used method in the analysisof cyclical patterns of data is Spectrum Analysis (see box on next page). Aclassical example of applying Spectrum Analysis in cycle theory is thestudy of sunspot activity; in which a cycle with a periodicity of around 11years appears regularly. So assuming sunspot activity reaches its peak thisyear, one may expect sunspot activity will peak eleven years later.

If cycle theory works well in the stock market, investors should be able togenerate significant profits. Spectrum Analysis allows investors not only todetermine the period of cycles but also their magnitudes.

Support from Spectrum Analysis

As shown in Figure 4, the Singapore stock market experiences a number ofcyclical patterns including cycles of 143 weeks (2.7 years) and 350 weeks(6.7 years). A 13-year period is approximately double 6.7 years and henceour spectrum analysis results suggest that the 13-year cycle may consist oftwo 6.7 year cycles and hence within the 13-year cycle period, there maybe two peaks and two troughs. If the above analysis is correct, Figure 2would suggest we are at the end of the first quarter of the cycle in whichthere is not much movement in the Singapore stock market. Againworking from Figure 2, we would expect the stock market to peak at theend of the second quarter of the cycle in three years time and then to fallin the third phase which lasts longer than other phases due to theasymmetric property of the cycle theory. But it should rally to a higherpeak in the final fourth phase of the cycle which should occur nine yearsfrom now.

Figure 4: Spectrum for Singapore Straits Times Index from 1973 to2004

Lucky 13?Does the Singapore equities market move in 13-year cycles? By Wing-Keung Wong and Lanz Chan

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Lessons from the US stock market

Since the Singapore index yields insufficient data points (only 30 years) togive a statistically significant test for a 13-year period we have looked at theUS stock market as an example of how the Singapore stock market mightbehave. Stock indices for the US market go sufficiently far back (40 years) toprovide some useful conclusions about shorter term cycles for this market.From figures 5 and 6, it can be seen that the cyclical patterns in the US stockmarket have changed dramatically over time. In the first period (1965-1984)there are cycles of around 200 weeks (3.8 years, about 4 years) and 55 weeks(about 1-year) while they are around 115 weeks (2.2 years) and 70 weeks(1.3 year) for the second period (1985-2004).

Figure 5: Spectrum for US Stock Market from 1965 to 1984

Figure 6: Spectrum for US Stock Market from 1985 to 2004

Are Cycles a Useful Investment Tool?

Like many other time series data, a stock index can be anatomized intofour parts – trend, cycles, seasonal components and error term. In the timeseries plot of the weekly S&P 500 as shown in figure 7, the most significantcomponent is the trend from 1965 to 2000. In this situation, theknowledge of cycles may not play an important role in generating profit.Investors could simply adopt the buy-and-hold strategy – buying in 1985and holding until 2000 would have generated a better profit than tryingto trade cycles within the trend. Thus, at certain times cycles become lessimportant in the investment decision.

FIgure 7: Plot of S&P 500 stock index from 1965 to 2004

However, for the Singapore stock market over the period 1975 to 2004 asshown in Figure 8, there is a much stronger cyclical element to themarket. So for this market, investors who had been able to predictprecisely short-term cyclical patterns would have been able to enhancetheir returns significantly. In practice, cyclical patterns in a stock marketcan change over time as has happened in the US stock market. Also itwould seem that the cycles in the SIngapore stock market are formed bythe combination of many ‘seed’ cycles. This will make the Singaporemarket more volatile with a higher number of smaller peaks and troughs,which in turn makes it harder for investors to time accurately the troughsand the peaks of the cycles and consequently when to buy and sell stocks.

Figure 8: Plot of STI from 1973 to 2004

Discussion

Within the Singapore stock market there would appear to be a natural 13-year cycle. The market is currently in a bull phase of that cycle and, basedon the experience of the previous two 13-year cycles, it should extendconsiderably higher. However, as can be seen from the experience of theUS, shorter term man-made cycles – such as the 4-year US Presidentialcycle – can disrupt these longer term patterns. Furthermore particularlystrong trends can swamp cyclical activity.

Professor Wong is an Associate Professor in the Department of Economics,National University of Singapore. He obtained his B.Sc. from ChineseUniversity of Hong Kong and M.Sc. from the University of Wisconsin-MadisonU.S.A. In 1989, he completed his Ph.D at the Wisconsin-Madison, majoring inBusiness and Statistics.

Lanz Chan works with UBS Wealth Management serving Greater Chinamarkets. He is the Managing Advisor for the Financial Economics Associationand the President and Faculty Advisor for the Financial ManagementAssociation (Singapore Chapter).

Spectral Analysis

The spectral analysis includes both periodogram and spectral density analyses.

Periodogram analysis assumes that a time series, Yt, can be expressed as a

linear combination of sinusoidal waves. The period and amplitude of these

cycles are determined through Fourier analysis. The basic equation of the

sinusoid may be written as follows:

Yt = R cos (�t + ø) + �, = A cos �t + B sin �t + �

where R is the amplitude; � is called the angular frequency (in radians per

unit time); � is the phase and �, is a random distributed error. We note that A= R cos ø and B = –R sin ø.. The principle of least squares is to minimize the

following equation:

n–1

T (A, B) = ∑ (Y, – A cos �t – B sin �t)2

t=0

and obtain the estimates for A, B and R.

The periodogram plots the sums of squares from each regression model

against the frequencies �. That is, m (m = n/2 if n is even and m = (n-1)/2 if n

is odd) regressions are fitted on the data, and the sums of squares for each

regression model are calculated and plotted. These sums of squares are

called periodogram ordinates, which can be interpreted as the amount of

variation in Y at each frequency. The regression sums of squares for each

model are proportional to the sum of the squared sine and cosine

coefficients for each model, which can be written as n /2(A2 + B2).

If there is a significant sinusoidal component at a given frequency, then :

(1) the coefficients A or B, or both, will be large;

(2) the regression sums of squares will be large; and

(3) the periodogram will have a large ordinate at the given frequency.

If there is no significant sinusoidal component in the data, then the

periodogram will not have any large ordinates at any frequency, which

means the data is simply white noise and no cycle has occurred.

If time series changes contain any periodicity, their spectral density estimate

will display a dominant peak at the corresponding frequency and a

concentration in the neighborhood of the peak. In practice, the Tukey and

the Tukey-Hamming windows or the triangular weighting sequence are

usually used to smooth the periodogram using weights spectral window.

Readers may refer to Granger (1964), Priestley (1981) and Shumway and

Stoffer (2000) for more information on the spectral analysis.


The Ichimoku kinkou-hyou analysis technique has attracted more andmore attention among leading technical analysts recently yet there isremarkably little written about the subject. Try a search on the internetand you will see this for yourself. The reason for this is that it is a relativelynew as a technique, having been developed in the late sixties in Japan.

A number of definitions have surfaced including the translation ‘one look’or ‘equilibrium of the price at a glance.’ Ichimoku analyses the mid-pointsof historical highs and lows to create a ‘cloud’ that is projected forward.This cloud allows for wider support and resistance zones and decreasesthe risk of trading false breakouts. The charts can be used in a similar wayto moving averages. It is essentially a trend-following technique. Once youget the hang of reading these charts, they convey a great deal ofinformation on trend existence and direction along with support andresistance areas, all at ‘a glance.’

The five main elements of the chart construction are:

Line 1 – The ‘mid-point’ of the previous 9 sessions

Line 2 – The ‘mid-point’ of the previous 26 sessions

Cloud Span 1 - The ‘mid-point’ of the last 52 sessions offset 26 barsforward

Cloud Span 2 - The ‘mid-point’ of Lines 1 and 2 offset 26 bars forward

Lagging Span – The price line shifted back 26 bars

Lines 1 and 2, called the ‘turning’ and ‘standard’, can be treated and read inmuch the same way as moving averages and give an indication of turningpoints from extremes in the price. The cloud span is a support orresistance area depending on where the price is in relation to the cloud.Think of the cloud as being created by two averages 52 and 17 (though 17is not the true calculation – it’s the average of the 9 & 26 mid points) bothoffset 26 bars forward. The area between these cloud boundaries isshaded (normaly blue and red) as they snake across each other. Thelagging span is the final, and often overlooked, ingredient that gives thelast confirming signal as we will explain.

The chart shows varying degrees of bullishness and bearishness in thefollowing order:

Price above/below turning line

Turning line above/below standard line

Price above/below cloud

Turning and standard lines above/below cloud

Lagging line above/below cloud

The full bullish position occurs when price is above the turning line, theturning line is above the standard line, and all are above the cloudincluding the lagging line. A full bearish position is the inverse of this. Inbetween full bullish and full bearish, there are partial conditions whereperhaps the price has fallen below the turning line, or the turning line hascrossed below the standard line. These are short-term indicationsprovided they all stay above the cloud. Falling through the cloud turnsthe chart from a bullish nature to a bearish nature.

Swapping between daily, weekly and monthly time frames will often givea clearer picture than drilling down to all the conditions. Generally, weeklyIchimoku charts give the clearest signals. The monthly charts provide avery big picture while daily charts are normally too sensitive. Of thevarious conditions that need to be satisfied to trigger a signal, the mostimportant is the price crossing the cloud.

As you use Ichimoku charts more and more you will begin to notice justhow clearly the price line can interact with the cloud. Touches on theouter edge or the inner edge are very common.

Cloud thickness and steepness is a characteristic that is rarely discussed,but just thinking about the construction we can make some observations.One of the great advantages of Ichimoku is it allows for non-linear trend. Ifthe price accelerates, the cloud follows the price. We can see this from thecurve up in the cloud in the late 1990s on the FTSE-100 (chart 2 below)when there was lots of indecision at the top with a thin cloud (this is quiteunusual). Where the cloud is thin, the market is normally moving quicklysuch that the new highs (uptrend) or lows (downtrend) are big enough tomove the mid points on both time horizons (52 and 17) Thin clouds meanacceleration. Thick clouds mean the short term is accelerating but not the52 period. This is similar to the price action before and after a movingaverage cross – a decisive cut of short term through longer term.Therefore wide clouds can be taken to mean periods of uncertainty andpotential turning points. Now for instance on FTSE-100 we may be at aturning point - ‘maybe’. Look for the cloud widening (more uncertainty –more likely a turn) or narrowing (less uncertainty – less likely a turn).

There are four main advantages to Ichimoku:

• They gives Resistance Areas, less whipsaws

• The trend position is clear – Bull or Bear

• Switching between daily, weekly and monthly charts works well

• Cloud area is projected into the future

The 5th line is the lagging Line (or span) and is perhaps the mostinteresting of all. It is the final condition that is met in a transition frombearish to bullish trend or vice versa. This was the big thing to come outof Rick Besignor’s Presentation at IFTA 2004 in Madrid. It is the true signalfor a complete cloud cross. Because it is shifted back 26 bars, it tends togive signals at the thinner parts of the cloud so the move occurs quickly.

Ichimoku chartsThis article is a summary of the presentation made to the Society’s monthly meeting in May By David Linton


This can sometimes be at a point where the price is crossing but usually itwill cross the cloud after the price. Normally the price crossing does pullthe lagging line through, but not always. One tell-tale signal that thelagging line will cross is the price testing the cloud from the other side i.e.after a cross up, we start to see support on the cloud. Conversely, islandreversals often occur with the island outside the cloud. This appears to beparticularly true on Japanese stocks.

The cloud’s projection forward enables you to make quite bold futurepredictions. Below is a monthly Ichimoku chart of the S&P 500 Index(chart 3) with all five lines shown. First, notice how the lagging line foundsupport in the cloud. In the scheme of the long term picture, the USmarket made a big correction within the trend. We can now see that theS&P needs to be above 1200 points at the end of 2005 to be back out ofthe cloud. The standard line has some work to do too. But the price isalready through the cloud and building a base on top of it.

Rather than rely on one particular time frame in isolation, I find looking atthe monthly (top right) weekly (bottom left) and daily (bottom right) givesthe long, medium and short term picture at a glance. For instance, on themonthly chart (see chart 4) the sterling/dollar rate is still in long termbullish territory above the mid$1.60s. It is just turning bearish on theweekly charts with the lagging line falling through the high $1.70s Thedaily chart has been fully bearish ever since the fall through $1.88 in April.

Ichimoku is not particularly effective in picking up intra-day moves unlessthere is a very clear trend. The big moves tend to occur on the side of thecloud you would expect when an instrument is trending on intra-day datai.e. big bullish moves occur above the cloud, bearish below. Congestionareas on these time frames give too much cloud interaction to provideclear signals.

Another big advantage of Ichimoku is that cloud crosses are easy to scanfor. Typically, a scan of the Eurotop 300 will produce only a handful of fullcloud crosses each month. So you don’t get too many signals just a niceshort list of stocks to investigate further. In scrolling instrument lists usingseveral different types of charts, I find Ichimoku are the quickest to read,‘at a glance.’

Using Ichimoku to screen tables for bullish and bearish criteria is againvery quick. Monthly, weekly and daily charts give long, medium and shortterm timeframes and you can enter Bullish or Bearish under each of theseperiods. You can take this further and create your own Ichimoku MarketBreadth. In May 2005, for example, the number of stocks that were bullishon all three time frames versus bearish was 3 to 1 for the FTSE 100. For theS&P 500 constituents it was 2 to 1.

One of the most common questions I am asked is, ‘Have you back-testedit?’ Having run tests on weekly charts, we have found if you take signals inline with the monthly chart the results seem to be consistently good i.e.with a monthly bullish Ichimoku chart only take the weekly bullish cloudcrosses for entry.

Finally, if the chart isn’t giving a clear signal, don’t use it. The market ideallyneeds to be trending up or down. Ichimoku does not work well in longterm sideways trends. I would also suggest not relying solely on Ichimokusignals. Use it in combination with other techniques or as a tool to findshortlists. Look at all three time frames simultaneously and you will seethe trends within trends. But most importance should be attached to theweekly charts which provide a different perspective from looking at ‘noisy’daily line charts. Some people will find it a hard to accept you are usingthese weird and wonderful charts to generate trading signals so it may bea good idea not to tell anyone!

David Linton is founder and Chief Executive of Updata plc. For furtherinformation see www.updata.co.uk


IntroductionI was recently asked to defend technical analysis to a UKSIP audience in aforum entitled “Technical Analysis: Astrology or Informed Analysis?” Thiswas a challenge on a number of levels. First, it is not my view that the useof planetary alignments or planetary cycles to strengthen entry and exitrules implies ‘uninformed analysis’. On the contrary: there are manyimportant books that suggest that life on earth is directly influenced bycosmic forces (Gaugelin, 1969; Lieber, 1979; Eysenck, 1982). In fact, since thegiant planets (Jupiter, Saturn, Uranus and Neptune) usually pull the centreof mass in our solar system outside of the physical body of the sun(Landscheidt, 1989), it would be amazing if the influences were not actuallyquite significant. Furthermore, many successful traders – including W.D.Gann – have explicitly used astrology to finesse their timing.

Second, there is the question of the degree to which astrology andtechnical analysis can actually be categorised together. Many technicalanalysts would argue that, even if astrology is a valid approach to markets,technical analysis is still a distinct and separate discipline because it usesdifferent analytical tools. And this, in a way, points to the real challengeimplicit in the forum’s title: is technical analysis just as a set of tools thatsometimes work but sometimes don’t? Or does technical analysis actuallyhave a genuine theoretical underpinning that justifies the use of certainanalytical tools?

What follows is part of my attempt to meet UKSIP’s challenge. However,there are two important qualifications. First, it only covers thephenomenon of price patterns, it does not cover the extraordinaryinfluence of the Golden Ratio. Second, the analysis is offered only as asmall part of an evolving process rather than as an unassailable body oftheory.

Technical analysis and economic theoryThe most fundamental definition of technical analysis is that it is the studyof past movements in asset prices in order to forecast future pricemovements in those prices. However, since past price movements are theoutcome of various forms of investor activity, most analysts would alsounderstand that the study of past price movements also includes – wherepossible – the study of various indicators relating to the supply of, anddemand for, the assets in question. This allows us to include not onlyinformation – such as volumes, open interest, and put/call ratios – that canbe derived directly from the various bourses, but also information – suchas opinion surveys and cash holdings in funds – that can be gleaned fromexternal sources. I generally use this broader definition, but want to makeclear that excluding by definition does not mean excluding despite value.After all, if a specific indicator helps to make money, why exclude it?

The basic idea underlying technical analysis is that human nature has aconsistency to it. This encourages certain general market phenomena, andcertain specific price patterns, to reproduce themselves through time.Accordingly it is considered appropriate to formulate hypotheses aboutmarket behaviour on the basis of historical data and to use thesehypotheses to anticipate the future. This process reproduces any otherform of scientific enquiry.

The problem, however, is that the assumptions about human behaviourused by technical analysts are profoundly different from the assumptionsused by economic theorists. Technical analysts assume (usually implicitly)that the market behaves as a coherent whole. Economic analysts assume(very explicitly) that total market behaviour is no more than the arithmeticsum of random decisions by individuals.

This difference in assumptions could not be better designed to fostermutual hostility between the disciplines. Technical analysts are accused ofrelying on some form of ‘hocus pocus’; economists are berated for ‘livingin ivory towers’. The result is that technical analysts tend to ignore theinfluence of fundamental analysis on trends and that economists tend toignore the power of technical analysis to forecast turning points. The truthis that both disciplines could usefully learn from one another. However,there is a need first to establish a common ground for understanding thephenomenon of financial markets.

The foundations of technical analysisIn any market, prices fluctuate up and down. The implicit assumption oftechnical analysis is that these fluctuations are not random. The explicit

claim of technical analysis, therefore, is that non-random fluctuationscreate patterns that repeat themselves. It is the repetition of patterns thatallows us to forecast – or, better, to anticipate – price movements. So thereare a number of questions that need to be answered:

(1) Are price movements random or non-random?(2) If price movements are non-random, what patterns can we

expect to emerge?(3) If patterned price movements are present, how quickly can we

decide which pattern is evolving?

Random or non-random behaviour

So much has been written on the subject of the randomness or otherwiseof price movements in financial markets that it seems dangerous even toattempt to address the issue. However, using a methodology suggestedby the work of Baumol and Benhabib (1989), it is possible to look at thesubject pragmatically, without going into a detailed discussion ofstatistical theory.

Figure 1 plots each day’s percentage change (ie, at time t) in the Dowagainst the previous day’s percentage change (ie, at time t-1), for everytrading day since 2nd January 1990. In other words, movements in theDow are plotted in what is called “t/t-1 phase space”. The result is exactlythe sort of pattern that can be expected if daily movements were indeedrandom – that is, they are scattered widely throughout the phase space.Nevertheless, what is also relevant is that the movements are actuallycontained in a very limited area of that phase space. So, although the pricemovements appear random, they are in some sense contained. Thisbecomes even more apparent if the scales on the chart are (say)quadrupled. See Figure 2. It is clear that price changes tend not to moveout beyond a very specific two-dimensional region. Should they do so,they tend to get pulled back in again towards the centre of that region.

Figure 1: Dow in t/t-1 phase space

Figure 2: Dow in expanded phase space

The operation of some sort of gravitational pull is particularly noticeable ifthe chart is extended backwards in time to (say) January 1946. Bydefinition, the data set is a long one, not only covering an importantperiod of economic and social evolution, but also including the 1987equity crash. See Figure 3. Importantly, the basic region of attraction forthe market remains unchanged, and the experience of 18th–20th October1987 stands out as an idiosyncrasy. It is arguable that, in one sense, the1987 crash was the truly random event.

A theoretical basis of technical analysisBy Tony Plummer

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DJ Industrial Average:Jan 1990 to Date

(Daily close, t/t-1)Data source: Yahoo.com

1-day percentage changes

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The point here is that the idea of randomness obviously has something todo with perspective: the longer the time perspective being taken (or, ifyou like, the broader the context), the less likely it is that fluctuations willseem random. Anyone who has traded in financial markets for any lengthof time will know when a price movement is ‘unusual’, based on his or herexperience.

Figure 3: Extended Dow data in t/t-1 phase space

Strange attractors

We can take this argument a stage further and look at the price changesover periods that are longer than one day. Figure 4 shows an example ofthe 5-day percentage changes in the Dow, in t/t-1 phase space. Here, the5-day percentage change for a particular day is compared with the 5-daypercentage change the previous day. What is clear is that the formerlycircular ‘bubble’ containing price movements begins to spread out alongan upward-sloping diagonal line. And this becomes even clearer forlonger time periods. Figure 5 shows 20-day percentage changes in theDow, in t/t-1 phase space. Oscillations in the Dow seem to be drawntowards the upward-sloping diagonal, where the rate of change at time tis equal to the rate of change at time t-1. Moreover, even though there areobviously unusual events, such as the 1987 equity crash, the oscillationsare essentially bounded on the upside and downside. In the language of‘chaos theory’, a strange attractor seems to be at work.

It seems that, over longer time periods, changes in the Dow tend towardsa stable path of change; but the changes are also persistently induced toaccelerate and decelerate along this path. We can hypothesise thatsomething encourages investors to chase the market up until some formof upper boundary is reached, and to chase the market down until someform of lower boundary is reached. This, in itself, is highly suggestive of adeep running ordered process at work. A related question is whether theacceleration/deceleration itself has a cyclical element of some sort. If itdid, this would strengthen the case for non-random market behaviour.

Figure 4: 5-day changes in the Dow

Figure 5: 20-day changes in the Dow

Groups and crowdsBefore we look more closely at the possibility of cyclical influences, weneed to consider the basis for an ordered process in human behaviour.According to economic theorists, individuals make their decisionsindependently of one another. Group behaviour is then considered to beforecastable because, according to probability theory, a large number ofuncertainties somewhat spookily create a certainty. As James Surowieckihas recently demonstrated in The Wisdom of Crowds (Surowiecki, 2005),this is not only absolutely correct, but can yield answers and decisionsthat are amazingly accurate.

The problem, however, is that once individuals start to be influenced byeach other’s behaviour, then their expression of individuality is muchreduced. Ultimately, deviation from some measure of average behaviourbecomes minimal. This presents a problem for theoreticians because,although basic probability theory breaks down, it is highly likely that alarge number of people doing the same thing will produce a forecastableoutcome.

This, in a way, was one of the central findings of the late 19th centuryFrench sociologist, Gustave le Bon, whose now-famous book, The Crowd,analysed the French Revolution (le Bon, 1895). Le Bon observed that, whenpeople came together in a common cause, the result was somethingdifferent to just the sum of the parts. People behaved differently to theway that they would as individuals: they would focus on, and follow, thedictates of a recognised leader; they would act to protect their beliefs; andthey would quickly see ‘non-believers’ as enemies. In short, the act ofpeople coming together under a unifying belief system would fosterconflict.

The truth behind le Bon’s assertions is only too clear in the bloodiedhistory of the twentieth century. There is, however, an important aspect ofhis analysis that is easily overlooked. This is that le Bon saw crowds, orgroups, as psychological phenomena whereby people behave in a unifiedway once they adopt a shared set of beliefs. This is because belief systems– however unlikely and unreasonable they may seem – mobilise powerfulinner emotions. Hence, crowds are held together, and energised by,emotions rather than by cold logic. Moreover – and this is important – theself-awareness of participating individuals is reduced (Neumann, 1990).The psyche is “invaded” by the values of the collective. Consequently, theability of individuals to make rational choices, evoke moral judgementand engage in active reality testing is suppressed. This, of course, wouldhelp to explain why outsiders have such difficulty in understanding andinterpreting group behaviour. Importantly, though, it might also help toexplain why stock markets bubble and crash, and why economies boomand slump. Somehow, emotionally laden beliefs are increasinglydistributed throughout a market place and throughout an economy.

Co-operationThe suggestion here is that warfare and violence may actually be aspecific outcome of the more general tendency towards co-operationamongst human beings. Indeed, this suggestion becomes seriouslycompelling once account is taken of contemporary ideas relating systemstheory, group psychology, and natural evolution. Nature organises itselfhierarchically into ever-greater wholes: lower-order parts contribute tohigher-order wholes, and the wholes organise the parts. As a result, eachwhole is qualitatively different to the mere summation of the parts. Inhuman beings, this organisational force finds expression in the need tomerge psychologically into a group. At one level this can be explained asthe need to reduce the sense of personal isolation. At another level it canbe explained as the need to have a sense of purpose. Either way, theoutcome is ‘natural’ in the genuine sense of that word.

However, the inner need to merge into greater wholes takes on a differentimperative when competition or conflict is involved. The associated threatto each individual’s psychological security is reduced when others areinvolved in meeting that threat (Trotter, 1947). Quite obviously, the greaterthe threat, the more urgent it is that each individual’s resources aredirected towards meeting the purposes of the whole. A competitiveenvironment therefore demands conformity from individuals (Bloom,2000). Non-conformity is punished by exclusion from the group:individuals are left to meet the threat alone.

The point is that, once account is taken of the inner dimension of humanexistence, the need to do things together becomes a viable explanationfor a part of human behaviour. Evolution can then be seen in terms notjust of random mutation and survival of the fittest individuals but also interms of the perpetuation of the most adaptive groups in the face ofexternal threats. Nature is, ultimately, a collaborative enterprise.

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Conformity enforcementImportantly, the idea that group beliefs can induce conformity has found agreat deal of support from independent researchers. In the 1950s, StanleyMilgram was able to show that more than 60 per cent of ordinary peoplecould be induced to deliver massive electric shocks to apparent patientsjust by being told by an authority figure in a white coat that it was alrightto do so. Fortunately, the ‘patients’ were actors and no electric shock wasactually involved. In the late 1970s, Ed Diener at the University of Illinoispublished evidence that, in a group setting, people strongly identified withother group members, had little sense of personal identity and tended toact without prior thought. Other evidence – notably from Bristol Universityin the UK and from Harvard in the US – showed that people’s perception ofnon-group members and, indeed, of reality itself, could all too easily beinfluenced by pressure from other group members. In the psychologicalarena, therefore, a group can be less than the sum of its parts.

In recent years, the central ideas of self-organization, group conformityenforcement, and non-rational collective behaviour have been used by anumber of important developmental theoreticians to explore theprocesses of history. Foremost amongst these have been Arthur Koestler(1978), Erich Jantsh (1980), Fritjof Capra (1982), Ken Wilber (1983), andHoward Bloom (1997 and 2000).

Non-rational behaviourWhat theory and research are both pointing to is the very real possibilitythat economic and financial activities are, at heart, less rational than manymight want to believe. This does not mean that such activity is alwaysirrational just that it is essentially activated by deeply held psychologicalneeds. These needs are orientated towards obtaining security andmeaning, and are universal. Hence, non-random behaviour in economicand financial markets may be the outcome of genuine group influencesrather than just the outcome of statistical interactions. This, in turn, wouldmean that excesses (which are, in any case more easily observed after theevent than before it) could well be part of a forecastable spectrum ofbehaviour instead of the unforecastable outcome of ‘keeping up with theJoneses’ or of ‘animal spirits’.

The problem, however, is that a clearly defined body of theory that coversthis spectrum of economic and financial behaviour is not currentlyavailable within the academic community. This suggests that some formof paradigm shift is almost certainly looming as a result of the aftermathof the financial bubble of the late 1990s. After all, how long can academicscontinue to ignore the tendency of markets to diverge from, and oscillatearound, fundamental values? However, it also implies that we need to lookfor guidance outside of the current ‘rational expectations’ paradigm thatembraces economics.

Financial marketsOne way forward is to look for patterns that regularly emerge in financialmarkets. There are three reasons for this. First, larger-scale movements infinancial markets basically reflect the evolving mood that embraces alleconomic and social behaviour within a community. Second, financialmarkets provide a continuous flow of uncontaminated data. Market priceaction therefore provides a marvellous testing ground for hypothesesabout human behaviour. Third, recurring price patterns would (if found)imply recurring behaviour. In other words, the patterns could beinterpreted.

This, indeed, is the route that technical analysts have chosen to take. Theresult is a large body of industry literature confirming that (a) marketsoscillate in reasonably regular cycles, that (b) markets spend time in base,top or holding patterns before entering a significant trend, and that (c)these price patterns tend to incorporate certain predictive priceconfigurations, such as ‘head and shoulder tops’. The over-ridingimpression is that market behaviour is not random.

Bubbles and crashesA valid point of access for an analysis of market price patterns is to look atinvestor behaviour during periods of emotional extremes. Traditionaleconomic theory regards such extremes as aberrations. However, insofaras such extremes are actually part of a spectrum of behaviour, they arelikely to reveal the basic energies that drive all behaviour. Extremes ofbehaviour are often noteworthy for their clarity of purpose.

Shown in the Figure 6 are the loci of US price action in the Dow JonesIndustrial Average from January 1921 to July 1934 and in the NASDAQfrom September 1995 to September 2002. The time periods involved aredifferent: the former covers a period of seven years, the latter covers aperiod of three years. However, when the time elapse relating to the Dow

is placed on the lower horizontal axis and the time elapse relating to theNASDAQ is placed on the upper horizontal axis, something veryinteresting emerges: the patterns of the acceleration into the peak andthe subsequent collapse are very similar.

Figure 6: The Wall Street Crash and the NASDAQ Collapse

This similarity has been recognized by Didier Sornette, who is Professor ofGeophysics at UCLA. Sornette has shown how non- linear mathematicscan track and predict a stock market ‘bubble and crash’ (Sornette, 2003).There are two distinct conclusions. The first is that the price accelerationinto the final peak is curvilinear, and that the time-elapse of oscillationsaround that accelerating trend gets progressively shorter. The second isthat this specific phenomenon only works because of the impact of “co-operative self-organization”. In other words, non- linear mathematics canpredict the timing of the peak (because the oscillations become so fastthat they effectively converge on zero), but such non- linear mathematicsonly work because stock markets are ‘natural’ systems.

HomogeneityThese conclusions are a dramatic confirmation of the impact of groupbehaviour in financial markets; and it is almost no accident that they havebeen generated outside of the discipline of economic theory.Nevertheless, they need to be placed in a wider context of investmentbehaviour. Professor Sornette notes that, as a stock market bubbleaccelerates into a peak, investors take more and more notice of whatothers are doing. Hence, behaviour becomes increasingly homogenousand local information has long-distance effects. Ultimately, of course, themarket becomes satiated, or ‘overbought’, and extremely vulnerable tosmall perturbations. So only a small amount of profit taking can initiate afull-blown ‘crash’.

What Professor Sornette is, in fact, describing is a specific – andparticularly dramatic – example of a more general mechanism. This is themechanism that induces conformity from market participants andthereby produces oscillations in financial markets.

Conformity enforcement in financial marketsFinancial markets are characterized by inter- group conflict: it is a contestbetween the bulls and the bears. Seen in this light, financial markets arenot just processes that encourage prices to converge on fundamental-induced values. They are reflections of a collective movement between theopposite polarities of optimism and pessimism. Hence, prices are likely toovershoot fundamental values in both directions. One implication is thatmarket participants actually pay less attention to ‘fundamental’ values thanis usually thought. Partly, this is due to the fact that such values are verydifficult to calculate in advance. Partly, though, it is due to the competitiveprocess. So much attention is ultimately given to ensuring that oneparticular view is right (eg, prices are going up) that participants lose sightof fundamentals. Energy is spent in generating propaganda to colleagues,clients, other members of the profession and the media. The sub-consciousintention is always to ensure that financial resources continue tounderwrite one’s own view. What is missed, however, is that this is alsoexactly what others are doing. Conformity enforcement is a very subtleprocess – which is why it is usually deemed not to exist. Nevertheless, it isconformity enforcement that lies at the root of all price trends.

In modern financial markets, the pressure to conform has becomeinstitutionalised. Market professionals are consistently monitored againstpeer group performances: indices are constructed to show the sectorallocations of other funds, and deviations from that ‘norm’ are monitoredfor success or failure. For the individual fund manager it is a risk to take amarginally different view, let alone an alternative view. So, when a market

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(Weekly closes)Data sources: Economagic & Datastream

Dow Jones Industrials(l.h.scale, lower time axis)

NASDAQ(r.h.scale, upper time axis)


either begins to run ahead of perceived valuations, or even begins to‘bubble’, there is huge pressure to join in. Not to do so is a direct risk topersonal wealth and personal income.

Ultimately, however, it is this threat to personal status that provides themain pressure to conform. Individuals participate in markets for reasons ofwealth, power and prestige – in other words, to enhance control overfuture resources. To be out of the market when it is going up, or in themarket when it is going down, threatens this control and generates fear.Collective behaviour, of course, reduces fear. So, for the majority, it is easierto trade on the evidence of actual price movements, than it is to invest onthe basis of theoretical valuations (which may, in any case, be wrong).

For whatever reason, the end result is that the psychological environmentof the market becomes dominated by a limited set of uncritically heldbeliefs, known as memes. In a bullish market the meme is that prices aregoing up; in a bearish market, the meme is that prices are going down. Ameme is the glue that holds otherwise disparate individuals together ingroups and crowds.

The basic mechanismFigure 7 shows some of the basic principles involved. It demonstrates themost likely pattern that will be traced out by a broad financial market priceindex during the course of a complete bull-bear cycle. No account is takenat this stage of the time scale involved. Starting at the lower left-hand sideof the chart, a market will be very oversold, probably after some form ofcrisis. There will then be a bear squeeze of some sort as short positions arecovered. This need not entail a significant proportion of investors suddenlybecoming bullish – it just needs some investors to close bear positions.This will cause the market to jump sharply. The rise may continue for a littlewhile because investors do not all respond simultaneously. Crucially, theywill respond, not so much to ‘fundamental’ considerations, as much as tothe fact that prices are rising. In other words, price movements –particularly sharp movements – are a critical item of information.

At some early stage, however, such technical buying dries up, and themarket begins to retrace back towards the lows. This is a ‘re-test’ of thelows and occurs while those who missed the initial rally will beconsidering whether or not they now need to react. Investors will takeinto account the fact that prices have rallied but they will also necessarilyre-assess fundamentals. Some may even decide that the market hadoriginally over-discounted fundamentals or that the fundamentals mightbe shifting. This triggers another bout of buying, which eventually takesthe market out of whatever holding pattern it has been in. In other words,a trend starts to materialize. As time progresses, either new informationbecomes available that confirms that fundamentals are improving and/orthe rally in the market enters a feedback relationship with fundamentalssuch that the latter improve anyway.

Figure 7: The basic mechanism

There are a number of very important points that emerge from this simplemodel of market behaviour. First, a reversal materialises once a market hasgone too far. The market is, in some sense, satiated. Second, the reversal isan information shock to the market. It reveals that the market may be outof line with fundamentals and that the market can no longer attractsufficient investor energy to continue the old trend. There is thus an‘energy gap’. Third, the subsequent re-test of the low occurs whileinvestors absorb the implications of what has just happened. Investors‘learn’ that something has changed. Fourth, the market ‘signals’ a trendmove by breaking out of the holding pattern. Since investors have, ineffect, learnt that fundamentals have changed, all subsequent informationwill be seen in the context of that learning. Data that confirm the trendwill increasingly be acted upon; data that contradict the trend willincreasingly be ignored. Fifth, market participants will increasingly focus

their attention on price action rather than fundamentals. Finally, themarket will run ahead of fundamentals and will become overbought, orsatiated. This presents the conditions that will trigger a reversal. The wholeprocess then begins in reverse.

The role of pricesWe thus have a three-phase mechanism that accounts for all the basicbehaviour within a financial market trend, whether up or down. We alsohave a specific mechanism that accounts for market reversals.Consequently, we can hypothesize the existence of a six- wave pattern ina full market cycle – three waves up and three waves down. This is animportant conclusion. However, there are other important inferences thatneed to be drawn. The first, which has already been mentioned, is theimportant role of prices. Individuals can process complex information, buta group can only react to simple information. The most important piece ofinformation to a financial market group is the actual behaviour of prices.The response of the group will be greater, the faster and morepronounced is the change in prices. In a sense, therefore, prices will fulfilthe leadership role in a psychological group. The group will accordinglyreact to this leadership and it will chase trends. The process is dramaticallyenhanced when high profile individuals confirm their own personalcommitment to the trend.

This, of course, stands economic theory on its head. In economics a price isdetermined by the behaviour of buyers and sellers. This is true, but is onlypart of the process. When prices generate information, prices alsodetermine behaviour. So a feedback effect is involved. As the biologist andphilosopher Gregory Bateson has argued, feedback is one of thecharacteristics of any living system (Bateson, 1979).

The process of learningThis brings us to the second inference from the market model. In anysystem that is oscillating in a feedback relationship with its environment,any new information from that environment has to be assimilated andabsorbed. A process of learning is therefore involved. So it hardly seemsaccidental that the mechanism just described mirrors the process oflearning that can be found in the human brain. In the early 1960s, HenryMills found that, although people would initially be very quick at pickingup the mechanics of a new task, they would inevitably go through a stagewhere their ability to apply their new learnings would slow (Mills, 1967).Only after this slowdown could activity speed up again.

Normally this ‘slowdown’ is missed because it is only temporary.Nevertheless, it is a real phenomenon, which reflects somethingimportant. At some stage during the learning process, information istransferred from short-term memory in the forebrain to long-termmemory deeper within the brain. Learning thereby moves from theconscious into the sub-conscious (Hebb, 1949). This is both automatic andnecessary, and frees up consciousness for other tasks. However, energyhas to be diverted away from other processes in order to facilitate thisadjustment, and the ability of a person to do a conscious task actuallydeteriorates temporarily. After the adjustment, people can apply theirlearnt techniques to the new task and not even think about it very much.

Markets are exactly the same. At a top or bottom, markets will go througha process of learning that fundamentals have changed. They respond tonew information, hesitate while that information is absorbed, and thenautomatically apply the resulting learning during the thrust of a trend.

Markets as collective learning processesFinancial markets (and, with them, whole economies) can be viewed asnatural self-organizing systems that learn from their interaction with theirenvironment.They are a particular form of what Howard Bloom of New YorkUniversity calls “collective learning machines” (Bloom, 2000). As such, theyorganize their lower-order parts in a coherent fashion, oscillate rhythmically,and express themselves in terms of a limited matrix of patterns.This isessentially why the discipline of technical analysis has the power regularlyto generate effective buy and sell signals. Analysts will look at indicators ofinvestor energy in order to estimate the intensity of the market’s hold overinvestors.They know that the stronger the market’s grip, the nearer themarket is to a turning point. Analysts will also look at the periodicity ofhistorical oscillations in order to forecast the timing of likely turning pointsin the future. If a cycle has made itself felt in the past, it is likely to continueinto the future. And, finally, analysts will look at the current evolution ofprice patterns, in the knowledge that certain patterns reproducethemselves. Once the market’s position in the context of a specific pattern isknown, it is possible to estimate what might happen in the future. Quiteobviously, the most powerful signals are going to be generated when eachof the three lines of analysis coincide.

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Price cycles

Central to technical analysis procedures is the phenomenon of pricecycles. It was earlier noted that financial market oscillations might bedriven by a natural learning mechanism and that inflexion points mightbe triggered by an energy gap that arose out of investor satiation. Theimportant point here is that an energy gap reverses the polarity of themarket from bullish to bearish, or from bearish to bullish. There are,however, two important questions: First, does this mean that bear marketsare inevitable? Second, what determines the difference between a bigbear market, such as those that emerge in the form of a ‘crash’, and minorsetbacks?

In the context of financial market cycles (and, indeed, of economic cycles),it is necessary to understand that downswings are as important asupswings. Nature cannot evolve without periods of rest, because it needsto replenish its energy. Hence, any period of activity will be followed by aperiod of rest. So, despite the best attempts of governments, bull marketswill always be followed by bear markets, and economic expansions willalways be followed by recessions. What then determines the extent of adownswing? One answer, of course, is the amplitude of the upswing: thebigger the upswing, the bigger (potentially) the correction. However, italso depends on the time span of the cycle: the longer the cycle, thelonger (potentially) the correction. Technical analysis has the capability ofdetermining the difference between big moves and small ones by puttingall moves into the context of history and accepting that this history has avalid and vital role to play. Hence, for example, if there is strong evidencethat the Dow Jones Industrial Average has, for a very long time, oscillatedwith a rhythmic periodicity of about 11 years (which it has), then there isevery reason to suppose that the oscillation will continue. This will give astrong indication of when an important reversal can be expected andwhat order of magnitude that reversal might take. The primarypresumption of cycle analysis, therefore, is that this time it will definitelynot be different.

Price patterns within cyclesAn important inference is that cycles can be defined by their patterning aswell by the precision of their periodicity (Plummer, 2003). This means thatthe observed variability in cycle periodicities does not invalidate theforecasting potential of cycle analysis because the evolution of a currentcycle can be tracked in real time against the pattern of a previous cycle.An example of this is shown in Figure 8, which compares the pattern offluctuations in the Dow Jones Industrial Average between September1990 and September 2001 with the pattern of fluctuations betweenSeptember 1957 and May 1970. Both periods represent one beat of the11-year cycle in the Dow, and both periods embraced rapid change in theUS economy: 1990-01 covered the revolution in information technology,and 1957-70 covered the social revolution of the ‘Swinging Sixties’. In asense, therefore, the two periods are directly comparable. When the pricepatterns of the two periods are overlaid on one another, by the simpleexpedient of plotting each beat of the cycle on a separate time axis, aremarkable similarity emerges. This is not unusual. Once the coincidenceof patterns is found, it becomes a simple matter of tracking a new cyclebeat against an earlier comparable one and tracking that cycle beat intoits final low. Any variations in the periodicity will not matter.

Figure 8: Patterns in the Dow

This, in a sense, is where technical analysis brings such great strength tomarket analysis. It focuses directly on the patterns of market behaviour –both in terms of price movement and investor activity – because itsworking assumption is that such patterns are both non-random andmeaningful.

The fallacy of the rational individual

The small and simple shift in emphasis – from the individual to the group– creates a massive shift in our understanding of economic and financialmotivation. Economic theory cannot properly explain why a large numberof people, who are assumed to be making rational decisionsindependently of one another, end up (for example) buying red cars ortrying to move house, all at the same time.“Mood” may be regarded asbeing part of the answer; but, then, by what mechanism can a change inmood be made to swarm through a population of separate and rationalindividuals?

Economic theory also cannot properly explain why particular patternsemerge in financial markets. If individuals really do make decisionsindependently of one another, then prices should just jump about in arandom fashion. There is no mechanism for explaining why markets shouldgenerate the specific behavioural patterns that have so far been analysed.Nor is there any mechanism for explaining why specific patterns recur.

Bearing this in mind, it is now appropriate to look at some of the findingsof technical analysts regarding specific price patterns.

Derivative price patternsIf a single beat of a cycle (of whatever length) contains a simple six-wave(three-up/ three-down) pattern, then it should be possible to isolate thatpattern from whatever trend is driving the market. Or, to put the samething another way, since a cycle beat consists of six basic waves, thesewaves can only present themselves in a small number of ways when theyare subjected to a trend. Moreover, each trend will itself be part of the six-wave structure of a higher- level cycle beat. If these conclusions arecorrect, then not only are price patterns non-random and meaningful, butalso they are limited in number.

This is a significant claim. It means that technical analysis – when properlyapproached and applied – is an extremely powerful method of interactingwith financial markets. This, indeed, is what some of the leadingproponents of technical analysis in the last one hundred years haveargued. One of these analysts, whose research spanned the traumaticyears of the first half of the twentieth century, was Ralph Nelson Elliott(Elliott, 1938). Elliott’s work is usually ignored by economists for the veryreasons it is so powerful – namely, it assumes non-random, patterned,group behaviour. However, Elliott made two significant observations. First,all impulsive movements, whether up or down, consist of five waves (threein the direction of the trend, interspersed with two corrections). Second,all corrections consist of three waves (two in the direction of thecorrective trend and one contra-trend move).

Five-three patterns and investor behaviourAt first sight, Elliott’s five-three pattern appears to contradict thehypothesis of a three-three archetype. However, the two are entirelyconsistent, once (a) the role of trends is included and once (b) someallowance is made for the possibility that Elliott himself may not have hadall the answers. Shown in Figure 12 is the formation generated when arising trend is applied to an otherwise balanced three-three cycle. Thecycle itself is notated as 1-2-3 up and A-B-C down. Once a trend is applied,however, wave B (which is theoretically a ‘re-test’ wave) actually makes anew high. In other words, the basic three-three profile incorporates a five-wave movement.

Figure 12: The effects of a trend

This is a very important insight and justifies a lot of the work thattechnical analysts conduct in relation to market satiation. There are twocritical phases after a trend has developed. The first is when it is

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‘overbought’ or ‘oversold’. This is the potential point of inflexion in a cycle,and may be captured by indices such as overstretched momentum or byindicators representing panic buying or panic selling.

The second critical phase, however, is when a ‘fifth wave’ extends themarket into a new high or new low. There are two possibilities. First, thefifth wave is not, by its very nature, truly impulsive. In this case, it may notbe ‘confirmed’ by indicators of investor enthusiasm for the trend.Momentum may be weaker, volumes may be lower, and open interest inthe relevant futures markets may be falling. Second, the fifth wave may bedynamic enough to create an investor panic. In this case, investmentpositions are finally driven to satiation. It is quite obvious that new highsor lows that are either not confirmed or generate excesses (or both) couldbe followed by a sharp reversal.

Note that some capitulation is likely to occur at the end of the third wave.It can also occur at the end of the fifth wave. On rare occasions, it mayhappen at the end of the first wave. Capitulation is therefore likely tooccur between one and three times in a specific trend.

Three-wave correctionsThis particular model does not, of course, automatically generate a three-wave correction. There was, however, something missing from Elliott’soriginal exposition. Elliott was intrigued by the patterning of markets anddid not look too closely into their causes. However, the foregoingexposition places great store on the impact on investors of sharp pricemovements. Such movements are information ‘shocks’. The impact ofenergy gaps has already been discussed. These are contra-trend shocks.There is, however, another class of information shocks. These are pro-trend

shocks, caused by a higher-level trend, where prices either move into, orextend the length of, an impulse wave. The presence of a pro-trend shockis recognizable by sharp price movements, increases in trading volumes,price gaps between one day’s close and the next day’s open, and risingopen interest.

All shocks have to be absorbed by the market and therefore generate aprice retracement of some sort. The contra-trend shock produces a re-testof the high or low, the pro-trend shock generates some form ofsubsequent holding pattern. Figure 13 shows the influence of a pro-trendshock. A break out from the base pattern indicates to investors that atrend is now developing. This is an item of information to which theyrespond, and buying volumes increase. Eventually, however, satiation setsin and the market hesitates (and, in effect, waits for ‘fundamentals’ to catchup). The market then moves ahead again, reaches its climax and turnsdown into a relatively deep correction.

Figure 13: Pro-trend information shock

The combination of simple distortions to lower- level cycles and the moredramatic effect of information shocks produced by high- level cycles yieldthe basic five-three pattern observed by Elliott. Underlying this pattern,however, is the operation of a three-three cycle.

Other patterns

This analysis is very brief and does not do justice to the forces involved.Nevertheless, it already confirms two other aspects of technical analysis.First, it confirms the influence of the famous ‘head and shoulder’ patternthat so often defines a reversal either out of a market high or away from amarket low. Figure 14 below shows the head and shoulders top formationthat was implicit in Figure 13.The top of wave 3 becomes the left shoulder,the peak of wave 5 is the head, and the end of wave B is the right shoulder.A line linking the lows of waves 4 and A become the neckline and a sellsignal is generated when prices fall through that neckline.

The reason why the pattern works so effectively is that in moving into atrend, the market has probably responded to a higher- level informationshock. This means that the qualitative structure of the market has shifted –it has incorporated new information and has evolved. Therefore, when acorrection occurs at the end of the trend generated by the information,that correction necessarily comes from a ‘higher’ level. It necessarily islonger in price and time than those that preceded it. This, indeed, is whatElliott found.

Figure 14: The head and shoulders formation

Second, the analysis substantiates the presence of genuine trends inmarkets. This, alone, is a hugely important conclusion. Markets enter atrend when investors have learnt that circumstances have changed: theyare only applying ‘learnt’ behaviour. Trends basically continue untilmarkets have run too far ahead of fundamentals.

The role of technical analysis

The discipline of technical analysis has been developed only gradually, overa very long period of time. Its main driving force has been one ofprofitability rather than theoretical nicety, and this has often militatedagainst effective communication with practitioners in other areas ofresearch, such as economics. Despite its apparent lack of theoretical rigour,technical analysts have observed, catalogued and used a huge volume ofvery effective tools and predictive techniques.There are, for example, noknown price patterns that lie outside of Elliott’s classifications.

Once these tools and techniques are seen in the context of self-organizinggroups, which learn (both from their environment and from their ownbehaviour) then much of it begins to make sense. Markets evolve in anon-random fashion, according to patterned cycles. Different styles ofinvestor behaviour can be identified at each stage of these patterns, andcan therefore provide a strong clue as to where a market is in anyparticular cycle. More to the point, technical analysis has the power –sometimes predictively, but always quickly – to signal serious pricereversals. We have shifted from forecasting based on the arcane workingsof statistics to forecasting based on the extraordinary forces of Nature.

BibliographyBateson, G (1979) Mind and Nature: An Essential Unity. Wildwood House, London.

Baumol, W and Benhabib, J (1989) “Chaos: Significance, Mechanism, and EconomicApplications” in Journal of Economic Perspectives.

Bloom, H (1997) The Lucifer Principle. Grove Press, New York.

Bloom, H (2000) Global Brain: The Evolution of the Mass Mind From the Big Bang to the

21st Century. John Wiley, New York

Capra, F (1982) The Turning Point. Wildwood House, London.

Elliott, R N (1938) The Wave Principle. Elliott, New York. Reprinted in Prechter R. (ed.)(1980) The Major Works of R N Elliott. New Classics Library, New York.

Eysenck, H and Nias, D (1982) Astrology: Science or Superstition? Temple Smith,London.

Gaugelin, M (1969) The Cosmic Clocks. Paladin, London

Hebb, D (1949) The Organization of Behaviour, John Wiley, New York.

Jantsh, E (1980) The Self-Organizing Universe. Pergamon, Oxford.

Koestler, A (1978) Janus: A Summing Up. Hutchinson, London.

Le Bon, G (1895) Psychologie des Foules . Felix Alcan, Paris. Reprinted (1922) as The

Crowd. Macmillan, New York.

Lieber, A (1979) The Lunar Effect. Corgi, London.

Mills, H R (1967) Teaching and Training, Macmillan, London.

Neumann, E (1990) Depth Psychology and a New Ethic. Shambhala, New York.

Plummer, T (2003) Forecasting Financial Markets. Kogan Page, London.

Sornette, D (2003) Why Stock Markets Crash. Princeton University Press, Princeton.

Trotter, W (1947), The Instincts of the Herd in Peace and War. Ernest Benn, London

Wilber, K (1983) Up From Eden. Routledge & Kegan Paul. London.

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Neckline


Orthodox technical analysis teaches us that markets trend and thereforethe best way to profit from market movements is to identify the presenceof a trend and trade in that direction until the trend has come to an end. Asimple idea in theory, yet one which most traders find difficult toimplement. The problem with trend trading is that you are always latejoining and always late leaving the trend.

Top and bottom pickers have an almost opposite philosophy; thesetraders try to identify the peaks and troughs in the market and attempt toparticipate in the moment at which the market turns. This method oftrading is probably the hardest to implement but, if successful, can behighly profitable.

The problem with both of these methods is the difficulty we encounter inidentifying when a trend has come to an end, and when one is beginning.There are many popular methods that traders use to achieve this,including moving averages, oscillator divergences and momentumindicators. One way I have found of identifying the end of a trend is to usewhat I call the naked bar. If I'm feeling particularly daring, the naked barwill not only signal that the top or bottom is in place, but it will encourageme to trade in the opposite direction to the recent trend.

The concept behind the naked bar is that when a trend ends or begins, itshould enter a new market environment. What we are looking for in thenaked bar is a bar which has broken away from the previous trend andhas, therefore, signalled the beginning of a new trend. It can also helpidentify when a market is range bound and when it is trending.

Identifying the Naked Bar

The naked bar is simply the first bar that trades completely outside the rangeof an extreme bar.What this means is that in an uptrend, we are looking forthe bar which has made the highest high so far, and following this the nakedbar will be the first bar which trades completely below the range of thisextreme bar (fig 1).The naked bar does not need to occur immediately afterthe extreme, it can occur any number of bars after the extreme bar. In adowntrend the naked bar is the first bar that trades entirely above the rangeof the bar that has made the lowest low so far (fig 2).

Naked Bars and Congestion

In sideways moving markets the naked bar is a little trickier to identify. Inany period of sideways trading there will be an extreme high bar and anextreme low bar, therefore the first bar that trades completely below therange of the extreme high bar will be the naked bar indicating adownside break, and the first bar trading completely above the range ofthe extreme low bar will be the naked bar indicating an upside break (fig3). Naked bars can be used to identify periods of congestion or sidewaystrends. The traditional definition of a sideways trend is any period thatdoes not have both higher highs and lows or lower highs and lows. Ifthere are no naked bars within a certain period, for example 10 days, thenthe market is in congestion (fig 4)

Trading the Naked Bar

The end of an uptrend is signalled by the appearance of a naked bar, butit is advisable to wait for a breach of the naked bar to the downside asyour exit point. This should ensure you do not give back too much profit.In a downtrend, wait for a breach of the naked bar to the upside beforeexiting the trade.

The naked bar can also be used to initiate new trades. This can be done byusing the criteria suggested above, whereby a long position is taken afterthe naked bar is identified in a downtrend and then breached to theupside. The breach can be used to enter a long trade, and the lowest lowof the downtrend would be an excellent place to put a stop loss. Theopposite can be down to initiate a short position.

Although the naked bar can be used as a stand alone pattern, I find that itis best used as a confirmation of a change in the market environment. Thishas the disadvantage of losing more on entry, but it helps avoid tradingfalse breaks.

The naked bar can be used in any timeframe for any market. One use is totrade intraday in the direction of a prevailing longer term trend. Thismethod works quite successfully since it allows for tight stop losses whilstallowing the potential to participate in major market moves.

Conclusion

Once familiar with the basic technique, naked bars can be used as a basearound which trading systems can be designed. Naked bars are veryuseful in defining trends and congestion areas and can be used to givedefinite trading signals.

Furhaan [email protected]

The Naked BarBy Furhaan Khan

Fig 1Bar A is the extreme bar for the uptrend and therefore the first bar that tradescompletely below the range of bar A will be the naked bar. Bar B is an example ofwhat an idealised naked bar would look like.

Fig 3 Bar A is the extreme upper bar for the congestion and therefore any bar thattrades completely below the range of bar A will be the naked bar (Bar C). Bar B isthe extreme lower bar for the congestion and therefore any bar that tradescompletely above the range of bar B will be the naked bar (Bar D). Bar C wouldindicate the beginning of a downtrend from a period of congestion and equallyBar D would indicate the beginning of an uptrend.

Fig 2Bar A is the extreme bar for the downtrend and therefore the first bar that tradescompletely above the range of bar A will be the naked bar. Bar B is an example ofwhat an idealised naked bar would look like.

market technician no 54

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