pairs trading - the technical analyst · pairs trading outlook for us ... on the quants strategies...

Adopting a market neutral strategy

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.technicalanalyst.co.uk wwwThe publication for trading and investment professionals

PAIRS TRADING

Outlook for US stocks Gartmore’s Mike GleasonOn the quants strategies

behind AlphaGen

TA in illiquid marketsHow do tradingrules perform?

Dow bullson the ropes

May/June 2005 THE TECHNICAL ANALYST 1

© 2005 Clements Biss Economic Publications Limited. All rights reserved. Neither this publication nor any partof it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic,mechanical, photocopying, recording or otherwise, without the prior permission of Clements Biss EconomicPublications Limited. While the publisher believes that all information contained in this publication was correctat the time of going to press, they cannot accept liability for any errors or omissions that may appear or loss suffered directly or indirectly by any reader as a result of any advertisement, editorial, photographs or othermaterial published in The Technical Analyst. No statement in this publication is to be considered as a recommendation or solicitation to buy or sell securities or to provide investment, tax or legal advice. Readersshould be aware that this publication is not intended to replace the need to obtain professional advice inrelation to any topic discussed.

CONTENTS 1 FEATURES

Charting illiquid marketsCan cycles and price patterns in alternative

markets be predicted by technical analysis andcan they then be exploited for profit?

US stock marketDow bulls on the ropes

Michael Riesner, technical analyst at UBSInvestment Bank in Zurich, explains why theremay be one last chance to earn money on thelong side before the Dow Jones heads south.

Pairs tradingArbitrage trading between two closely related

securities can be an effective strategy for managers wishing to maintain a

market neutral approach.

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WELCOMEThe overlap between quantitative strategies, behavioural finance and

technical analysis is a significant one with TA techniques such as momentum andtrend following strategies being applied by quantitative analysts and hedge fund

managers. This is illustrated in our feature on Gartmore's AlphaGen hedge funds.Investment manager and quants strategist, Mike Gleason, explains how all were

brought together to devise the AlphaGen model.

The Technical Analyst's first seminar took place in London on May 12th and we arepleased to report the event was a great success. We would like to thank all our

speakers and delegates for their participation.

Matthew Clements, Editor


Editor: Matthew ClementsManaging Editor: Jim BissAdvertising & subscriptions:Louiza Charalambous Marketing: Vanessa GreenEvents: Adam CooleDesign & Production: Paul Simpson

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ISSN(1742-8718)

INDUSTRY NEWS

MARKET VIEWS US stocks: Dow bulls on the ropesOutlook for crude oilAUD/USD: Losing momentumEUR/USD: USD sentiment breaks higher

TECHNIQUES Pairs tradingThe Measure RuleThe Selling Climax: Fusion analysis in actionCharting illiquid markets Chart pattern notes

INTERVIEWMichael GleasonHead of quantitative strategies, Gartmore

SUBJECT MATTERS Predictive models vs. trend-following techniques

SOFTWAREVantagePoint

BOOK REVIEW & TOP 10Quantitative Trading Strategiesby Lars Kestner

COMMITMENTS OF TRADERS REPORTLONG-TERM TECHNICALSEVENTS

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CONTENTS 2 REGULARS>

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Using the Measure Rule to predict aprice target.

“Our alpha model is designed to exploitcognitive dissonance that exists in themarketplace.” Mike Gleason, Gartmore

TraderMade, the UK-based providerof technical analysis software, haslaunched its new browser based mar-ket data solution for institutionalusers called The Cube.The product has beendesigned as a container todisplay market informa-tion including personal-ized layouts, customizablequote boards, a choice ofnews providers along withtechnical analysis studies.Each user can constructand save their pages andaccess the applicationfrom any web-linked PC

in the world, requiring no externalsoftware installation.

TRADERMADE LAUNCH THE CUBE

4 THE TECHNICAL ANALYST May/June 2005

Industry News

PROQUOTE GOES INTERNATIONAL

CQG releaseChartTradersoftware

Charting software provider, CQG,have released ChartTrader, a newservice allowing CQG users toplace orders directly from a CQGchart page. ChartTrader appearson the right side of a convention-al CQG chart and features marketprices, market depth, and buy andsell market orders. The servicealso allows users to enter, modifyand cancel trades straight fromthe chart; see market entry andexit points on the chart; and viewlimits and stops graphically. Moreinformation is available fromwww.cqg.com.

New Bloomberg study Bloomberg has released a proprietarystudy called Binary Strings. Thestudy tries to isolate a move by agiven percent that the user defines. Itthen looks at previous moves of the

same size and determines, through aproprietary algorithm, the degree ofsimilarity to what is happening now.It then returns a probability of suc-cess based on how many similar pat-

terns can be found and how similarthey are. For more information type:GEG<GO> on your Bloombergterminal.

Proquote, the London StockExchange's market data and tradingterminal business, has launchedProquote International which pro-vides real-time and delayed datafrom over 100 markets and OTCsources. The institutional service

covers international equity, fixedincome, currency and commoditymarkets, and supplies pre-trade ana-lytics and decision making tools.These include news feeds, companyfundamentals and intra-day charting.

Industry News


E X C H A N G E N E W S :

Record open interest at the CBOT

The CBOT has reported a record one-day level ofopen interest. May 13 saw exchange-wide open interestrise to 14,584,115, up from 14,487,181 set on April 21.Average daily volume in April rose to 2,920,406 con-tracts, up 14.4% on April 2004.

The LSE reported an annual rise of 19% in tradingvolume in April at 6.5 million contracts. ETF averagedaily volume rose 7% on April 2004.

Eurex traded 104 million contracts in April, up 21%on the year.

Hamid Malik has joined the researchteam at Fimat in London after leav-ing his post as global director oftechnical analysis at market researchfirm, IDEA Global. At Fimat, he isresponsible for research and analysiswithin the Alternative InvestmentSolution division of Global FundServices.

Martin Scott has moved on fromhis role as chief technical strategistat the Royal Bank of Scotland inLondon and has taken up an internalposition within RBS Financial

Markets as a proprietary trader.Technical research is now splitbetween Tom Pelc (Rates andCommodities) and Steve Merrigan(FX).

Barclays Capital has continued toexpand its technical analysis researchteam with the addition of MacNeilCurry as senior technical strategist inNew York who will cover the FX,bond and commodity markets.Jordan Kotick, global head of tech-nical analysis, says the London officewill also expand to bring the G7markets team to four analysts.

ON THE MOVE...

Jordan Kotick

New indices forLondon’s AIM

The FTSE Group and the LondonStock Exchange have introduced aseries of new indices for AIM, theLSE's market for small high growthcompanies. The FTSE AIM IndexSeries went live on 16 May and willcomprise three new indices: theFTSE AIM UK 50 Index, the FTSEAIM 100 Index, and the FTSE AIMAll-Share Index. The FTSE AIMUK 50 Index and the FTSE AIM100 Index will both be liquid, trade-able indices that can be used as thebasis for index-related investmentproducts, such as Exchange TradedFunds (ETFs). The FTSE AIM UK50 Index will consist of the 50largest eligible UK domiciled stocksquoted on AIM by market capitaliza-tion. The FTSE AIM 100 Index willinclude the 100 largest eligible stocksquoted on AIM whether UK domi-ciled or international.

Market Views


Since the Dow Jones hit nearly 11000 in mid March,the market has been heading in only one direction,namely south. The technical damage suffered over the

last couple of months is serious, spanning the entire rangeof long-term indicators that we follow.

Divergences in breadthThe market is building increasing divergences in monthlymomentum and breadth indicators, and with the recentplunge in new 52-week highs (Figure 1), it's clear that mar-ket breadth is decreasing significantly. Over the last 40 yearsthere have been only five major divergences in this indicator(where a high price in the Dow Jones has not been con-firmed by the NYSE New 52-Week High indicator.). Ineach case it was a fairly solid fore-runner of a major correc-tion or a cyclical bear market of 20% to 50% in equity mar-kets.

Since January 2004, the indicator has built a 6th diver-gence, thereby failing to confirm the December 2004 highin the Dow. Noting the decreasing monthly momentum (asign of increasing down pressure in the underlying 4-yearstock market cycle) and the NYSE Advance/Decline line,which has broken its major long-term uptrend, we expectthe market environment in equities to be very challenging,particularly during the second half of 2005.

Thus, although the market is currently trading a mere 8%

below the mid-April high, we have growing evidence thatthe Dow has already seen a major top or is at least in themiddle of a long-term peaking process, suggesting signifi-cantly lower price levels throughout the rest of the year.

Major peaking process underwayLooking at the medium-term, the weekly chart in Figure 2shows that by breaking below 10400, the index has takenout a classic cross support - a truly big point from a techni-cal perspective. The level represents the pullback from thebroken minor long-term downtrend from 2001, coincidingwith the 2003 March uptrend as well as the widely respected200-day moving average.

Sentiment becoming bearishHowever, apart from the technical price damage and thedeteriorating long-term monthly structure, we also detectsigns that the market could experience a substantialrebound into later Q2 and into the summer period.Sentiment has been decreasing significantly over the lastcouple of weeks, regardless of the sentiment measureanalysed, and market psyschology is becoming increasinglybearish. The AAII Bullish Consensus recently hit a 13-yearlow with merely 17% bulls left (Figure 3). TheNasdaq/NYSE volume ratio has seen levels below the 1.00barrier, which is usually a sign that market participants are

US STOCKSDOW BULLS ON THE ROPES by Michael Riesner

Figure 1. Figure 2.


Market Views

becoming too risk averse, selling high beta tech stocks ver-sus value.

Finally, looking at US equity and index (OEX) put/callratios, we also note growing pessimism and hedging activityamong investors, suggesting the market is near a tradablerebound. When oversold momentum price indicators are

included in the analysis (see Figure 2), we have yet furthersupport for a significant momentum rebound later in thesecond quarter.

Substantial rebound into summerThe question now is from which level we can expect therebound to start. We believe the market is likely to test andhold its next major support level at 9700 to 9500 in the sec-ond half of May, which should then serve as a solid basisfor the anticipated rebound. As a target, it is likely to revisitits broken former support levels in the area of 10300 to10500.

Given the decreasing long-term technical background, weview this tactical rally as the last chance to earn money onthe long side for this year and an opportunity to sell strate-gically. After that, we expect a significantly weaker Dow inthe second half of 2005.

Michael Riesner is responsible for technical analysis atUBS Investment Bank in Zurich.Figure 3.

Market Views


The sustained rise in the price of crude oil since thelows set at the beginning of 1999 has put this com-modity back into the spotlight. Heightened media

coverage and a break to record highs during the secondquarter of 2004 has fuelled speculation about future pricemovement, even prompting some analysts to forecast levelsof over $100 a barrel. It's at such times that it becomeseven more important to look at the technicals.

Figure 1 shows a weekly bar chart of the Nymex futuredisplaying the most recent leg of the current uptrend, whichstarted back in November 2001. The 200-day moving aver-age (MA 200) clearly displays a rising slope. Prices tradedabove the MA 200 and, during the first week of May, theMA 200 successfully supported the weakness which fol-

lowed the most recent top in early April. These technicalconditions favour a further rise in the Nymex future.

Taking a more in-depth look at the longer-term charthowever gives some grounds for concern. Figure 2 shows amonthly bar chart together with a momentum indicator -the 14 period RSI. The first warning signal comes from thefact that prices have seen a parabolic move since the fourthquarter of 2003. Parabolic characteristics are usually a prel-ude to the end of the trend or a correction within thetrend. A second indication that prices may start a correctivemove is based on the price activity during the month ofMay. The weakness seen during this month has resulted inthe formation of a key reversal chart pattern. At the end ofa strong uptrend, this chart pattern should raise some ques-

OUTLOOK FOR CRUDE OILby Geertjan Nikken


Market Views

tions about the sustainability of the trend.This weakness is supported by the non-confirmation of

the RSI. The most recent high set early in April managed tobreak the price level of the top set back in late October lastyear. The RSI 14 however failed to confirm this higher high.The last time the RSI 14 showed negative divergence was inOctober 2000, which was also a month in which a reversalbar formed. This negative divergence and key reversal pat-tern was a set up for the decline seen during most of 2001.

The daily bar chart in Figure 3 highlights the recentuptrend from September 2003. Several support trendlineshave been added. The longer-term trendline supports thetrend since September 2003. The three shorter-term trend-lines support the acceleration in 2005 within this mid-termtrend. As shown, these trendlines have been broken andsuccessfully tested as new resistance. In the first week ofMay, the final short-term trendline was broken and is cur-rently being tested as new resistance. Failure to break thislevel would imply a test of the longer-term trend line isnext. This trendline currently fluctuates around $45.00.

In conclusion, the outlook for the Nymex future is vul-nerable. A further decline towards $45.00 is most likely and,even though a move towards this level is impressive in per-centage terms, this weakness should be seen as a correctionwithin the mid term uptrend from September 2003. Themonthly chart, however, indicates that this time around thetrendline is less likely to hold and a break of $45.00 willmost likely trigger further downward momentum towardshorizontal support at $ 40.00.

Geertjan Nikken is technical analyst at Fortis Bank inAmsterdam.Figure 3.

Figure 1.

Figure 2.

Market Views


The daily AUD/USD chart continues to show spotconsolidating within a tight range (0.7625-0.7850).The short- to medium-term outlook remains mixed,

as trading oscillators are positive, but most are lookingexhausted. Moreover, the trading mode remains lacklustregiven neutral MACD and sideways 9- & 18-day movingaverages. That said, the short-term bearish channel formedoff the 0.7989 (March 8), 0.7841 (April 29) and 0.7827(May 5) highs remains intact. The focus is on stability abovethe short-term trend support 0.7675 and reaction low0.7625. A firm breakof these levels wouldrevive the bearishmode and put pressureon AUD/USD to testthe long-term bullishtrend support at0.7500 and the short-term bearish channelsupport at 0.7480.

The weeklyAUD/USD chart inFigure 1 shows spotconsolidating within awell-defined, long-term bullish trendformed off the 0.5230(August 2002) and0.6889 (September 2004)lows, but further gainsabove the February 2004 high of 0.8004 were restrained bythe bearish divergence signal on the exhausted positivemomentum oscillator. Noteworthy is the underlying long-term bullish trend that could be compromised by the per-sistent consolidation phase in the short-term. Moreover, thesell signal on the MACD indicator as of April 3, 2005 lookssolid. Against this backdrop, sustainability above 0.7500-0.7480 in the short-term remains crucial, as a weekly closingprice below 0.7480 could yield extended losses to a down-side scope of 0.7350-0.7000. To reduce this risk,AUD/USD would need to stabilise above near-term resist-ance 0.7850 and to revive the bullish tone within the under-lying long-term bullish trend - a series of higher highsabove 0.8004 must be attained before end Q2, 2005. That

said, we believe further topside gains could be limited to0.8165, which is the channel resistance of the medium-termbullish channel formed off the 0.7442 (December 10,2004), 0.7505 (January 18, 2005) and 0.7635 (April 18, 2005)lows.

In conclusion, though a long-term base has been firmlyestablished above 0.6765, a level which marks the 38.2%Fibonacci retracement of the 0.5230 (August 2002) to0.8004 (February 2004) up-move, further topside gains arerestrained by the overbought state and bearish divergence

signal on the exhaust-ed positive momentumoscillators. In addition,upward momentumwas lost during thepersistent consolida-tion phase as of end-November 2004. Thefocus now is on stabil-ity above medium-term bullish channelsupport at 0.7675 andthe reaction low at0.7625. A violation ofthese short-term sup-ports could put pres-sure on AUD/USD toretrace deeper towardsthe long-term bullishtrend support at

0.7500 and the short-term bearish channel support at0.7480. A weekly closing price below 0.7480 would confirma bullish trend reversal and further downside tests to0.7350-0.7000 are likely. To reduce this risk, AUD/USDwould need to stabilise above immediate resistance at0.7850, but to revive the bullish tone within the long-termbullish trend a series of higher highs above 0.8004 must beattained by end-Q2, 2005. That said, we believe extendedgains could be limited to 0.8165, which is the medium-termbullish channel resistance.

Eric Chong is FX technical strategist at StandardChartered Bank in Singapore

AUD/USDLOSING MOMENTUM by Eric Chong

Figure 1. AUD/USD weekly chart


Market Views

In the March/April issue of the Technical Analyst wereported on significant developments that had takenplace on EUR/USD which we believed indicated the

upside had become very limited. The risk of a significant cor-rection to the bull-market which began in January 2002 washigh. Recent developments, both in terms of the pure priceaction on EUR/USD and sentiment indices on the USDIndex (.DXY), are now confirming this view, and indicatingthat minimum downside targets on EUR/USD can now beextended towards the base of the 4th Quarter 2004 rally at1.1987.

Moving averages The specific range of levels which have broken onEUR/USD are 1.2860-1.2732 (February/April 2005 lowsand 200-day moving average) and the 55-week moving aver-age at 1.2675. The closing break of this support region wasachieved recently (week ending 13th May 2005). Also worthyof note is that the USD Index closed above the 55 weekmoving average for the first time since April 2002. Thesebreaks, coupled with the negative monthly developments onEUR/USD already in place (as described in the original arti-cle), open a move to the low from which the Q4 2004 rallybegan at 1.1987. The downside targets can also be extendedfurther if support from 1.1987-1.1760 is broken.

A relationship we monitor closely in the foreign exchangemarkets is the absolute differential between the 55 and 200week moving averages, and the period of time a market hasspent above or below the 55 week moving average on a clos-

ing basis. With respect to these relationships the USD Indexhas moved towards historic extremes, both in terms of thegap between the two averages and the period below the 55week moving average. Last week saw the USD Index post itsfirst close above the 55 week moving average since April2002. When historically wide gaps such as this develop, wehave often seen very sharp corrections of the prevailingtrend, with extreme targets of the 200 week moving average.The 200 week moving average on the USD Index stands at98.47. The situation is similar on EUR/USD, except that itmade one marginal false close below the average in lateAugust last year. At that time it was not accompanied bybreaks of other important supports and the other long termnegative developments were not in place as they are now. The200 week moving average on EUR/USD stands at 1.1039.

Sentiment indices (Figure 1)Last week also saw very significant developments posted onUSD sentiment indices, specifically those related to the USDIndex. In the last issue of the Technical Analyst we highlight-ed that not only was stochastic momentum diverging posi-tively on the USD Index against the May/June 2003,January/February 2004 and December 2004 lows, indicatingdecreasing willingness to sell the USD as it moves lower, butthat USD sentiment as measured by the Market Vane indiceswas also diverging positively, indicating the decreasing will-ingness of market analysts to forecast significant furtherUSD falls. This we believed this was very important. Thesame Market Vane indices had diverged in 2000/2001 andsuccessfully marked the 2000/2001 USD highs. Now, notonly is the sentiment index diverging, it has also broken high-er from its multi-month triangle-type consolidation, confirm-ing that market participants see the break higher on the USDIndex (break lower on EUR/USD) as very significant and areadjusting their views accordingly. Overall, short-term correc-tions/consolidations aside, the medium to long-term nega-tive technical outlook for EUR/USD continues to build.

John Noyce is technical analyst for Citigroup ForeignExchange

Copyright © 2005 Citibank N.A. All rights reserved. Any unauthorised use,duplication or disclosure is prohibited by law and may result in prosecution.CitiFX, Citigroup and the Umbrella Device are trademarks and service marksof Citicorp or its affiliates and are used and registered throughout the world.Citibank is authorised and regulated by the Financial Services Authority.

Figure 1. USD Index (.DXY) overlaid with Market Vane USDBullish-Consensus sentiment index. Data source: Reuters.

EUR/USDUSD SENTIMENT BREAKS HIGHER by John Noyce

Techniques


Techniques


Pairs trading allows the trad-er to take advantage ofprice moves between two

related securities.Typically, a paired position consists of a

long position in one security and a short posi-tion in the other. The idea is to exploit knowl-edge of the relationship between the twosecurities. If the relationship between thesecurities is statistical in nature then the tradefalls in the category of statistical arbitrage. Ifthe relationship is due to the announcementof a merger then the trade is termed risk arbi-trage or merger arbitrage. Pairs trading can beapplied to all markets (FX, bonds etc) wherethere are two securities whose price move-ments are correlated. In this article we will dis-cuss pairs trading in the context of statisticalarbitrage.

An illustrationConsider two stocks, XLF (Select FinancialSPDR) and BAC (Bank of America →→

PAIRS TRADINGADOPTING A MARKET NEUTRAL STRATEGY

by Ganapathy Vidyamurthy

Techniques


Corp). A quick comparison plot of thetwo securities is shown in Figure 1.Note that the price movements are sim-ilar. We can certainly expect this to bethe case because BAC is a firm in thefinancial sector with both retail andinvestment arms so its business mixreflects that of the broad index XLF.

Next we assemble the closing pricesfor the two securities in a spreadsheetfor a period of about 6 months (11-Oct-04 to 8-Apr-05). The chosen timeperiod depends on two considerations:the number of data points must be suf-ficient for the regression to be mean-ingful, ideally around 45-60. Also, theseries must be long enough to tell if itis mean reverting or trending. We thencalculate the natural logarithm of theprices. A scatter plot of the logarithms

of the prices is shown in Figure 2.The scatter plot shows signs of a lin-

ear relationship between the log pricesof the two securities. Based on thatobservation we run a linear regressionon the data and end up with a relation-ship as shown.

log(BAC) = 0.6419 * log (XLF) + 1.6492

We then calculate the logprice spreadand the price spread on the spreadsheet using the formulae:

Plots of the logprice spread and pricespread are shown in Figure 3 andFigure 4.

The log price spread clearly showssigns of oscillating about zero and theprice spread oscillates about a meanvalue of about 26.75.

Let us review what we have done sofar. Firstly we had a hunch that theprices of XLF and BAC are likely tomove together. In order to verify thiswe tabulated the closing prices over asix month period for the two securities,calculated the logarithm of the pricesand applied a simple linear regressionto establish a statistical relationshipbetween the prices of the two securi-ties. We then checked for violations ofthis relationship at each time step bycalculating the log price spread and dis-covered that the violations oscillateabout zero.

So how we can exploit this informa-

Figure 2.

Figure 4.Figure 3.

Figure 1.

Logpricespread = log(BAC) - 0.6419 * log(XLF) - 1.6492

Pricespread = BAC - 0.6419 * XLF


Techniques

tion for trading? We can enter into apairs trade when the log price spread issubstantially away from zero, say at -0.0175 or 0.0175 and expect the spreadto converge. More precisely if log pricespread is at 0.0175, we short BAC andgo long XLF in the ratio 1:6419 nettingapproximately $27.5 per share of BACsold. When the log price spread goes tozero we reverse the trade giving back$26.75. The profit on the trade pershare of BAC is about $0.75. But canthe trade be made in sufficient volume?Yes, since BAC has a large market cap-italization and is traded in reasonablevolume every day, and XLF is anexchange traded fund.

Similarly, if the swing is to the nega-tive side i.e. log price spread is at -0.0175, the trade on this side involvesshorting XLF and buying BAC. Thistrade has an inherent advantage withrespect to execution when compared tothe previous one. Note that in generalshorting is subjected to the uptick rule.However it does not apply in this casebecause XLF is an exchange tradedfund and exchange traded funds areexempt from the uptick rule. Thismakes it easier to short the ETF and setup the position.

In summary, by entering into a pairedposition when there is a deviation fromequilibrium and getting flat when equi-librium is established we are able togenerate profits. This is the essence ofstatistical arbitrage pairs trading.

Theoretical foundations Statistical arbitrage pairs trading, in thearea of econometrics, relates closely tothe idea of cointegration. This is wherethe time series of measured quantitiesshare a common relationship. Eventhough it may be hard to predict thefuture values of the individual timeseries we can say that knowledge ofone series certainly says a great dealabout the other because of the exis-tence of the equilibrium relationship.Deviations from the equilibrium rela-tionships are self correcting and theytend to revert back to equilibrium.

From the view point of financial the-

ory, support for pairs trading ideas maybe found in APT (Arbitrage PricingTheory). APT essentially states thatevery security involves exposure to riskfactors and the total return of the secu-rity is the sum of the returns awardedby the market for taking on those risks.It follows therefore that if two securi-ties share the same risk exposures thentheir returns are going to be the same ina given time period. The implication ofthis being that the logarithm of theprices could potentially exhibit cointe-

grating behavior.But why should the trader care about

theoretical explanations? Theoreticalexplanations are important because thetrader is trading on the basis of themodels built on the theory. Knowledgeof the theory is essential to realizingwhere the theory can break down orsuddenly go awry. The trader can thenbe alert and on the look out for suchinstances.

Issues in strategy execution It is important to point out that theequilibrium relationship exists onlybetween related securities and not nec-essarily for any random pair. It is there-fore necessary to identify candidatepairs and subject them to the exercisedescribed earlier to see if the deviationsactually oscillate about a mean value.Next the equilibrium relationship mayitself be subject to change. The traderwould need to figure out how often to

reevaluate the statistical relationship. Ifthe changes are too frequent then thepair may not be fit for trading.

Additionally there are issues ofmoney management. It is commonknowledge that two traders trading thesame broad strategy can produce vastlydifferent returns simply because of dif-ferences in money managementapproaches. Statistical arbitrage pairstrading is no different from otherstrategies in this respect. Within a singlepair the trader needs to decide where toplace the threshold levels that trigger atrade. Answer questions like if thereshould be one single threshold level ormultiple levels, what should the posi-tion size be at each of the differentthreshold levels etc. The trader alsoneeds to evaluate his ability to executetrades and come up with smart execu-tion strategies that reduce slippage.Execution ability also feeds into thetime frame that one uses to recordprices. Different time frames can resultin dramatically different outcomes.

At a portfolio level the trader needsto decide what sets of pairs he wouldmonitor and trade and come up with amethod to quantify the risk on his port-folio at all times. This also involvesdecisions on position sizing, i.e., howmuch to bet on each pair based on therisk reward profile. Additionally, theleverage profile can also be variedacross the different pairs in the portfo-lio. Issues such as these confront thetrader when executing any strategy andif not addressed correctly can lead tolack luster performance and/or unde-sirable drawdowns.

Practiced with sound money manage-ment, however, statistical arbitragepairs trading certainly has a place in thepragmatic trader and fund manager’sportfolio of strategies.

Ganapathy Vidyamurthy is a quanti-tative analyst and author of "PairsTrading" published by WileyFinance.

Techniques


THE MEASURE RULEby Thomas N. Bulkowski

Use the measure rule topredict a price targetafter the breakout

from a chart pattern.

In the second edition of my book,Encyclopedia of Chart Patterns, Iexplore how often a price predictionmethod, called the measure rule, worksfor over 60 chart and event patterns inboth bull and bear markets. This articletakes a closer look at the results for var-ious chart patterns, including howoften it works and what to look for.

For most chart patterns, the measure

rule is the height added to (upwardbreakouts) or subtracted from (down-ward breakouts) the breakout price.Figure 1 shows an example of the rulefor an Eve & Eve double bottom.

Eve & Eve double bottomAn Eve & Eve double bottom has twovalleys near the same price. Each valleyappears wide and rounded. If spikesare present, they are usually short andclustered. Contrast the Eve bottomwith an Adam bottom in October. AnAdam bottom appears narrow, usuallywith a one- or two-day downward

spike. The various combinations ofAdam and Eve peaks or valleys fordouble tops and bottoms give perform-ance and identification differences.

The time between the two bottoms ofa double bottom varies, but the bestperformance comes from bottomsspaced 2 to 7 weeks apart. Valleys widerthan 7 weeks show diminished per-formance after the breakout. Theheight of the pattern from the lowestvalley to the highest peak between thetwo valleys is at least 10 percent ofprice, but allow for variations. Tall chartpatterns often perform substantial- →→

Techniques


ly better than short ones.In the example shown in Figure 1, a

throwback occurs after the breakoutwhen the stock returns to the breakoutprice. A throwback happens 55 percentof the time in the patterns I looked at.

To apply the measure rule to doublebottoms, subtract the lowest valley inthe chart pattern (B) from the highestpeak (A) to get the height. Since thebreakout is upward, add the height tothe highest peak (A) to get a targetprice. Price reaches or exceeds the tar-get 67 percent of the time in a bull mar-ket, or about two out of every threetrades.

Head-and-shoulders bottomA head-and-shoulders bottom, such asthe one shown in Figure 2, has a leftshoulder that is opposite a right shoul-der with a head in between. The shoul-der valleys should bottom near thesame price and be almost an equal dis-tance from the head. Symmetry isimportant for easy identification.

The head must be below the twoshoulder valleys otherwise you may belooking at a triple bottom. A necklinejoins the armpits in the pattern, and itsignals a trade when price closes aboveit (for down-sloping necklines only).For up-sloping necklines, use a closeabove the price of the peak locatedbetween the head and right shoulder asthe buy signal. Otherwise, you maynever get a buy signal for steep neck-lines. Volume is usually heavier on thehead or left shoulder and diminishedon the right shoulder.

The measure rule is unique for head-and-shoulders patterns (both tops andbottoms). Find the lowest valley in thehead and measure the vertical distancefrom that low to the neckline. I showthe measure between the head andpoint A in Figure 2. That gives theheight. Add the height to the breakoutprice - the point where price pierces theneckline (for down-sloping necklines)or the peak price between the head andright shoulder (for up-sloping neck-

lines). The result is the target price.Price hits the target 74 percent of thetime in a bull market.

Falling wedgeA falling wedge is a somewhat rare pat-

tern, and Figure 3 shows an example. Awedge is a price pattern bounded bytwo converging and down-slopingtrendlines. The trendlines will eventual-ly join at the wedge apex. The breakoutaverages 57 percent of the way to the

Figure 2. A head-and-shoulders bottom uses the neckline as the vertical measure projectedupward from the breakout point to compute a price target.

Figure 1. An Eve & Eve double bottom has a price target that matches the height of the chartpattern.

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wedge apex.Look for price to come close to or

touch each trendline a total of fivetimes or more - three times on one sideand two on the other. Anything lessthan five touches and the pattern may

be invalid. Price should also cross thepattern from side to side several timeslike that shown. Volume usually trendsdownward from the start of the patternto just before the breakout.

The measure rule for upward break-

outs from falling wedges sets a priceobjective level with the top of the pat-tern, point A in Figure 3.

A downward breakout uses the heightof the pattern (A - B) projected down-ward from the lowest valley in the pat-tern (B).

Price reaches the top of the pattern70 percent of the time in a bull market.For downward breakouts, the measurerule is less successful, working just 30percent of the time. To increase thelikelihood of a successful target, usehalf the height projected downward.

Symmetrical triangleFigure 4 shows a symmetrical triangleABC. A symmetrical triangle is a chartpattern that has two converging trend-lines that join at the triangle apex (C).The top trendline slopes downwardand the bottom one slopes upward.Look for at least two touches of eachtrendline and make sure price crossesthe chart pattern plenty of times, fillingthe pattern with price movement.Avoid cutting off a rounding turn andcalling it a symmetrical triangle.

The traditional way to use the meas-ure rule is to compute the height (A -B) and add it to the upward breakoutprice or subtract it from the downwardbreakout price. Price stages a breakoutwhen it closes outside of the triangletrendline. This works 66 percent of thetime for upward breakouts and 48 per-cent of the time for downward break-outs in a bull market.

An alternative way to compute aprice target is to draw a line parallel tothe bottom trendline (for upwardbreakouts like that shown by thedashed line) or parallel to the down-sloping line AC, beginning at point B,for downward breakouts. Begin draw-ing the line from the start of the pat-tern at the top (point A), ending direct-ly above the breakout point (D). Theprice directly above the breakoutbecomes the target.

Figure 4. A symmetrical triangle uses the pattern's height projected in the direction of the break-out to reach a target, or a trendline parallel to the side opposite the breakout to get a target.

Figure 3. A falling wedge with an upward breakout needs price to rise to the top of the patternto hit its target.

→→

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The numbers Table 1 shows how often the measurerule works for popular chart patterns.For example, I looked at a database ofthe various combinations of Adam andEve double bottoms and found 1,098chart patterns that I tested against themeasure rule prediction. The ruleworked between 66% and 67% of thetime in a bull market using data from1991 to 2004 in as many as 1,100stocks, but not all stocks covered theentire period, and I excluded data fromthe recent bear market. That meansprice met or exceeded the target pricebefore dropping at least 20% (a trendchange, measured from the highestpeak to the close) or a close below thelowest valley in the chart pattern.

The table shows that price meets orexceeds the target better after an

upward breakout than a downward one.For percentages below 50, use a projec-tion that is half the height of the pat-tern. That will increase the probabilitythat price will hit the target.

Closing positionThe measure rule is a tool used withchart patterns that suggests a price tar-get. Typically, the height of the patternis used in the computation. Add theheight to upward breakouts or subtractit from downward ones to get a pricetarget. For a more conservative (closer)target, use half the chart pattern heightprojected in the direction of the break-out.

Once you have a price target, look fornearby support or resistance zones.This may be round numbers (10, 15, 20,and so on), prior peaks and valleys, hor-

izontal price consolidation regions,trendlines, and even other chart pat-terns. Often price will stall at overheadresistance or underlying support as itnears the target price.

Thomas Bulkowski is a privateinvestor and author of two books,Encyclopedia of Chart Patterns,Second Edition (John Wiley & Sons,2005) and Trading Classic ChartPatterns (John Wiley & Sons, 2002).([email protected])

Copyright © 2005 by Thomas N. Bulkowski. All

rights reserved. All figures use Bulkowski's propri-

etary software.

Chart Pattern Upward Breakouts

Downward Breakouts

Double bottoms (all variations) 66% to 67% N/A

Double tops (all variations)

N/A 70% to 73%

Head-and-shoulders bottoms

74%

N/A

Head-and

-shoulders tops

N/A

55%

Triangles, ascending

75%

68%

Triangles, descending

71%

54%

Triangles, symmetrical

66%

48%

Triple bottoms

64%

N/A

Triple tops

N/A

40%

Wedge, falling

70%

30%

Wedge, rising

58%

46%

Table 1. The table shows how often the measure rule works for various chart patternsand breakout directions. "All variations" means the four combinations of Adam and Eveshapes for peaks and valleys.

“FOR MOST CHART PATTERNS THE MEASURE RULE IS THE HEIGHTADDED OR SUBTRACTED FROM THE BREAKOUT PRICE.”

Asia and Japan Hedge Fund Directory

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2005

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Techniques


The number of man-agers, especially hedgefunds, using a com-

bined fundamental and tech-nical approach to investinghas been rising. Achievingrisk-adjusted excess returnsis not easy and it makessense to be pragmatic. Yetwhile there are numerousbooks and journals devotedto fundamental and techni-cal analysis, each in isola-tion, there is little writtenabout the two combined.

Here, I present an example of how aquantitative fusion trading strategy canbe built from a combination of funda-mental, technical and also behaviouralconsiderations, to identify and trade theSelling Climax - a fairly common occur-rence in the markets..Technical considerationsBased on the definition of leading tech-nical analyst, John J. Murphy, a sellingclimax is a significant reversal occurringat a chart bottom. (One can also havethe reverse, a Buying Climax at a charttop). It is "…usually a dramatic turn-around at the bottom of a down movewhere all the discouraged longs havefinally been forced out of the marketon heavy volume… The subsequentabsence of selling pressures creates a

vacuum over the market, which pricesquickly rally to fill."

While it may not mark the final bot-tom of a falling market, it usually sig-nals that a significant low has beenseen. Edwards and Magee in theirTechnical Analysis of Stock Trends(8th Edition) state, "It is a harvest timefor traders who, having avoided thebullish inflection at the top of the mar-ket, have funds in reserve to pick upstocks available at panic prices".

So, a selling climax based on theobservations of leading techniciansappears to provide good return oppor-tunities.

Fundamental considerationsSelling climaxes may reflect variouscorporate imbroglios, such as earningsdisappointments and governanceissues. Optimistic earnings models ofPEG (price/earnings to growth) andDDM (dividend discount model) arescaled down substantially, leading tolower earnings estimates and theremoval of buy recommendations.

Upon sell-off, however, a stock mayreach valuation levels that are moreattractive (e.g. lower price/book ratio,smaller market capitalization). Somevalue players would also claim that thelower P/E ratio of the stock shouldenable it to show future risk-adjustedreturns as well. This is based on the →→

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THE SELLING CLIMAXFUSION ANALYSIS IN ACTIONby John Palicka

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belief that over long periods low P/Estocks perform better than high P/Estocks, because investors tend to over-pay for the perceived expected growthassociated with a high P/E ratio. Underthe Gordon Growth model, a P/Eincreases as growth increases, assumingthe other variables remain constant.

Behavioural considerationsJames Montier, a leading observer ofbehavioural finance on Wall Street hascommented, "... if a stock price drops,then in theory if the analyst were cor-rect in their initial price target, it shouldbecome even more attractive to buy.However, in practice, analysts actuallyreduce their target prices in response toa drop in the current market price."

One behavioural influence on analystforecasts is Representativeness. This isa "…tendency to evaluate how likelysomething is with reference to howclosely it resembles something else,rather than using probabilities." Forexample, one could see the initialaccounting scandals of Tyco as similarto those of Enron, even though based

upon subsequent events they weren'teven close.

Representativeness generates inap-propriate forecasts, which partlyexplains why stocks trade at muchlower levels than would otherwise beexpected.

Example - ImpathWe can demonstrate our fusion processby examining the stock of Impath.Impath was a medical diagnostic com-pany that reached lofty valuation levelsand eventually went into bankruptcy.Along the way, it had several investordisappointments. These included, atfirst, earnings disappointments andlater, announcements of managementgovernance violations, thereby causingselling climaxes. The chart below indi-cates a selling climax in late April, basedon a disappointing earnings announce-ment.

On a technical basis, when IMPHwent below 30 at the end of April, ashort-term fusion trader would havebought the stock at that point becausethe combination of the stock plunge

and relatively high volume would seemto indicate a selling climax. Thus, onewould expect some sort of rally oncecalmer days set in.

On a fundamental basis, the stock'sP/E and P/B relative to the market wasmarkedly more attractive at the approx-imate 30 selling climax low andappealed more to value investors. Theexpected P/E was 26 (1.2 x the SP500), the trailing P/E was 33 (1.3 x theSP 500) and the P/Book was 3.3 (about60% of the SP 500).

Just a few months before, IMPH hadtraded at 60 with much higher absoluteand relative valuations. The significantfall in IMPH brought it closer to thesweet spot of value investors.

The stock subsequently rallied nicely.About two years later, upon announce-ment of fraud issues, IMPH experi-enced another massive selling climax,this time to about $0.75 with a subse-quent rally to over $5.00 within onemonth. On a valuation basis, one couldeasily make the ultimate calculation ofliquidation value (according to Grahamand Dodd) which subsequently provedtrue, as IMPH paid off its creditors andas of today will soon return about$4.50 per share to stockholders. Ofcourse, not all selling climaxes end upin bankruptcy, and many times they arejust storms in a long prosperous voy-age.

Thus, to play a selling climax onlytechnically may lead to profitableopportunities, but combined with valu-ations that have been shown to exhibitbetter than average results greatlyenhances profit potential.

One can also get a better feel for thepsyche of the holders by scanning SECdocuments such as Schedule 14A todetermine the level of nervousness thatbehavioral finance may impose. A stockwith mostly index fund holders shouldshow little need to panic sell, as com-pared to "hot money" funds that shootfirst and ask questions later. In the caseof IMPH, of the three major 5% hold-ers as of April 26, 2001, all lowered oreliminated their positions by the time

Figure 1.

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of the next filing on April 12, 2002.Perhaps, the Representativeness issueof IMPH reminded them of similartorpedo stocks in their portfolios.

HedgingSelling climaxes are becoming morecommon, especially with the increaseduse of momentum strategies by hedgefunds.

In a selling climax, one can attempt tobuy the stock at the low and hedge riskswith derivatives, namely buying a pro-tective put option with an elasticity thatis a close as possible to -1.0. Naturally,upon a stock rally from the low, theprice of the put should expire worth-less, but its loss would be offset by themuch larger gains of the stock.

In the case of Impath, one can use anupside target price objective of around40 based upon drawing a downwardtrendline that connects the peaks ofnear 58 in February and 48 in April.Thus, buying around 30, and usingtrendlines as a guide, the selling climaxwould lead to a 10 point potential gain.

For illustration purposes, if a 30 putstrike with one month to expiry costunder 3, the ensuing risk/reward ofover 3 to 1 would be very attractive.Since the selling climax tends to reversein a few days, one would not pay muchfor an option's Theta (time value) butone would seek a near term put with astrike price close to the selling climaxprice. Should the stock not haveoptions, one could create a syntheticoption from other securities.

The spread tradeOne can also execute a spread trade onan intermarket basis. For example, ifthe stock was a housing stock that soldoff because of a selling climax, one canevaluate it against a related area (mostlikely the industry ETF) where the sell-off was overdone, and try to position aspread trade. A spread trade may try tobuy the "cheaper" security, say thehousing stock, from the proceeds of ashort on the more "expensive" asset,say the ETF. Hopefully, the spread willnarrow and thus generate profits aftercarrying and transaction costs.

Other, more complex strategies thatplay up behavioral aspects could also beused, such as Vega analysis of theimplied and historical volatility of thestock's option. One would then exploitthe Vega mispricing. Since a selling cli-max would lead to panic selling, thiscould artificially boost the option'sprice based on investors expecting rela-tively higher volatility in the securitygoing forward.

As we can see, a selling climax com-bined with derivative strategies couldlead to many trading permutations thatshould satisfy the utmost leveragedspeculator.

A quantitative trading systemTraders and hedge funds may seek sell-ing climaxes as a major strategy forprofitable trades. Using fuzzy logic onecan create algorithms to identify techni-cal patterns. Fuzzy logic has beenapplied to more complex patterns suchas head-and-shoulders, and a selling cli-

max can be easily programmed. Theseshould lead to a quantitative decision-making process.

Using fundamental databases, one canselect fundamental filters to screen foroptimal valuations. Combining both,one can have artificial intelligence pro-grams automatically present real-timetrading opportunities. In fact, thesetrades could be done without humanintervention under strict parameters,creating something akin to a fusionblack box.

John Palicka is president and chiefportfolio manager at GlobalEmerging Growth Capital (GEGC).He also runs an FTKnowledgecourse on fusion analysis for tradersand fund managers.

“SELLING CLIMAXES ARE BECOMING MORE COMMON,

ESPECIALLY WITH THE INCREASEDUSE OF MOMENTUM STRATEGIES

BY HEDGE FUNDS.”

CHARTING ILLIQUID MARKETSby Roar Adland and Steen Koekebakker

Hedge funds are increasingly diversifying intoalternative markets in the search for higher returns.Can technical analysis be applied successfully inthese areas? And to what extent does limited liquidity affect the profitability of technical tradingstrategies? Drawing conclusions that are relevant toall illiquid markets, the authors look at the performance of technical trading rules in the second-hand market for ocean going cargo ships.

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Techniques


Since the writings ofCharles Dow in the1800s established tech-

nical analysis as the originalinvestment tool - long beforemodern financial theory andthe efficient market hypothe-sis were born - the applica-tion of these techniques hasbeen largely confined to thefinancial markets.

This is because the financial marketsprovide the detailed high-frequencyprice and volume information thatmany technical trading rules are builton, as well as quick, low-cost executionof trades - a crucial element for theprofitability of any trading strategies ina near-efficient market. However, aninteresting question is whether techni-cal analysis has merit in more illiquidexotic markets, such as those for physi-cal assets (for instance real estate andships), where less volatile prices andmore pronounced market cycles sug-gest a higher degree of price pre-dictability.

The second-hand market for ocean-going bulk cargo ships is an obviouscandidate for applying technical analy-sis outside the box. As ships are mobileand fungible assets (at least within aparticular ship type), this is a global andcompetitive asset market, served by alarge number of brokers. Despite lim-ited liquidity in comparison to anyfinancial market, it is highly transparentcompared to other markets for physicalassets, with estimated prices for varioustypes of ships now published on aweekly basis by a centralised body (theBaltic Exchange in London) based onindependent price assessments by aninternational panel of shipbrokers.

Predicting the cyclesBulk cargo ships carry the world's rawmaterials in intercontinental trade andare typically grouped into ships trans-porting crude oil and oil products(tankers) and those carrying dry bulk

commodities such as coal, grain, andiron ore (bulk carriers). Both ship typescan be subdivided according to cargocarrying capacity. For instance, VeryLarge Crude Carriers (VLCCs) andAframax vessels denote large and mid-size tankers and Capesize and Panamaxvessels represent large and mid-sizebulk carriers, respectively. As can beseen in Figure 1, the prices for suchsecond-hand ships move in rather well-defined cycles - at first glance, promis-ing territory for trend-following techni-cal trading rules. Indeed, returns in thesecond-hand market for bulk shipsgenerally exhibit the characteristics thatare typical to speculative dynamics:large long-run price swings exhibiting aclear mean reverting pattern. Despitethese intriguing qualities our study rep-resents the first attempt to investigatethe merits of technical analysis in thismarket.

In order to assess the profitability oftechnical analysis in this market wefocus on common technical tradingstrategies that can be easily implement-ed in a computerised trading model: fil-ter rules, moving average crossoverrules and support and resistance levels.In order to avoid the effects of datasnooping we search for the best tradingrule, as defined by the highest cumula-tive wealth net of broker commission,during an initial sample period (1976 -1994) and then assess the economic

performance of the best trading ruleout of sample in the subsequent peri-od. In all cases the investor starts withcapital equal to the initial cost of theship. No short selling is possible andso the technical trader is either long oneship, in which case he receives freightrevenue from spot market operation, orout of the market. Given that ships arewasting assets we estimate the loss invalue due to ageing based on straight-line depreciation to scrap value at age25 years.

Table 1 illustrates the out-of-sampleperformance of the best trading rulecompared with the simple buy-and-hold strategy. The best-performingtechnical trading rules are generally fil-ter rules, although moving averagebased rules also perform well.

The technical trading rules performsignificantly better than the buy-and-hold investment strategy in the second-hand market for dry bulk carriers, butunderperform marginally in the tankermarket. This is due to the strongerfreight market for tankers during thesample period, as the technical tradingstrategy can outperform the buy-and-hold strategy only during market down-turns.

The effects of illiquidityThese results are based on the assump-tion that a purchase/sale can be imple-mented as and when a trading sig-

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→→

Techniques


nal occurs, at the price quoted by theshipbroker as of the beginning of eachmonth. In practice, a shipping investoris not likely to be able to execute a tradeon very short notice when a trading sig-nal occurs, either due to a lack of suit-able candidates in the market or theoften lengthy pre-purchase procedures.To address this issue, we imposed adelay of one month in the implementa-tion of a trading signal, conditional onthe signal remaining valid, to allow forprice slippage.

When we impose more realistic trad-ing conditions for an illiquid physicalmarket, the technical trading rules per-form less well in all sectors. In particu-lar, there is now evidence of superiorout-of-sample economic performanceonly in the Panamax bulk carrier sector,while in all other sectors the best tech-nical trading rule underperforms thebuy-and-hold strategy. As shown inFigure 2 in the case of the VLCC mar-

ket, only a small percentage of the largenumber of technical trading rulesinvestigated would actually have out-performed the buy-and-hold strategy.

These results highlight one of thechallenges that technical analysis faceswhen applied to illiquid markets. Whiletechnical analysis is undoubtedly a veryuseful tool to establish theoreticallyprofitable trading signals, probably witha higher success rate than for liquidfinancial assets, the inability to trade onthe information without a considerabledelay will tend to reduce realized prof-its of trend-following trading strategiesto insignificant levels. Combine thiswith short sales constraints and youhave an explanation of why cyclical andpredictable price patterns can persistand not be traded away.

However, this is not only an issue inmarkets for physical assets. In all finan-cial and commodity markets, illiquiditycomes with high bid-ask spreads anddelays in the execution of trades.Equally important, there is a higherprobability that (bid and ask) pricesbecome "stale", increasing the uncer-tainty of the price at which the tradewill take place, if there is a market to befound at desirable price levels at all. Bydefinition, infrequent trading will alsohamper the use of traditional chartingtechniques, as the underlying pricedynamic of the assets is not sampled

sufficiently frequently or regularly,increasing the risk of "false" patternsand trading signals. In the secondhandmarket for ships this has been partlyresolved by the construction of priceindices that are based on a panel ofbrokers' price assessments for a stan-dardised asset. However, this remainsless desirable as it leaves the price set-ting to designated individuals ratherthan the market as a whole.

Derivatives to the rescue?Many of the limitations listed above,such as the impossibility of short sell-ing and the delays in execution due toavailability, are constructs of a physicalmarket dealing with physical assets.With the recent development of swapcontracts (BSPAs) on ship values theselimitations should be removed. Whileit remains to be seen whether theresults presented here can be trans-ferred profitably to the forward market,increasing interest from institutionalinvestors and hedge funds suggests thattechnical analysis may soon find a newfrontier in the global shipping industry.

Roar Adland is director of ShippingInvestor Services (shippingin-vestor.com) and Dr. SteenKoekebakker is Senior Consultant atAgder Research Foundation.

Cumulative wealth ($m)

Ship Sector

Buy & hold

Best technicaltrading rule

VLCCAframaxCapesizePanamax

117.474.448.823.4

114.373.571.142.8

Figure 2.

Table 1. Out-of-sample economic perform-ance

Techniques


CHART PATTERN NOTES by David Gelfman

HEAD-AND-SHOULDERS AND DOUBLE BOTTOMS

Most traders and analysts areaware of what to look for intwo of the most reliable chartpatterns - head-and-shoul-ders and double bottoms -especially when it comes tovolume considerations. Butwhat other factors can wesearch for in order to increasethe scope or reliability ofthese patterns?

Head-and-shoulders bottom Neckline: The neckline usually slopesdown in a head-and-shoulders bottom.The pattern is complete when theresistance marked by the neckline isbroken, preferably on a closing pricebasis.

In a well-formed pattern, the slopewill not be too steep, but don't auto-matically discount a formation with asteep neckline. Also, some analystsbelieve an upward sloping neckline ismore bullish than a downward slopingone.

Duration: It is not unusual for a head-and-shoulders bottom to take severalmonths to develop. Volume activity instocks is characteristically less after aperiod of declining prices than after abull market. Because of this lower vol-ume, bottoms take longer to form andtend to be smaller than tops

Location of moving average: The head-and-shoulders bottom should be below themoving average. Compare the locationof the pattern to a moving average ofappropriate length. For short durationpatterns use a 50 day moving average,for longer patterns use a 200 day mov-ing average.

Variations on pattern: It is not uncom-mon to see more than two shouldersand more than one head. A commonversion of a multiple head-and-shoul-ders pattern includes two left shouldersof more or less equal size, one head,and then two right shoulders thatmimic the size and shape of the leftshoulders.

Furthermore, the classic head-and-shoulders pattern is made up of threesharply pointed components - the headand two shoulders. This is not alwaysthe case. Sometimes, the shoulders canlack sharp low points and instead bequite rounded. This does not affect thevalidity of the pattern.

Double bottomDouble bottoms are considered to beamong the most common of chart pat-terns. Since they seem to be so easy toidentify, the double bottom should beapproached with some caution.

Increase from first low: According to someanalysts, after the first bottom isformed a rally of at least 10% shouldfollow. Others maintain that the riseregistered between the two bottomsshould be at least 20%. The risebetween the lows tends to look round-ed but it can also be irregular in shape.

Alignment of the two lows: Sometimes thetwo lows comprising a double bottomare not at exactly the same price level.This does not necessarily render thepattern invalid. Nevertheless, if the sec-ond low varies in price from the firstlow by more than 3% or 4% the patternmay be less reliable.

Time between bottoms: Pay close attentionto the size of the pattern - the durationof the interval between the two lows.Generally, the longer the time between

the two lows, the more reliable the pat-tern.

Common criteriaNo volume spike on confirmation: The mostimportant part of the equation for suc-cess in these two patterns is rising vol-ume on the breakout. The lack of a vol-ume spike on the day of pattern confir-mation is an indication that this patternmay not be reliable. In addition, if thevolume remains constant or increasesover the duration of the pattern thenthe pattern should be considered lessreliable.

Support and resistance: A region of priceconsolidation or a strong support andresistance line at or around the targetprice is a strong indicator that the pricewill move to that point.

Moving average trend: The moving averageshould change direction within theduration of the pattern and shouldhead in the direction indicated by thepattern.

Inbound trend: A shallow inbound trendmay indicate a period of consolidationbefore the price move indicated by thepattern begins. Look for an inboundtrend that is longer than the duration ofthe pattern. The longer the downtrendbefore the basing occurs, the moreexplosive the breakout should be. Agood rule of thumb is that the inboundtrend should be at least twice the dura-tion of the pattern.

David Gelfman is a principal ofTrending123.com, a US based com-pany that provides pattern recogni-tion scanning software. The trend-ing123 pattern recognition scan ispowered by Recognia.

Interview


TA: You spent a great deal of time setting up theAlphaGen funds. How did you arrive at the point when youwere ready to launch?

MG: Prior to the launch my team was involved in develop-ing the quantitative models we now use. This was a long-term project that involved four years of simulations andpaper trading in order to perfect our strategy. This meantundertaking the time consuming process of applying theappropriate technology and databases that allowed us notonly to develop the model, but also to conduct extensivebacktesting of the portfolio recommendations it generated.Only when the model was consistently generating valuablerecommendations was it time to launch. We subsequentlylaunched the first fund in December 2003.

TA: What markets do AlphaGen cover?

MG: The AlphaGen funds are Regulus and Crucis, whichbetween them have around $650 million under manage-ment. The Regulus fund covers European equities while theCrucis fund covers Asia (ex-Japan) and Australia.

TA: How involved are you in day-to-day trading?

MG: Because of our model driven approach we are nottrading everyday so I have a lot of time on my hands toconduct more research. This involves constantly examiningour current strategies and checking to make sure the anom-alies still exist whilst looking for new ones. This is verymuch a team based approach and we are lucky to have tal-ented people at Gartmore including Luke Smith, my fellowAlphaGen investment manager.

TA:: Can you explain how your quantitative strategy works?

MG: We use a composite quantitative model derived fromfour separate models: estimate revision, price momentum,valuation and earnings quality. This gives us a number, an

expected return, every morning which is then fed into our'optimiser'. The optimiser looks at cross sectional stockselection using a set of very sophisticated constraints thatcontrol sector, country and so forth. This then producesour recommended portfolio. By blending the fast acting

GENERATING ALPHAQUANTITATIVE STRATEGIES AT GARTMOREMichael Gleason, head of quantitative strategies at Gartmore in London, explains why heexpects high returns from the firm's AlphaGen hedge funds.

MikeGleason

Interview


technical momentum driven factors with our very stable val-uation side we are able to achieve net returns of 12-15%.We really prefer to describe our approach as 'glass box'rather than 'black box'. Although it is quantitative and tech-nical it is also explainable and intuitive in nature. Theapproach is still automated but trades are not sent directlyto the dealing desk because we review the recommendedportfolios coming out of the optimiser and make subtlechanges. Ultimately, we like to think we are controlling thebox and it isn't controlling us although the basic specifica-tions of the model are updated only very slowly. In fact, wehaven't changed the overall weights in the model for overthree years.

The output of the model and optimiser often means tak-ing a contrarian view. For example, instead of looking atwhat Marconi stock itself is doing, we look more closely attheir competitors. We buy stocks with characteristics that wethink will outperform in the long run andthen we stick to them regardless of whateveryone else may be doing. On the techni-cal side, our model incorporates a propri-etary price momentum study and trendidentification. We see these as quantitativestudies and so don't get involved withanalysing chart patterns and such like. Ourmomentum model is the most technical weget and this comprises about 25% of theoverall model. The valuation model appliesbehavioural finance which is TA relatedbut not in the conventional sense. As ourfunds are market neutral, we don't have toconcern ourselves too much with day-to-day fundamentals and so have no need toworry about such things as currency man-agement or whether stocks are under orovervalued. My main focus is on research and ensuring ourmodels are working properly.

TA: There seems no doubt that behavioural finance is nowan important subject within the asset management commu-nity and technical analysis has embraced the subject for theobvious reason that trader psychology is common to both.What aspects of behavioural finance interest you most?

MG: We have spent millions of dollars and a great deal oftime developing our quantitative models. Even so, we couldhave got 85% of the way there with a simple spreadsheet. Itis the other 15% that is our edge and we have used behav-ioural analysis to capture this. More specifically, we look at aconcept called 'cognitive dissonance'. This is a psychologicalphenomenon which, when applied to the financial markets,means that individuals tend to overreact to short-term newsflows so market movements have a disproportionate impacton prices. We believe that there is too much emphasisplaced on the news flow of the day so if IBM is laying offworkers, for example, the market reaction is to punish it.

Although our products are market neutral, our alphamodel is designed to exploit this cognitive dissonance bytaking advantage of the relative cross-sectional mis-pricingof securities. In our view, this dissonance is responsible forthe consistent underperformance of most long-only andlong-short funds. In many respects our process is theantithesis of those that are most commonly used by long-short managers. Dissonance comes from active managerswho are underperforming their benchmarks and hedgefunds that are underperforming by trading too much. Itarises because the market is organisationally dysfunctionalso we are there to pick up the pieces so to speak. The bene-fit of applying this behavioural approach is that the humanpsyche evolves only very slowly so it will take a long timefor us to lose this edge.

TA: How is the market organisationally dysfunctional?

MG: We are looking at hundreds of fac-tors that are having a direct impact on thestocks in our portfolios, and we use com-puters to do it. This compares with theaverage equity analyst who may be cover-ing 20 or 30 stocks. It's not possible tokeep all of this information well organisedon a large number of stocks. This resultsin a low level of coverage for each stock.We take the view that there are many,many factors that influence the stock priceso one item of news doesn't justify achange of position. We are making moneyin a difficult environment at very low riskbecause we are feeding off this dysfunc-tionality in the marketplace. Our portfolioscontain over 700 stocks and if one falls by

50%, we don't panic.

TA: What do you think is the attitude towards technicalanalysis in the asset management community? Is Gartmoredifferent in any way?

MG: I would say the attitude is positive and open minded.Of course every manager is different and you will alwaysfind those who are mostly technically driven and those thatrely almost entirely on fundamentals. My guess is that mostuse a combination of both and this is probably the bestapproach. Asset managers will consider anything that maygive them an edge in the market. Obviously technical analy-sis falls into this category so I would say almost everybodyuses TA to some extent whether they are willing to admit itor not! I think fundamental analysis gets talked about to agreater extent because of the nature of the subject. It's justeasier to discuss earnings data than indicator/price diver-gence as it is something that is more tangible. Nevertheless,every manager will consult a chart and conduct some tech-nical analysis before taking a stock under coverage. So →→

Luke Smith

in that sense, TA does ultimately drive their investmentdecisions because if the technical signals are not encourag-ing, then a manager will certainly think twice about adjust-ing the stock in his portfolio. At Gartmore we are fairly typ-ical in our usage of technical analysis with non-quantitativemanagers tending to consider a mixture of both technicaland fundamental factors.

TA: Do you use quantitative execution strategies such asVWAP (volume weighted average price)?

MG: Our implementation strategy has recently beenupgraded. We have now adopted smart algorithmic tradingafter having previously traded purely on a risk basis forimmediate execution. This change came about because wewanted to lower our footprint in the marketplace. However,we don't put too much hope in VWAP benchmark. Insteadwe like to look at our implementation shortfall relative tothe time that we decide to make a decision. For example, ifits 2pm and we want to be in 300 stocks then that's ourbenchmark - the 2pm prices for those stocks. This gives usreal-time TCA (transaction cost analysis) on the implemen-tation. Actually, because of our leverage and size, we effec-tively become VWAP by definition so this is why theVWAP benchmark is not very relevant to us.

TA: Why have you chosen to close the funds at their cur-rent size?

MG: We had a target for the size of each fund when theywere being set up and these targets have now been met.Taking on an extra dollar would not yield any additionalprofits from the funds because the targets were designed toreflect their optimal size beyond which problems in man-agement and execution begin to arise. Our simulations showthat with a larger fund our models fail to work as well,transaction costs increase because we would be dealingmore and crucially, our market footprint would be bigger.Another factor is that our alpha would start to decaybecause more of our capital would be in the mega-capstocks which are a very efficient segment of the market.

TA: How do you see the future of the hedge fund industry?

MG: Much doom and gloom has been written about theoutlook for the hedge fund industry but I don't subscribe tothe view we are going to see an implosion of the market.From the roadshows we have done over the past year it'svery clear that the industry is becoming institutionalised.Funds that continue to rely on a single manager will be introuble and eventually lose business. Whilst I'm bullish forthe industry as a whole, I'm also sympathetic to the viewthat the industry is set for a shake-up and this will lead to areduction in the number of funds that are out there. It is anuncomfortable truth that no one likes to discuss but thefacts prove that most hedge funds do not outperform, andthis is the problem. For example, most long-only managersthat are benchmarked to a large-cap index like the FTSE orS&P have been underperforming for years. Stock selectionis very difficult and unless you have alpha you will bedestroyed in the marketplace because simply betting on betais too risky in the long-term. I think most institutions willbe gravitating towards a mix of high alpha generators withproven ability along with low risk alpha trackers.

TA: Some prominent analysts late last year were predictingthe beginning of a bear trend for stocks in the spring ofthis year leading to a significant decline over the next fewyears. April was certainly a bad month for the market so doyou go along with this view?

MG: No, not really. While 2005 is likely to be a disappoint-ing year for US stocks I don't see that a long-term bulltrend will be with us. The situation in the US is certainlyprecarious with a housing market bubble, enormous levelsof consumer debt, large budget and trade deficits and risinginterest rates. Even so, these factors alone are not enoughto severely damage the stock market, and besides, many ofthese factors should have been priced into stocks already.Forecasting the underlying market is not really my game andlong-term views of a year or more are very difficult becauseso much can change. So, if you would like my forecast forthe Dow in 2006 then I'm afraid it's "no comment!"

Interview


The Gartmore quants team

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Subject Matters


PREDICTIVE MODELS VERSUSTREND-FOLLOWING TECHNIQUESby David Sepiashvili

Investors usually either tryto predict stock marketbehaviour or try to react to

market trends, starting to tracethem after a significant part ofthe trend has already developed.While the main principles oftrend-following trading systemsare well studied and wellproven, theoretical concepts ofpredictive trading models arevirgin land, where research isonly in its initial stages.

The reasons for the lack of populari-ty of predictive models in comparisonwith trend-following systems likelyresults from a widespread false state-ment that no predictive model is possi-ble since there is no dependencybetween adjacent data points, andtherefore a trading system must bereactive, not predictive. We stronglybelieve that there are patterns with pre-dictive value in the market and that wecan recognize them based on advancedstatistical concepts. Our only concern isdefining the areas of their excellence.

The demarcation line between thesetwo groups of trading systems lies inthe frequency of the underlying data(intraday, daily, weekly, monthly) andthe time horizon being traded (veryshort, short, minor intermediate, inter-mediate, and long). As our studiesshow, predictive models are preferablefor trading on intraday data, particular-ly on high frequency data, and performsatisfactorily on daily data only in thevery short term. While trend-following

models are better suited to lower fre-quencies, namely daily, weekly, ormonthly data and are applicable tolonger terms. Here we present empiri-cal evidence to support this statement.

Testing periodTo find the right testing period, wemust take into consideration the cycli-cal nature of the stock market. It isimportant to determine in which phaseof the long wave we are currently inand which strategy is most appropriateto use in this phase. During the last 70years the US stock market has exhibit-ed five distinct trending/non-trendingperiods: the bearish market over the 20years from 1929 to about 1949, thesubsequent 17-year bull market (from1949 to about 1966) followed by a 17-year bear market, and finally an 18-yearbull market starting in 1982. In 2000the bull market was over and we arenow in a new long wave bear market.

To validate the potential advantagesand limitations of the trading systemswith respect to their profitability andconsistency, we will test them undervarious types of market conditions.Based on the above we chose for com-parative evaluation of predictive andtrend-following trading systems the lastfive years of the preceding bull 18-yearperiod from 1/1/1995 to 1/1/2000,and the starting five years of the cur-rent bearish cycle that is the periodfrom 1/1/2000 to 1/1/2005.Demonstrating both 'good' and 'bad'conditions when bull phases inter-spersed with bearish phases and quietnarrow ranging markets, they are suit-able for performance evaluation under

various market environments.

Trading modelsThe main difficulty when using a trend-following technique is to determine theappropriate moving average calculationperiod that will fit the market cycle. Ashorter calculation period results inoverreacting to minor, temporary fluc-tuations of price, whereas a longer cal-culation period may avoid the numer-ous false signals, but will react moreslowly and lag behind the security'sactual cycle, thereby missing the majorpart of the trend. In other words, thereis a trade off between sensitivity andreliability. With this in mind we includ-ed in the performance evaluationreport three different moving averagecrossover systems: a very short-termsystem, a short-term system, and anintermediate term system.

Predictive models, instead of reactingto market movements, are trying toforecast their direction ahead of time.Trading rules in our case are based onsmall-scale predictive patterns that havea high degree of recurrence. One ofthe simplest predictive patterns of thekind are 'down-down-up' and 'up-up-down', that is buy long after two con-secutive down moves, and sell shortafter two consecutive up moves. Thisgroup of trading rules, using price ratesof change as predictor variables, fore-cast with a certain degree of accuracy atarget variable - the direction of theprice movement, but not its value: priceis predicted to go up or to go down. Weconsider here predictive models of twoterms - very short and short.


Subject Matters

Performance The results in Table 1 illustrate the per-formance of the mechanical tradingrules on the Nasdaq 100 index. Forcompleteness, we also present resultsfor popular long-term investment trad-ing strategies, comprising the 50-dayand 200-day moving average crossoversystem (long positions only) and a buy& hold strategy.

It's worth noting that the hypotheti-cal performance of such trading doesnot represent actual trading resultsbecause it makes no allowance for trad-ing expenses and because, in reality, it isimpossible to execute a trade at theclosing price. Nevertheless, the twoapproaches are evaluated in identicalterms and the analysis lays an objectivebasis for comparison.

Comparative analysisNasdaq 100 index. As we can see fromTable I, predictive models perform ondaily data satisfactorily only in the veryshort term, while trend-following mod-els are preferable for longer terms(short, intermediate, and long).

In 2000-2005 the very short-termpredicative model outperforms theshort-term predictive model and alltrend-following models by a wide mar-gin, featuring an exceptionally high netprofit, profit factor, and efficiency fac-tor. Net profit per trade is sufficient toexceed all execution costs even in thecase of retail trading. During the pre-ceding five-year span the model returnsmeager gains.

The trend-following strategy demon-strates a sufficient level of returns dur-ing the strongly- trending market of

1995-2000. All the models yield goodresults, the long-term strategies toppingamong them. The existence of longwaves explains why long-term strate-gies deliver the best results during1995-2000, while the results in the bearmarket of 2000-2005 are disappointing.The recent years of the downswing ofthe stock market long wave has proventhat simple long-term investmentstrategies, being efficient in strongly-trending markets, fail when the marketmoves down or sideways. The factdemands a revision of investment poli-cy. In this deflationary phase of theeconomic long wave, which will proba-bly last until around 2012-2016, it is agood time to start using more flexibletrading strategies.

FTSE 100 index. We can observe ananalogous situation in other worldstock markets. The FTSE 100 index, asan example, is chosen. Figure 1 showsits absolute values over 1995-2005 withpercentage values marked in black andtotal net profit obtained by using a veryshort-term predictive model and calcu-lated without reinvestment andallowance for trading costs. We canclearly distinguish phases efficient for along-term trend following strategy andfor predictive models. This brings us toan essential point that we would havemade much more headway with com-bining these two strategies giving pref-erence to one of them depending onmarket current conditions.

Philadelphia gold & silver index. Let's con-sider an example of Philadelphia Gold& Silver index since gold is generallyrecognized as a global hedging tool.The common sense underlying thestatement is that investment in goldprovides an efficient hedge when confi-dence is lost in the stock market, as theprice of gold varies inversely to thestock market. Figure 2 shows absolutevalues of the index over 1995-2005with percentage values marked in blackand total net profit obtained by using avery short-term predictive model andcalculated without reinvestment andallowance for trading costs. As we

1/1/1995 – 1/1/2000

1/1/2000 - 1/1/2005

Table 1. Nasdaq 100. (n = total number of trades; n(w) = win probability (number of winningtrades to total number of trades); n(w)/n(l) = win probability to loss probability ratio; W/L =average win per winning trade to average loss per losing trade ratio; NP = total net profit (grossprofit less gross loss); PF = profit factor (gross profit to gross loss ratio); EF = efficiency fac-tor (net profit to gross profit ratio); NP/n = net profit per trade (allows assessment relative totrade costs))

→→

Subject Matters


can see, the percentage chart looks likeinvesting in the gold has not been agood investment during the first sixyears of the past decade. The last twoyears of a bullish four-year period,when price of gold moves in tandemwith stock market, are an exceptionfrom the empirical rule. But as we con-tinue to move through the deflationaryphase of the economic long wave, goldvalue, unlike the stock market, is likelyto go on rising, diverging again fromthe stock market.

Using the very short term predictivemodel as an alternative investmentstrategy we could have obtainedencouraging results: total net profit of317.2% with 282 trades, and averagenet profit per trade of 1.12%. The trad-ing model demonstrates high profit fac-tor (2.36) and efficiency factor (0.58)with a stable growth of returns (Figure2).

Performance characteristicsPredictive models allow investors andtraders to forecast the direction ofprice movement with a high degree ofaccuracy, providing an extremely highwin probability (around 0.7). Compareit to the win probability values of (0.35- 0.45), which are considered satisfacto-ry for trend-following models. But amore accurate prediction of reversalpoints is accompanied here by signifi-cantly lower win-loss ratios than intrend-following models: compare W/L< 1 for predictive models with W/L >2 for trend-following models. Suchasymmetric performance is an impor-tant aspect of predictive models thatrequires further attention.

David Sepiashvili is president ofAlticom Inc. (www.alticom.com).He is also a doctorate candidate insignal processing at CarnegieMellon University in Pittsburgh,USA.

Figure 1. Combining two techniques for profits in different market environments: here you cansee how we can get high profits by combining long-term investment during upward trendingperiods and swing trading based on the predictive model during downward and sideways phas-es of price movement.

Figure 2. Gold vehicle: here you see how it's possible to benefit from trading gold's regularshort-term cycles, while long-term investment is a failure.

“IN THIS DEFLATIONARY PHASE OF THE ECONOMICLONG WAVE...IT IS A GOOD TIME TO START USINGMORE FLEXIBLE TRADING STRATEGIES.”

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VANTAGEPOINT

Software


It has long been lamentedin technical analysis cir-cles that indicators that

appear to work so well are infact lagged in their responseand therefore hindered intheir predictive ability. Thechallenge to the humblemoving average has alwaysbeen to somehow make itmore predictive.

VantagePoint (VP), launched in theearly nineties from Florida basedMarket Technologies, goes some waytowards that goal, demonstrating ahigh degree of accuracy from theresults of its five independent neuralnetworks.

Neural nets have been around for awhile now. Often their main draw-back is that of the complexity of theoutput and time taken to analyse andcompute the results. These limita-tions often make these models diffi-cult to apply in an active trading envi-ronment.

Even though the technology "underthe bonnet" is rather complex with

VP, the outputs are simple to under-stand and easy to apply. VP's accura-cy and simplicity are without doubttwo of the key reasons for the prod-uct's success and longevity. The com-pany has continued to prosper sinceits inception in 1979 whilst mostother end-of-day software companieshave fallen by the way.

Markets coveredEach of VP's 43 markets is sold as abespoke module. Additional marketsare being progressively introducedeach year. Of interest to the institu-tional market is the imminent releaseof 13 currency pairs based on 24hour cash forex data, as opposed tothe seven futures market based cur-rency modules available at themoment. In addition to this, MarketTechnologies also plans to release amodule for the DAX Index (cash orfutures) sometime in the second quar-ter of 2005.

Intra-market relationshipsIn the late 1980's it became obvious,especially with the advent of the 1987crash, just how interrelated marketshad become - even on an intra day

basis. The interrelatedness of marketsis now a critical consideration for anytrader or investor.

VP uses data input from nine sepa-rate markets in order to run its fiveneural net outputs. The outputs,which can be displayed in chart for-mat or as a report, are generated atthe end of each trading day and givepredictions that go several days for-ward.

Their British pound model, forexample, uses inputs from the USDollar Index, Eurodollar, EuroFX,Comex Gold, Japanese Yen, SwissFranc, S&P 500, 10 Year US Treasurynotes as well as the British pounditself. In most cases cash, futures orcontinuous contract data can be cho-sen as the input from an easy to useinput configuration table.

Data is provided by either CSI orGenesis via a daily internet datadownload. Both are well known andaccurate sources of end of day data.Errors are automatically back correct-ed by these companies.

The five neural netsThere are five neural nets, each pro-viding their own piece of the picture.

INTERMARKET NEURAL NETWORKS

Software

The first and second neural netshave the job of predicting the pricerange for the next trading day - thefirst net predicts the high and the sec-ond net predicts the low (Figure 1). Astatistically accurate idea of the area ahigh or low will take place in the fol-lowing day's market can clearly be putto very good use in many intra-daytrading strategies. More than onetrader we spoke to was using the highand low target areas to judge entryand exit points on intra-day trades -sometimes in conjunction with otherindicators.

When we compared for example,VP's predicted next day highs andlows with Support 1 and Resistance 1

pivot points, VP performed far better.One reason being that pivot pointlevels for the following day rely onthe previous day's range. Since nar-row days often follow expansive days,pivot points have this inherent flawthat VP does not appear to sufferfrom. However, consistently predict-ing the next day's high and low with100% accuracy is impossible and evenVP sometimes misses the mark. VPis, however, a relatively accurate andreliable predictive probability tool.

A third neural network issues a"Neural Index" (Figure 2). This indi-cates whether or not the market islikely to top or bottom out (in otherwords, change trend direction) in the

next two days. Some traders use thisindex as a filter to take intra-day posi-tions in conjunction with the intra-day predicted high or low, sellingclose to or at the predicted high if themarket in question is predicted to fallor buying at or near the predicted lowif the neural index shows a likelyuptrend.

The fourth network predicts thefive-day moving average for two daysinto the future and the fifth and finalneural network predicts the ten-daymoving average for four days into thefuture (Figures 3 and 4). Several ofthe traders we interviewed for thisarticle used a crossover of the predic-tive averages with a correspondingchange in the neural index in order togenerate a signal.

VP in practiceThe five outputs are often used incombination to confirm each other.However, VP is a set of indicatorsand not a system. As such, each trad-er develops his or her own systemsand trading techniques from VP'soutput.

For example, one futures trader wespoke to looks at all 43 markets eachday and has developed a form ofmomentum indicator from the aver-ages in order to reliably detect whenthe market is coming into a turn. Hewill then use this in combination withthe neural index output and try alsoto time his entry according to the pre-dicted high or low. Therefore, evenfor a longer term position, he usesVP to optimize his entry point.Surprisingly, this trader now only usesVP, having apparently given up onother packages.

All VP indicators can be chartedand tabulated in a user defined out-put. Output formats include oscilla-tors (that are based around a zeroline) and allow users to compare pre-dicted output with the current data.For example, Phigh Diff is a deriva-tive indicator that shows the differ-ence between the predicted high

Figure 1.

Figure 2.


→→

for tomorrow and today's actual high.In addition, a historical output reportis available that will show all results ina user defined table, going back justunder three months.

Case studyMark DiMaggio, a broker withOpportunities in Options fromCalifornia, uses VP to manage a widevariety of private futures and optionsportfolios. He also sometimes worksin conjunction with clients who havethe VP software.

As many traders are unwilling tocarry the overnight exposure in theirpositions in what DiMaggio describesas "the sleep factor", he uses calendar

ratio spreads in markets such as crudeoil, silver, bonds and the euro in orderto maximise the return while beingable to quantify the risk. He claimsthat although he may use various indi-cators and chart patterns to get thebigger picture, VP plays a critical rolein timing position entry. He creditsVP for taking him (and his clientportfolio) into a recent and profitableshort position in crude oil.

Even more important though,DiMaggio asserts that VP gives himstaying power in a position. Whilstsome clients will inevitably choose tobank profits early on in a move,DiMaggio claims that VP, being bothpredictive and accurate, enables him

to stay short or long thus allowing thefull potential of the move to be real-ized.

VP is sold with a minimum of three mar-kets (chosen from the 43 available) andstarts at US$3,500 for individual clients.Additional discounts are available for 6,10, 15, 21, 30 and all 43 markets.Contact Market Technologies, LLC forinstitutional pricing. Free forecasts are avail-able by request at their website, www.tradertech.com.

Larry Levy is a freelance journalistand private trader.


Software

Figure 3.

Figure 4.

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VERSION

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Books

Quantitative Trading StrategiesHarnessing the Power ofQuantitative Techniques to Createa Winning Trading Program

By Lars KestnerMcGraw-Hill344 pages, £46.99ISBN 0-07-141239-5

Quantitative Trading Strategies isavailable from the TechnicalAnalyst Bookshop at the reducedprice of £39.94 plus £2.00 P+P. Toorder please call 01730 233870and quote "The Technical Analystmagazine" or order online atwww.global-investor.com/technical-analyst. Books are usually postedwithin one working day of yourorder.

From pattern spotters who act on a hunch to number crunching quantitative ana-lysts who create objective trading strategies, technical analysis is certainly a broadchurch. Many occupy the middle ground, using a mix of patterns, oscillators and

indicators on a fairly ad-hoc basis, but Lars Kestner - former equity derivatives trader atSolomon Smith Barney and creator of the K-ratio performance measure - presents a veryelegant argument for pushing your trading and portfolio management firmly towards asystematically quants based approach, something which many hedge funds and propdesks are already doing.

Traders with more informal styles could understandably argue that market subtleties,which are apparent to the experienced trader, may not be apparent to rigid mechanicalrules. However, rather like the chess computers that are now so good they can beat theGrand Masters, quantitative trading strategies are likely to present an ever increasing chal-lenge to the discretionary trader. As Kestner points out in the opening pages, between1996 and 2001 systematic Commodity Trading Advisers (CTAs) enjoyed average annualreturns of 7.1 percent compared to only 0.6 percent for discretionary CTAs.

Once you've decided that quantitative trading strategies may be a route you wish toexplore, this well structured and lucid book is the perfect starting point.

The book is divided into two parts. Part one takes us on a vast and substantial tour cov-ering almost everything you need to know for developing a quantitative trading strategy.Important concepts such as optimization, filtering, portfolio management and the prosand cons of different performance measures are all discussed clearly and with a gooddeal of substance. He also goes on to look at important advances in quantitative tradingand presents ways of building arbitrage and relative value based strategies, involving, forexample, pairs trading, credit spreads and volatility strategies.

Part Two is the core of the book and, as he says, is "...unique in that few existent textsexamine performance of trading strategies in both stocks and futures markets in anapples-to-apples side-by-side comparison."

Kestner uses the familiar oscillators (such as RSI and stochastics) and trend-followingtechniques (such as moving averages) as building blocks for generating the strategies thathe examines. In all, there are 27 trading strategies that are tested on 29 futures and 34 USstocks for the period January 1990 to December 2001. Tables of results are presented foreach strategy and include among other measures, the K-ratio, Sharpe ratio, maximumdrawdown, plus an equity curve. The results are presented without accounting for trans-action costs and slippage so that, according to Kestner, he can spot a strategy that per-forms so poorly that it may be reversed to generate profits.

The aim of the book, however, is not to present trading strategies that should be adopt-ed wholesale. In fact, most of the strategies' net profits erode over the period under studyand it's clear that strategies need to be regularly re-assessed. Instead, the book is meantto provide a framework upon which new quantitative techniques can be developed.

If you've already launched yourself along the path of mechanisation and are a quantsanalyst responsible for devising trading strategies, then this book will tell you little new.For everyone else, Kestner's Quantitative Trading Strategies provides an excellentgrounding in how to develop back-test, implement and monitor a quantitative technicaltrading strategy. Armed with data and suitable software (or even an Excel spreadsheet),the opportunities to develop a properly tested strategy are infinite.

QUANTITATIVE TRADING STRATEGIES


Books


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Technical Analysis of the Financial Markets 2nd Ed.John Murphy (New York Institute of Finance) 1999RRP: £49.99 Our Price: £39.99 saving: 20%

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Technical Analysis and Stock Market Profits Richard Schabacker (Harriman House) 2005 RRP: £39.99 Our Price: £33.99 Saving: 15%

The top 10 best selling technical analysis books over the past 12 months:(Source: Global Investor)

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Commitments of Traders Report

COMMITMENTS OF TRADERS REPORT10 May 2004 - 11 May 2005Futures only (open interest) non-commercial net long positions and spot rates

10-year US Treasury Source: CBOT

Dow jones Industrial Average Source: CBOT

5-year US Treasury Source: CBOT

Swiss franc Source: CME

Pound sterling Source: CME Yen Source: CME

-250000

-200000

-150000

-100000

-50000

0

50000

100000

150000

200000

11/05/2004 03/08/2004 26/10/2004 18/01/2005 12/04/20053.60

3.80

4.00

4.20

4.40

4.60

4.80

5.00Non-commercial net long Spot

-200000

-150000

-100000

-50000

0

50000

100000

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11/05/2004 03/08/2004 26/10/2004 18/01/2005 12/04/20050

0.5

1

1.5

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2.5

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3.5

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4.5

5Non-commercial net long Spot

-30000

-20000

-10000

0

10000

20000

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50000

11/05/2004 03/08/2004 09/11/2004 01/02/2005 26/04/20051

1.05

1.1

1.15

1.2

1.25

1.3


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35000

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11/05/2004 03/08/2004 26/10/2004 18/01/2005 12/04/20051.65

1.70

1.75

1.80

1.85

1.90

1.95


-60000

-40000

-20000

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20000

40000

60000

11/05/2004 03/08/2004 26/10/2004 18/01/2005 12/04/200596

98

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11/05/2004 03/08/2004 26/10/2004 18/01/2005 12/04/20059200

9400

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Commitments of Traders Report

Euro Source: CME

Nasdaq Source: CME

3-month eurodollar Source: CME

Nikkei Source: CME

Gold Source: CEI US dollar index Source: NYCE

-4000

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11/05/2004 03/08/2004 26/10/2004 18/01/2005 12/04/200510000

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11/05/2004 03/08/2004 26/10/2004 18/01/2005 12/04/20050.00

0.50

1.00

1.50

2.00

2.50

3.00


-20000

-10000

0

10000

20000

30000

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60000

70000

11/05/2004 03/08/2004 26/10/2004 18/01/2005 12/04/20051.05

1.10

1.15

1.20

1.25

1.30

1.35

1.40

Non-commercial net long Spot

-20000

-15000

-10000

-5000

0

5000

10000

11/05/2004 03/08/2004 26/10/2004 18/01/2005 12/04/2005102

104

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11/05/2004 03/08/2004 26/10/2004 18/01/2005 12/04/2005300

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Long-Term Technicals


LONG-TERM TECHNICALS

Provided by Thomas Anthonj, ABN Amro, Amsterdam

The market seems uninterested in any further upside so a breakunder 1.8760 then a further fall to 1.8672 and 1.8695 and themore crucial 1.8512 could occur. Key support levels are then1.8428 and more likely 1.8196. To avoid this, prices need to holdabove 1.8760 and then break above 1.8892. The market wouldthen need to clear the downward sloping resistance line at 1.9160and 1.9217 to shake off any remaining bears.

GBP-USD

The downside has cooled off but this could just be a pullback withina bigger downward rotation. That said, if prices move above106.16/41 then they could make it up through the remainingFibonacci levels but to receive confirmation of a new bull phase themarket would have to break above 108.90 breaking the row of lowertops at 111.73. That would then target at least 115.00. Support is at104.43.

USD-JPY

Prices are at an area where a bounce higher could occur. But so farthe bulls seem unwilling to step in. To get any sustainable upsidestarted the market would need to clear the 1.2964 hurdle. If thatoccurs then 1.3125 would be the next challenge above which thedownward sloping line at 1.3340 would come into focus. A fall under1.2810 and 1.2766 would largely end this chance and would target1.2734/08 and 1.2510.

EUR-USD

The longer-term weekly chart shows a break bellow the last lows at422.25. This continues the current downward bias and should seeprices break below 410.4 and reach the last two downside Fibonaccilevels at 403.85, and 391.37. The bigger picture suggests howeverthat levels could make it as far as the 371.65 low before any signifi-cant rebound. Resistance at the moment is 422.25. Above it wouldcall for a rise to the line at 433.30 before selling might start again.

Gold


Long-Term Technicals

A possible head-and-shoulders reversal pattern could be in theworks but the last shoulder still needs to form. A move below10489.84 could be enough to drive levels down towards 9382.95which is also the 61.8% Fibonacci level of the previous up move.Holding there would be bullish and give a good chance of making itback to at least 12189.98. Above that, levels as high as 16408 couldbe realized this being the potential pattern's suggested reach.Directly breaking above 12189.98 would target the peak at 12556.11.

Nikkei

The recent slide to 1010.51 ended with a key-day reversal bar.This has launched a correction phase that could go as far as thelast two upside Fibonacci levels at 10612.53 and 10758.55 beforesellers pounce again. Higher than 10758.55 would indicate a will-ingness to take on the 10984.37 peak and clearing this wouldleave little in the way of the 11750.28 all-time-high. Support startsat 10100 and extends to the low at 1010.51.

Dow Jones

A slight reversal bar was set at 1889.85 and this was the start of acorrection higher. However, the market would need to clear theresistance line at 2020.00 to become bullish. Higher than that then2116.75 and the top at 2191.60 and 2395.73 would need to betaken out to confirm a resumption of the up trend - targeting2647.00. Failing to clear 2020.00 and slipping under 1889.85would clear a path to at least 1751.

Nasdaq

The bulls backed off the 61.85 Fibonacci level at 1253 and thiscaused the up trend to wobble. Failing to break above 1191.00 and1217.90 could see levels slip under the previous low at 1137.50 andhead on down to the 38.2% Fibonacci level and previous low at1060.72. That is a good place for a rebound but below it calls for adeeper setback to at least 1006 and potentially 1011.00. Above1217.90 would likely result in 1253 being rammed and taking it outshould be enough to spark a rally to 1370.

S&P 500


Portfolio ManagementAcademy

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Technical Analysis in the Commodity Markets

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Technical Analysis in the Equity Markets

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Monthly Meeting

June8

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Technical Analysis

August4-5

New York,US

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Monthly Meeting

September14

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July6

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