Emerging Trends and Future Direction

part 4:

Emerging trends and future direction

Chapter 10Basket algorithms – The next generation

Chapter 11The future of algorithmic trading

97

107

■ ALGORITHMIC TRADING ■ A BUY-SIDE HANDBOOK ■ THE TRADE 2005

■ Chapter 10


Algorithmic trading, once con-sidered an obscure method of

automatic benchmark-drivenorder execution, is going main-stream in the world of trading.The estimates of 35% to 40% oftotal US equity volume tradedthrough algorithms by 2008 nolonger look wildly optimistic.

The labour-efficient and objectivenature of algorithms appeals to buy-side traders as it gives them morecontrol of their order flow. A skilledtrader may be able to outperform anyof the modern trading algorithms fora single stock. However, it would beimpossible to expect the same traderto monitor every single position in abasket of a hundred names or more.The algorithmic method enablesthese positions to be traded automat-ically in a cost effective way versus thetraditional trading desk.

Since most strategies aim tomatch specific benchmarks likeVWAP, TWAP, Close, or ArrivalPrice, the algorithm’s performance

is transparent and measurable. Thetrader can therefore easily compareperformance across differentproviders and implementations.

This chapter will focus onImplementation Shortfall strategiesfor baskets of stocks. The emer-gence of these advanced strategies,such as ROBE™ from MiletusTrading, is a sign that algorithmictrading has evolved beyond thefirst generation of core strategies.In short, algorithmic portfoliotrading is not only labour-efficientand objective, but a very promisingand largely unexplored domain inthe field of rule-based execution.

In order to fully understandalgorithmic portfolio trading strate-gies, one must examine the ideasunderlying the ImplementationShortfall (IS) strategy for an indi-vidual stock. Several years ago,Almgren and Chriss introduced theconcept of an efficient trading fron-tier (ETF)1, which is the bedrock ofmost IS strategies today.

97

*Anna Bystrik, PhD, research analyst,Miletus Trading

**Richard Johnson,senior vicepresident in chargeof Product Sales,Miletus Trading

Basket algorithms – The next generation

Implementation Shortfall strategies for baskets of stocks

*Anna Bystrik and **Richard Johnson

1 R. Almgren,‘Optimalexecution withnonlinear impactfunctions andtrading-enhancedrisk, AppliedMathematicalFinance 10’(2003), 1-18.

2 R. Almgren and N.Chriss, ‘Optimalexecution ofportfoliotransactions’, J.Risk 3 (Winter2000/2001) 5-39.

3 A. Freyre-Sanders,R. Guobuzaite, K.Byrne, ‘A reviewof trading costmodels: reducingtransaction costs,J. Investing’ (Fall2004), 93-115.

■ Chapter 10



Efficient trading frontier –

single stock

The breakthrough paper ‘Optimalexecution of portfolio transactions’by Robert Almgren and Neil Chriss2

quantifies the relationship betweentwo important components of trad-ing costs – market impact and tim-ing risk. Any attempt to devise astrategy with low timing risk leadsto aggressive trading and aninevitable increase in market impactcosts. Conversely, the decision tominimise market impact necessi-tates passive trading, and a highdegree of timing risk.

These ideas become easier tograsp if a simple example is used toillustrate the concepts. Consider asell order of 78,000 shares of XYZ tobe completed by the end of the day.The difficulty of this trade dependson the expected trading volume forXYZ. As a proxy for expected vol-ume, it is common to use a 20-dayaverage daily volume (ADV). If ADVis 650,000 shares then the positionconstitutes 12% of the ADV.

Clearly, there are many ways tocomplete this trade within a day.One possibility is to use a VWAPstrategy. Ideally, this approach willmaintain a steady participationrate of 12% throughout the day.There are, however, severalassumptions which need to be sat-isfied for a smooth execution: (a)650,000 shares of XYZ is an accu-rate volume forecast; (b) the intra-

day volume distribution (U-curve)is consistent with the historic aver-ages; and (c) the trading strategy isable to maintain the indicated par-ticipation rate at all times.

The market reality may invali-date these assumptions. For exam-ple, U-curves can be quite unpre-dictable, and on any given day mayvary from historic averages. Forsimplicity, assume XYZ has a per-fectly flat intra-day volume profile– with 50,000 shares traded in each30-minute bin (in the UnitedStates the stock markets are openfor six and a half hours, from 9:30to 16:00, giving thirteen 30-minbins). In this simplified example,maintaining a steady 12% partici-pation rate by trading 6,000 sharesper bin completes the order.

In this example the trading ispassive, thus minimising tempo-rary market impact of trades.Market impact in general is verydifficult to measure or estimate (cf.3). The existing pre-trade analysistools (TIE™ from Miletus Trading)apply advanced statistical tech-niques to large trade databases inorder to estimate market impactfor any pre-selected executionstrategy. In this example, a pre-trade engine may produce a marketimpact estimate of 30 bps. Anyother trading strategy is likely tolead to a higher impact estimate.

On the other hand, for VWAPexecution the timing risk is relative-

98


■ Chapter 10


ly high. After three hours of tradingmore than half of the original posi-tion is still exposed to marketvolatility. The timing risk (under-stood as standard deviation of theaverage trade price) depends pri-marily on the volatility of the stockand is thus easy to estimate. If dailyvolatility for XYZ is 170 bps, thenthe timing risk for a VWAP strategywill be around 100 bps.

Is VWAP the optimal strategy ifthe benchmark is Arrival Price? Itmay be, but only for a trader who isnot worried about market risk. Arisk-averse trader may prefer a moreaggressive strategy, which starts totrade at a higher participation rate(e.g. 20% in the morning), reducingthe position faster. This strategy maycause 40 bps of market impact, butreduce timing risk to 70 bps. An evenmore aggressive strategy will lead tothe estimates of 48 bps of impact and60 bps of risk. Which is preferable?The correct way to approach thisquestion is to construct a ‘utilityfunction’ U = I + R, where I is theexpected impact, R is the timing riskof the strategy and is a risk-aver-sion coefficient which reflects thetrader’s preferences. Since the opti-mal trading strategy seeks to min-imise this function, it may be moreappropriate to refer to it as a ‘penaltyfunction’. Take = 0.5 and computethe penalty for each of the three sam-ple strategies (see Fig. 1): for theVWAP strategy

U = 30 + 0.5■100 = 80 bps,for the second strategy U = 40 + 0.5■70 = 75 bps,and for the third strategy U= 48 + 0.5■60 = 78 bps.The results are close, but the penaltyis the smallest for the second strategy.Thus, for = 0.5, the second strategyis preferable. The VWAP strategy istoo passive, while the third strategy isoverly aggressive. Note that for =1.5 the third, more aggressive, strate-gy is preferable due to a higher risk-aversion level.

In their work, Almgren andChriss outline how to build aunique optimal trading strategy foreach level of risk-aversion; the setof these optimal strategies definesan efficient trading frontier. Mostmodern pre-trade tools are capableof calculating these optimal strate-gies and assessing the difficulty of atrade schedule. However, these cal-culations are of limited value whenit comes to designing and imple-menting a real-world trading strat-egy. There are many high frequencyvariables in the data which are notgoing to conform to their historic,pre-trade estimates. Factors such asintra-day volatility, spread and pre-dicted volume may vary with eachtick; hence the static executionschedule will no longer be optimal.

A workable high frequencytrading strategy necessitates a moreflexible application of ETF con-cepts. Rather than calculate a static

99

■ Chapter 10



execution schedule for a given ina utility function U = I + R, it isessential to consider incrementalchanges in utility. Instead of tryingto minimise U for the day, look atthe short-term increment ∆U, anddetermine the rate of tradingwhich leads to the maximalimprovement in utility. The usualtrade off between impact and tim-ing risk is still present but on ashorter time scale. The target par-ticipation rate will constantlychange, according to thespecified and actual market

conditions. If observed volatilitylevels are low, the strategy will slowdown without increasing the tim-ing risk estimate. Similarly, itshould also respond to the changesin volume and be able to takeadvantage of unanticipated sourcesof liquidity.

In summary, a modern IS sin-gle-stock strategy should combinea mathematical ETF frameworkwith the flexibility required fortrading. That way, the overallurgency of trading conforms tothe stated demands, and the strat-

100

VWAP

Medium

Aggressive

9:30

10:0

010

:30

11:0

011

:30

12:0

012

:30

13:0

013

:30

14:0

014

:30

15:0

015

:30

16:0

0

Rem

aini

ng s

hare

s

Figure 1: Three different execution strategies


■ Chapter 10


egy delivers better execution qual-ity by reacting to changing marketconditions.

Optimal trading strategies for

baskets

The calculations are more complexin the design and implementationof an IS portfolio trading strategy.At this point, let’s consider the fol-lowing questions:■ How does one estimate impact

and timing risk for the basket?And what is the correct way toconstruct an efficient tradingfrontier in this case?

■ How does one take advantage ofmarket opportunities and differ-ent sources of liquidity whilemaintaining the prescribed con-straints and urgency of execution?

■ How does one accommodate theassorted constraints on the bas-ket (dollar balance/ratio, sectorconstraints) without sacrificingperformance?

A good portfolio trading strategyshould address these problems; sim-ply combining single stock IS strate-gies will not solve any of them.

Efficient trading frontier –portfoliosThe market impact estimate for aportfolio can be calculated by sim-ply adding the market impact esti-mates of individual trades.However, aggregating timing riskestimates is considerably more

involved. There are two possibleapproaches here, although bothhave their limitations.

The first approach to risk estima-tion is based upon the use of historiccorrelation coefficients among thestocks in the portfolio. One problemis that it is difficult to obtain a reli-able estimate of a correlation matrix– and virtually impossible to accu-rately estimate all of the correlationcoefficients in large portfolios. Evensmaller portfolios require a samplesize of several months in order toobtain a meaningful estimate – andthere is no guarantee that theobtained values are suitable forintra-day timing risk calculation.

The second approach involvesselecting and using risk factors toconstruct and utilise a model fortiming risk estimates. There aresome drawbacks here as well:which risk factors to select besidesthe standard triple (market, size,value); and how to filter out noisein this model. However, with theproper choice of risk factors andsampling intervals, this approach ispreferable to correlation matrices.

Once both expected input andtiming risk are calculated, it becomespossible to compute the optimaltrading strategy for a given . Sincethe market impact is usually mod-elled as a nonlinear function of par-ticipation rate (cf. 4), this calculationimmediately leads to a large nonlin-ear optimisation problem. For a

101

4 G. Iori, M. G.Daniels, J. D.Farmer, L.Gillemot et al., ‘Ananalysis of priceimpact function inorder-drivenmarkets’, PhysicaA, 324 (2003),146-151.

■ Chapter 10



portfolio of 300 names the optimisa-tion problem for one day will involveat least 300●13 = 3900 variables (ifthe trading day is split into thirteen30-minute bins). This is pushing theupper limit of capabilities for mod-ern optimisers. Because of this, manyexisting pre-trade engines settle foran approximate solution in the caseof large portfolios. It is crucial toremember that the static optimalschedule has very limited value –more so for a basket of stocks asthere are many more variables whichcan change throughout a given day.

Dynamic portfolio tradingstrategiesA dynamic portfolio strategy shouldsatisfy the same requirements as asingle stock IS algorithm; beingcapable of reacting to changing mar-ket conditions and consequentupdates in statistical estimates.

Let ∆T be a relatively short timeinterval (on the order of a fewminutes). The strategy should aimto minimise the change in the utili-ty function (∆U = ∆I + ∆R) overthis interval. This problem mayseem intractable, primarily due tothe complicated nature of portfoliorisk estimates; nevertheless, given agood factor risk model, ∆R can beestimated in a relatively simplemanner. The key here is to use awell-known concept of MCR (mar-ginal contribution to risk) for eachsecurity in the basket.

If x(k) is the size of the positionk in the basket, then the MCR ofthis position can be estimated asthe partial derivative q(k) =∂R/∂x(k). Knowing the MCR of theposition allows us to estimate howthe portfolio risk changes if theposition is reduced by some smallincrement ∆x(k).

Moreover, in terms of shorttime interval ∆T, the overall changein risk can be approximated as

n

∆R=∑q(k)∆x(k)k=1

and the overall change in utilitythen can be represented as

n

∆U=∑∆U(k)k=1

where∆U(k) = ∆I(k) + q(k)∆x(k).

The advantage of this approach isthat now the interaction betweenstocks in the portfolio is eliminat-ed, at least for very short time-frames. Then it becomes possibleto find the optimal participationrate for each stock in the basket –that is the trading rate which min-imises each individual utility incre-ment ∆U(k). If the strategy thenexecutes at this rate over the period∆T, the result will be optimal forthe entire portfolio.

102


■ Chapter 10


A dynamic strategy adjusts thetarget rates continuously; therefore,it can quickly react to opportuni-ties that arise from changing mar-ket conditions. The MiletusROBE™ (Risk-Optimized BasketExecution) strategy uses thisdynamic approach to control mar-ket impact and timing risk.

Note that ‘off-the-shelf ’ riskmodels are not suitable for high-frequency trading applications; it isnecessary to develop a robustmodel calibrated for a shorter timehorizon. Based on experience, athree-factor model with sectors asadditional risk factors yields supe-rior results when implementingdynamic trading strategies.

It is interesting to note that it isentirely possible for some positionsin the portfolio to have zero MCR.In this case they don’t contribute tothe overall portfolio risk, and willalways be traded at a steady rate(close to that of a VWAP strategy)regardless of the specified value of

. Some positions may even havenegative MCR; subsequently theyperform a valuable function of riskreduction on the portfolio level.These positions will be traded at aslower rate – the higher the , theslower the rate. Figure 2 (overleaf)shows participation rates for differ-ent values of marginal contributionto risk. In general, this shows thatsimply merging optimal schedulesfor stocks in the basket may give an

overall portfolio strategy which isvery far from optimal.

Dealing with constraintsThe nature of dynamic portfoliotrading strategies allows accommo-dation of many constraints (suchas single stock participation rates,portfolio dollar balance/ratio, sec-tor constraints) without a dramaticincrease in computational over-head. The challenge is to make thenecessary adjustments in a smoothway, without sharply varying par-ticipation rates.

Building participation rate ceil-ings into a strategy is practical andstraightforward to implement. It isless trivial to accommodate ‘hard’dollar balance/ratio constraints. Theoptimal way to control risk with aperfectly balanced two-sided basketmay result in a temporary imbal-ance at some point during the day.To achieve a market neutral portfo-lio, the side with a higher overallbeta will execute faster. In this case,the risk model and the customer-imposed constraints contradict eachother. The strategy must work with-in the constraints by selectivelyadjusting the participation rates ormodify a utility function to includea constraint penalty.

One-sided portfolios

Basket algorithms are also applicablewhen dealing with a one-sided port-folio; in this scenario the primary

103

■ Chapter 10



objective is to minimise active risk –a tracking error versus a benchmark.Typically the benchmark will be amarket index, but custom bench-marks provided by the client canalso be incorporated. A basket algo-rithm used in conjunction with afutures hedge is the best way to min-imise market risk when executing aone-sided basket.

It is vital to refine the baskettrading algorithm to handle one-sided lists, since it serves as animportant tool for several more

complicated trading tasks. Portfolioalgorithms are occasionally appliedto baskets where the imbalance is2:1 or more. Moreover, it may berequired to maintain this dollarratio throughout the trading peri-od. The risk management tech-niques used for balanced basketsare less effective in this setting. Themost practical way to trade a port-folio with these constraints is tosplit it into two one-sided portfo-lios, and then apply the IS strategyfor each of them. This way the

104

Zero MCR

Negative MCR

Positive MCR

9:30

10:0

010

:30

11:0

011

:30

12:0

012

:30

13:0

013

:30

14:0

014

:30

15:0

015

:30

16:0

0

Part

icip

atio

n ra

te

Figure 2: Marginal contribution to risk


■ Chapter 10


trading schedule for each side isoptimal. If the value of is thesame for each sub-basket, the dollarratio constraint is easy to satisfy,and only minor adjustments to theparticipation rates will be required.

In special situations, transitiontrading may require having separatebenchmarks for each side of a bal-anced basket. For example, if a plansponsor wishes to reduce his large-cap holdings and replace them withsmall-cap stocks, it makes sense forhim to consider a different bench-mark for each side of a transitionportfolio. Once again, the portfoliois split into two smaller baskets, andeach is traded separately versus adifferent benchmark – such as S&P500 and Russell 2000, respectively.The strategy performance is thenjudged based on the tracking errorfor each side.

Other varieties and customisa-tions of portfolio trading strategymay be demanded by transitiontraders. In fact, some transitionmanagers now apply the ETFapproach to planning the transition,viewing it as a multi-period optimi-sation problem5. Since a dynamicportfolio-trading strategy is alsobased on the ETF approach, its useallows the manager a better controlover the process of transition.

Next generation

The next generation of tradingalgorithms has come a long way

from the static schedules derivedfrom historical U-curves thatunderpinned the first generation ofalgorithms. These original algo-rithms helped traders manage theirworkload by allowing them to sendlarge lists of stocks for automatedexecution; the trading logic, howev-er, was applied individually to eachstock in the list. Now traders canaccess advanced algorithms thatwill trade each stock in the portfo-lio according to how every otherstock is behaving and adapt to con-tinually changing market condi-tions. The benefits of incorporatingreal-time risk and market impactanalytics will be immediatelyapparent to index fund managers,hedge funds and transition traders,who desire a risk neutral way tomove from one portfolio to anoth-er. After these early adopters, usageof basket algorithms will spread tothe wider investment community,especially if delivered through anintuitive trading application thatallows them to monitor risk andperformance in real time and adjustconstraints to align the executionstrategy with their trading goals. ■

105

5 C. Blake, D.Petrich, A.Ulitsky,‘The right tool forthe job: usingmulti-periodoptimization intransitions’,TransitionManagement,InstitutionalInvestor 2003


■ Chapter 11


The future of algorithmic trading

In the last few years, we have wit-nessed the rapid adoption of

algorithms to trade single stocks.Future pundits might call 2005 the‘year of the algorithm for the insti-tutional equities trading business’.As the institutional trading envi-ronment has become more com-petitive, traders have turned to effi-cient algorithmic execution.Algorithms like VWAP, TWAP,POV, PEG, SMARKET, andImplementation Shortfall are allpart of the traders arsenal whenexecuting single stock orders. Arecent survey of buy-side tradersindicates that the drivers behindthe trend of algorithmic adoptionare: (1) control over the tradingprocess, (2) ability to focus onvalue added activities, and (3) costcontrol. In addition to these gains,trading algorithms have allowed

firms to trade stealthily to reduceboth the explicit and implicit trad-ing costs by lowering commissionsand reducing impact costs.

Fast forward to 2006

In 2006, the battle for market sharein the algorithmic space will extendacross the European, LatinAmerican and Asian markets. In theAmericas we will likely see morecreative algorithmic deal making asbroker/dealers will struggle toremain competitive in the ‘low-touch’ segment. As buy-side firmscontinue to reduce the number ofexecution partners in their efforts toincrease cost-efficiencies, manysmall broker/dealers will not be ableto commit the required financialresources needed to remain com-petitive in the low-touch DMA andalgorithmic segment of the market.

107

*Robert L Kissell,vice president,Global ExecutionServices, JP Morgan

**Andrew Freyre-Sanders,head of AlgorithmicTrading, EMEA, JP Morgan

***Carl Carrie,head of AlgorithmicTrading, USA,JP Morgan

What will be the shape of algorithmic trading in the year ahead, asbrokers strive for market share and buy-side demand grows for ahigher order of intelligence in engineering algorithms?

Robert L. Kissell*, Andrew Freyre-Sanders** and Carl Carrie***

■ Chapter 11



Algorithmic trading requiressubstantial research and develop-ment. While many firms were ableto develop first generation algo-rithms with reasonably small mea-sures of dispersion around target-ed benchmarks, it has becomeincreasingly clear that benchmarkperformance (or transaction costanalysis) will become more of acompetitive differentiator andrequire more sophisticated finan-cial engineering. Additionally, sus-taining algorithmic performancewill require new investment inlow-latency market data and orderconnectivity to fragmentedexchanges, ECNs, alternativecrossing networks and inter-listedmarket centres. Service desks mayalso require new ‘high- touch’ ser-vices such as consultative meetingswith their algorithmic analysts,interactive algorithmic order andexecution analysis and algorithm-of-algorithms analytics for tradingbaskets.

A rapidly changing and highlycompetitive landscape for algorith-mic trading in 2006 will encourage

some broker/dealer algorithmicproviders to creatively partnerwith vendors and otherbroker/dealers, while some clientswill look to outsource dealingand/or partner to create uniquecompetitive advantages in capabili-ties and cost structure.

Trading analytics

New pre-trade capabilities providetraders and investors with therequired transparency to specifyappropriately chosen algorithms.They provide portfolio managerswith liquidity information as wellas algorithm risk and cost break-downs. Some of the new measuresthat are becoming part of the newstandard execution terminology,include: Market Impact, TimingRisk, Risk Contribution andTrading Difficulty. It’s possible thatthese sensitivities will become ascritical to stock traders as Delta,Vega and Gamma are for optionstraders.

Determination of appropriatealgorithms and algorithmic para-meters is much easier with accurateinformation on pre-trade liquidity,difficulty, cost and risk analytics.Investors need to first determine ifthe execution is suitable for algo-rithmic trading, and if so, whichalgorithm and algorithmic parame-ter are most consistent with theoverall investment objective. Alltoo often, funds incur unnecessary

108

“Sustaining algorithmicperformance will require

new investment in low-latencymarket data and orderconnectivity.”

■


■ Chapter 11


slippage due to improper selectionof execution strategy, which trans-lates directly to the bottom line interms of decreased returns.

Performance enhancing

algorithms

Increasingly, clients will alsodemand flexibility with algorithmicparameters such as volume limits,adjustments for special tradingdays such as half-day trading ses-sions or FOMC days, or dynamicmarket adjustments based on pricemomentum and other variables.Refinements in the core of thesealgorithms, whether they are calledLimit Order Models or Micro-Order Submission Models, will alsoprovide improved trading results.

Another area for active devel-opment will be to prevent infor-mation leakage and algorithmicgaming. Even experienced tradersrisk unintentionally signallingtheir order to the marketplace,whether they are using an algo-rithm or not. They can see it inslippage or feel it in the patternand delays in fills. As algorithmshave become more popular, infor-mation leakage and gaming havebecome part of the broader debateabout algorithms and the bench-marks to which they are oftentied. In fact, the default bench-mark for many traders, VWAP, hasoften been criticised because ofthe ‘push’ associated with the dis-

ciplined bucket trading, no matterhow much the venue, size andtime between trades is ran-domised. One of the areas of algo-rithmic development that hasreceived little press coverage is theincreased amount of work beingundertaken on algorithms toreduce information leakage.

Algorithmic trading requiresinvestors to specify rules on amacro level while each micro-orderis automatically determined bywhatever parameters theOptimisation sets the algorithm to.For example, on the macro levelinvestors are required to specifytheir benchmark price (e.g.,Decision Price, Arrival Price, etc.),choice of algorithm and set ofparameters. While price bench-mark is tied closely to the portfoliomanager’s investment goal, algo-rithm and parameters should adaptto changing market conditions andprices. It is more difficult to ascer-tain how the algorithm shouldadapt to changing market condi-tion and prices. Micro level deci-sions govern the price of limit

109

“Information leakage andgaming have become part of

the broader debate aboutalgorithms and the benchmarks towhich they are often tied.”

■

■ Chapter 11



orders, frequency of market orders,randomness of size and quantity,intervals between order submis-sion, and appropriate tradingvenue. These micro level rules arein place to ensure that actual trans-actions follow the optimally pre-scribed strategies.

One popular parameter unique-ly available to JP Morgan’sImplementation Shortfall algo-rithm is the ability to change thedistributional characteristics versusthe price benchmark. An impor-tant point is that with any adapta-tion strategy (e.g., adjust participa-tion rates based on price ‘money-ness’), traders need to be comfort-able with the changing cost profileto ensure potential costs are consis-tent with the investment objectives.For example, an adaptation strate-gy that becomes more aggressive intimes of favorable prices and lessaggressive in times of adverse pricemovement (e.g., Aggressive In-the-Money, ‘AIM’) will incur betterprices on average but increasesnegative risk exposures (e.g., the

probability of realising unfavorableprices). An adaptation strategy thatbecomes more passive in times offavourable price movement andmore aggressive in times of adverseprice movement (e.g., Passive In-the-Money, ‘PIM’) will incur lessfavourable prices on average butwith reduced downside risk.Potential shifts in cost profile areshown in Figure 1 against a normal‘no adaptation tactic’.

In 2006, hedge funds andsophisticated asset managers willstart to use new kinds of algo-rithms that are not tied to a tradi-tional benchmark. For example,JPMorgan has released a smartmarket algorithm (SMARKET)that tries to improve the price ofsending a market order by dividingan order into multiple, but aggres-sively priced, limit orders that willconvert to market orders if theorders expire without being filled.

Algorithmic developers are rac-ing to adapt new and existing algo-rithms to handle new market com-plexities, ranging from regulatorymissives to capital commitment toilliquid stocks. New protected quoteand fast/slow market handling willimply additional complexity forsmart order routers and algorithms.The rise of NYSE flow in crossingengines and other third marketvenues will increase the need foralgorithms to consolidate the dis-parate pools of liquidity, leveraging

110

“Algorithmic developers areracing to adapt new and

existing algorithms to handle newmarket complexities, ranging fromregulatory missives to capitalcommitment to illiquid stocks.”

■


■ Chapter 11


fill rates and latency informationaccordingly. Similarly, increasedcapital use for small and mid capswill be increasingly automated byalgorithmic market making.

Portfolio algorithmic trading

in 2006

Portfolio algorithmic trading islikely to emerge as the most signifi-cant algorithmic capability in themarket. Several market participantshave announced portfolio algorith-mic offerings. Some market partic-ipants will mimic the single stockparadigm of sending orders via FIXdirectly to a portfolio algorithm,while others will use a combinationof FIX and rich web interfaces toprovide extended capabilities.

Are single stock algorithmsappropriate for portfolios? While‘algorithm conjurers’ have devel-oped systems to trade and trackbenchmarks like VWAP andArrival Price or the current Close

or a specific targeted volume per-centage – all of these algorithmswere developed to work with singlestocks in mind. However, singlestock algorithms are cumbersomeand unwieldy when applied toportfolios. The trader needs tominimise information leakageacross the list, which is generally abigger challenge than working asingle name. When a portfolio istraded, the trader will typicallywant to apply basket level con-straints such as setting the maxi-mum share as a percentage of ADVfor any individual name. Not everytrader has the same risk tolerances.When the market starts to drop, inorder to limit unintended risk (i.e.,sector, dollar, or beta skews) thetrader must more actively managethe execution of the basket, whichgreatly increases the potential ofinformation leakage.

Some industry experts havedescribed the emergence of a new

111

AIMStaticStatic

Aggressive In-the-Money

C1C2 BadGood

Passive In-the-Money

C1 C3 BadGood

PIM

Figure 1

■ Chapter 11



class of algorithmic trading forportfolios as the ‘algorithm-of-algorithms’. While a lofty monikerfor an algorithm, it conveys theessence of a higher order intelli-gence controlling an array of algo-rithms. Figure 2 compares thereduction in risk achieved from anoptimal algorithm-of-algorithmsapproach to a VWAP strategy for along/short basket. The optimalapproach should reduce the risk ofadverse price movement muchmore quickly than trading theportfolio by merely applyingVWAP across all tickers to reducemarket impact costs alone. AVWAP strategy only provides a lin-ear reduction in risk, whereas anoptimal strategy provides a rapid

non-linear risk reduction for asmall increase in market impactcosts. The individual names in abasket do not trade independentlyof the market or each other – theyare inherently correlated.

A new type of tool is neededthat provides a much richer frame-work for optimally working a port-folio and aligning the executionprocess with the portfolio con-struction process. At the core ofthis new tool would be an opti-miser that would determine theefficient trajectory to: reduce trad-ing costs, resulting from marketimpact cost and price appreciation(alpha decay); manage intradayrisk, resulting from price volatilityand covariance of price movementacross all names in the portfolio;and manage liquidity risk, theuncertainty associated with dailyvolumes and intraday volume pro-file. In this context, a trading opti-miser does not generate a tradeschedule like traditional investmentoptimisers, but rather dynamicallytranslates the intraday trading tra-jectory directly into algorithmparameters.

The TAO of trading

JPMorgan is currently using anoptimal algorithm-of-algorithms inits portfolio trading business and amodified version of it in its algo-rithmic market making effort. Thesystem is called TAO (‘Trading

112

List optimisedVWAP

1 2 3 4 5 6 7 8 9 10 11 12 13

Dol

lar r

isk

Period

FIGURE 2


■ Chapter 11


Algorithmic Optimizer’). TAO hasbeen designed to reduce tradingcosts and manage risk and liquidityintraday, as outlined above. It willbe available to trading clients laterthis year. TAO is an algorithmictrading system for portfolios thatincorporates an interactive webpage which can integrate directlyinto the traders OMS. TAO allowsthe trader to review pre-trade ana-lytics, configure algorithmic para-meters, optimise to an optimal‘Efficient Trading Frontier’ list ofalgorithms, and monitor executionperformance against multiplebenchmarks in realtime, all fromone web screen.

TAO will dynamically readjustall of the algorithms and their

parameters based on trader-sup-plied constraints, the system’sknowledge of the portfolio com-position, what has been traded,market prices and any otherinformation it can derive orobtain. Now imagine if there wassome optimal level of trading foreach ticker. In this scenario, theoptimal algorithm-of-algorithmswould rebalance the portfolio andall of its worker algorithmsaccording to centralised informa-tion and intelligence, but executedin a distributed fashion. ■

113

The information contained herein is provided forinformation only, and any views or opinionsexpressed herein are solely those of the individualauthors and may differ from the views and opinionsexpressed by other departments or divisions ofJPMorgan and its affiliates.JPMorgan is the marketing name for JPMorganChase & Co. and its subsidiaries and affiliatesworldwide. J.P. Morgan Securities Inc. is a memberof NASD, NYSE and SIPC. The JPMorgan Chase Bankis a member of the FDIC. J.P. Morgan Futures Inc. isa member of the NFA. J.P. Morgan Securities Ltd. isauthorized by the FSA. J.P. Morgan Securities AsiaPte Ltd., (JPMSA) and J.P. Morgan Securities (AsiaPacific) Limited, are regulated by the Hong KongSecurities & Futures Commission. JPMSA isregulated by the Monetary Authority of Singaporeand the Financial Services Agency in Japan. Issuedand approved for distribution in the UK and theEuropean Economic Area by J.P. Morgan Securities

Ltd., J.P. Morgan Europe Limited and J.P. Morgan plc,members of the London Stock Exchange andregulated by the Financial Services Authority. Issuedand distributed in Australia by J.P. Morgan AustraliaLimited and J.P. Morgan Markets Australia Pte.Securities Limited which accept responsibility for itscontents and are regulated by the AustralianSecurities and Investments Commission. J.P. MorganMarkets Australia Pty. Ltd. is a licensed investmentadvisor and a futures broker, and it is a member ofthe Sydney Futures Exchange. J.P. Morgan Securities(Far East) Limited Seoul Branch is a member of theKorean Stock Exchange and J.P. Morgan Futures(Korea) Limited is a member of the Korean FuturesExchange. In the UK and other EEA countries, thiscommentary is not available for distribution topersons regarded as private customers (orequivalent) in their home jurisdiction. Copyright 2005 JPMorgan Chase & Co. All rightsreserved.


■ THE TRADE GUIDE – BROKER ALGORITHMS

The TRADE guide to broker algorithms

Reprinted from The TRADE, Issue 3, Jan-Mar 2005

115



The Electronic Trading Services™ (ETS)group, part of Banc of America

Securities’ global equities platform, wasformed in February 2004. ETS is dedicat-ed to developing and delivering a suite ofelectronic trading products to institutionalinvestors. The formation of ETS followedthe 2003 acquisition of Vector Partners, aquantitatively driven broker-dealer pro-viding algorithmic, block and portfoliotrading technology, which served as afoundation for the development of newclient solutions. Shortly after ETS waslaunched, the acquisition of Direct AccessFinancial Corp. (DAFC) was announced, aprovider of direct access technology,extending ETS’ technology platform.Today, ETS offers a comprehensive algo-rithmic toolkit and has hundreds ofclients subscribing to its algorithmic trad-ing services.

ETS’ consultative approach to salesensures that clients are ‘couriered’through the system by experiencedprofessionals who are accountable atevery stage of the customer cycle. ETShas a team focused on improving theperformance of its algorithms. Newanticipated product introductions includeaverage price stop, enhanced transactionanalysis tools and broader connectivityoptions.

Trading benchmarksETS has both agency and principalstrategies with both single stock and listcapabilities. Eight strategies are currentlyoffered to clients: VWAP, TWAP, TVOL,Razor, Market on Close, Arrival Price,Market Call and Premier Block Trading™(PBT), an electronic and anonymous blockliquidity utility for orders up to $20million for the entire Russell 1000 stockuniverse.

Flexibility & customisationAlgorithms have different settings(passive, neutral and aggressive) tocreate distinct execution profiles for eachtrade. ETS is committed to understandingits clients trading objectives andproviding the tools to achieve their goals.To this end, it partners with clients tocustomise existing algorithms, as well asto develop proprietary models – a featurewhich many clients take advantage of.

Performance measurementPerformance measurement is seen ascritically important and ETS offers acomprehensive range of post-trade dataand analytics.

Connectivity optionsClients can connect via Bloomberg frontend and a variety of trade and ordermanagement systems. These includeAdvent, BRASS, Charles River, Fidessa,FlexTrade, InstaQuote, LatentZero,LongView and Macgregor. In February2005, ETS added Reuters to the list ofthird party connections. ETS is alsoavailable via FIX connections.

116

Banc of America SecuritiesElectronic Trading Services™ (ETS)



Algorithmic trading is an important growth area for BNY Brokerage over-

all and for its DExsm (Direct ExecutionServices) platform, launched in the thirdquarter of 2004. Delivering the benefits ofalgorithmic trading to clients has beenpart of an ongoing effort and clients haveaccess to the same platform and tradingtools that BNY has used on its tradingdesk for years. Whether a client deliversalgorithmic orders to the program tradingdesk or works algorithmic orders into themarketplace themselves, a ‘pure agencyposture’ means that BNY’s services are‘conflict-free’.

Today, 60% of its clients make use itsalgorithms. BNY reports increased usageof algorithms as part of its clients’ tradingstrategies and anticipates a steady rate ofat least 30% growth in the next year. Aspart of its development plans, BNY islooking to integrate pre- and post-tradereporting into its DMA platform.

Trading benchmarksBNY does not place benchmarkconstraints on its algorithmic offering.Algorithms can be executed across a‘myriad’ of customer-driven benchmarks.

Flexibility & customisationClients can customise the behaviour ofalgorithms by setting parameter values.Using the DEx platform clients can alsocreate their own algorithms.Customisation remains an area ofdifferentiation for BNY and it plans tocontinue to focus on providing clientswith bespoke algorithms.

Performance measurementClients are provided with same dayexecution quality reports, as well ascustomised pre- and post-trade reports,which include impact studies, liquidityscreens, confidence intervals andstraightforward P&L.

Connectivity optionsBNY is regularly expanding itsconnectivity to the third party providerused by its clients. Connections havebeen established with all the leadingOMS and network providers, includingBloomberg, Macgregor, Charles River,LongView, Eze Castle and, most recently,since January 2005, SunGuardTransaction Network (STN). Traditionalmeans of connectivity via FTP, VPN, emailand the internet are also available. BNY isfully FIX compliant.

117

BNY BrokerageDirect Execution Services (DExSM)



Algorithmic trading is a ‘critical’ compo-nent of Citigroup’s brokerage busi-

ness. In 2004, it established itsAlternative Execution division. There arenow over 30 institutions connected inEurope alone using its algorithms.Citigroup has backed its commitment inthis area with high levels of investment.In 2004, it acquired Lava Trading, aprovider of high-performance tradingsolutions, to enhance its capability in allaspects of electronic trading.

Algorithmic models are built to takeinto account idiosyncrasies at market andsingle stock level and are ‘extensively’tested on internal flow before being madeavailable to clients. When designing thecore components of its algorithms,stealth is as much a concern asperformance and reliability. Clients areprovided with the same algorithms thatare used on the algorithmic tradingexecution desk.

Inherent in the design of Citigroup’salgorithmic solutions is ‘the requirementto improve the trading process.’ To thisend, a premium is placed, now and in thefuture, on offering clients acomprehensive consultancy service toenable them to select algorithms to bestsuit their trading strategies.

Trading benchmarksThere are four core benchmarks: VWAP,TWAP, MOC and Participation. Clients canchoose one of these or create a hybrid ofa standard benchmark.

Flexibility & customisationClients have ‘full control’ of orders, whichcan be modified in real time. Thealgorithmic models are designed to beflexible. This flexibility enables templatesto be designed for clients, which allowthem to select benchmarks to fitindividual trading strategies. Algorithmsare also tailored in line with marketdynamics, ensuring that an algorithmbuilt for a highly liquid market is not usedto trade in a low liquidity environment, forexample.

Performance measurementIn early 2004, Citigroup acquired BestExecution Consulting Services (BECS), anindependent web-based provider oftransaction cost analysis. Through thisservice clients can evaluate theperformance of Citigroup relative to otherbrokers.

Connectivity optionsAnyone with a FIX compliant system canconnect with Citigroup’s algorithms. Thisincludes the majority of ordermanagement systems in use around theworld. Clients who are not FIX compliantcan access Citigroup’s algorithms viaCitigroup’s Algorithmic Trading executiondesk, or via Bloomberg front end.

118

CitigroupAlternative Execution



CSFB launched its Advanced ExecutionServices (AES™) in 2002, making it

the first major broker to offer an algorith-mic trading service. Today, AES operatesconsistently in over 20 countries acrossEurope, US and Asia. With close to 400clients using AES directly in 2005, CSFBremains one of the principal providers ofalgorithms to the buy-side and extends‘best of breed’ algorithms to clients, asused by in-house traders.

Algorithmic trading is a majorcomponent of equity trading revenue,with a number of CSFB’s large clientscurrently aiming to direct 25% to 35% oftheir order flow to algorithms.

Client anonymity is given the highestpriority and CSFB prides itself inprotecting AES users from any form ofinformation leakage. Orders are processedwithout manual intervention, protectingthe identity of AES’ clients. CSFB hasapproached external auditors to discussthe feasibility of third party verification ofthe anonymity that AES offers clients.

To retain and attract clients, CSFBseeks to continually improve executionperformance. One way of doing this willbe to take advantage of internal crossing.Service enhancements will be introducedin such a way to ensure that anonymity isnever compromised.

Trading benchmarksAES tactics are designed to work towardsa number of benchmarks. The main onesused are Slippage from Arrival Price,reducing market impact, VWAP and InLine with Volume. More complexalgorithms are being made available suchas PhD, which is designed to optimiseprogram trades.

Flexibility & customisationAlgorithms are optimised to work withdefault settings designed to produce bestperformance. However, parameters canbe adjusted to fit a client’s trading style,allowing for a more aggressive strategy,for example. If necessary, CSFB will buildunique algorithms for a client.

Performance measurementCSFB can provide clients with same dayexecution performance for their AEStrades. An internal execution performanceanalysis tool, ExPRT, is used to measureexecution performance against a range ofdata points including start, mid andinterval VWAP. Performance can bemeasured on an order-by-order basis oroverall by ticket size, sector, market,tactic etc. A feature called ‘storyboard’provides clients with real-timeinformation on events in the stock theyare trading.

Connectivity optionsClients can access AES via any FIX-enabled OMS. A large number of vendors,including Bloomberg and Reuters, havedeveloped full AES functionality on theirorder entry tickets, allowing clients toadjust the variable parameters availablefor an AES tactic.

119

CSFBAdvanced Execution Services (AES™)



Goldman Sachs was one of the firstbrokers to enter the algorithmic trad-

ing space following their acquisition ofSpear, Leeds & Kellogg. GSAT views algo-rithmic trading as integral to the growthof its equity business and expects buy-side demand in this area to increasesteadily. Currently, over 35% of GoldmanSach’s equity flow on any day is executedthrough their algorithmic trading deskand the firm expects the combined vol-ume of equity trade run through algo-rithms, program trades and electronictrading to increase to as much as 65% by2006.

In terms of product development,GSAT is focused on deepening its marketcoverage through a global, multi-assetoffering. And it is committed to creating‘smarter’ algorithms that give clients abroad range of customisable parametersto trade ever more complex benchmarks.

Trading benchmarksThe prevailing benchmarks of choice usedby clients are VWAP and ImplementationShortfall. Other commonly availablebenchmarks include Piccolo (Small OrderSpread Capture algorithm) and TWAP.GSAT’s newest algorithm, 4CAST,explicitly balances market impact againstopportunity cost.

A number of customised algorithmshave been created to match unique,customer-driven requirements and GSATintends to widen its focus to meet otherbenchmarks as identified by clients.

Flexibility & customisationBy adjusting a wide range of availabletrading parameters, GSAT says that a‘significant’ degree of customisation canbe undertaken to fit a particularexecution style. GSAT occasionallydevelops customised solutions for its‘best clients’ as required.

Performance measurementGoldman Sachs’ electronic tradingplatform and order entry system,REDIPlus®, offers a range of analyticaltools for order performance monitoring(pre-trade, real time and post-trade),encompassed in a system entitled ‘TheGuide’. This system also provides tradingestimates from GSAT’s proprietary costmodel and other statistics to help theuser understand what the algorithm isthinking prior to submitting the order.

Connectivity optionsREDIPlus provides clients with access toGSAT’s algorithms. The models are alsoaccessible via third party OMS vendors,FIX connections or Bloomberg.

120

Goldman SachsGoldman Sachs Algorithmic Trading (GSATsm)



Instinet believes that its ‘unconflicted’agency-only business model leaves it

well positioned in the algorithmic space.Its algorithmic trading platform providesaccess to 40 markets worldwide and isutilised by over 1,500 clients in NorthAmerica, Europe an Asia. The rules-basedtrading solutions offered by Instinet allowclients to select and use those ruleswhich meet their specific trading require-ments. As an agency-only broker, there isno proprietary trading and thus no risk tothe client of information leakage benefit-ing an internal trading desk. Externally,trading performance is constantly moni-tored and adjustments undertaken asnecessary to ensure there is no ‘front run-ning’ in the market.

In the future, Instinet intends toexpand the range of rules in order toreduce the ‘true’ total cost of trading forclients (implicit and explicit). Andleveraging its unconflicted businessmodel, increasing emphasis will beplaced on customised solutions. Instinetconcurs with industry estimates andexpects institutions’ use of algorithmictrading to double by 2006.

Trading benchmarksAlgorithms meet the followingbenchmarks: VWAP, TWAP, TVOL, ArrivalPrice, Risk Arbitrage, Pegging, Discretionand Spread (Pairs) Trading.

Flexibility & customisationAlgorithms are configurable throughmultiple rules-based parameters. In apairs trade, for example, throughInstinet’s risk arbitrage pairs rule a clientcan specify a variety of settings includingthe cash component of a deal, the cashimprovement they are seeking or thepercentage improvement.

Instinet works with clients to createcustomised algorithms to suit particularinvestment strategies and minimisetransaction costs.

Performance measurementInstinet’s proprietary Newport™ portfoliotrading system allows intra-day and post-trade analysis to multiple benchmarks.Clients are also encouraged to make useof reports from third-party transactioncost specialists such as Plexus Group andAbel/Noser, who measure the executionquality of hundreds of brokers.

Connectivity optionsInstinet’s algorithmic trading service isaccessible via FIX connection, Newportand the Instinet Trading Portal® frontend, in addition to many third-party ordermanagement systems.

121

InstinetAlgorithmic Trading Group



As an agency-only provider of quantita-tive trading solutions, ITG avoids con-

flicts of interest arising in connection withproprietary trading. Algorithms are avail-able via ITG’s SmartServer™ service. ITGdescribes its SmartServer strategies as‘intelligent trading destinations that auto-execute trades according to a pre-definedtrading strategy.’ Currently around 150clients across the globe use ITG’s algo-rithms. Between 30 and 40 million sharesa day are traded via these algorithms,representing approximately 40% of ITG’stotal trading volume. The company antici-pates that this will climb to as high as80% of its overall volume in the next fiveyears.

Clients have access to the liquidity ofITG’s POSIT®, the intra-day equitycrossing system. A proprietary front-endsystem, TRITON™, meanwhile, offers acomplete set of integrated execution andanalytics tools. It allows clients to routeorders to more than 75 destinations andaccess ITG’s proprietary pre-trade,execution and post-trade analytics.

Trading benchmarksThe standard benchmarks offered by ITG’sSmartServer service include VWAP, TWAP,Implementation Shortfall (Decision Price)and Market Close. However, SmartServerusers are not constrained by these andcan apply customised benchmarks, givingtraders the ability to switch betweendifferent strategies in response tochanges in market conditions.

Flexibility & customisationITG offers strategy customisation on bothclient desktops as well as the server side.Using SmartServer and TRITON, clientsare able to modify strategy parametersand distributions in real time. TRITONallows clients to customise further on topof a ‘black box’ strategy and write theirown trading rules and algorithms to auto-trade. Custom strategy servers are builtfor specific clients.

Performance measurementA variety of tools are available to measurethe performance of SmartServeralgorithms. These include a performanceattribution tool that monitors execution,strategy profile deviation and executionprice deviation. Clients using TRITONhave access to ITG ACE® for pre-tradecost estimation and ITG Risk™ forpredicting and managing volatility. Clientscan use ITG eXtra real-time performancemeasurement or ITG TCA® for post-trademeasurement across multipledestinations, markets and brokers.

Connectivity optionsSmartServer can be accessed directly viaa FIX connection from users’ ordermanagement systems and via the TRITONtrading interface.

122

Investment Technology Group (ITG)SmartServer™



JPMorgan considers algorithmic tradinga core part of its business strategy, both

for internal trading and client distributionservices. A ‘highly quantitative focus andpedigree’ has had a major impact onproduct development, which has beensteered by its proprietary statistical arbi-trage group. There are currently around50 clients globally using the company’salgorithms. The focus with this clientgroup is on equipping them with anunderstanding and approach that willhelp them use algorithms to best effect,not just as a ‘black box’ trading tool.

The focus moving forward is onbuilding a comprehensive trading toolset,encompassing strategies for executionalong with tools for trade optimisationand decision support. Considerableemphasis is placed on developing a ‘morepervasive, flexible and transparentproduct’ that is integrated seamlessly intothe trader’s workflow. A significantincrease in algorithmic trading isanticipated in the next two to five years.

Trading benchmarksA ‘strong concentration’ on flexibility,permits both complex and simplified‘parameterisation’ based on clientpreference. Strategies target a variety ofbenchmarks, including VWAP andImplementation Shortfall (Arrival Price,Close Price) and a ‘trader pre-definedbenchmark.’

Flexibility & customisationThe quantitative team works with clientsto create customised algorithmic andconnectivity solutions specific to theirrequirements. JPMorgan’s developmenteffort has concentrated on expandingupon its core limit order model or ‘micro-placement’ strategy. Algorithms are builtas ‘wrappers’ around this model for usealongside clients’ existing benchmarks.

Performance measurementEnd of day reports are sent to clients onan order-by-order basis, supported bybenchmark performance statistics. Onlinepost-trade tools, which allow clients toindependently verify any trade that isundertaken intra-day against a range ofbenchmarks, are also provided.

In February 2005, JPMorganannounced that it would be launching apre-trade analytics service accessible viaBloomberg’s Execution ManagementService. The service will allow clients toselect the algorithm best suited to meettheir trading objective.

Connectivity optionsJPMorgan is connected to all the majorthird-party order and trade managementsystems for order and algorithmic routing.

123

JPMorganElectronic Execution Services



Lehman Brothers’ LMX™ was launchedin 2004. LMX users receive the benefit

of Lehman Brothers’ ‘one firm’ philosophy,which allows clients to maximise the effi-ciency and effectiveness of their relation-ship with the company. Lehman Brothersseeks to differentiate itself through itsdistribution and service models, leverag-ing its existing sales and support chan-nels. Hundreds of clients benefit from theuse of LMX algorithms, either directly viaits Direct To Model™ access channel, orindirectly through its Execution Serviceplatform. On average, over $3 billion aday is executed globally using LMX strate-gies.

LMX was established in response toclient demand and its future direction willalso be shaped by that demand. There aretwo areas of enhancement that users ofits algorithms have highlighted: betterguidance around strategy and strategyparameter selection, and tighter analyticintegration before, during and after thetrade. Lehman Brothers is in the processof building new functionality tocomplement its strategies in these areas.

Trading benchmarksLMX strategies support all major tradingbenchmarks, including VWAP, TWAP,Arrival Price (Implementation Shortfall)and Closing Price.

Flexibility & customisationThe recently released strategy concept,Conditional Autotrading, permits LMXusers to customise their strategies ‘on thefly’ as well as to save ‘favourite’ strategiesfor easy re-use. It provides traders with a‘toolkit of algorithmic building blocks’that can be assembled to create hybrid

strategies. For clients whose needs arenot met by the new concept, LehmanBrothers’ team of strategy engineers canwork with them to create bespokestrategies.

Performance measurementStandardised and customised executioncost analysis services are availableglobally on both a self-service and a full-service basis. Lehman Brothers’ costanalysis capability is driven by PortfolioWebBench, a web-based toolkit for pre-trade, intra-trade and post-trade analysis.

Connectivity optionsDirect to Model is an algorithmconnectivity solutions suite, which allowsclients to directly access LMX strategiesfrom a wide variety of front ends,including proprietary or third party OMSor execution management systems suchas LehmanLive® LINKS™.

Recently formed alliances with majorOMS vendors such as Macgregor andNeovest demonstrate Lehman Brothers’continuing resolve to bring its product tobuy-side traders’ desks.

124

Lehman BrothersLMX™



Merrill Lynch went live with ML X-ACTSM, its algorithmic and comput-

er-based equity trading platform, in thethird quarter of 2003, extending access toclients in the first quarter of 2004.Originally designed for US equities, asstrategies have been added it hasexpanded its coverage to Europe and,most recently, Asia, and now offers itsinstitutional clients access to over 40markets.

The X-ACT algorithmic trading engineshave been developed in-house and utilisea variety of benchmark-related strategiesdriven from a single architecture, which,at its core, is based on marketmicrostructure research and extensivequantitative data infrastructure. All ML X-ACT strategies are based on this structureto minimise transaction costs. Eachstrategy uses historical and forecastedstock-specific statistics to determinewhen, how much and how frequently totrade.

Trading benchmarksStrategies are continuously re-calibratedin response to real-time market data,execution costs and benchmark relativeperformance. ML X-ACT strategies aim toachieve or outperform a number ofdefined benchmarks: OPL (Optimal),QMOC, VWAP, CLOCK (a TWAP engine),POV (Percentage of Volume) and TWIN(trades two stocks based on a price perratio or spread).

Flexibility & customisationUsing ML X-ACT’s integrated, interactivescreens, clients can customise theirorders by setting a number of inputparameters and constraints, includingstart and end times, target participationrate, maximum participation rate and riskaggressiveness factors, which determinethe level of risk and aggressivenessversus the benchmark.

Performance measurementMerrill Lynch’s Global Equity Analytics(GEA) application provides portfolioanalytics and trading tools that combineproprietary quantitative data models withboth real-time and historical data fromBloomberg.

Connectivity optionsClients can connect to ML X-ACT throughthree primary channels: via an equitysales trader, direct through a two-way FIXconnection, or from their desktop througha third-party OMS or front-end system.

125

Merrill LynchMerrill Lynch Execution via Algorithm and

Computer-based Trading (ML X-ACTSM)



Algorithmic trading is a key element of Morgan Stanley’s electronic trading

business and central to its overall equityoperation. The same tools that are avail-able to clients are also used by internaltraders in the client businesses and forman important part of the overall executionplatform for Morgan Stanley’s traditionalcash business, portfolio trading businessand futures business.

The Benchmark Execution Strategies(BXS) algorithmic trading platform wasdeveloped in 1996 as a tool for MorganStanley’s portfolio trading desk and wasextended to clients in 2001. BXS focuses onminimising transaction costs and impact torelevant trading benchmarks. Consultingwith clients throughout the trade life cycleensures implementation methodology andinvestment objectives are aligned foroptimal execution. Over 500 individualclient organisations utilise BXS algorithms.

Plans include the further developmentof its global platform, increasing therange of products offered across assetclasses and improving execution.

Trading benchmarksAlgorithms are constrained to meet anumber of benchmarks, including VWAP,Arrival Price (Implementation Shortfall),Close and Target Percentage of Volume.

Flexibility & customisationThe BXS platform allows forcustomisation of a number of parameterssuch as duration, trading aggressiveness,limit prices and volume limits.

Performance measurementTwo levels of performance measurement areoffered. The first is a daily ‘ScoreCard’ of alltrades executed electronically, whichanalyses trades against a series ofbenchmarks and applies internal statisticsto each trade. Second is a product calledExecution Performance Attribution (EPA), aweb-based tool that allows a client toperform interactive analysis of all theirtrades across multiple brokers. Using EPA,clients gain further insights into theirtrading costs by segmenting trades bybroker, sector, trader and portfolio manager.

Connectivity optionsBXS is accessible via Passport, MorganStanley’s trading portal, a front end that isaccessible via the internet or MicrosoftExcel. Access is also available viacustomised FIX connections to proprietaryOMS and vendor OMS. Well-establishedpartnerships with leading OMS vendorssuch as Charles River and Macgregor ensurethat BXS is easily accessible to clients.

126

Morgan StanleyBenchmark Execution Strategies (BXS)



P iper Jaffray seeks to deliver the most‘advantageous execution price’ for its

clients’ orders. It lists a number of keyfeatures that help achieve this objective:price predictive modelling technology,customised execution strategies andorder types, a highly automated tradingprocess and easily scalable infrastructure.Together this contributes to minimiseinformation leakage and overall marketimpact.

In 2004, Piper Jaffray acquired VieSecurities, a provider of algorithm-based,electronic execution services. Theacquisition, which included proprietaryalgorithms, direct market access systemsand licensed trading technology, wasundertaken to help meet increasing clientdemand for automated, cost-effectiveexecution capabilities and, in particular,requests for ‘value-added’, algorithm-based trading services.

Algorithms employ short-termpredictive signalling techniques todetermine the optimal execution timing,trading period, size, price and executionvenues, while minimising market impact.In terms of future development, work isongoing to improve the performance ofthe algorithms, with a focus on buildinggreater speed and predictive capability.

Trading benchmarksAlgorithms are constrained to thefollowing benchmarks: ImplementationShortfall (Arrival Price), guaranteed VWAP,best efforts VWAP, TWAP, Market on Open(MOO) and Market on Close (MOC).

Flexibility & customisationPiper Jaffray collaborates with clients toachieve the best execution for each order,

providing customised solutions andtailored trading strategies to meetspecific client benchmarks.

Performance measurementPre- and post-trade analytics are used tooptimise execution performance. Real-time updates are available on allexecutions. By applying real-time analysisacross a range of market factors, a modelcan be formulated of the expected priceand volume for a given stock in one tothree minutes. Execution reports andstatus updates are delivered to clientsupon request and/or when marketactivity suggests an adjustment in tradingapproach could yield improved results.

Independent, third party, post-tradeanalytics are also available for all tradesdetailing market impact and performanceagainst all major benchmarks. This isconsidered a ‘vital feedback loop’ inhelping refine trading strategies tomaximise execution performance andminimise risk.

Connectivity optionsA team of communication and softwareengineers is on hand to assist clients withconnectivity from all order managementsystems through FIX, FTP, email or InstantMessaging. Customised FIX connectionshave been created that support advancedorder types and algorithmic trading.

127

Piper Jaffray



Direct Strategy Access (DSA) forms acore part of UBS Investment Bank’s

execution offering. DSA is offered toclients as part of UBS’ sales trading-based service. Clients also have theoption to trade completely anonymously.

To facilitate access to liquidity, UBS hasbuilt iXt (intelligent eXecutiontechnologies), which locates the bestpossible price across multiple exchanges,ECNs or internally within UBS InvestmentBank. Its proprietary strategy tools havebeen designed to predict trading trends,combining historical tick data with real-time market data analysis and quantitativemodels, to provide optimal execution.

UBS highlights its commitment to thecontinuing development of its electronicexecution products. As part of itsalgorithmic offering, UBS intends todevelop new and customised strategies,as well as providing more analytics toolsand reporting, open new markets andexpand into derivatives.

Trading benchmarksThe following strategies are available:VWAP, TWAP, INLINE, HIDDEN, PIN, MOC,PRISM (Implementation Shortfall). UBS’strategy engine uses a variety ofbenchmarks to achieve best execution;for example, a VWAP strategy, whilsttrying to achieve a VWAP benchmark, willalso monitor other benchmarks such asvolume change and price movement.

Flexibility & customisationAlgorithms are ‘fully customisable’ andsupport start/stop times, volume targetsand volume caps. Price limits and use ofall the exchange order types whereapplicable.

Performance measurementUBS Equity Trader, a web-based electronictrading platform, provides real-timeupdates. Clients that want to conducttheir own post-trade analysis can useUBS Strategy Console, a real-timemonitoring and graphing tool, for thispurpose.

Connectivity optionsClients can connect to DSA directly fromtheir order management systems usingthe FIX protocol. UBS deploys anadvanced FIX infrastructure, whichsupports three different options fordefining a strategy in a FIX New Ordersingle message: ‘847’ making use of theFIX 4.4 algorithmic tags, as well as Tag 57and 6000 options. Algorithms can beaccessed directly from a third partyvendor such as Bloomberg or from UBSEquity Trader. Through a combination ofOMS and UBS Equity Trader, clients canenjoy the functionality of Equity Traderwithout having to re-key orders orexecutions back into their OMS.

128

UBS Investment BankDirect Strategy Access (DSA)

■ A buy-side handbook – Algorithmic trading

Contacts


130

Part 1: Market and mechanicsChapter 1: Algorithmic trading – Upping the ante in a more competitive marketplaceTABB Group Contact: Wendy Garcia, analyst, TABB Group Tel: +1 203 535 3668 email: [email protected] www.tabbgroup.com

Chapter 2: Understanding how algorithms workCitigroup Contact: Dr Tom Middleton, head of European Algorithmic Trading Tel : + 44 20 7986 0196 Email: [email protected] www.citigroup.com

Chapter 3: Build or buy?Inforeach Contact: Allen Zaydlin, CEO, Inforeach Tel: +1 312 332 7740 ext. 2000 email: [email protected] www.in4reach.com

Part 2: Honing an algorithmic tradingstrategyChapter 4: Choosing the right algorithm for your trading strategyUBS Investment Bank Contacts: Tracy Black, executive director, European Sales Trading Tel: +44 20 7568 4869 Mob: +44 7884 111478 Email: [email protected] & Owain Self, executive eirector – Equities Tel: +44 20 7568 4961 Email: [email protected] www.ubs.com/directstrategyaccess

Chapter 5: Anonymity and stealthCSFB

Chapter 6: Customising the broker's algorithmsCSFB Contact: Samantha Ward, Electronic Trading & AES™ Sales Tel: +44 20 7888 4368 Email: [email protected] www.csfb.com

Part 3: Quantifying and enhancing valueChapter 7: Measuring and interpreting the performanceof broker algorithmsITG Contacts: Ian Domowitz, managing director and global head of Research Email: [email protected] &Henry Yegerman, director of Research Product Management email: [email protected] www.itginc.com

Chapter 8: Making the most of third-party transactionanalysis: the why, when, what and how?GSCS Information Services Contact: Jo Turnbull, Sales & Marketing Tel: +44 1932568488 email: [email protected] www.gscs.info

Chapter 9: Enhancing market accessNexa Technologies Contacts: Mark Muñoz, senior vice president, Corporate Development Tel: +1 949 885 2177 email: [email protected] & Mark Ponthier, director – Engineering, Automated TradingSystems Tel: +1 972 747 8860; m: +1 214 578 3676 email: [email protected] www.nexatech.com

Part 4: Emerging trends and future directionChapter 10: Basket algorithms – The next generationMiletus Trading Contacts: Anna Bystrik, PhD, research analyst email: [email protected] & Richard Johnson, senior vice president, Product Sales email: [email protected] Tel: +1 212 825 1707 www.miletustrading.com

Chapter 11: The future of algorithmic tradingJP Morgan Securities Contacts: Carl Carrie, head of Algorithmic Trading, USA Tel: +1 212 622 6419 Email: [email protected] &Andrew Freyre-Sanders, head of Algorithmic Trading, EMEA Tel: +44 20 7779 2117 Email: [email protected] & Robert Kissell, vice president, Global Execution Services Tel: +1 212 622 5700 Email: [email protected] www.jpmorganonsite.com

1 Sekforde StreetLondonEC1R 0BEUK

www.thetrade.ltd.uk

Tel: +44 (0) 20 7075 6115

Published by

Date post:	16-Nov-2014
Category:	Documents
Upload:	ameetsonavane
View:	885 times
Download:	5 times

Emerging Trends and Future Direction

Documents