3.946.061.62(3.73)
382
Advanced Trading RulesTable 13.2 Diversi®ed sectorDIVERSIFIED
Systematic
50/50 Sys/disc
Discretionary
NumberAssets (millions)
1813918
782
44487
Average 12M RORAverage 12M STD12M Sharpe ratioWorst drawdown
16.149.261.30(16.54)
6.1116.000.13(28.04)
9.5910.110.54(14.59)
that the systematic traders have fared far better than their discretionarycounterparts. Due to the much higher average annual return of 16.14 per centversus 9.59 per cent with similar average annual standard deviation of 9.26per cent versus 10.11 per cent, the systematic group's Sharpe ratio is a muchhigher 1.30 versus 0.54 for the discretionary traders. While this result mayseem surprising at ®rst glance, a closer examination of the possible reasonsbehind this discrepancy unveils a solid explanation. Since diversi®ed traderstend to be involved with so many dierent markets, a computerizedsystematic approach may well be the most eective trading style. On theother hand, it is extremely dicult for discretionary traders to keep track ofand be pro®table in trading a diversi®ed portfolio, whereas they experiencemuch more success when their focus is narrowed to trading speci®c sectors.Agricultural traders (see Table 13.3). Discretionary traders dominate thissector, the most traditional of all the commodity markets. While most of thediversi®ed programs also trade the agricultural markets systematically, a puresystems-based program trading only in agriculture is very rare and usuallynot terribly successful. Since these markets often react to fundamental factorssuch as weather conditions and crop reports, discretionary traders that tend
Table 13.3 Agricultural sectorAGRICULTURAL
Systematic
50/50 Sys/disc
Discretionary
NumberAssets (millions)
10
18201
Average 12M RORAverage 12M STD12M Sharpe ratioWorst drawdown
9.2323.760.21(39.62)
9.146.330.79(14.29)
Commodity trading advisors and their role in managed futures
383
Table 13.4 Energy sectorENERGY
Systematic
50/50 Sys/disc
Discretionary
NumberAssets (millions)
212
215
Average 12M RORAverage 12M STD12M Sharpe ratioWorst drawdown
17.1320.430.63(20.86)
7.7013.820.26(19.62)
to employ mainly fundamental information in making their trading decisionsare typically the most successful.Energy traders. One may expect that markets in the energy sector may bemore amenable to discretionary traders due to the nature of oil prices beingin¯uenced by fundamental factors such as weather patterns, OPEC policies,and political and military developments in the Middle East. While this maynot seem to be the case in light of the scarcity of energy specialists in Table13.4, there are some prominent traders such as Willem Kookyer fromBlenheim whose roots can be traced back to the energy sector. However,there are very few pure energy specialists remaining currently and this smallsample size makes the statistics from Table 13.4 not very meaningful.Equity traders (see Table 13.5). As equity managers are not a major portionof the universe that Refco tracks, the statistical signi®cance of this huge sectoris low due to the small sample size. Furthermore, the data is further skewedby the fact that a number of the large systematic traders that are tracked byRefco employ non-directional long/short equity programs, which tend to bemuch less volatile than the directional equity portfolios.Financial traders (see Table 13.6). Due to the tendencies for ®nancialmarkets to be in¯uenced by signi®cantly more variables than the commodity
Table 13.5 Equity sectorEQUITY
Systematic
50/50 Sys/disc
Discretionary
NumberAssets (millions)
11959
224
24891
Average 12M RORAverage 12M STD12M Sharpe ratioWorst drawdown
9.205.370.95(2.31)
19.208.291.80(4.35)
18.1114.500.97(20.02)
384
Advanced Trading RulesTable 13.6 Financial sectorFINANCIAL
Systematic
50/50 Sys/disc
Discretionary
NumberAssets (millions)
773220
49
20523
Average 12M RORAverage 12M STD12M Sharpe ratioWorst drawdown
13.1714.370.63(15.77)
11.1113.870.51(16.41)
9.405.450.97(4.57)
markets, the use of technical inputs tends to be more popular thanfundamental analysis. Consequently, over 75 per cent of the traders in thissector employ a systematic approach, managing over 85 per cent of the assets.However, in sharp contrast to the diversi®ed sector (where systematic tradershandily outperformed their discretionary peers), the dierences in the®nancial sector are much more subtle. While systematic programs did managea higher return (13.17 per cent versus 9.40 per cent), this did come at a cost ofmuch higher volatility (14.37 per cent versus 5.45 per cent), resulting in apoorer risk-adjusted return as measured by the Sharpe ratio (0.63 versus0.97). The worst drawdown was also considerably higher at 15.77 per centversus an impressive 4.57 per cent for the discretionary group. One of themain reasons for this relatively unimpressive performance by the systematicgroup can be attributed to the very dicult trading conditions in the foreignexchange sector over the past four years.Foreign exchange traders (see Table 13.7). Another sector that has beendominated historically by systematic traders, the past few years have beenvery trying times in the new low volatility environment. The volatility for thesystematic programs has been equivalent to that of the discretionary traders(21.69 per cent versus 23.99 per cent) and the worst drawdowns have been
Table 13.7 Foreign exchange sectorFOREIGN EXCHANGE
Systematic
50/50 Sys/disc
Discretionary
NumberAssets (millions)
523958
21655
Average 12M RORAverage 12M STD12M Sharpe ratioWorst drawdown
11.5921.690.35(21.59)
29.5323.991.06(21.13)
Commodity trading advisors and their role in managed futures
385
Table 13.8 Interest rate sectorINTEREST RATE
Systematic
50/50 Sys/disc
Discretionary
NumberAssets (millions)
22779
5173
Average 12M RORAverage 12M STD12M Sharpe ratioWorst drawdown
13.142.573.52(0.39)
10.367.150.87(12.72)
very similar as well (21.59 per cent versus 21.13 per cent). However, the bigdierence between the two groups lies in the raw returns that have beengenerated, with the discretionary group faring far better with an averageannual return of 29.53 per cent as compared to the systematic group's 11.59per cent, resulting in a much higher Sharpe ratio of 1.06 versus 0.35. Since themid-1980s into the early 1990s, the favourable trending conditions in the FXmarkets generated signi®cant pro®ts for a large number of the systematictraders, spawning dozens of new currency programs. Given the performanceresults over the past ®ve years and if the current environment persists, itappears that discretionary methods may well be better suited to currencytrading.Interest rate traders (see Table 13.8). On the surface, it appears thatsystematic traders have achieved incredibly good returns (13.14 per cent)with very little volatility (2.57 per cent), resulting in a stratospheric Sharperatio of 3.52 and a practically non-existent worst drawdown of 0.39 per cent.However, this result is very skewed in that around 90 per cent of the assets inthe systematic interest rate sector is managed by three very low-risk, yieldenhancement ®rms. Hence, even though it appears that the results appear tobe reasonably sound statistically with twenty-two traders in this category, thefact is that the dollar-weighting method unfortunately reduces this group tolittle more than an index for the three yield enhancement ®rms.Metal traders. As can be seen clearly from Table 13.9, not very manytraders are focused exclusively on the metal sector. Hence, any comparisonbetween the systematic and discretionary programs in this sector will not bevery meaningful.Softs traders (see Table 13.10). Similar to the energy and metal sectors, thesofts group does not contain sucient members in order to provide ameaningful analysis between the systematic and discretionary programs.Stock index traders (see Table 13.11). Unlike the equity sector in whichthere are more than twice as many discretionary as systematic traders, thestock index group is predominantly systems based by a ratio of nearly 6 : 1.
386
Advanced Trading RulesTable 13.9 Metal sectorMETAL
Systematic
50/50 Sys/disc
Discretionary
NumberAssets (millions)
27
10
Average 12M RORAverage 12M STD12M Sharpe ratioWorst drawdown
11.648.820.85(9.60)
45.7316.392.44(17.92)
Table 13.10 Softs sectorSOFTS
Systematic
50/50 Sys/disc
Discretionary
NumberAssets (millions)
111
22
Average 12M RORAverage 12M STD12M Sharpe ratioWorst drawdown
28.3526.600.91(26.99)
21.0529.780.56(15.01)
Table 13.11 Stock index sectorSTOCK INDEX
Systematic
50/50 Sys/disc
Discretionary
NumberAssets (millions)
35204
629
Average 12M RORAverage 12M STD12M Sharpe ratioWorst drawdown
12.208.720.93(11.88)
3.6922.60(0.02)(43.94)
Judging from the vastly superior results over the data period, it is quite easyto understand why such a discrepancy in popularity exists. Though thesample size is rather small with only six programs, the most likely explanationfor the discretionary group's dismal results can be attributed to the relentlessrise in the US stock market and the very tempting discretionary tendency towant to short the market.
Commodity trading advisors and their role in managed futures
387
13.8 CONCLUSIONSDrawing from the observations made in the aforementioned study betweensystematic and discretionary traders, some generalizations can be reachedthat could prove to be extremely valuable in the construction of a diversi®edportfolio of CTAs. Even though the managed futures industry appears to bedominated by systematic traders, a strong case can certainly be made for theinclusion of a number of discretionary advisors in a diversi®ed portfolio,especially in the agricultural, currency and ®nancial sectors. Of course,systematic traders will still play a very prominent role in the allocationmix, not only as core CTAs in the diversi®ed category but also at some sectorssuch as stock index, interest rate or even in ®nancial or foreign exchange.The type of information generated from this type of analysis is only one ofthe many variables that enables asset allocators to make better decisions. Asexpressed in the previous sections, numerous quantitative and qualitativefactors still need to be carefully examined prior to the actual hiring of theadvisors. Nevertheless, the statistics on the performance dierences betweensystematic versus discretionary CTAs are tremendously useful when used inconjunction with the other more traditional measurements of advisor results.
Chapter 14BAREP futures fundsDAVID OBERT AND EDOUARD PETITDIDIER*
14.1 INTRODUCTIONBAREP (Banque de ReÂescompte et de Placement), a French bank and a fullyowned subsidiary of SOCIETE GENERALE, has an asset managementstructure, which creates, markets and manages a full range of products andservices provided to clients.This structure has developed a futures funds' asset management based ontwo leading concepts:(1) Technical systematic asset management, with investment strategiesbased on models of historical behaviour in futures markets.(2) Concentration of the decision-making process within the BAREPmanagement team acting as its own commodity trading advisor(CTA).Section 14.2 of the chapter is an introduction of BAREP's speci®capproach, with a presentation of its organization and its advantages.Section 14.3 seeks to explain precisely BAREP's management and techniques used to trade futures: the systematic, the technical analyses and thetrading horizon.Section 14.4 speaks about money management with asset allocation andleverage policies.Finally, sections 14.5 and 14.6 describe BAREP's funds performance overthe last two years.14.2 BAREP'S ORGANIZATIONBAREP decided to concentrate all the necessary competencies and skillsinside the bank, in terms of research, development, management, presence onthe markets and back oce. Today, this strategy is clearly justi®ed, although
This chapter was written while the authors were at BAREP Asset Management.
BAREP futures funds
389
it requires the use of signi®cant technical and intellectual resources. Finally,this method presents a lot of advantages in terms of security and coherence.14.2.1 The organizationBAREP implements important human means in order to dispose of a totalmanagement independence (Figure 14.1). This team consists of three principal poles, each concerned with dierent competence ®elds.The research and fund management team is in charge of operational®nance research and the daily life of the funds, that is:(1)(2)(3)(4)
database maintenance with the quotation on all marketsexecution of the orders on all marketscreation of new trading models (conception, data processing)implementation of tests to measure the eciency of models in the pastfrom a risk-reward point of view(5) measurement of the robustness and quanti®cation of the models'stability in the long run(6) following up on the dierent strategies(7) setting up and following up on the asset allocation strategy.Because markets are always changing, the team always tries to improveexisting strategies and procedures as well as ®nd totally new ways to makemoney.The data-processing team is in charge of data-processing developmentsduring all stages of the project, which are the creation, the setting up and thefollowing up, without any subcontracting.The main executions are:(1) reliable tool to test all trading strategies(2) an operational instrument allowing the managers to deal in excellentand secure conditions on more than 600 strategies and 30 markets(3) real time screen to follow up each fund value, results by markets andby strategy(4) home-made computerized system for the back oce positions enablingus to value all the instruments of the funds (futures, cash, exchangeswap).The back oce team is in charge of all administrative operationsconcerning the funds, especially(1)(2)(3)(4)
control of the orders on futures marketsmanagement of credit transfers (margin calls)administration of all monetary instruments used by the fundscontrol and follow up of the net asset values calculated by an externalcompany.
Research and management pole
Figure 14.1 BAREP's organization
BAREP futures funds
391
The interconnection between the three teams is very important because thesetting up of the whole structure has been conceived from a very coherentpoint of view. This organization allows us to develop swiftly but harmoniously at the same time.
14.2.2 AdvantagesPosition taking and asset allocation are integrated and assure us of apermanent control of all management aspects. The risk-return ratio ismanaged on a whole portfolio scale, that allows us to reduce eventualhedge costs and to optimise yield.An integrated approach to management allows us to apply the principle oftransparency: the investor is able to take his or her investment decision withvery objective parameters regarding the risk/return ratio.Principles of management are de®ned and tested since the beginning; theirpertinence is regularly checked to allow steadiness of performance.All the characteristics of the decision process, in terms of model selection orasset allocation, are rational. Human factors regarding the speci®c state ofmind of each manager are not taken into account in the management methodchosen by BAREP. The quality of methods has been tested and isindependent from all modi®cation of our managers' states of mind.The performance of management is improved by the absence of intermediaries. In-house asset management provides investors with an attractiveperformance which is not eroded by pro®t sharing with external partners.The liability of all administrative operations is secured by the action of®mat, broker of the SOCIETE GENERALE Group, and the ®rst world'sleading broker on futures markets.This type of investment has to be considered with a medium-term point ofview. This length of time period enables investors to bene®t from theexceptional yield of these markets.
14.3 TRADING CONCEPTSBAREP follows three principle trading concepts. First, our trading issystematic with decisions made on a computer system and no use of humaninputs and emotions. Then, we make our decisions with technical tradingstrategies and we computerize them in order to test their robustness and tostudy their risk-return ratio. Finally, we try to have short-term horizons aswell as long-term horizons.
392
Advanced Trading Rules
14.3.1 SystematicThis type of management is based on the setting up of a formal tradingorganization. The managers create models that allow them to take a positionwith a precise timing and at a de®ned buy±sell level. The managers are notfree to interpret the signals before executing the orders in the market; theiraction is concentrated on the follow-up of performance and the improvementof models.Systematic management requires a signi®cant study and developmentperiod in order to set up the trading models. The structure of the organizationand the rigour of the decision-taking process assure the managers of thepermanence of the methods used. The trading strategies contribute to ecientdaily management and allow the manager to extract him or herself from theday-to-day administration.Futures markets have been studied very precisely by the asset managers, interms of evolution, liquidity and volume, in order to understand theircharacteristics and choose the most ecient and liquid ones.We intervene simultaneously on the main principal futures markets inEurope, the USA and Asia, and on more than 45 dierent markets:(1) long interest rates: Notionnal, Bund, Gilt, BTP, Bonos, JGB, Tbond,Tnote5y, TNote10y, CGB(2) short interest rates: Pibor, Euro DM, Short £, Euro ITL, EuroUSD(3) currencies: USD/DEM, USD/GBP, USD/CHF, USD/JPY, USD/CAD, USD/AUD, DEM/JPY, GBP/DEM, DEM/ITL(4) Stock indices : CAC, Dax, Footsie, Nikkei, S&P 500(5) commodities: crude oil, natural gas, heating oil, NY harbour gasoline,sugar, coee, cacao, gold, silver, copper, corn, wheat, soybean,soybean oil, soybean meal, live cattle.Each day, the database is updated with the quotes from all these markets.14.3.2 Technical analysisThe aim of technical analysis is not prediction; it tries to derive bene®ts fromthe repetitive behaviour of markets. The main postulate adopted is thatmarkets are endowed with a memory inscribed in historical records ofquotations and that it is possible to extract a behaviour continuity in themarket. Managers identify these cyclical components to create adaptedtrading models. These systems are tested with historical prices in order toevaluate them in terms of yield and risk.The development of strategies is speci®c to BAREP's research teams, basedon technical, statistical and chartist methods, excluding any discretionarytrading.
BAREP futures funds
393
StrategyWe assume that a ®nancial market memory does exist, which is linked to theoperators' one. Thus, some developments in the ®nancial markets do reoccurwith constant characteristics.The objective of the models' research is to create a system of decisionswhich aim to understand the market's behaviour in the past and enable themanagers to design computerised decision-making models, which are stillstudied and parametered according to market developments.Therefore, fund managers search for as many trading approaches aspossible to catch many dierent market moves.The analytic forms of these trading concepts are called strategies. Thenumber of parameters represents the number of rules which aect either theprice or the time of entry and exit. Each strategy created is evaluated in orderto see its return, its risk and its stability.Strategies are based on statistical and technical studies, and it usesindicators able to give buying or selling signals to the managers. Of course,stop-loss mechanisms exist guaranteeing the system's safety.The trading rules of strategies are the same for each asset manager,whatever their anticipations in the evolution of the market.
SelectionThe managers select their strategies and assets by the measure of therobustness and the quanti®cation of the models' stability in the long run.(1) Tests are implemented to measure the eciency of the models in thepast on a risk±reward point of view. The strategies are selectedaccording to the following three fundamental criteria.(a) The return, including brokerage and slippage. Slippage representsthe risk the market will trade through a protective stop, or getting®lled at prices which are worse than expected. The knowledge ofeach potential market is essential (liquidity, quality of execution,length of life, brokerage) to estimate the eventual slippage.(b) The Sharpe ratio is here the average return divided by the return'sstandard deviation. It represents a measurement of the return'sconsistency of a trading method.(c) The drawdown is the maximum loss ever registered, analysedaccording to its depth, drop time and recovery time.(2) The test of parameters' stability aims at measuring and quantifying themodels' stability in the long run. The principal tool is the mappingwhich studies the movements of two parameters in a period of time ina speci®c market. We want to check the stability of these parameters.
394
Advanced Trading Rules
ExampleLet's explain our research approach by presenting one of our strategies. It isbased on a pattern recognition: support and resistance.Theory says that subjective analysis is not easily checked using mathematical methods. We want to computerize the research of patterns (channels,triangles, support and resistance points) and use this research to initiate andmanage positions on futures markets.A support is a level on the chart under the market where buying interestsare suciently strong to overcome selling pressure. As a result, a decline ishalted and the price turns back again. Usually a support level is identi®edbeforehand by a previous low.A resistance is the opposite of support and is identi®ed by a previous peak.Whenever a support or resistance level is penetrated by a signi®cantamount, they reverse their role and become the opposite. If the resistancepoint is broken, the level becomes a support point, and vice versa.The strategy uses these situations to initiate buy orders when a resistancelevel is broken and sell orders when a support level is penetrated. Theproblem resides in determining whether a penetration is signi®cant or not.It has a few parameters, including:(1) the length of the period of time to search for the best supports andresistance levels(2) the management of trades: stop-loss and take-pro®t objectives.When the strategy is computerized, the test of its potentialities is made ondierent markets to decide if this strategy will be introduced into ourportfolios.In our example, we study, thanks to mapping, the stability of twoparameters of the strategy:(1) the length of the period of time, in order to search for the best supportand resistance levels(2) the stop-pro®t objective.Mapping studies the variations of these two parameters and gives the netreturn, the Sharpe ratio and the drawdown ratio for each couple ofparameters (the drawdown ratio represents the average return divided bythe maximum drawdown).f Market: JGBf Time: 1 June, 1989, to 3 June, 1996f First parameter: thirty±ninety daysf Second parameter: one±three market points.
BAREP futures funds
395
Inside the mapping (Table 14.1), the ®rst number represents the net return,the second the Sharpe ratio and the last one the drawdown ratio.The ®rst remark is the stability of these two parameters. Whatever theircoecients are, the return stays roughly the same, with a Sharpe ratio aboveone and a very good drawdown ratio.The strategy seems interesting on the JGB and a few assets will be chosen inthis mapping for the constitution of the JGB portfolio explained as follows.14.3.3 Trading horizonBAREP works its trading horizons in terms of types of markets, models andtime horizons.MarketThe principal ®nancial markets, in Europe, the USA and Asia can be dividedin dierent classes and trading horizons.(1) Stock indices:(a) frequent and violent accelerations(b) very volatile(c) short-term trends(d) importance of entry and exit points(e) pro®t-taking(2) Long interest rates:(a) technical and chartist(b) medium- and long-term trend-following(c) volatility(3) Currencies(a) medium- and long-term trend-following(b) no pro®t-taking(4) Commodities(a) violent accelerations(b) long-term trends(c) pro®t-taking.ModelDierent kinds of models are used in our portfolios. These models are studiedon an open±high±low±close basis.A `volatility breakout trading system' is based on the following principles.If the market moves a certain percentage from a previous price level, it breaksout of a trading range and is a buy or sale.A `trend-following system' (Figure 14.2) represents techniques which try tocatch trends in a market. The systems buy when the market is going up and
Table 14.1 A mapping
ES 1ES 1.5ES 2ES 2.5ES 3
ENC 30
ENC 40
ENC 50
ENC 60
ENC 70
ENC 80
ENC 90
23.7/1.13/3.6733.0/1.33/5.1439.0/1.38/5.8139.9/1.30/5.3638.6/1.21/5.18
25.4/1.18/3.9333.3/1.29/5.0938.4/1.31/5.6445.2/1.39/5.9844.1/1.33/5.84
20.4/0.99/2.6827.4/1.08/2.8631.0/1.09/3.1437.7/1.19/3.5641.8/1.25/3.94
28.1/1.26/5.4435.8/1.33/5.0341.3/1.35/5.5947.6/1.41/5.8546.0/1.32/5.65
29.1/1.28/5.1436.6/1.34/4.8141.9/1.35/5.3249.7/1.44/5.7634.6/1.05/4.00
31.4/1.39/5.5536.5/1.38/4.8038.9/1.30/4.9347.1/1.41/5.4632.2/1.01/3.72
24.7/1.12/3.9830.3/1.17/3.4937.4/1.25/4.1745.6/1.36/4.7030.5/0.96/3.14
A trend-following strategy:± does not try to anticipate market's movements± allows to win on increasing or decreasing markets± allows to be always present in big trends± cannot be a long time against the market.
Figure 14.2 Trend-following strategy
398
Advanced Trading Rules
sell when it is going down. They don't anticipate short-, medium- or longterm trends, they just follow them.A `contrarian system' takes position in the opposite way of trend-followingsystems.A `chartist system' is the research of recurring formations or patterns.T|meTime is one of the most important factors aecting uncertainty, preciselybecause we can never be certain about the future. A large range of timehorizons is used, as well as short-, intermediate- and long-term ones.Figure 14.3 shows two examples of trading time horizons, a short-termsystem and a long-term system. On one side, the graph shows the evolution ofthe Notionnal from 1 January, 1990, to 15 November, 1996. On the otherside, it shows the returns of a short-term system on the Notionnal. We usetwo dierent returns, which are the realized and unrealized pro®ts, and therealized pro®t.A short-term strategy has an average duration from one day to two weeksand is very active. Transaction costs and slippage can be very important. Along-term strategy (see Figure 14.4) lasts between three months to severalyears and takes only a few positions. Transaction costs and slippage are low.14.4 MONEY MANAGEMENTMoney management covers the allocation of funds. It includes such areas asportfolio make up,