of general principles can be laid down, there exists no sure ®redeterministic approach to the data analysis problem. Rather, it is necessary toexert a degree of judgement, the facility for which is greatly improved byexperience.
13.3.1 Quantitative measuresRefco employs a variety of quantitative statistical measures of CTAperformance as presented in the Refco Derivative Advisor Database. Thevarious categories that are analysed include return statistics, recent returns,worst drawdowns, linear and scatter correlations and horizon analysis.Return statisticsA primary variable is the monthly rate of return for a CTA. The time series ofreturns, ri;t for each CTA i, for month t, provides the source of data tocompute the following.Average rate of return for CTA i, Ari . The average rate of return, alsocalled the historical rate of return, is found for dierent intervals of time, onemonth, three months, six months, twelve months, two years, three years and®ve years. The analysis is done by linking together previous returns for all theconsecutive holding periods. For example, with thirty-six months of returndata we would generate thirty-four quarterly return numbers and twenty-®veannual returns.Standard deviation of returns for CTA i, SDri . The standard deviation iscalculated on the linked returns for rolling periods of time corresponding tothe calculation for the expected rate of return. As a statistical measure ofvolatility, the standard deviation of a CTA's returns provides a good tool tomeasure risk.Sharpe ratio for CTA i, SM ri . The Sharpe ratio provides a tool to measurethe eciency of a CTA. It is calculated by taking a CTA's actual return minusthe risk-free T-Bill return and dividing the result by the standard deviation ofthe CTA's returns. The Sharpe ratio attempts to evaluate a CTA's performance by considering risk as well as returns and allows more level comparisons between CTAs with vastly dierent returns and/or risk pro®les. A valueof 1.0 on an annual basis is considered very good.
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Gain statistics for CTA i {average, max, min, SD}. The gain statistics arealso useful in addressing the risk of a CTA. Upside volatility is weightedlower than downside volatility such that a CTA will not be penalized for`making too much money'. This distinction is especially important in light ofthe fact that the standard deviation measure makes no distinction betweenupside and downside volatility and, thus, unfairly penalizes traders whoperform exceedingly well. The distribution of the period of times the CTAgains money also serves as a meaningful tool to de®ne rebalance decisions.Loss statistics for CTA i {average, max, min, SD}. The loss statistics areuseful in addressing the risk of a CTA. Downside volatility is especiallyimportant in the evaluation of the risk of a CTA and is a vital supplement tothe standard deviation measure. The distribution of the period of times aCTA loses money is also a meaningful tool to de®ne rebalance decisions.Distribution percentage of up/down periods. The percentage of one-month,three-month, six-month or twelve-month periods in which a CTA is up ordown is also a good measure for the consistency of a CTA over time.Recent returnsThe recent returns is an important tool both in the analysis of the quality of aCTA as well as being invaluable in de®ning rebalancing decisions. The longerterm returns over the past twelve-month, eighteen-month, twenty-fourmonth, and thirty-six-month periods can be useful in identifying dierenttypes of CTAs that are more likely to generate above average returns in thefuture. The shorter-term returns over the past one-month, three-month, sixmonth and twelve-month periods are used primarily in the rebalancingdecisions.Improving CTAs. The CTAs that have improved their returns withoutincreasing their risk or have maintained their returns while lowering volatilitywould be looked upon more favourably. This would be especially true if theCTA employs a computerized technical system, as this would suggest that therecent improvements in performance will more likely be an ongoingphenomenon.Cyclical/seasonal CTAs. The CTAs that have demonstrated a pattern intheir past track record to be either cyclical or seasonal may, at a given point intime, provide a greater probability to produce above-average returns. Anexample of this type of situation would be identifying a grain specialist thatproduced exceptional results due to a severe drought some ®ve years agofollowed by mediocre performance over the next few years. Due to thecyclical nature of weather patterns, the CTA may oer better risk±rewardstatistics in the short term than his average returns, standard deviation andSharpe ratio may suggest.
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Impact of growth and/or extraordinary circumstances. The CTAs recentreturns may be re¯ective of internal changes that might be worth consideringin the allocation process. For example, poor recent returns in contrast to agood previous track record may indicate that the CTA may be taking on toomuch money and the burden of the new administrative work may be aectinghis trading. Likewise, the CTA may be undergoing some dicult times in hispersonal life, such as a divorce or death in the family, that may be aectinghis trading. CTAs who are running small operations consisting of only one ortwo employees may also be aected if the principal is frequently travelling inan attempt to raise new capital.Worst drawdownsDrawdowns are measured as the percentage decline from the peak to thevalley with the duration measured by the time from the initial decline to theestablishment of a new high. Analysis of a CTA's worst drawdowns is anextremely useful tool in the evaluation of a CTA's risk and can also be helpfulin rebalancing decisions.Magnitude of drawdowns. The magnitude of drawdowns is the mostcommonly used criteria in assessing the downside risk of a CTA. Refcoroutinely analyses the four largest drawdown periods for each CTA in aneort to assess what the worst case scenario for each CTA would be andwhether the fund±client will be able to tolerate such a scenario.Duration of worst drawdowns. The duration of drawdown periods, inconjunction with the magnitude of the decline, is very helpful in determiningthe suitability of a CTA given the goals and constraints of a fund.Length of recovery time. The length of time a CTA takes to emerge fromdrawdown periods is also an important criteria in determining clientsuitability. A consistently short recovery period from drawdowns will bepreferable to CTAs who languish incessantly after dicult trading periods.Disparity between largest drawdowns. CTAs whose largest drawdowns varysigni®cantly from one another generally hold less predictive power thanCTAs with consistent drawdowns. Technical traders with historical drawdowns in similar ranges tend to have the most predictable downside risk. Onthe other hand, discretionary CTAs, especially ones with large disparitiesbetween historical drawdowns, are generally less de®ned with regards todownside risk. Caution must be exercised with some of these CTAs in that thesingle largest drawdown may have been caused by the CTA being overleveraged during a drawdown in a futile attempt to make up the previouslosses.Proven history of drawdown recoveries. CTAs with long track recordscomplete with numerous drawdowns and subsequent recoveries may be
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especially useful in the reallocation decision process. The risk±reward ratiosfor such CTAs are typically more favourable while they are in the midst of adrawdown and are likewise less attractive after a prolonged period of superiorperformance. Decisions with regards to rebalancing should properly addressthe timing for such CTAs.Linear and scatter correlationLinear correlation coecient. The calculation of correlation of the CTAs'returns with other CTAs and benchmarks provide insight to diversi®cation ofreturns and subsequently the reduction of risk. The linear correlationcoecient measures the degree of relationship between two series of returns,with the results being any value between 1 and 1. The closer the value is to 1,the more related the two series of returns, and the more predictive one series isto the other. Similarly, the closer the value is to 1, the more the two seriesare diametrically opposite and would also be predictive of each other. A valuearound 0 means that the two series are unrelated and the returns of one serieshold no predictive value in the returns of the other. In accordance withmodern portfolio theory, signi®cant reductions in volatility can be achievedby combining negatively correlated CTAs in a fund, signi®cant reductions involatility can be achieved.While linear correlation coecients are extremely useful in portfolioallocation and combining CTAs with the goal of minimizing volatility,there are some limitations to using them in evaluating CTAs. The mainproblem with the linear correlation coecient is that in the search fornegatively correlated investments, the important fact that negative correlationis not necessarily good and positive correlation is not necessarily bad was lost.For example, if the client has already invested with CTA A and is searchingfor a second CTA (CTA B), then a linear measurement does not dierentiatebetween desirable negative correlation (e.g. CTA B is up when CTA A isdown) and undesirable negative correlation (e.g. CTA B is down when CTAA is up). Furthermore, although the linear measurement does properlypenalize undesirable positive correlation (e.g. CTA B is down when CTA Ais down), it also wrongly penalizes desirable positive correlation (e.g. CTA Bis up when CTA A is up).Scatter correlation graphs. Scatter correlation graphs provide a terri®csupplement to simple linear correlation coecient values. Assuming thatthe client already has exposure to a benchmark index or is invested in a CTA(CTA A), and is looking to add a CTA B, the scatter correlation graphprovides invaluable information. By graphing the distribution of returns forCTA A against those of CTA B, a clear picture of CTA B's suitability withCTA A develops. It is desirable if CTA B's returns are positive when CTA A
Commodity trading advisors and their role in managed futures
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is down (negative correlation) as it is if CTA B's returns are positive whenCTA A is up (positive correlation). Overall, a desirable scatter correlationgraph would be in the general shape of an upward sloping parabola consistingof negative correlation on the left half of the graph and positive correlationon the right half of the graph.Horizon analysisInterval returns analysis. Horizon analysis reviews returns over intervals ofvarious length, (one month, three months, six months, twelve months, etc.) toassess the momentum of a CTA. The rebalance decisions are aided by thistool. However, caution must be taken in assessing the relative impact of themost recent month's eect on the average versus the back month that is beingdropped. Often a large drop in the average horizon is the result of dropping aspectacular month rather than the addition of an extremely poor month, andinterpretations that a CTA may be losing momentum may be premature.Rather, this reallocation tool is most useful when viewed from a number ofdierent intervals to arrive at a more precise consensus of which direction aCTA has a greater probability of heading towards in the future.13.3.2 Qualitative measuresWhile the use of quantitative statistical analysis is important in the CTAselection process, Refco also arranges numerous personal meetings with theCTAs themselves in their oces, at Refco's oces or during industryconferences. Refco believes that the qualitative due diligence trips aretremendously important in determining whether the CTAs being consideredare likely to be able to duplicate past results in the future. Refco considersfactors such as trading style, key personnel, depth and quality of organization, research, execution, cost and credibility to be relevant factors in theevaluation of a CTA.Trading style. Perhaps the most signi®cant qualitative distinction is that oftrading style. It is important that there is diversi®cation by trading style aswell as having mathematically non-correlated CTAs. The categories whichare generally considered include technical versus fundamental, systematicversus discretionary, diversi®ed versus specialized, and short- versus intermediate- and long-term.(1) Technical versus fundamental. These classi®cations relate to how aCTA makes trading decisions, i.e. what things are important to theCTA's decisions. Technical analysis is based on the study of themarkets themselves in order to provide a means of anticipating futureprices. Technical analysis of markets often includes a study of theactual daily, weekly and monthly price volume and open interest data
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and price pattern data. Charts and/or computers are often used for theanalysis of these items. Sentiment indicators, such as call-to-put ratiosand relative strength, are also technical analysis instruments. Fundamental analysis is based on the study of the commodity itself. Fundamental analysis looks at the external factors that aect the supply anddemand of a particular commodity in order to predict future prices.Items such as government actions, the release of information concerning weather conditions or the release of statistical information whichresult in probable or actual signi®cant price movements are considered. Many CTAs use a combination of technical and fundamentalanalysis. These CTAs usually use fundamental analysis to determinethe general direction of a given commodity and then use technicalanalysis for entry and exit.(2) Systematic versus discretionary. These classi®cations relate to how aCTA executes trading decisions. If the CTA executes a trade based ona system signal (usually a computer system) we categorize the CTA asa systems trader. To a certain degree, the CTA has eliminateddiscretion from trading decisions. A discretionary CTA makes tradingdecisions with the use of human input and emotions. Personalexperiences are used to make and execute trading decision but somesort of computer system may be employed as well. The key in this caseis that the CTA may or may not, with almost equal probability, followthe signals being generated by the `system'. A CTA who primarilyrelies on a system but very infrequently overrides the system's signals issystematic/discretionary.(3) Diversi®ed versus specialized. This classi®cation relates to whichmarkets a CTA will trade. Diversi®ed indicates that the CTA maytrade a wide variety of futures contracts. For the most part, this typeof CTA will purposely diversify a portfolio of futures contracts andoptions to reduce non-systematic risk. In most cases, a CTA whotrades a diversi®ed portfolio relies primarily on technical analysis.Specialized indicates that the CTA will trade a narrow group offutures contracts. CTAs in this category are most often arbitrage ortactical managers. The futures management programs often involvesome sort of arbitrage technique.(4) Short- versus intermediate- and long-term. This classi®cation refers tothe typical duration of a CTA's trades. The average duration for ashort-term trader ranges from one day to perhaps one week. CTAswith a short-term horizon tend to be much more active. While the riskexposure of the client's capital tends to be minimized due to the shortterm exposure to possible market ¯uctuations, transaction costsbecome more signi®cant to the CTA. The average duration for anintermediate-term trader ranges from one to two weeks to three to sixmonths. Additional market risks are usually present for such CTAs,however the impact of transaction costs tend not to be as great as for
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short-term CTAs. The average duration for a long-term trader rangesfrom over a month to up to several years. Because of the longer timehorizon, these CTAs are exposed to the greatest market risks but theadditional risk is somewhat oset by the generally lower transactioncosts.(5) Key personnel. An important consideration with the selection processis the experience and education of the key personnel of the organization. Much information can be gathered through interviews with thehead trader or the system designer and a good idea of how the trader/system would handle various potential market situations can beobtained. Additional care must be taken in selecting CTAs thatplace a heavy reliance on one or two key principals, especially iftrading decisions are discretionary rather than systematic.(6) Organization. The depth and quality of a CTA's organization is alsoimportant in assessing reliability. The importance of having a goodsupport sta capable of handling the administration, marketing andother non-trading aspects of a business cannot be overstated. Again,caution must be taken in selecting CTAs who must perform all theoperational functions in addition to trading, especially if tradingdecisions are discretionary rather than systematic. Furthermore, special attention needs to be paid to emerging CTAs who have taken onsigni®cant amounts of new funds, as the sudden increase in administrative work often leads to a deterioration in performance.(7) Research. While CTAs with proven track records convey a greatersense of security and are more attractive to investors, those CTAs whoshow a genuine interest in making improvements to their tradingmodels or markets should be particularly noted. Because markets arein a state of constant change, methods that proved to be successful inthe past may not be applicable in trading environments in the future.CTAs who recognize and emphasize the need for additional researchshould have a higher probability for success in the future.(8) Execution. The quality of a CTA's execution broker and order ¯owsystem can be a factor, especially during turbulent markets when poorexecution could potentially lead to large losses.(9) Cost. The CTA's fees for managing client funds is also a considerationin that excessively high fees reduce the client's chances for pro®tability.Similarly, excessively high commissions and/or a high number oftransactions by the CTA can severely detract from the returns forthe client.(10) Credibility. The most subjective of all the qualitative measures, thequestion of the CTA's credibility and trustworthiness can be takeninto account as a ®nal screen before the ®nal allocations. Previousnegative experiences with the CTA and/or information gathered fromreliable sources that may suggest a high probability of impropriety bythe CTA could be a factor in the ®nal allocation decision.
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13.4 MODERN PORTFOLIO THEORYModern portfolio theory was introduced in 1952 and tells how to maximizereturns while minimizing risk. The foundation for this theory rests on theconviction that investors be concerned only with expected returns and risks ofportfolios, not with individual investments, with the underlying assumptionthat all investors are risk-averse. The theory emphasizes the formation ofportfolios that are suciently diversi®ed so that no other portfolio has:(1) the same expected return with lower expected risk(2) the same expected risk with higher expected return.Historical data is used in screening potential investments to be included inthe portfolio building process, hereafter called `optimization'. By combininginvestments with low or negative correlation coecients, substantial reductions in expected risk levels are attainable. The initial choice of investmentsmay consider many factors other than historical returns. This may include,but is not limited to, qualitative factors such as key personnel, organization,research, execution and cost. While these covert factors are frequentlyoverlooked and are not considered in the basic model, Refco employs themas qualitative inputs to its model.Keeping the foregoing factors in mind, one may proceed to the ®rst step;construction of the ecient frontier. The ecient frontier is constructedthrough a mathematical algorithm utilizing:(1)(2)(3)(4)
the weights of individual assetsthe expected returns of individual assetsthe variance of assetsthe covariance between assets.
Points of expected risk return levels are the result and represent all possiblecombinations of the assets in the portfolio that are of maximum eciency. Anexpected risk versus expected return graph may be plotted to accompany thisresulting set of data. The frontier provides the investor with the ability toselect any point along the frontier that is equal to, or greater than theminimum variance portfolio.Selection of the appropriate point (a mix of assets) is the second step and isbased on the personal risk preferences that satisfy the investor ± whereexpected risk is lowest and expected return is highest. The ®nal allocationswill be based on the investor's decisions with regards to returns expectationsand/or risk tolerances.13.4.1 DiversificationIntegrating multiple CTAs. The typical institutionally managed futures fund is
Commodity trading advisors and their role in managed futures
377
structured to combine multiple CTAs. It has been determined that thebene®ts of the multiple-advisor fund exceed the greater expenses of thestructure. The single-advisor account will usually have lower costs for tworeasons:(1) CTAs will charge less for larger accounts(2) the operating costs such as accounting and custodial expenses are foronly one account.The multiple-advisor structure is signi®cantly more attractive for the following three reasons:(1) Specialization. A multiple-advisor fund allows the inclusion of CTAsthat specialize in market segments such as currencies, agriculturals,®nancials or metals.(2) Diversi®cation of styles. Also called `process diversi®cation', thediversi®cation of the investment decision tools is highly recommended.Since each CTA's approach is susceptible to being incorrect in itsassessment of the markets, a commitment to a single CTA increasesthe concerns regarding the results of the total fund should the CTAperform poorly. To reduce this exposure, it is advantageous todiversify among several conceptually appealing but dierentapproaches. Typical classi®cations of CTAs by process would bewhether the CTAs are systematic/discretionary or employ fundamental/technical inputs.(3) Diversi®cation of markets. The multi-advisor format allows moreCTAs to participate in more markets using dierent approaches. Amajor bene®t of multiple CTAs trading multiple markets rests with thefact that most CTAs tend to carry larger positions in the samedirection during trending markets and smaller positions in dierentdirections during choppy markets. Hence, pro®ts tend to be maximized and losses minimized when the multi-advisor approach tradingin dierent markets is adopted.
13.5 OVERVIEW OF CREATING A MANAGED FUTURES PROGRAM13.5.1 Selection and allocation processThe objective of the selection and allocation process is to determine the mostecient allocation of the fund's assets among the multiple CTAs who willachieve the risk/return parameters set for the fund. This process determinesthe appropriate percentage of the fund's assets to allocate to each CTA, inorder to produce the lowest overall volatility for a given rate of return. Inaddition to historical data, the procedure makes use of the data generated by
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the performance measurement tools as applied to the monitoring of speci®cCTAs and qualitative factors such as key personnel, organization, research,execution, cost and credibility. The design for a group of CTAs should followthe following algorithm.Target return. Select a target return level or range for the fund.Minimum criteria. Select minimum screening criteria for all CTAs: dollarsunder management, years of experience, registration, depth of organization,institutional clients, minimum return, largest acceptable drawdown, Sharperatio, etc.Diversi®cation requirements. Select diversi®cation requirements for the fund(minimum and maximum exposure by market segment, contract, CTA ortrading style).Database search. Screen the database for CTAs who ful®ll the minimumcriteria.Qualitative inputs. Considerations of qualitative factors are used to eitherscreen out certain CTAs or add other CTAs who may not have quali®edunder a certain screen that extenuating circumstances may deem the CTA tobe a viable choice.Correlation study. Calculate coecients of correlation on both a monthlyand quarterly basis to identify a diversi®ed and non-correlated group ofCTAs.Optimization. The EnCORR/analyser and optimizer completes the processby determining the optimal allocation for the chosen group of CTAs.13.5.2 Monitoring and management processThe process of selecting CTAs and allocating assets among them is crucial tothe design of the fund, but there are two further tasks that are equally asimportant:(1) monitoring the fund(2) actively managing each CTA.Before being included in the fund, each CTA is assigned certain parametersbased upon his or her historical returns. These parameters are generated fromthe distribution of the monthly losses and the distribution of monthly gains.It is based upon these parameters that Refco will ®rst monitor the CTAs andthen, when required, actively manage each CTA's role in the fund.The monitoring process involves the collection of data as generated by thedaily performance measurement system reports. If any material changesoccur in the CTA's system which exceed the de®ned parameters, the management process immediately ensues. The managing process will include thefollowing.
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Drawdown/stop loss. Reduce/eliminate a CTA's allocation if the CTA'sNAV declines to a pre-de®ned stop-loss point.Exceptional performance. Reduce a CTA's allocation if the performancerises beyond a pre-de®ned strike point (account momentum is unsustainable).Changes in group correlation. Increase/decrease a CTA's allocation basedon signi®cant changes in the correlation between group correlation.Maintenance of desired risk±reward ratio. Add CTAs when necessary tomaintain the desired risk±reward ratio. This will require an on-going processof identifying and analysing new CTAs both in the US as well as overseasCTAs. Currently, the Refco derivative advisor database monitors over 550programs.13.6 COMMODITY TRADING ADVISORSAlthough there are over 2500 registered CTAs currently, perhaps only around1000 are active. As a means of tracking the universe of CTAs who can beconsidered for possible allocations in a portfolio, the Refco derivative advisordatabase maintains and updates the performance histories of approximately550 individual CTA programs. Despite the relative diversity amongst theCTA universe with regards to the combination of dierent trading styles andstrategies as well as the markets being traded, two major broad classi®cationscan be made on the group as a whole.Trading styles. There are two major schools of thought as to how CTAsshould approach their decision-making process ± systematic versus discretionary. The premise behind the systematic approach is that the key tosuccessful trading lies not only in having good trading rules and strategies,but, more importantly, in being able to eliminate the human emotions thatoften lead to many traders' downfalls. Most systematic traders tend also to behighly computerized and utilize mostly technical indicators, which are muchbetter suited for inputs that can be analysed by computers. On the otherhand, discretionary traders believe that successful results are generated fromtrading skills and that the ability to make decisions that make common sensemore than oset the possible disadvantages of human emotion. Discretionarytraders tend to be more varied in their decision-making inputs in that whilemost rely predominantly on their fundamental views of the markets, they alsotend to employ a variety of technical indicators for timing entry and exitpoints.Markets traded. In classifying the universe of CTAs, the second majordistinction that can be made is in the markets that are being traded. Refco'sclassi®cation scheme generally breaks down the CTAs into groups thatinclude diversi®ed, agricultural, energy, equity, ®nancial, foreign exchange,
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interest rate, metal, softs and stock index. Generally, CTAs are considered tobe diversi®ed if they are trading markets from both the commodity(agricultural, energy, metal and softs) as well as the ®nancial (foreignexchange, interest rate and stock index) sectors. Financial traders are, thus,CTAs who trade in two or all three areas of the ®nancial sector.
13.7 SYSTEMATIC VERSUS DISCRETIONARY TRADERSThe debate over the relative merits of the two contrasting methodologiesproduces an array of management preferences for asset allocators. Based onthe latest data from the Refco derivative advisor database using data from thepast ®ve years ending in March 1996, systematic traders account forapproximately 71 per cent of all the trading programs while discretionaryprograms make up only 26 per cent of the total, with the remaining 3 per centconsidering their programs a 50/50 mix of the two approaches. Furthermore,over 80 per cent of the total assets are invested with the systematic programs,whereas only 19 per cent are placed with discretionary traders. The reasonsbehind the seemingly higher popularity of the systematic programs can beexplained by three major factors.Absence of large discretionary traders. While systematic traders are typicallymanaging more assets than their discretionary counterparts, the few successful discretionary `stars' such as Paul Tudor Jones and Louis Bacon,accumulate tremendous assets and become closed to new investments. As aresult, these large traders stop providing information to databases and are notrepresented in the overall statistics.Longevity of systematic traders. Because the systematic traders are able toencode their methodologies into computers, they enjoy a tremendousadvantage over their discretionary counterparts in longevity. Since thesystematic approach is much more conducive to a company's continuedexistence, the vast majority of programs that have existed for more than oneor two decades are systematic, even though the original developers of thesystems may or may not still be active in the company. While discretionarytraders can maintain excellence over time periods of as long as several years, itis very unusual to ®nd consistently successful discretionary traders overlonger periods of ten or twenty years. Often, the continuing high demandsplaced on the discretionary traders wears them down over time, leading toeither poorer performance and/or the retirement from the business. Thus, inthe absence of eective means of transferring their skills, the businesslifespans of discretionary traders tend to be far shorter than those of theirsystematic counterparts.
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Preferences of large asset allocators. Due to their more substantial assetsunder management, the longer track records, as well as their more readilyquanti®able approaches, larger asset allocators tend to favour systematictraders. As a result, systematic programs receive more assets under management, which makes them more attractive candidates for further allocations,and the overall group thus grows even larger versus their discretionarytraders.Performance comparisons. The performance summaries in Tables 13.1 to13.11 illustrate the dierences between systematic and discretionary traders ineach of the respective market sectors, where a number of fascinating truismsseem to surface.Total of all traders. Perhaps the single most surprising result of thecomparison between systematic and discretionary programs is highlightedby the statistics from Table 13.1. While the systematic traders have produceda very respectable average annual return of 13.43 per cent with an averageannual standard deviation of 12.79 per cent, the discretionary programs havebeen able to achieve similar returns at 13.94 per cent but with much lowervolatility at 6.06 per cent average annual standard deviation. Consequently,the Sharpe ratio for the discretionary traders is more than twice that for thesystematic programs (1.62 versus 0.73). The historical worst drawdownstatistic is even more impressive, with the discretionary traders enjoyingover a 3 : 1 edge over the systematic group (3.73 per cent versus 11.70 percent). The shocking aspect is that despite poorer performance from both anabsolute and especially volatility adjusted basis, systematic traders have beenable to maintain a dominant position in the overall industry in terms of bothnumber of traders as well as assets under management.Diversi®ed traders. Keeping in mind the vastly superior results displayed bythe discretionary traders when compared to the systematic group in theoverall results, the data from Table 13.2 may appear to be inconsistent. Instark contrast to the total results, an evaluation of diversi®ed programs shows
Table 13.1 Systematic versus discretionary programsTOTAL
Systematic
50/50 Sys/disc
Discretionary
NumberAssets (millions)
38413 067
14123
1432977
Average 12M RORAverage 12M STD12M Sharpe ratioWorst drawdown
13.4312.790.73(11.70)
9.209.720.52(19.19)
1