A Systematic Fund of Managed Accounts

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By: Peter Urbani*

* Peter Urbani was most recently Chief Investment Officer for Infiniti Capital a Fund of Hedge Funds group. He is a 20year investment industry veteran and has served variously as Head of Portfolio Management, Head of Research both

Quantitative and Qualitative, Head of Investment Strategy and as a buy-side Mining Research Analyst.

NOTE: The Lyxor Fund universe used within this presentation is used for illustrative purposes only. We have no

affiliation of any sort with Lyxor and this is not a solicitation or recommendation to buy or sell any of their products.

RiskCONSULTING

Know

Back-testing A Systematic Fundof Managed Accounts


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Lessons from the Credit Crisis

Funds of Funds business model under pressure

Scale AUM > $5bn or Niche e.g. Asian focus, Emerging Managers

Fees under pressure

Historic Fees 2:20 - the new normal 1:10 1:15

Demand for greater TRANSPARENCY

Demand for greater LIQUIDITY

Historic Liquidity 90 120 Days now 30 Days wanted

New Fund Vehicles UCITS III, Managed Accounts, Act40 ETFs


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One Possible SolutionA Fund of Managed Accounts

Meets the demands for Liquidity Mostly weekly

Improved Transparency Daily pricing and aggregatedexposures

More frequent pricing allows for systematic methodbacktesting

Reasonable Returns achievable but estimated Liquidity Risk

premium of approx 2.00% paid by consumer in the form oflower returns

Range of offerings is improving but still small relative to theHedge Fund Universe. Multiple platform providers


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A Systematic Fund of Managed Accountsutilising the IAS SFA scoring methodology

ProprietarySoftware


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Import Fund Selection Universe

DataImport Wizard


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ImportedDatabaseOf FundReturns


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PortfolioOptimisationSetup andConstraints


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ObjectiveFunctionTo Use

+ = Maximise- = Minimise


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Simulated Annealing Optimisation AlgorithmIterative process that initially allows for worse solutions to be accepted to ensure

that the feasable search / solution space is fully covered. Can accommodatehighly non-linear objective functions and multiple constraints


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Single Period ( in-sample) optimisation


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Next Period ex post ( out-of-sample) return


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AdvancedSettingsMultiple Run

Walk ForwardOptimisation


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Walk Forward Optimisation

Next Period ex post ( out-of-sample) return

. . . .

. . . .

Out-of-sample next period returns


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Walk Forwardout of sample

returns

SelectionBased onCustom

SFA ScoreRanking of

Risk

ReturnPersistence


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SFA ScoreConstituents

AndStandardisation


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Trust but Verify

Always provide sufficient information for clients to independently verify results


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LowerDownside

Beta

HigherUpsideBeta


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Non-Linear

BivariateDependence


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HigherAverageReturns


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LowerDrawdowns


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LessDownside

Risk

HigherUpside

Potential


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Fund

Factsheetwith SFAScore


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Best-FitDistributions

Goodness of Fit


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Scores forRisk


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Scores forReturn


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AndPersistence


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4-Moment RiskDecomposition


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4-Moment Risk Decomposition allows for more meaningfuldisclosure. Includes Modified StdDev and CVaR, Liquidity VaRand Drawdown at Risk (DaR)


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FourMomentDensity

Forecast


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Current Weights Versus Equal Weighted

Note significant reduction in CTA weighting from benchmark and previous period


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A Systematic Fund of Managed Accounts

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