A systematic fund of managed accounts ( Jun 2011 )

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Transcript of A systematic fund of managed accounts ( Jun 2011 )

By: Peter Urbani*

* Peter Urbani was most recently Chief Investment Officer for Infiniti Capital a Fund of Hedge Funds group. He is a 20 year 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 Fund of Managed Accounts

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 ETF’s

One Possible SolutionA Fund of Managed Accounts

• Meets the demands for Liquidity – Mostly weekly

• Improved Transparency – Daily pricing and aggregated exposures

• More frequent pricing allows for systematic method backtesting

• Reasonable Returns achievable but estimated Liquidity Risk premium of approx 2.00% paid by consumer in the form of lower returns

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

A Systematic Fund of Managed Accounts utilising the IAS SFA scoring methodology

ProprietarySoftware

Import Fund Selection Universe

DataImport Wizard

ImportedDatabaseOf FundReturns

PortfolioOptimisationSetup andConstraints

ObjectiveFunctionTo Use

+ = Maximise- = Minimise

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 accommodate highly non-linear objective functions and multiple constraints

Single Period ( in-sample) optimisation

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

AdvancedSettings

Multiple RunWalk ForwardOptimisation

Walk Forward OptimisationNext Period ex post ( out-of-sample) return

. . . .

. . . . Out-of-sample next period returns

Walk Forward out of sample

returns

SelectionBased onCustom

SFA ScoreRanking of

RiskReturn

Persistence

SFA ScoreConstituents

And Standardisation

Trust but Verify

Always provide sufficient information for clients to independently verify results

Lower Downside

Beta

HigherUpsideBeta

Non-LinearBivariate

Dependence

Higher AverageReturns

LowerDrawdowns

Less Downside

Risk

Higher Upside

Potential

Fund Factsheetwith SFA

Score

Best-FitDistributions

Goodness of Fit

Scores forRisk

Scores forReturn

AndPersistence

4-Moment RiskDecomposition

4-Moment Risk Decomposition allows for more meaningful disclosure. Includes Modified StdDev and CVaR, Liquidity VaR

and Drawdown at Risk (DaR)

Four MomentDensity

Forecast

Current Weights Versus Equal Weighted

Note significant reduction in CTA weighting from benchmark and previous period

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