[PPT] - Overview of Discussion It is always wise t o reflect upon best PowerPoint Presentation

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SEATTLE 999 Third Avenue Suite 4200 Seattle, Washington 98104 206.622.3700 telephone 206.622.0548 facsimile LOS ANGELES 2321 Rosecrans Avenue Suite 2250 El Segundo, California 90245 310.297.1777 telephone 310.297.0878 facsimile

“ WHAT ARE YOU REALLY GETTING?” ( SUMMARY DISCUSSION)

Jeffrey J. MacLean S enior Consultant

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Overview of Discussion

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It is always wise t o reflect upon best pract ices following significant capit al market s disrupt ions
Quant itat ive st rat egies have proliferat ed in last t went y years
They had difficult ly during t he recent market downt urn, most not ably August 2007
Excess ret urns have been st eadily declining in recent periods
Analysis should be t heoret ical and philosophical in nat ure
Past ret urns alone t ell us lit t le t o not hing about t he fut ure, but help us underst and maj or risk fact ors
Fundament al forces driving success should be ident ified, underst ood and planned for accordingly
Barriers t o success should be heeded and avoided when appropriate
There is never an “ answer” t o any st rat egy
The goal is t o creat e a prism t hrough which t o dimension opport unities and st rat egies
Not creat e a black or whit e opinion on t hem
We humbly offer our philosophical views

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General Types of Quantitative Models

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Quant s buy and sell securit ies based on deviat ions from t heoret ically expect ed pat t erns of behavior

Trading Models
Analysis of past ret urns t o predict t he fut ure
Generally short t erm orient ed
Moving averages, Oscillat ors, and Channel Breakout s t o name a few
Valuat ion Based S

creening & Const ruct ion

Balance sheet and valuat ion analysis; public dat a only; no management int erviews
Hold syst emat ically t ilt ed port folio relat ive t o benchmark
Can be comput er/ model driven, or “ black box”
Avoid pit falls of human j udgment by eliminat ing it from decision; no mosaic t heory
Volat ilit y & Correlat ion Opt imizat ion
Focus is on st at ist ical met rics; st andard deviat ion, variance, co-variance, correlat ion, et c.
Oft en comput er/ model driven, or “ black box”
Goal is t o opt imize port folio for superior risk adj ust ed ret urns

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Barrier: Efficient Market Hypothesis (EMH)

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All act ive managers are const rained by EMH in some form, but only quant s by all t hree

Weak EMH
Fut ure prices cannot be predict ed by past price pat t erns
Hist oric analysis offers no guidance t o fut ure pricing or behavior
No persist ent or repeat able pat t erns of behavior t o generat e “ alpha”
Bad for t rading st rat egies and models based on hist oric ret urns
S

emi-S t rong EMH

Market s rapidly adj ust t o new public informat ion
Discovery “ alpha” is available, but finit e
Bad for managers t hat rely on public dat a; i.e., valuat ion-based const ruction
Allows “ alpha” for t hose using subj ect ive j udgment t o discern privat e informat ion; i.e., mosaic t heory
S

t rong EMH

Market reflect s all informat ion, public and privat e
Bad for all act ive management ;“ alpha” cannot be found

Not e: Theory pert ains t o “ alpha,” not Bet a-Alpha (you can beat an index wit h differing bet as)

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Barrier: The Crowding Out Effect

5% 10% 15% 20% 25% 30%

Number of Quant. Mgrs. as % eVestment US Large Cap Universe (Sept. 89 - June '09)

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urce: eVest ment Alliance; Wurt s & Associat es

0.30% 0.24% 0.19% 0.10% 0.15% 0.20% 0.25% 0.30% 0.35% 10%

15%

15%

20%

20% + Average Quarterly Excess Return %

f Quant Mgrs in eVestment US Large Universe

Average Median Quarterly Gross Excess Return for US Large Cap Quant Mgrs to Preferred Benchmark (Sept. 89 - June 2009)

S

urce: eVest ment Alliance; Wurt s & Associat es

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There are only so many ways t o find “ alpha”

through quantit at ive t ools

S

uch t ools are widely available

People wit h quant it ative expert ise are common
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ecret methods t o uncover alpha are unlikely t o remain secret for long – model evolut ion is key

Logically speaking,

firms only employing quant itative met hodologies will compet e for finit e “ alpha”

Finit e alpha & more managers = less alpha t o go around
Evidence loosely support s t heory of “ Crowding Out Effect ”
Conclusion:

The number

f

managers employing quantit at ive st rat egies limit s pot ent ial “ alpha”

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Barrier: “ Goldilocks” Volatility

0.20% 0.34% 0.22% 0.17% 0.10% 0.20% 0.30% 0.40%

0.0-1.0 1.0-2.5 2.5-5.0 5.0+ Average Quarterly Excess Return Range of Quarterly Change in VIX Average Median Quarterly Excess Return for US Large Cap Quant Mgrs to Preferred Benchmark vs. Quarterly Change in VIX Index (March 1990 - June 2009)

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urce: eVest ment Alliance; Wurt s & Associat es

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Recall

quant managers t rade

n

deviations from t heoretically expect ed pat t erns of behavior

Wit hout deviat ions t here is not hing t o t rade upon
Imagine t wo ext remes:
No volat ilit y = no opport unit y
Infinite volatilit y = t heoretical relationships break

down & no opport unity

Hence quant s needs somet hing in bet ween
Evidence

loosely support s concept

f

“ Goldilocks” volat ilit y at market level

Note

some quant s require int ra-st ock volatilit y;

r

volat ilit y wit hin a given index

Conclusion: Implement ation of quantitative st rat egies rest

upon cert ain volat ilit y assumpt ions t o produce “ alpha”

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S

me Practical Barriers to S

uccess

Median Quantitative Strategies Fundamental Strategies Expense Ratio 0.50% 0.70% R-Squared 94% 85% Active Share 22% 30% Active Expense Ratio 2.2% 2.1%

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ur ce: eVest ment Al l iance, Wur t s & Associat es

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Benchmark Const raints

Given EMH, volatilit y, and crowding out concerns, the more you

const rain a manager’s st rategy, the more difficult it is achieve success

Most t radit ional quant st rat egies are highly const rained
Most alt ernat ive quant st rat egies are unconst rained

Fees

Act ually expensive for t he act ively managed port ion
Another barrier t o success given finite “ alpha”

and benchmark const raint s for t radit ional managers Crowding Out Conundrum

Managers with highest likelihood for success have the most secret

and difficult t o replicat e models, or are const ant ly changing t hem

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ecret models impede due diligence and formulation of forward looking forecast s

Must be aware of and willing t o accept t his conundrum

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S ummary

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Maj or Risk Fact ors Associat ed Wit h Quant Managers

EMH port ends inability of quant s t o produce “ alpha” solely wit h public dat a, but not bet a-alpha
Quant derived “ alpha” is finit e; number of quant managers affect s aggregat e opportunity set
Cert ain volat ilit y condit ions are necessary for success
Benchmark const raint s limit abilit y t o overcome barriers t o success
Opaque models complicat e due diligence and forward looking analysis
Fees can be high on an act ively managed basis

Increasing Chances of S uccess

Combat “ crowding out effect ” by allocating t o t hose wit h highly dynamic and evolving st rategies, and be willing t o

accept associat ed lack of t ransparency

Relax benchmark const raint s t o allow opportunit y for beta-alpha and maximize efficiency of fees, or lower fees on highly

const rained mandat es

Fairly evaluate performance in relation t o opportunit y set, benchmark const raint s, fees, and market volatility, not j ust

past ret urns t o avoid poor manager t iming decisions

Above all, be aware risk fact ors and barriers t o success for quant itat ive st rat egies are different from fundamental