DIVERSEDIVERSE

Illuminating the World of

presented by

HEDGE FUNDSHEDGE FUNDSHEDGE FUNDSAlfred Winslow Jones

The Institutional Investor Journal, August 1968

BRIEF HISTORY of HEDGE FUNDSBRIEF HISTORY of HEDGE FUNDSBRIEF HISTORY of HEDGE FUNDS

The Most Popular

HEDGE FUNDSTRATEGIESHEDGE FUNDSTRATEGIESHEDGE FUNDSTRATEGIES

Where is All the Money inCTAs & MANAGEDFUTURES FUNDSCTAs & MANAGEDFUTURES FUNDSCTAs & MANAGEDFUTURES FUNDS

UNDERSTANDING MOMENTS DISTRIBUTIONUNDERSTANDING MOMENTS DISTRIBUTIONUNDERSTANDING MOMENTS DISTRIBUTION

2.3172.3172.317

WEALTHWEALTHWEALTH

29%15%12%

12%

10%

6%6%

6%4%

Long/Short Equity

CTA/Managed Futures

Emerging Markets

Global Macro

Fixed Income

Event Driven

Relative Value

Multi Strategy

Other

$127

Switzerland

$89

Luxembourg

$34

Sweden

$27

Bermuda

$483

UK

$88

Brazil

NorthAmerica

UniversityEndowments

IndividualInvestors

Fire, Police, Teacher& Other Pensions

Municipalities

Funds ofHedge Funds

6,178Europe

4,092

Africa

113

Australia

165

South America

1,267Asia

637

$49

Netherlands

$34

Germany

$1124

USA

$66

France

Total reported Assets Under Management (AUM) of hedge funds and funds of hedge funds at the end of �rst half 2012. If broken down into individual U.S. dollar bills and placed side by side, they would circle the equator 9,017 times or go to the moon and back 470 times.

Keeping a Portfolio ProtectedIMAGINING THEUNIMAGINABLEIMAGINING THEUNIMAGINABLEIMAGINING THEUNIMAGINABLE

Traditional risk statistics assume that events to the left and right of the mean are equally likely to occur.

Value at Risk (VaR) is the maximum loss a fund can expect within a speci�ed holding period using a speci�ed con�dence level. It is calculated using Traditional Risk Statistics based on a Normal Distribution,

with No Skew, and Mesokurtic Kurtosis.

The traditional VaR model has come under criticism because, in the real world, events can't be easily modeled on a simple, symmetrical curve (normal distribution). Newer, Fat-Tail VaR models better capture the fact that highly improbable and damaging events do occur, and can occur more frequently than traditional risk statistics assume.

Nassim Nicholas Taleb popularized the use of the term “black swan” for these highly improbable events.

size of the assetsunder managementin billions of USD based on management company location

$ trillion

Hedge Fund Investors are

DIVERSE

CONTINENTCONTINENTHedge Funds are on Every

CONTINENT

*Except for Antarctica

“ The logic of the idea

was very clear. It was a

hedge against the vagaries of

the market. You can buy more

good stocks without taking

as much risk as someone

who merely buys. ”

is Relatively Concentrated

IT'S ALL GREEK TO MEIT'S ALL GREEK TO MEIT'S ALL GREEK TO ME Some Common Investment Statistics

3 4 FUNDS3 4 FUNDS3 4 FUNDS

$

are denominated in the US Dollar or Euro

out of

Regulation of U.S. securities industry begins with the Securities Act of 1933. Regulation D exempts companies selling to

“accredited investors” from registering.

1933

Regulation of U.S. funds begins with passage of the Investment

Company Act of 1940.

1940

Fortune magazine publishes article on Alfred

Winslow Jones’ hedge fund - number of hedge funds

grows in the following years.

1966

Alternative Investment Management Association (AIMA) formed to represent all practitioners

in the alternative investment management industry.

1990

SEC adopted rules requiring registered advisors with

$150 million or more in AUM to report comprehensive

fund information.

2011

Securities Exchange Act of 1934 establishes the

Securities and Exchange Commission (SEC).

1934

Managed Funds Association (MFA)

is formed to advocate on behalf of hedge funds and managed

futures �rms.

1991

Alfred Winslow Jones forms �rst hedge fund (coining them “hedged funds”) and eventually

introduces the 20% performance fee.

1949

JOBS Act passed. Allows hedge funds to market themselves to a broader audience.

2012

Rothschild Family introduces the world’s

�rst fund ofhedge funds.

1969 2002

“UCITS III” Directives released in Europe allowing

UCITS to employ alternative strategies.

Financial Services Authority (FSA)

established to regulate the UK �nancial

services industry.

Committee of European Securities Regulators (CESR) established – later

replaced by the European Securities

and Markets Authority (ESMA) in 2011.

2001

56%

USDollar

21%

Euro

7%

Brazilian Real

4%

Chinese Yuan

4%

UKPound

PERCENT OF TOTAL AUM BY FUND SIZE

NUMBER OF HEDGE FUNDS AND FUNDS OF HEDGE FUNDS REPORTING A GIVEN DOMICILE

3%

SwissFranc

5%

Other

SYMBOL DEFINITION WHAT IT ATTEMPTS TO ANSWER

*

< $25M

$25-50M

$51-100M

$101-250M

$251-500M

$501M-1B

>$1B78.1%

1.1%

1.1%

1.8%

4.7%

5.0%

8.2%

Family Of�ces

Foundations & CharitableOrganizations

Measures the fund’s value relative to a benchmark.

Alpha

Beta

Sigma(Standard Deviation)

α

0.135%

β

Ω

σ

Omega

How much extra did you earn from a fund that you wouldn’t have otherwise earned from investing in the broad market?

Measures the fund’s sensitivity to movements of the market as a whole

How likely is your fund to track the benchmark?

Uses actual return distribution and divides expected returns into gains and losses to provide a relative measure of the likelihood of the fund achieving a given return.

How do your fund’s good returns stack up to its bad returns, and how likely is it that your fund will make more than a given percentage?

Measures the degree of variation of the fund’s returns around the fund’s mean (average) return for a speci�ed period.

How much should you expect your fund’s returns to vary from the norm?

THE FORMULA FOR YOUR INNER MATH GEEK

LEPTOKURTIC

For example, traditional risk approaches assume that the

likelihood of an extreme event (negative or positive) is very slim.

These extreme events are modeled by the tails of the

normal distribution.

MESOKURTIC PLATYKURTIC

TRADITIONAL RISK STATISTICS

chance

The overstated probability that an extremely positive event (gain) occurs according to the

traditional risk approach.

Traditional ETL

Fat-Tail ETL

ExtremeNegative Event

(Loss) Occurs Here

An asymmetrical curve, based on real-world returns, can more accurately predict the chance

that an extreme negative event (loss) or an extreme positive event (gain) may occur.

Notice that the tail is “fatter” to the left of 95% VaR. This indicates that the probability of sustaining losses has increased.

chance

ExtremePositive Event

(Gain) Occurs Here

In this example,both the Fat-Tail and

normal distributions havethe same mean and VaR 95%

numbers, but the Fat-Tail distribution is Leptokurtic and

has a left skew.

As a result, the Fat-Tail distribution’s Expected Tail

Loss (ETL), which is the average of returns that exceed the VaR,

more accurately captures a higher downside risk than

traditional ETL.

FAT-TAIL RISK STATISTICS

VARIANCE

MEAN

STANDARD DEVIATIONmeasures the volatility of returns from the mean

SEMI DEVIATIONmeasures the volatility of returns below the mean

LOSS DEVIATIONmeasures the volatility of returns from the mean only during periodsof a loss

DOWNSIDE DEVIATION (5%)measures the volatility of returns below a Minimum Acceptable Return (MAR), offered here at 5%

Where M = The mean return of the benchmarkWhere M = The mean return of the fund

Alpha = M - Beta x M

R

R

RD

RD

2

I-1I 1R I R

N

I-1

N

R

RD

I

IWhere R = The return of the benchmark for period IWhere RD = The return of the fund for period IWhere M = The mean return of the benchmarkWhere M = The mean return of the fundWhere N = Number of periods

Beta = ( ∑ ( R - M )(RD - M ) ) ÷ ( ∑ ( R - M )RD

1/2

1/2

2

I-1I R

N

R

IWhere R = Return for period IWhere M = Mean of return set RWhere N = Number of periods

Standard Deviation = ( ∑ ( R - M ) ÷ (N - 1) )

I=1IR

N

M = ( ∑ R ) ÷ N

Annualized Standard Deviation = Monthly Standard Deviation x ( 12 )

Where r is the threshold return, and F is cumulative density function of returns.

b

r

Ω(r) =

∫F(x)dx

∫(1-F(x))dx

r

a

If You've Made it This Far... It's Time for Some More Advanced Analysis

of athe

Moment

1st

Moment

2nd

Moment

3rd

Moment

4th

Measures the distance of returns from the mean. In other words, variance shows how frequently possible returns occur.

SKEWNESS

NEGATIVE SKEWNO SKEW

Characterizes the degree of asymmetry of a distribution around its mean. In other words, by using skewness investors should be able to better predict whether a return is more likely to occur to the left or to the right of the mean.

KURTOSISCharacterizes the relative peakedness or �atness of a distribution. In other words, kurtosis helps investors better predict the likelihood of a given return (or loss).

The average of returns. In other words, it's the average.

μ

μ = Mean

x

y

x

y

x

y

POSITIVE SKEW

x

y

μ

σ = Standard Deviation

Mode

μMedianMode

(high peak)

(returns more likely to be to the left of the mean)

(returns more likely to be to the right of the mean)

(returns equally likely to be to the left and to the right

of the mean)

(�at topped)(normal distribution)

Median

Mode

Median

μ μ

x

y

x

y

x

y

-1σ-2σ-3σ 1σ 2σ 3σx

y

"One single observation can invalidate a general statement derived from millennia

of confirmatory sightings of millions of white swans. All you need is one single

(and, I am told, quite ugly) black bird.”

Nassim Nicholas TalebThe Black Swan: The Impact of the Highly Improbable

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SovereignWealth Funds

-3σ μ-1σ-2σ 1σ 2σ 3σ

Commodity Futures Trading Commission

(CFTC) established. Commodity Trading Advisors (CTAs), or commodity pools,

are generally required to register with the CFTC.

1974

European Federation of Investment Funds and

Companies (FEFSI) established – later

renamed European Fund and Asset

Management Association (EFAMA).

founded.

1996

Hedge Fund Association (HFA)

founded to advocate on behalf of smaller and

emerging hedge funds.

0.135%

95%

VaR

Images are for illustration purposes and are not to scale

GOOD THINGS COME IN SMALL PACKAGESGOOD THINGS COME IN SMALL PACKAGESGOOD THINGS COME IN SMALL PACKAGESSmaller Funds Tend to Outperform Larger Funds… …But Larger Funds are Generally Less Volatile

CUMULATIVE PERFORMANCE BY SIZE OF FUNDJanuary 1996 to December 2011

ANNUALIZED VOLATILITY BY SIZE OF FUNDJanuary 1996 to December 2011

100%

6.92%

7.39%

5.94%

4.75%4.07%

4.00%

3.63%3.67%

4.12%

5.95%

6.05%5.96%

0% 1% 2% 3% 4% 5% 6% 7% 8%

0%

DEC1996

DEC1998

DEC2000

DEC2002

DEC2004

DEC2006

DEC2008

LARGE> $500M AUM

MID-SIZE$100 - 500M AUM

SMALL< $100M AUM

DEC2010

300%

500%

700%

DOWNSIDEDEVIATION

(5%)

LOSSDEVIATION

SEMIDEVIATION

STANDARDDEVIATION558%

356%

307%