pitch book january 2017

Prepared by George G. Namur, Ph.D.

January 2017

Dual Strategy

Equity Derivatives Fund

Confidentiality

Dual Strategy Equity Derivatives Fund2

This information is privileged and confidential. This

presentation includes descriptions of methodologies and

concepts derived through substantial research and

development efforts. No part of this presentation may be

reproduced by any means or transmitted without the written

permission of Dr George G. Namur.


Introduction

Fund Overview

Strategy Overview

Highlights of Prospective Organization

Dual Strategy Structure and Performance

Equity Valuation

Bios

Contact Information

Appendix

Overview

Introduction


Introduction


The fund is looking to raise capital in exchange for equity in the NewCo

Prospective funding General Partners (GPs) will receive shares in the Investment Manager Company and the Investment Advisor Company

GPs will jointly receive the performance and net management fees that the fund generates

Required funds are part seed capital and part operation expenses (OpEx) capital

Fund Overview


Fund Objective


Achieve superior risk-adjusted returns using equity derivatives and

quantitative models

Keep correlation with market and volatility in check through

diversification and proper strategy allocation

Fund is targeting an annual performance of approximately 30% before

management and performance fees, with a volatility in the mid-teens

and a Sharpe ratio of approximately 2

The Fund in a Snapshot


The FCA-regulated London based fund will combine two complementary

strategies to generate high returns while limiting volatility

Long-Biased Options Strategy

Quantitative Volatility Strategy

Equity options primarily The strategy serves to de-risk the options strategy

• The two strategies are loosely correlated (correlation of 0.115)*

• The combination features a barbell strategy where more weight is allocated to

the lower risk strategy* Monthly returns from Jan 2012 to Dec 2016 were used in computations

Portfolio Construction


The portfolio allocates funds between the options strategy and the

quantitative volatility strategy. The actual asset allocation is at the

fund manager’s discretion and is a function of market conditions

The options strategy focuses on long positions in US equity options

and capitalizes on their inherent asymmetric exposure and

magnified returns

The quantitative volatility strategy focuses on major US index

derivatives and is non-directional. It identifies and executes

arbitrage, relative value, and carry trades

Theoretical Portfolio Allocation


Options Strategy Quantitative Volatility

Strategy

Up Markets

Theoretical Allocation

(20-80)

• Relatively higher allocation

• Higher returns (leverage effect)

• Asymmetric exposure

• Likely to outperform

• Serves to reduce fund’s volatility

• Performance loosely correlated

with market

Sideways or

Down Markets

Theoretical Allocation

(10-90)

• Opportunities for bearish positions

• Using spreads rather than outright

positions may be appropriate

• Relatively higher allocation

• Investment opportunities

(arbitrage, relative value, carry

trade, etc.) unaffected by market

direction

• Likely to outperform

Strategy Overview


Options Strategy


The options strategy will follow a stereoscopic approach (top down

and bottom up), and will utilize a combination of global macro,

fundamental, technical, and sentiment analysis to identify high

potential trades

The strategy will primarily focus on long call options on US securities,

but will also enter bearish positions using long put options when

appropriate

The options portfolio will be diversified across stocks, sectors and

asset classes (via ETFs and futures) to mitigate security-specific and

correlation risk

Quantitative Volatility Strategy


The Quantitative Volatility strategy uses the Gamma1 Vanna2 Volga3

(GVV) cost framework to accurately calculate the implied volatility for

options of all maturities

The GVV cost framework offers an innovative approach to

determining the true value of derivative contracts and identifies price

discrepancies between options of different maturities and underlying

securities

1. The Gamma of an option is the second derivative of the option value with respect to the underlying price

2. The Vanna of an option is the sensitivity of the option delta with respect to change in volatility; or alternatively, the sensitivity of Vega with respect to

the underlying price

3. The Volga of an option is the second derivative of the option value with respect to the underlying’s implied volatility

Highlights of Prospective Organization


Fund Structure


U.S. Feeder Fund Offshore Feeder Fund

Master Fund

Investment Advisor

Company

General Partners

Capital GainsCapital Gains

Performance

Fees

Performance

Fees

Investment Manager

Company

Management

Fees

Management Fees

Net Expenses

Fee Structure


• Assets under management will be subject to an annual management fee

of 2%

• The fee will be charged on the first business day of every quarter on

the net assets under management as of the last business day of the

previous quarter

• Profits will be subject to an annual performance fee of 20%

• The fee will be charged on the first business day of every quarter on

the previous quarter profits in excess of the high-watermark, after the

management fees are deducted

Subscription Agreement Terms


Minimum Initial Investment $300,000

Minimum Additional Investment $100,000

Lockup Period 2 Years

Lockup Type Soft

Lockup Redemption Penalty 15%

Redemption Date Quarterly

Redemption Notice 120 Days

Service Providers


Investment Manager Company

UBS London

(Prime Broker)

PwC London

(Auditor)

HSBC London

(Banker)

Dechert London

(Law Firm)

Citco London

(Administrator)

FCA

(Regulator)

Dual Strategy Structure and

Hypothetical Performance



Combination 1(Low Allocation)

Combination 2(Base Allocation)

Combination 3(High Allocation)

Weight of Options Strategy 10% 15% 20%

Weight of Quantitative Volatility

Strategy90% 85% 80%

Best Month 10.04% 14.45% 18.86%

Worst Month -6.75% -7.14% -7.55%

CAGR 25.89% 29.65% 33.29%

Correlation with S&P 500 0.55 0.46 0.56

Standard Deviation 10.81% 14.46% 18.30%

Sharpe Ratio 2.20 1.88 1.68

Sortino Ratio 3.30 3.15 2.90

* Monthly returns from Jan 2012 to Dec 2016 were used in computations

Variations of Strategy Allocations

with Key Metrics


Up Months Down Months

Combination 1 (10-90) 0.42 0.16

Combination 2 (15-85) 0.42 0.15

Combination 3 (20-80) 0.41 0.15

* Monthly returns from Jan 2012 to Dec 2016 were used in computations

Correlation* of the Dual Strategy with

the S&P 500


-10%

-5%

0%

5%

10%

15%

Jan

-12

Fe

b-1

2M

ar-1

2A

pr-

12M

ay-1

2Ju

n-1

2Ju

l-12

Au

g-1

2Se

p-1

2O

ct-

12N

ov

-12

De

c-1

2Ja

n-1

3F

eb

-13

Mar

-13

Ap

r-13

May

-13

Jun

-13

Jul-

13A

ug

-13

Sep

-13

Oc

t-13

No

v-1

3D

ec

-13

Jan

-14

Fe

b-1

4M

ar-1

4A

pr-

14M

ay-1

4Ju

n-1

4Ju

l-14

Au

g-1

4Se

p-1

4O

ct-

14N

ov

-14

De

c-1

4Ja

n-1

5F

eb

-15

Mar

-15

Ap

r-15

May

-15

Jun

-15

Jul-

15A

ug

-15

Sep

-15

Oc

t-15

No

v-1

5D

ec

-15

Jan

-16

Fe

b-1

6M

ar-1

6A

pr-

16M

ay-1

6Ju

n-1

6Ju

l-16

Au

g-1

6Se

p-1

6O

ct-

16N

ov

-16

De

c-1

6

Combination 2 S&P 500

Base Allocation – Monthly Returns

vs S&P 500

Year 2012 2013 2014 2015 2016

YTD 41.81% 55.45% 12.94% 34.58% 9.34%


* VAMI: Value Added Monthly Index, or the value of $1,000 invested at beginning of time series.

VA

MI

1,000

1,500

2,000

2,500

3,000

3,500

4,000

Jan

-12

Fe

b-1

2M

ar-1

2A

pr-

12M

ay-1

2Ju

n-1

2Ju

l-12

Au

g-1

2Se

p-1

2O

ct-

12N

ov

-12

De

c-1

2Ja

n-1

3F

eb

-13

Mar

-13

Ap

r-13

May

-13

Jun

-13

Jul-

13A

ug

-13

Sep

-13

Oc

t-13

No

v-1

3D

ec

-13

Jan

-14

Fe

b-1

4M

ar-1

4A

pr-

14M

ay-1

4Ju

n-1

4Ju

l-14

Au

g-1

4Se

p-1

4O

ct-

14N

ov

-14

De

c-1

4Ja

n-1

5F

eb

-15

Mar

-15

Ap

r-15

May

-15

Jun

-15

Jul-

15A

ug

-15

Sep

-15

Oc

t-15

No

v-1

5D

ec

-15

Jan

-16

Fe

b-1

6M

ar-1

6A

pr-

16M

ay-1

6Ju

n-1

6Ju

l-16

Au

g-1

6Se

p-1

6O

ct-

16N

ov

-16

De

c-1

6

Combination 2 S&P 500

Base Allocation – VAMI Curve vs

S&P 500

Equity Valuation – Base Allocation


London Office Budget Analysis


Expenditure Projection Annual Fees (USD)* -Year 1

On

e-T

ime

Ex

pe

nse

s

Startup Costs $77,713

Office Fit Out / Furniture $85,714

Computer Hardware $73,571

Peripherals $7,857

Business Software / Applications $189,029

Re

cu

rrin

g E

xp

en

ses Legal / Compliance $307,714

Office Space $416,667

Marketing $113,000

Miscellaneous $18,643

Payroll Wages $1,435,714

Bonuses $56,316

Total $2,295,897

* Cost estimates are based on an exchange rate of 1.43 USD to the GBP

Year 1 2 3 4 5 6 7 8 9 10

Total Expenses

(Millions)$2.30 $2.15 $3.41 $5.08 $8.03 $16.24 $21.38 $25.43 $29.86 $32.83

Equity Value – Base Allocation


• The mean value of equity computed by discounting the total fees

net of costs is around $260 million

• The required OpEx capital was calculated as $5 million by

taking the mean deficit of management fees over 10,000

iterations and adding three standard deviations of the mean

deficit

• Three standard deviations were added in order to minimize the

odds of being underfunded

Term Sheet Framework


A successful launch of this venture requires $5 million in OpEx capital and a minimum of $50 million of assets under management (seed capital).

Prospective investors must invest both seed and OpEx capital to become GPs in the fund

An equity stake of 25% in return for $50 million seed investment provides a funding GP with an IRR of around 35%

To keep GPs interests aligned with those of LPs, funding and sweat equity GPs are expected to be meaningfully invested in the fund (AUM) while the fund is operating

Term Sheet Framework


In return for the $5 million OpEx capital, a funding GP will

receive callable cumulative preferred shares at a preferred rate of

8% per annum, paid out quarterly

No bonuses will be paid to fund executives when the fund cannot

meet its dividend obligation on the outstanding preferred shares

The fund will buy back the preferred shares when capital is

available, hence insuring that GPs cover operating expenses in

proportion their equity stake

Fund Manager’s Bio



Professor of Finance at the Olayan School of Business at the American University

of Beirut. Introduced several new finance classes including “Hedge Fund

Strategies”, “Derivative Securities” and “Securities Analysis & Trading”

Teaches CFA Level I, II, and III classes related to Fixed Income Portfolio

Management, Derivatives and Alternative Investment at the Institute for Financial

Analysts (IFA)

Two decade experience trading various markets (currencies, equities, fixed

income and commodities). Stock picking skills based on stereoscopic analysis

In-depth expertise in derivatives and risk management

Affiliated with the Gerson Lehrman Group as an expert on commodities,

resources and financial services

Dr. George G. Namur


Previously CEO and Managing Partner at Vermont Harding Management Ltd, an

alternative asset management firm based on systematic foreign exchange trading

He started Namur Capital Management in 2001, a wealth management and advisory

firm. He managed clients money as Separately Managed Accounts. He focused on

global macro trends and his trading was driven by his macro views, including

market risk appetite. He supplemented his fundamental analysis with technical,

cyclical as well as sentiment analysis

Formerly General Manager at Shell Trading (previously Coral Energy) in Houston,

managing energy-related assets worth several billion dollars. Introduced the

concept of front office risk management. Developed a portfolio of synthetic power

assets allowing the firm to become a major player in electricity trading

Dr. George G. Namur


Worked at the prestigious Capital Market Risk Advisors in New York where he

advised Wall Street firms, banks and asset managers on risk management and

derivative valuation. Experience at CMRA inculcated a risk-based discipline in

trading and asset management

Trained in derivatives at Chase Manhattan Bank, now JPMorgan Chase in New York

MBA in Analytical Finance and Business Economics from the University of Chicago

Booth School of Business, and Ph.D. in Civil Engineering from the University of

Michigan, Ann Arbor

Fluent in Arabic and French

Dr. George G. Namur

Quantitative Volatility Portfolio

Manager’s Bio



Senior equity derivatives trader for a major bank in New York

Joined the bank in 2000 and has been managing the risk on the S&P 500 index

options portfolio since 2004

Main responsibilities include market making, customer facilitation and proprietary

trading as well as overseeing risk on other derivative products

Helped develop volatility marking methodologies that analyze relative value

opportunities in secondary risks

MS in Financial Engineering degree from Columbia University and MBA in Finance

from the City University of New York

Contact Information



For further enquiries, please contact

George G. Namur, Ph.D.

[email protected]

+1 (917) 421-9866

+961 (71) 100977

Contact Information

mailto:[email protected]

Appendix


Valuation Model Key Assumptions


Simulated quarterly returns for each allocation over ten years

Ran 10,000 iterations

Returns were sampled from a normal distribution

The mean return was the dual strategy’s CAGR and the standard

deviation was its volatility

New investments in the fund were modeled as a function of

previous year’s return, fund vintage, and AUM size

Redemptions were taken as a fraction of starting AUM for each

period

Discount Rate Assumptions


The beta of each allocation was computed using historical log returns

The discount rate was obtained from CAPM and adjusted using the

hazard rate, which is the probability of the business failing in any given

year, or returning less than -5% in any given quarter

The hazard rate was computed as the weighted average of :

Mean market hazard rate for a hedge fund of 33%*, at 25%

Computed hazard rate, at 75%

* Source: Financial Times

Discount Rate


Allocation 1 Allocation 2 Allocation 3

Discount Rate 4% 5% 7%

Hazard Rate 10% 11% 13%

Adjusted Discount Rate* 15% 19% 23%

∗ 𝐴𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑅𝑎𝑡𝑒 =(1 + 𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑅𝑎𝑡𝑒)

(1 − 𝐻𝑎𝑧𝑎𝑟𝑑 𝑅𝑎𝑡𝑒)− 1


Assets Under Management – Base Allocation

Year 1 2 3 4 5 6 7 8 9 10

Ending AUM

(Millions)$89 $168 $289 $591 $1,194 $1,962 $2,552 $2,964 $3,247 $3,446

-

500

1,000

1,500

2,000

2,500

3,000

3,500

0 4 8 12 16 20 24 28 32 36 40

USD

(M

illi

on

s)

Quarter

Capital Gains Before Fees– Base

Allocation


-

50

100

150

200

250

300

0 4 8 12 16 20 24 28 32 36 40

USD

(M

illi

on

s)

Quarter

Management Fees Net of Expenses

Breakeven Point– Base Allocation


-2

1

3

5

7

9

11

13

15

17

0 4 8 12 16 20 24 28 32 36 40

USD

(M

illi

on

s)

Quarter

Total Fees Net Expenses – Base

Allocation


0

20

40

60

80

100

120

140

160

180

0 1 2 3 4 5 6 7 8 9 10

USD

(M

illi

on

s)

Year

Equity Value Distribution – Base

Allocation


Equity (Millions) $150 $200 $250 $300 $350 $400

Probability of Exceeding 77% 62% 47% 34% 23% 15%

0%

2%

4%

6%

8%

10%

12%

14%

16%

50 100

150

200

250

300

350

400

450

500

550

600

650

700

750

800

850

900

950

1,00

0

1,05

0

1,10

0

Pro

bab

ilit

y

Equity (USD in Millions)

pitch book january 2017

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