kepos capital lp - granicus

This discussion document is to be read in conjunction with our “Exotic Beta Revisited” white paper. This summary description of Kepos Capital LP and any investment vehicle that it may organize and

manage is intended only for discussion purposes and does not constitute an offer or solicitation of an offer with respect to the purchase or sale of any security; it should not be relied upon by you in

evaluating the merits of investing in any securities. These materials are confidential and are not intended for distribution to, or use by, any person or entity in any jurisdiction or country where such

distribution or use is contrary to local law or regulation. © Kepos Capital LP, 2013. All rights reserved.

Exotic Beta

Prepared for SDCERA

Confidential and Not for Re-Distribution

April 18, 2013

Please carefully review important disclosures including, but not limited to, methodology and calculation

limitations at the end of this document.

Important Disclosures

Introducing exotic beta

- Definition

- Alternative approaches to risk premia exposure

From theory to practice

- Building exotic betas

- A list of interesting exotic betas

- Combining exotic betas into an optimal portfolio

- Measuring portfolio exposures

Empirical evidence

- Comparing alternative approaches

- Potential portfolio impact

Summary

2

Overview

INTRODUCING EXOTIC BETA

Alpha

• Statistical arbitrage

• High frequency currencies

• Term structure arbitrage

• Volatility arbitrage

Exotic Beta

• Catastrophe bonds

• Volatility

• Commodities

• Credit

Beta

• Global equities

Active

Zero net supply

Higher cost

Passive

Plentiful supply

Lower cost

“…returns exist along a continuum – from beta, to exotic beta, and ultimately to alpha.”

– Litterman (2005)

4

Proprietary Well-known

Our View: The Return Spectrum

expected to generate a positive long-term compensation for assuming risk that is uncorrelated to

the equity risk premium

transparent, intuitive and reasonably well known

not generated by short-term inefficiencies or unexpected events

An exotic beta is an investment strategy that is:

Structural

- Market segmentation

- Preferred habitat

Rationales and examples:

5

Defining Exotic Beta

Liquidity

- Private assets

- Variable liquidity

Insurance

- Equity crash risk

- Default risk

- Left-tail events

FROM THEORY TO PRACTICE

Move away from market capitalization weights

- Reweighted indexes (RAFI)

- Reallocations across asset classes (Asset Class Risk Parity)

Hedge fund replication

- Mimic the broad hedge fund index with derivatives (JPM Hedge Fund Alt Beta)

- Construct tracking portfolio for individual hedge funds strategies (AQR’s Diversified Arbitrage ETF)

Pure risk premia

- Identify broad, robust risk premia across the broadest range of asset classes

- Build tracking portfolios for individual risk premia (DFA)

- Combine risk premia in a dynamic reallocation strategy (exotic beta)

7

Different Approaches to Alternative Risk Premia

9

The graph shows historic performance of backtested risk premia and does not represent actual returns of any investable product. Prospective investors should exercise caution in using or relying on

any hypothetical or backtested results as being indicative of future performance. HYPOTHETICAL OR SIMULATED PERFORMANCE RESULTS HAVE CERTAIN INHERENT LIMITATIONS. AMONG

OTHER SHORTCOMINGS, UNLIKE AN ACTUAL PERFORMANCE RECORD, SIMULATED RESULTS DO NOT REPRESENT ACTUAL TRADING. IN ADDITION, THE STRATEGIES PORTRAYED

INVOLVE A HIGH LEVEL OF RISK, WHICH COULD RESULT IN SUBSTANTIAL INVESTMENT LOSSES. You are urged to read all of the additional disclosures on pages i-ii.

Cumulative Historic Performance of Exotic Betas Utilized by Kepos

Scaled to 10% Volatility

0.5

5.0

50.0

Dec

-89

Jul-

90

Feb

-91

Sep

-91

Ap

r-9

2

No

v-92

Jun

-93

Jan

-94

Au

g-9

4

Mar

-95

Oct

-95

May

-96

Dec

-96

Jul-

97

Feb

-98

Sep

-98

Ap

r-9

9

No

v-99

Jun

-00

Jan

-01

Au

g-0

1

Mar

-02

Oct

-02

May

-03

Dec

-03

Jul-

04

Feb

-05

Sep

-05

Ap

r-0

6

No

v-06

Jun

-07

Jan

-08

Au

g-0

8

Mar

-09

Oct

-09

May

-10

Dec

-10

Jul-

11

Feb

-12

Gro

wth

of

$1

(Lo

g Sc

ale)

Commodities

Credit

Equity Value

Bond Yields

Bond Yields Value

Currency Value

Real Assets

Volatility

10

Forecasting Risk Premia

While exotic beta may be thought of as a passive strategy, some slow moving

reallocations may be warranted

– Risk premia vary over time

– Timing opportunities are just value investing in risk premia

– Risk premia must be monitored for structural changes

– Financial shocks often ‘spill over’, causing other risk factors to become

more highly correlated and perform poorly

We rely on the Black-Litterman Asset Allocation model to blend different sources

of information (prior beliefs and empirical evidence) into a consistent set of

expected returns (posterior views)

11

Forecasting Exotic Beta Returns with Black-Litterman

0.00

5.00

10.00

15.00

20.00

25.00

-40% -30% -20% -10% 0% 10% 20%

Sample mean Momentum Value Spillover

0.00

5.00

10.00

15.00

20.00

25.00

-40% -30% -20% -10% 0% 10% 20%

Prior

The Black-Litterman model combines

different views into a single distribution of

expected returns

Empirical evidence

Prior Views

The Black-Litterman distribution

− is centered around the weighted

average of forecasts

− has less dispersion than any of

the forecasts

0.00

5.00

10.00

15.00

20.00

25.00

-40% -30% -20% -10% 0% 10% 20%

Static views Dynamic views

Posterior Distribution

Guidelines of a proposed process. Not to be understood as a mandatory checklist of required steps; at times we may follow an investment management process that, for one reason or another, does

not follow the processes described herein.

Commodities Credit Equity Value Bond YieldsBond Yields

ValueCurrency

ValueReal Assets Volatility

Median 16.9% 3.0% 4.0% 18.0% 21.0% 23.8% 3.5% 11.2%

75th percentile 22.8% 7.8% 6.4% 22.1% 26.2% 28.5% 6.5% 13.8%

Max 36.3% 13.1% 19.8% 34.5% 53.4% 57.1% 13.0% 20.8%

Min 6.2% -14.3% -1.2% 6.6% 6.3% 10.1% -0.5% -1.6%

25th percentile 12.7% 0.3% 2.3% 14.2% 15.4% 19.5% 1.4% 4.5%

-20.0%

-10.0%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

Mar

gin

al C

on

trib

uti

on

to

Ris

k

12

Exotic Beta Risk Allocation Over Time (Simulated)

Note: Hypothetical portfolio; not based on actual trading. Uses monthly data from January 1990 through February 2012. Prospective investors should exercise caution in using or relying on any

hypothetical or backtested results as being indicative of future performance. HYPOTHETICAL OR SIMULATED PERFORMANCE RESULTS HAVE CERTAIN INHERENT LIMITATIONS. AMONG



Decomposition of Risk Over Time (Marginal Contribution to Risk) Median, 25th and 75th percentile, and maximum and minimum MCR

13

Current Exotic Beta Views

Note: Theoretical portfolio weights broken down by return forecasting model using information as of March 29, 2013. Prospective investors should exercise caution in using or relying on any

hypothetical or backtested results as being indicative of future performance. HYPOTHETICAL OR SIMULATED PERFORMANCE RESULTS HAVE CERTAIN INHERENT LIMITATIONS. AMONG



March 31, 2013 Portfolio Weights (Annualized Volatility Level)

Decomposed by expected return forecasting model

14

How should we measure exposure to risk premia?

Note: Theoretical portfolio weights as measured by marginal contribution to risk (MCR) and by notional leverage. Prospective investors should exercise caution in using or relying on any hypothetical or

backtested results as being indicative of future performance. HYPOTHETICAL OR SIMULATED PERFORMANCE RESULTS HAVE CERTAIN INHERENT LIMITATIONS. AMONG OTHER

SHORTCOMINGS, UNLIKE AN ACTUAL PERFORMANCE RECORD, SIMULATED RESULTS DO NOT REPRESENT ACTUAL TRADING. IN ADDITION, THE STRATEGIES PORTRAYED INVOLVE

A HIGH LEVEL OF RISK, WHICH COULD RESULT IN SUBSTANTIAL INVESTMENT LOSSES. You are urged to read all of the additional disclosures on pages i-ii.

Portfolio Risk (MCR) Portfolio Leverage

EMPIRICAL EVIDENCE

Note: Hypothetical portfolio; not based on actual trading. Uses monthly data through February 29, 2012.

16

Prospective investors should exercise caution in using or relying on any hypothetical or backtested results as being indicative of future performance. HYPOTHETICAL OR SIMULATED

PERFORMANCE RESULTS HAVE CERTAIN INHERENT LIMITATIONS. AMONG OTHER SHORTCOMINGS, UNLIKE AN ACTUAL PERFORMANCE RECORD, SIMULATED RESULTS DO NOT

REPRESENT ACTUAL TRADING. IN ADDITION, THE STRATEGIES PORTRAYED INVOLVE A HIGH LEVEL OF RISK, WHICH COULD RESULT IN SUBSTANTIAL INVESTMENT LOSSES. You

are urged to read all of the additional disclosures on pages i-ii.

Kepos Exotic Beta Strategy

Equal Risk

Combination of

Exotic Betas

Kepos Exotic Beta

Strategy

(Dynamic Risk)

Average Annual Return 21.5% 23.8%

Annualized Volatility 9.1% 9.4%

Sharpe Ratio 1.9 2.1

Maximum Drawdown -14% -7%

Correlation with ACWI 0.16 0.05

Adding dynamic

allocation of risk

(“timing”) improves

backtested

performance

modestly.

Backtest Comparison: January 1990 – February 2012

Global equities have a modest Sharpe ratio and significant drawdowns

Asset class risk parity offers improved performance but high equity correlation and large drawdowns

Exotic beta simulation generates improvements in performance, correlation and drawdown over global

equities and risk parity

Exotic beta simulation uses about twice as much leverage and 3-4 times as much turnover as risk parity

Exotic beta compares favorably to global equities and the risk parity portfolio of asset classes

17

1 The above leverage number represents average leverage per side for the period from January 1990 through February 2012. It is calculated as the average of long notional exposure and the absolute

value of short notional exposure, divided by the model portfolio’s net asset value. For futures, equity index swaps, currency forwards and equity instruments, we calculate leverage as the notional

exposure of the position. For fixed income instruments, including interest rate swaps, we use the same notional methodology but generally convert all exposures to 10-year zero-coupon equivalents.

For variance swaps, option straddles and swaption straddles, we generally use the vega notional multiplied by implied volatility. We also may net offsetting positions where appropriate. Note the Asset

Class Risk Parity Portfolio is only longs. 2 “Turnover” is calculated on a daily basis as the current day’s aggregate trades divided by the prior day’s aggregated absolute positions. Trade size includes

internal crosses that would have otherwise been executed in the marketplace. The turnover figure specified above is the median based on model data from January 1990 through February 2012.

Prospective investors should exercise caution in using or relying on any hypothetical or backtested results as being indicative of future performance. HYPOTHETICAL OR SIMULATED




Comparing Exotic Beta to Equities and Risk Parity

Global Equities at

10% Volatility

Asset Class Risk

Parity Portfolio

Kepos Exotic Beta

Strategy

Average Annual Return 4.4% 9.6% 23.8%

Annualized Volatility 10.0% 10.0% 9.4%

Sharpe Ratio 0.2 0.7 2.1

Maximum Drawdown -33% -27% -7%

Correlation with ACWI 1.00 0.77 0.05

Average Leverage1 1.6 3.1

Turnover (Days)2 299 77

Note: Hypothetical portfolio; not based on actual trading. Uses monthly data through February 29, 2012.

Model Portfolio Comparison: January 1990 – February 2012

18

Can We Replicate Hedge Funds with Exotic Beta?

Projections. Prospective investors should exercise caution in using or relying on any hypothetical or backtested results as being indicative of future performance. HYPOTHETICAL OR SIMULATED




Hedge fund returns are infrequently observed

Hedge fund portfolios are typically very dynamic

Only common factor or risk premia will be replicable, not alpha

Therefore, replication can only capture the long-term components in aggregate hedge fund returns

Dependent Variable: HFRI monthly returns, August 1995 to March 2011

EXPLANATORY

VARIABLES

Regression

Coefficient t-Stat

Intercept 0.36% 4.40

MSCI ACWI 0.41 22.45

MSCI ACWI (lag 1)MSCI ACWI (lag 2)MSCI ACWI (lag 3)Equity ValueBond YieldsBond Yields ValueCommoditiesReal AssetsCurrency ValueVolatilityCredit

R-squared

Fitted meanHFRI mean

0.14%

0.50%

73.0%

Regression

Coefficient t-Stat

0.33% 4.25

0.40 22.47

0.05 2.97

0.04 2.49

(0.03) (1.41)

0.17%

0.50%

75.6%

Regression

Coefficient t-Stat

0.18% 1.90

0.39 21.13

0.05 1.79

0.02 0.52

(0.04) (1.29)

0.10 3.89

(0.02) (0.84)

0.04 1.47

0.02 1.00

0.04 1.55

0.32%

0.50%

76.8%

19

Getting Alternative Risk Premia Into Your Portfolio

5.0%

6.0%

7.0%

8.0%

9.0%

10.0%

11.0%

12.0%

5.0% 7.0% 9.0% 11.0% 13.0% 15.0% 17.0% 19.0%

An

nu

aliz

ed R

etu

rn

Annualized Volatility

10% exotic beta

taken from bonds

Risk/Return Tradeoffs for Various Portfolios

10% exotic

beta taken

from equities

Typical Institutional Portfolio

(55% equities / 40% bonds /

5% hedge funds)

Hedge funds

Global equities

Global

bonds

Projections. Prospective investors should exercise caution in using or relying on any hypothetical or backtested results as being indicative of future performance. HYPOTHETICAL OR

SIMULATED PERFORMANCE RESULTS HAVE CERTAIN INHERENT LIMITATIONS. AMONG OTHER SHORTCOMINGS, UNLIKE AN ACTUAL PERFORMANCE RECORD,

SIMULATED RESULTS DO NOT REPRESENT ACTUAL TRADING. IN ADDITION, THE STRATEGIES PORTRAYED INVOLVE A HIGH LEVEL OF RISK, WHICH COULD RESULT IN

SUBSTANTIAL INVESTMENT LOSSES. You are urged to read all of the additional disclosures on pages ii-iii.

20

Summary

We believe a good model for return predictability is a spectrum from beta to

alternative risk premia to alpha

There are a variety of approaches to introducing alternative risk premia in your

portfolio. These approaches are not all created equal:

− Risk parity around asset classes is straightforward, but our research

suggests that it is not as Sharpe ratio enhancing as other strategies may be

− Hedge fund replication brings the allure of hedge funds, but is this the right

objective?

− Exotic beta is more complex and requires more judgment to refine, but we

believe it generates better return expectation and risk characteristics

Regardless of approach, these liquid and transparent strategies can enhance the

performance and risk of a traditional portfolio

APPENDIX

Appendix i

OVER 30 PERSONNEL

Including:

15 from Goldman Sachs

8 from Satellite Asset Management

3 from Highbridge

2 from BGI/BlackRock

Kepos Capital Team

MARK CARHART

Chief Investment Officer

GIORGIO DE SANTIS

Director of Research

GIORGIO DE SANTIS

Director of Research

ED CHU

Chief Technology Officer

MATTHEW DESCHAMPS

Chief Operating Officer

NOEL FLYNN

Chief Financial Officer

SIMON RAYKHER

General Counsel & Chief Compliance Officer

JOSEPH DELUCA

Chief Strategist

Technology (5)

Raj Bakhru

Aurelio Campanale

Greg Hobart

Eric Ng

Felix Apfaltrer

Jonathan Berkow

Nick Bonamo

Rohan Chauhan

Ui-Wing Cheah

Harry Farrell

Arlen Khodadadi

Adam Lichtenstein

Pete Meindl

Mike Nigro

Raja Reddy

Derek Schaeffer

Adrien Vesval

Research & Portfolio

Management (17) Infrastructure (7)

Kerry Arenas

Sandra Catlyn

Brenna Ingersol

Tom Mikolinski

Andy Rykowski

Legal & Compliance (2)

Brian Carvajal

Product Management (2)

Daisy Chang

Risk and Portfolio Construction Team

BOB LITTERMAN

Chairman, Risk Committee

ATTILIO MEUCCI

Director of Portfolio Construction

Ui-Wing Cheah

Felix Apfaltrer

23

Exotic Beta Example: Directional Implied Volatility

2012 contribution: 143 bps gross

Strong positive performance as implied volatilities

declined sharply for much of the year

A spike in implied volatilities late in the year

(‘fiscal cliff’) resulted in losses for December

Investors should exercise caution in using or relying on any hypothetical or backtested results as being indicative of future performance. HYPOTHETICAL OR SIMULATED PERFORMANCE

RESULTS HAVE CERTAIN INHERENT LIMITATIONS. AMONG OTHER SHORTCOMINGS, UNLIKE AN ACTUAL PERFORMANCE RECORD, SIMULATED RESULTS DO NOT REPRESENT

ACTUAL TRADING. IN ADDITION, THE STRATEGIES PORTRAYED INVOLVE A HIGH LEVEL OF RISK, WHICH COULD RESULT IN SUBSTANTIAL INVESTMENT LOSSES. You are urged to read

all of the additional disclosures on pages i-ii. Please note that actual performance data appears on page 19.

24

Exotic Beta Example: Commodities Backwardation

2012 Contribution: -269 bps gross

Difficult performance in Q2 driven by strong

underperformance of crude oil and agriculture

relative to natural gas and livestock

Recovery in Q3 driven by strong performance in

the agriculture sector

Difficult Q4 as base metals rallied while

agriculture weakened

Investors should exercise caution in using or relying on any hypothetical or backtested results as being indicative of future performance. HYPOTHETICAL OR SIMULATED PERFORMANCE

RESULTS HAVE CERTAIN INHERENT LIMITATIONS. AMONG OTHER SHORTCOMINGS, UNLIKE AN ACTUAL PERFORMANCE RECORD, SIMULATED RESULTS DO NOT REPRESENT

ACTUAL TRADING. IN ADDITION, THE STRATEGIES PORTRAYED INVOLVE A HIGH LEVEL OF RISK, WHICH COULD RESULT IN SUBSTANTIAL INVESTMENT LOSSES. You are urged to read

all of the additional disclosures on pages i-ii. Please note that actual performance data appears on page 19.

Appendix ii

Kepos Exotic Beta Fund Snapshot as of January 7, 2013

EQUITIES CURRENCIES COMMODITIES SOVEREIGN FIXED INCOME

Australia 1.6% Australia -7.4% Soybean oil -0.4% Australia 3yr 31.4%

Austria 0.0% Canada -17.1% Corn 4.3% Australia 10yr -25.0%

Belgium 0.0% Euro -10.2% Cocoa 0.1% Canada 2yr 0.0%

Brazil 3.9% Japan -17.9% WTI crude 2.9% Canada 10yr -31.1%

Canada -1.5% Norway -0.3% Brent crude 4.2% Euro Schatz 2yr -29.8%

China 0.4% New Zealand 17.3% Cotton -2.1% Euro Bund 10yr 49.0%

Denmark 0.0% Sweden 10.7% Feeder cattle -1.8% Japan 2yr 0.0%

Finland 0.0% Switzerland -17.3% Gold 1.7% Japan 10yr -34.6%

France -4.5% United Kingdom 19.4% Heating oil 2.0% UK gilt 2yr 0.0%

Germany -4.2% Argentina 0.0% Orange juice 0.0% UK gilt 10yr 46.1%

Greece 0.0% Brazil 8.9% Coffee -2.6% US Treasury 2yr -8.9%

Hong Kong -1.8% Chile 5.7% Kansas wheat -1.2% US Treasury 10yr 61.2%

Hungary 0.0% Colombia -1.8% Aluminum -1.1% H: Hong Kong 2.1%

India -4.5% Czech Rep. -33.7% Live cattle 1.3% H: Japan 2.3%

Italy 8.1% Hungary 1.6% Lean hogs -2.3% H: Eurostoxx 50 4.6%

Japan -1.8% Indonesia 1.6% Lead 0.0% H: United Kingdom 2.4%

South Korea -1.0% India 11.6% Nickel 1.0% H: United States 11.4%

Malaysia -0.8% Israel -8.4% Copper 1.8%

Mexico -4.4% Korea 17.7% Tin 0.0% Total 58.2%

Netherlands -1.2% Mexico 20.0% Zinc -1.2% Beta 0.00

Norway 2.6% Peru 0.0% Natural gas -2.6% Duration 4.35

Poland 0.0% Philippines -11.4% Palladium 1.8%

Russia 5.3% Poland 11.7% Platinum 1.5%

South Africa -0.3% Russia 13.7% Gasoil 3.5%

Singapore -4.2% South Africa 10.1% Soybean 3.1% VOLATILITY, CREDIT, REAL ESTATE

Spain 0.0% Singapore -13.1% Sugar -1.1% VXX -4.5%

Sweden 2.0% Thailand -3.1% Silver 1.3% VNQ 24.2%

Switzerland -5.1% Turkey -5.6% Soybean meal 2.9% LQD 3.0%

Eurostoxx 50 15.1% Taiwan -9.2% Wheat -2.5% HYG 6.2%

Thailand -1.7% US dollar 6.8% Gasoline 0.1% US Treasury 10yr -6.2%

Turkey -1.2% H: Hong Kong -2.4% H: Hong Kong -1.1% H: Hong Kong -3.3%

Taiwan -1.0% H: Japan -3.4% H: Japan -1.6% H: Japan -4.7%

United Kingdom -3.8% H: Eurostoxx 50 -5.4% H: Eurostoxx 50 -2.8% H: Eurostoxx 50 -6.8%

United States -12.2% H: United Kingdom -2.3% H: United Kingdom -1.6% H: United Kingdom -6.3%

H: United States -11.0% H: United States -5.9% H: United States -17.8%

Total -16.4% Total 0.0% Total 14.5% Total 28.8%

Beta 0.00 Beta 0.00 Beta 0.00 Beta 0.00

Duration 0.00 Duration 0.00 Duration 0.00 Duration -0.46

This information is unaudited and the data is accurate only as of the date indicated above.

MARK CARHART

CHIEF INVESTMENT OFFICER

Prior to Kepos, Mark was the Co-Chief Investment Officer of the Quantitative Investment Strategies Group at Goldman Sachs Asset

Management, where the team managed over $185 billion in assets at its peak. At Goldman Sachs, Mark was named Managing Director in

1999 and Partner in 2004. Prior to joining Goldman, Mark was an Assistant Professor of Finance and Business Economics at the Marshall

School of Business at USC, a Senior Fellow of The Wharton Financial Institutions Center, and a consultant for Dimensional Fund Advisors

(DFA) and Mercer Global Advisors. His publications include articles in The Journal of Finance and The Review of Financial Studies as well

as several chapters in our book, Modern Investment Management: An Equilibrium Approach. Mark earned a B.A. from Yale University in

1988, became a CFA Charterholder in 1991 and received his Ph.D. from the University of Chicago Booth School of Business in 1995.

GIORGIO DE SANTIS

DIRECTOR OF RESEARCH

Giorgio joined Kepos after spending eleven years in the Quantitative Investment Strategies Group at Goldman, Sachs & Co. In that group, he

was the co-head of the research team as well as a senior portfolio manager. Giorgio became a Managing Director of the firm in 2002 and a

Partner in 2006. Prior to joining Goldman, he was an Assistant Professor of Finance and Business Economics at the Marshall School of

Business at USC. He has published articles in The Journal of Finance, the Journal of Financial Economics, the Journal of International

Money and Finance and other academic and practitioner journals in finance and economics. He also contributed chapters to several books

on investment management, including Modern Investment Management: An Equilibrium Approach. His research covers various topics in

international finance, from dynamic models of risk in developed and emerging markets to optimal portfolio strategies with multiple asset

classes and time-varying market conditions. Giorgio received a B.A. from the Libera Universita’ Internazionale degli Studi Sociali in Rome in

1984, a M.A. in Economics from the University of Chicago in 1989 and a Ph.D. in Economics from the University of Chicago in 1993.

MATT DESCHAMPS

CHIEF OPERATING OFFICER

Matt was previously Chief Financial Officer and Principal of Satellite Asset Management, L.P. and Chair of the Satellite Operating Committee.

He was a founding member of the firm, which commenced operations in 1999 and managed in excess of $7 billion across multiple strategies

in New York and London. Prior to joining Satellite, Matt worked in the equity financing division at Morgan Stanley, where he was an account

relationship manager. Prior to Morgan Stanley, he was a Senior Associate for Coopers & Lybrand, LLC. Matt earned a M.B.A. in Finance and

Accounting from the Stern School of Business at New York University in 1999 and a B.A. in Government from Franklin & Marshall College in

1993. He is a Certified Public Accountant and a member of The American Institute of Certified Public Accountants.

Appendix iii

Kepos Capital: Profiles

BOB LITTERMAN

CHAIRMAN, RISK COMMITTEE

Bob Litterman is the Chairman of our Risk Committee and of our Academic Advisory Board. Prior to joining Kepos Capital in 2010, Bob

enjoyed a 23-year career at Goldman, Sachs & Co., where he served in research, risk management, investments and thought leadership

roles. He oversaw the Quantitative Investment Strategies Group, a portfolio management business formerly known as the Quantitative

Equities and Quantitative Strategies groups, and Global Investment Strategies, an institutional investment research group. While at Goldman,

Bob also spent six years as one of three external advisors to Singapore’s Government Investment Corporation (GIC). Bob was named a

partner of Goldman Sachs in 1994 and became head of the firm-wide risk function; prior to that role, he was co-head of the Fixed Income

Research and Model Development Group with Fischer Black. During his tenure at Goldman, Bob researched and published a number of

groundbreaking papers in asset allocation and risk management. He is the co-developer of the Black-Litterman Global Asset Allocation

Model, a key tool in investment management, and has co-authored books including The Practice of Risk Management and Modern

Investment Management: An Equilibrium Approach (Wiley & Co.). Bob earned a Ph.D. in Economics from the University of Minnesota and a

B.S. in Human Biology from Stanford University. He is also the inaugural recipient of the S. Donald Sussman Fellowship at MIT's Sloan

School of Management and serves on a number of boards, including Commonfund, the Sloan Foundation and World Wildlife Fund.

SIMON RAYKHER

GENERAL COUNSEL AND CHIEF COMPLIANCE OFFICER

Before joining Kepos in 2012, Simon served as General Counsel and Chief Compliance Officer at Lombard Odier Asset Management (USA)

Corp, the US asset management subsidiary of one of the oldest and largest private banks in Switzerland. For the previous seven years, he

was General Counsel, Chief Compliance Officer, and Principal of Satellite Asset Management, L.P. Prior to Satellite, Simon was a Senior

Associate with the law firm of Schulte Roth & Zabel LLP. Before that, he was a prosecutor with the Investigation Division of the New York

County District Attorney’s office where Simon conducted tax fraud and money laundering investigations. Prior to joining the New York District

Attorney, he was an Associate at the law firm of Brown & Wood; earlier, he was an auditor at the accounting firm of Coopers & Lybrand.

Simon holds a B.B.A. and M.B.A. in Accounting from Pace University and a J.D. from Fordham University School of Law.

Please note that profiles for all team members as well as our standard due diligence questionnaire are available upon request.

Appendix iv

Kepos Capital: Profiles

Business Principles Investment Principles

We have developed this philosophy with a core team that has managed

client capital together for over a decade

Our investors are our partners

We strive to eliminate potential business conflicts

We promote transparency with our investors

Investment contracts should align investor and

manager incentives

Our most important asset is our team

We promote a meritocracy and reward employees

for individual contributions as well as teamwork

Our culture values both creativity and dissent

We promote transparency with our employees

Return sources can and should be separated

Alpha is the result of proprietary research

complemented by skill and understanding of markets

Beta and exotic beta reflect returns that reward

investors for exposure to systematic risk

Our base belief is that markets are efficient in

the long run

We build investment strategies where we

believe we have a comparative advantage

Disciplined and diversified investment strategies

improve performance in the long term

Our research approach springs from our

academic roots

Our strategies evolve continuously

Risk posture must adjust dynamically as market

opportunities change

Asset size matters in skill-based management

Kepos Capital: Guiding Philosophy

To the extent simulations, hypothetical returns and/or backtests were used to prepare this presentation:

THESE RESULTS ARE BASED ON SIMULATED OR HYPOTHETICAL PERFORMANCE RESULTS THAT HAVE CERTAIN INHERENT LIMITATIONS. UNLIKE THE

RESULTS SHOWN IN AN ACTUAL PERFORMANCE RECORD, THESE RESULTS DO NOT REPRESENT ACTUAL TRADING. ALSO, BECAUSE THE TRADES HAVE

NOT ACTUALLY BEEN EXECUTED, THE RESULTS MAY HAVE UNDER- OR OVER-COMPENSATED FOR THE IMPACT, IF ANY, OF CERTAIN MARKET FACTORS,

SUCH AS LACK OF LIQUIDITY. SIMULATED OR HYPOTHETICAL TRADING PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT THEY ARE

DESIGNED WITH THE BENEFIT OF HINDSIGHT. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR

LOSSES SIMILAR TO THESE BEING SHOWN.

How this Simulation was Compiled (Pages 9-10, 14, 16-18):

• Multiple Concurrent Models. For the exotic beta strategy, we employed 16 trading models that we intend to utilize in a developed product offering. The models were used

to construct the individual exotic betas. Further, a model was employed that varied, based on systematic criteria, the amount of risk allocated to each exotic beta

throughout the simulation.

• Hypothetical Returns. The results presented were achieved by retroactively applying the trading models to market data for various time periods, depending on a

discretionary decision by us as to the reliability of the market data for use in backtesting for each strategy. The inception dates for each strategy may differ based on

availability of data. For the full sample results: Jan-90 to Feb-12 is the date range for Commodities, Equities, Fixed Income, Currencies and Real Assets; Aug-93 to Feb-12

is the date range for Credit; and Aug-95 to Feb-12 is the date range for Volatility. Although we mention Catastrophe Risk as an exotic beta, it is not included in this

simulation.

• Calculation of Returns. Returns are based on excess return above the respective financing rate that would have been experienced by the applicable tradable instrument

utilized, inclusive of mark to market gains and losses and all cash flows (including cash flows that were related to dividends or other distributions) that would have been

realized. The simulated performance is presented gross of any management and incentive fees.

• Transaction Costs Estimate. Transaction costs are imputed in all values presented and are calculated through the application of an overall algorithm for each strategy

created using historical data for the entire period surveyed that computes – for each given hypothetical transaction – a bid/ask spread and expected market impact for such

transaction, which may not equal the actual spread and market impact that would have applied in any given case.

• Sizing Individual Exotic Betas to Form the Exotic Beta Strategy. The amount of risk allocated to individual exotic betas has varied throughout the simulation. The target

risk allocated to each exotic beta was determined frequently (e.g., monthly) during the time span of the applicable simulation using only backward-looking information from

a variety of sources, including strategy backtests, prior beliefs, and expected diversification benefits.

• Other Constraints. The simulated underlying portfolios may also include a number of additional constraints. These were imposed at the discretion of Kepos personnel and

are meant to represent (but may not accurately forecast) what may happen in actual trading, e.g., volume limitations in certain securities. For each portfolio/strategy that

you consider to be material or of interest, we can provide additional information on any such constraints.

How this Simulation was Compiled (Pages 20-22):

• Multiple Concurrent Models. For the exotic beta strategy, we employ 17 trading models. The models were used to construct the individual exotic betas. Further, a model

was employed that varied, based on systematic criteria, the amount of risk allocated to each exotic beta throughout the simulation.

• Hypothetical Returns. The results presented were achieved by retroactively applying the trading models to market data for April to December 2012.

• Calculation of Returns. Returns are based on excess return above the respective financing rate that would have been experienced by the applicable tradable instrument

utilized, inclusive of mark to market gains and losses and all cash flows (including cash flows that were related to dividends or other distributions) that would have been

realized. The simulated performance is presented gross of any management and incentive fees.

• Transaction Costs Estimate. Transaction costs are imputed in all values presented and are calculated through the application of an overall algorithm for each strategy

created using historical data for the entire period surveyed that computes – for each given hypothetical transaction – a bid/ask spread and expected market impact for such

transaction, which may not equal the actual spread and market impact that would have applied in any given case.

• Sizing Individual Exotic Betas to Form the Exotic Beta Strategy. The amount of risk allocated to individual exotic betas has varied throughout the simulation. The target

risk allocated to each exotic beta was determined frequently (e.g., monthly) during the time span of the applicable simulation using only backward-looking information from

a variety of sources, including strategy backtests, prior beliefs, and expected diversification benefits.

• Other Constraints. The simulated underlying portfolios may also include a number of additional constraints. These were imposed at the discretion of Kepos personnel and

are meant to represent (but may not accurately forecast) what may happen in actual trading, e.g., volume limitations in certain securities. For each portfolio/strategy that

you consider to be material or of interest, we can provide additional information on any such constraints.

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Certain Limitations and Shortcomings that Prospective Investors Should Carefully Consider:

• Different Criteria for Backtesting. The backtesting approaches, data sets, and time periods may not be consistent from model to model. For each portfolio/strategy that you

consider to be material or of interest, we can provide additional information on any such criteria.

• Presentation of Gross Performance Figures. The simulated performance is presented gross of any management and incentive fees.

• Not a Proxy for Prior Portfolios Managed. Prospective investors should not misinterpret the hypothetical results presented as being a proxy for any portfolio that was

previously managed by any Kepos personnel. This means that any positive performance in any such prior portfolio should not be seen as validating the simulated portfolio.

In addition, prospective investors should understand that the exotic beta strategy differs markedly from the funds previously co-managed by Mr. Carhart and other Kepos

personnel.

• Individual Model Behavior May Diverge. In actual trading, the performance of different models may diverge, leading to a situation where the gains in one model are erased

by the losses in another, and potentially at a higher frequency than occurred in the simulation. In the simulation, in general, most of the models are profitable at any given

point in time, which may not be the case in actual trading.

• Investment Process. Investors should understand that every aspect of the investment process should be viewed as being in a state of constant refinement and innovation

(even after the inception of trading) and therefore subject to change. Such changes could include, but are not limited to, addition and removal of underlying models,

factors, data or other related estimates.

• Textual Statements to be read as Aspirational or Forward-Looking. Textual statements regarding the portfolios/strategies may be in the present tense or otherwise be

declarative statements, but all such statements should be understood to be anticipatory, aspirational, or forward-looking statements and no guarantee of future

performance is implicated thereby.

• Assumption of no Interim Adjustments; Outperformance of Simulation in Market Crises. The returns of the hypothetical portfolio are derived from the systematic application

of trading models under a specific set of assumptions. The simulation does not make any other interim adjustments to the models (other than to reweight – as discussed

above – the appropriate trading models following the initiation of a new trading model). Were Kepos’ management actually managing a portfolio for the period indicated,

active risk management, which will involve a number of personnel, would have been employed and therefore interim decisions to adjust weightings or to modify models

would have changed the portfolio’s interim and aggregate performance measures and there can be no certainty that such changes would not have resulted in losses. For

example, these models span periods of extreme market dislocation, including the 2007 liquidity crisis and the 2008 failure of Lehman Brothers, and demonstrate

outperformance in these periods; in actual trading, scenarios such as these would likely be accompanied by manager intervention and there is no guarantee that such

interventions would not result in losses.

• We May Employ Additional or Substitute Models. In actual trading, we may employ additional models not included in the simulation, the impact of which could adversely

affect actual performance or otherwise diverge from the backtested models presented here.

Additional Disclosures and Disclaimers Regarding Simulated Performance that Prospective Investors Should Read and Understand:

• Simulations may not be Indicative of Future Performance. One danger of relying on a simulated portfolio is that the simulation may produce positive results over the

backtested period, but that future application of the models employed may result in losses. In a number of cases, quantitative managers have produced impressive

backtests only to see substantial losses in actual trading.

• Certain Metrics may not be Indicative of Future Portfolio Characteristics. Metrics of performance for the hypothetical portfolio including, but not limited to, the Sharpe Ratio

and correlation coefficients have been inferred or calculated from the simulation and are therefore hypothetical and not necessarily indicative of future model behavior,

which may differ significantly.

• No Representation as to Actual Performance. No representation is made as to the accuracy, completeness, or effectiveness of the models or the composite simulated

portfolio, nor to the results of running such models under actual trading conditions.

References to Indices

• References to indices, benchmarks or other measures of relative market performance over a specified period of time are provided for your information only and do not

imply that the actual Fund portfolio will achieve similar results. The index composition may not reflect the manner in which a portfolio is constructed; unlike these indices

and benchmarks, each Fund's portfolio may contain options (including covered and uncovered puts and calls) and other derivative securities, futures and other commodity

interests and currencies, and may include short sales of securities, margin trading, securities of smaller capitalization companies and types of securities that are different

than those reflected in these indices and benchmarks, and is not as diversified as these indices and benchmarks. While an adviser seeks to design a portfolio which

reflects appropriate risk and return features, portfolio characteristics may deviate from those of the benchmark. Indices are unmanaged and investors cannot invest directly

in indices. The figures for the index reflect the reinvestment of dividends but do not reflect the deduction of any fees or expenses which would reduce returns.

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In connection with any consideration of an investment in the Fund, prospective investors should be aware of a number of additional general and specific risks (many of which

are described in the Fund's private placement memorandum and Kepos Capital's Form ADV), including the following:

• Limited Regulatory Oversight. While the Fund may be considered to be similar to an investment company, it is not required to, nor does it intend to, register as such under

the U.S. Investment Company Act of 1940. Accordingly, the provisions of that act (which may provide certain regulatory safeguards to investors) are not applicable to

investors in the Fund. Kepos Capital LP is currently exempt from registration with the U.S. Commodity Futures Trading Commission as a commodity pool operator.

Therefore, investors in the Fund do not have the benefit of the protections afforded by such registrations.

• Limited Operating History. The Fund has only a limited operating history upon which prospective investors can evaluate their anticipated performance.

• "Master-Feeder" Structure. The Fund invests through a "master-feeder" structure, which presents certain unique risks to investors. Smaller feeder funds investing in the

master fund may be materially affected by the actions of larger feeder funds investing in the master fund. The master fund is a single entity and creditors of the master

fund may enforce claims against all assets of the master fund.

• Conflicts of Interest. The investment manager will be subject to a number of conflicts of interest from time to time, some of which are described in the Fund's private

placement memorandum.

• Financing Arrangements. The use of leverage is integral to many of the Fund's strategies, and the Fund depends on the availability of credit in order to finance its

portfolio. The purchase of options, futures, forward contracts, repurchase agreements, reverse repurchase agreements and equity swaps generally involves little or no

margin deposit and, therefore, provides substantial leverage. Accordingly, relatively small price movements in these financial instruments may result in immediate and

substantial losses to the Fund.

• Counterparty Risks in the OTC Market. Many of the markets in which the Fund may effect transactions are not "exchange-based," including, without limitation, "over-the-

counter" markets. The lack of evaluation and oversight of over-the-counter markets that are available in exchange-traded transactions exposes the Fund to the risk that a

counterparty will not settle a transaction in accordance with its terms and conditions because of a dispute over the terms of the contract or because of a credit or liquidity

problem, thus causing the Fund to suffer a loss.

• Model and Data Risk. Given the complexity of the investments and strategies we manage, we must rely heavily on quantitative models (both proprietary models

developed by our personnel, and those supplied by third parties) and information and data supplied by third parties. When these models and data prove to be incorrect,

misleading or incomplete, any decisions made in reliance on them expose investors to potential risks. Also, the research and modeling process we engage in is extremely

complex and involves financial, economic, econometric and statistical theories, research and modeling; the results of that process must then be translated into computer

code. Although we seek to hire individuals skilled in each of these functions and endeavor to provide appropriate levels of oversight, this complexity raises the chances

that the finished model may contain one or more errors that could adversely affect performance.

• Obsolescence Risk. We are unlikely to be successful in managing client accounts unless the assumptions underlying our models are realistic and either remain realistic

and relevant in the future or are adjusted to account for changes in the overall market environment. If such assumptions are inaccurate or become inaccurate and are not

promptly adjusted, it is likely that profitable trading signals will not be generated.

• Crowding/Convergence. There is significant competition among quantitatively-focused managers, and our ability to deliver returns for investors that have a low correlation

with global aggregate equity markets and other hedge funds is dependent on our ability to employ models that are simultaneously profitable and differentiated from those

employed by other managers. To the extent that we are not able to develop sufficiently differentiated models, investors' investment objectives may not be met, irrespective

of whether the models are profitable in an absolute sense.


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kepos capital lp - granicus

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