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Risk Factor Investing Caisse de Dépôt / CFA Asset Allocation Forum

Mark Carhart, CIO, Kepos Capital LP

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, 2015. All rights reserved.

These confidential presentation materials (this “Presentation”) are being presented in conjunction with a limited number of discussions conducted on a confidential basis with a limited

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qualified—for the purpose of providing certain information regarding a potential investment in a vehicle (the “Fund”) managed by Kepos Capital LP (“Kepos”). The information in this

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discussion purposes only, is only a summary of certain information, is not complete, does not contain certain material information about the Fund, including important conflicts disclosures

and risk factors, and is subject to change without notice.

This Presentation does not constitute an offer to sell or a solicitation of an offer to purchase any security of the Fund. Any such offer or solicitation shall only be made pursuant to—and

subject to the terms and conditions contained in—a confidential private placement memorandum of the Fund (and an accompanying subscription agreement), which qualifies in its entirety

the information set forth herein, which should be read carefully prior to an investment in the Fund, and which contains additional information about the investment objectives, terms and

conditions, tax information and risk disclosures of or relating to the Fund.

An investment in the Fund is not suitable for all investors. Investors must meet certain eligibility requirements and have the financial ability, sophistication, experience and willingness to

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investment methodology may be considered “conservative,” “safe,” “risk free” or “risk averse.” The Fund employs leverage and other investment techniques that may increase the volatility

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Certain of the information contained in this Presentation represents or is based upon forward-looking statements, which can be identified by the use of forward-looking terminology such as

“may”, “will”, “should”, “expect”, “anticipate”, “target”, “project”, “estimate”, “intend”, “continue” or “believe” or the negatives thereof or other variations thereon or comparable terminology.

Due to various risks and uncertainties, actual events or results or the actual performance of the Fund may differ materially from those reflected or contemplated in such forward-looking

statements. The information contained herein represents management’s current expectation of how the Fund will continue to be operated in the near term; however, management’s plans

and policies in this respect may change in the future. In particular, (i) policies and approaches to portfolio monitoring, risk management, and asset allocation may change in the future

without notice and (ii) economic, market and other conditions could cause the Fund to deviate from stated investment objectives and guidelines.

This document is confidential, is intended only for the person to whom it has been provided and under no circumstance may a copy be shown, copied, transmitted, or otherwise given to

any person other than the authorized recipient without the prior written consent of Kepos. Notwithstanding anything to the contrary herein, each recipient of this summary may disclose to

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other tax analyses) relating to such tax treatment and tax structure. The distribution of the information contained herein in certain jurisdictions may be restricted, and, accordingly, it is the

responsibility of any prospective investor to satisfy itself as to compliance with relevant laws and regulations.

PAST PERFORMANCE IS NOT AN INDICATOR OR GUARANTEE OF FUTURE RESULTS. THERE IS NO GUARANTEE THAT THE FUND WILL ACHIEVE COMPARABLE RESULTS

TO THOSE SET FORTH IN THIS DOCUMENT OR THAT IT WILL ACHIEVE ITS INVESTMENT OBJECTIVES IN THE FUTURE.

Disclosures

Challenge:

Allocation

decisions

reflect

unintended

factor

exposures

INTRODUCTION Our Broader Objective is to Build Better Policy Portfolios

Proposed

Solution:

Purposeful

and explicit

allocation to

risk premia

Better

knowledge

of return

sources

Conventional Approach

Capital-Based Allocations

Risk Factor Approach

Multiple Risk Premia within Asset Classes Asset

Classes

Risk

Premia

Themes

2

INTRODUCTION Sources of Excess Returns

Alpha

Alternative Risk Factors

CAPM Beta

We believe a useful conceptual framework for classifying return sources is

a continuum between beta and alpha

Factors in equity portfolios Style Tilts (value, momentum...) Fundamental Indexing Smart Beta

General alternative risk factors Systematic Beta Dynamic Beta Strategy Replication Exotic Beta

Risk parity factors Asset Classes

Example: FX Carry

Example: Renaissance Medallion Fund

Example: Global Equity Risk Premium

3

Risk Factor Investing Outline

1. Defining the Factors

2. Building a Portfolio of Factors

3. Performance in Various Environments

4. Implementation Issues

4

A Taxonomy of Macro Risk Premia

Momentum Asset performance can be persistent

Equities

Equities

Equities

Insurance Risk averse investors pay a premium to insure against extreme events

Equities

Equities

Equities

Income Investor bias results in excess demand for low yield/high volatility assets

Equities

Equities

Equities

Equities

Value Cheap assets tend to outperform expensive assets

Bonds

Currencies

Commodities

RISK

PREMIA

THEME

ASSET CLASS

FACTORS

We believe there are four risk premia themes across the major asset classes.

5

Equities

-100 -50 0 50 100

New Zealand

Australia

Norway

Canada

Sweden

United Kingdom

United States

Euro

Japan

Switzerland

ACWI - Hong Kong

ACWI - Japan

ACWI - Eurostoxx 50

ACWI - United Kingdom

ACWI - United States

FX Carry Portfolio

FACTORS Illustration: Building an Equity-Beta-Hedged Risk Factor for Developed FX Carry

Choose a

carry measure

Construct a

dollar-neutral FX

portfolio on

carry rank

Hedge away

ACWI beta using

liquid equity index

futures

-100 -50 0 50 100

New Zealand

Australia

Norway

Canada

Sweden

United Kingdom

United States

Euro

Japan

Switzerland

1 2 3

0.0 1.0 2.0 3.0 4.0

Australia

Canada

Euro

Japan

New Zealand

Norway

Sweden

Switzerland

United Kingdom

United States

MSCI ACWI Hedge 3-Month LIBOR FX Carry Portfolio

6

Where Do

Factors

Live?

FACTORS How Do You Know That It’s Truly a Compensated Factor?

Where there is risk transfer or persistent behavioral biases

Theory does not identify the individual factors

Framework

for Factor

Selection

1. Economically intuitive (necessary, but not sufficient)

2. Demonstrated historical premium in excess of global equity beta

3. Institutional liquidity and capacity

4. Empirically robust

Testable on other markets, asset classes, time periods

Holds up when specific time periods and markets are excluded

(jackknifing)

Insensitive to factor definition

The Goal Factors that perform well in the future

7

FACTORS Will These Premia Persist Over Time?

Examples of Out-of-Sample Factor Performance

Value Strategy in US Stocks, 1925-20141

Global Macro Trend-Following Strategy 1800-20122

Cum

ula

tive

Pe

rfo

rman

ce

Cum

ula

tive

Pe

rfo

rman

ce

1 From Davis, James L., Eugene F. Fama and Kenneth R. French. "Characteristics, Covariances and Average Returns: 1929-1997." Journal of Finance 55 (2000): 389-406. The chart shows the return to a value

strategy based on Book/Price employed in US stocks from 1925 to 2014. 2 From Y. Lemperie, C. Deremble, P. Seager, M. Potters, J.P. Bouchaud. “Two Centuries of Trend Following.” Capital Fund Management. April

15, 2014.

8

Fama-French

(1993) original

period

Period

since

financial

futures

existed






9

PORTFOLIO How Should You Allocate Between Risk Factors?

The primary benefit will come from diversified exposures to factors

Equal risk is a reasonable starting point

Strategic tilts away from equal-risk may reflect relative confidence

as well as forecasted correlation

Slow-moving, modest, dynamic reallocations may also be

warranted:

o structural changes can occur

o shocks can ‘spill over’ from one set of markets to another

o valuation matters: risk premia can become relatively

expensive or cheap due to variation in supply and demand

10

Forecasting expected factor returns is difficult

You can control the degree of cross-sectional and time-series variation in return forecasts

o It’s not all or none

o Traditional 60/40 asset allocations already reflect a high degree of confidence in forecasts

We propose a Bayesian forecasting approach that weights individual forecasts by their confidence

o The “prior” reflects an equal expected return for all factors

o Both long-term and nearer-term forecasts can be incorporated to build a single overall forecast

PORTFOLIO Forecasting Risk Premia

Prior

Historical Mean

Value

Momentum

Spillover

Strategic

Forecasts

Dynamic

Forecasts

Overall Forecast

-4%

-3%

-2%

-1%

0%

1%

2%

3%

4%

5%

199

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199

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199

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201

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An

nu

alized

Retu

rn F

ore

cast

Illustration: Simulated Forecasts for FX Carry Factor (at 2% Volatility)1

1The graph shows historic performance of backtested FX carry risk premia that include real-world trading constraints, estimated t-costs and a hedge to the MSCI ACWI. Prospective investors should exercise caution

in using or relying on any hypothetical or backtested results as being indicative of future performance. 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. You are urged to read all of the additional

disclosures provided at the end of this presentation.

11

=

Simulated Cumulative Returns (Humbled)

The graph shows historic performance above 1-month US T-bills of backtested risk premia that include real-world trading constraints, estimated t-costs and a hedge to the MSCI ACWI, from January 1990 to June

2014. Prospective investors should exercise caution in using or relying on any hypothetical or backtested results as being indicative of future performance. 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. You are

urged to read all of the additional disclosures provided at the end of this presentation. Please see important disclosures at the end of this presentation that define “humbled.”

PORTFOLIO

Risk Factor Themes and Total Factor Portfolio

Hedged to Equities against MSCI ACWI Adjusted to common volatility level (IR in parentheses)

Key Takeaways

1. Attractive potential returns

2. Low cross-correlation

3. Poor performance

uncorrelated with equities

4. Transparent and low cost

12

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

20

12

20

14

Gro

wth

of

$1

(Lo

g Sc

ale)

Value (IR=0.53)

Income (0.51)

Insurance (0.53)

Momentum (0.50)

TOTAL (1.00)

MSCI ACWI (0.34)

$1

Risk Factor Investing Strategies Have Been Lowly Correlated PORTFOLIO

Statistic Lowest Average Highest

Correlation to MSCI ACWI (0.02) 0.33 0.83

Correlation to Barclays Global Agg Bond Index (0.26) (0.03) 0.23

Correlation to HFRX Composite Index 0.06 0.35 0.81

Average Cross-Correlation (0.07) 0.21 0.62

Alternative Beta Funds1

1 The correlation statistics are estimated using publicly available daily data on mutual funds domiciled in various countries in addition to research from Kepos Capital LP. We believe the products included in this

analysis are representative of risk premia investment products, but the analysis does not include all investable risk premia products. Correlations are estimated over the period April 2012 through June 2015.

13






14

PERFORMANCE Performance in Market States (Humbled)

1990-2014 Risk Factor Theme Sharpe Ratios Conditioned on

Rolling 12-Month ACWI and US 10-Year Treasury Return

15

This slide presents the hypothetical, backtested results of a composite simulated portfolio (as further described on the "Backtest Disclosures" page), which is being provided to share additional color on Kepos’ portfolio

structuring and not as any prediction of future returns or performance. In fact, prospective investors should exercise caution in using or relying on any hypothetical or backtested results as being indicative of future

performance. Please see important disclosures at the end of this presentation that define “humbled backtest.” 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




Value Value

Income Income

Insurance Insurance

Momentum Momentum

Factor Portfolio 0.27 Factor Portfolio 0.66

Value Value

Income Income

Insurance Insurance

Momentum Momentum

Factor Portfolio 0.96 Factor Portfolio 0.96

Bonds

Underperform

Bonds

Outperform

Equities

Underperform

Equities

Outperform

PERFORMANCE Difficult Periods in Risk Factors

This slide presents the hypothetical, backtested results of a composite simulated portfolio (as further described on the "Backtest Disclosures" page), which is being provided to share additional color on Kepos’ portfolio

structuring and not as any prediction of future returns or performance. In fact, prospective investors should exercise caution in using or relying on any hypothetical or backtested results as being indicative of future

performance. 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. You are urged to read all of the additional disclosures provided at the end of this presentation.

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

Jan

-82

Mar

-85

May

-88

Jul-

91

Sep

-94

No

v-97

Jan

-01

Mar

-04

May

-07

Jul-

10

Sep

-13

Commodities - Relative Momentum

Can they have bad drawdowns? , but no worse than equity drawdowns …

MSCI ACWI

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

Jan

-82

Mar

-85

May

-88

Jul-

91

Sep

-94

No

v-97

Jan

-01

Mar

-04

May

-07

Jul-

10

Sep

-13

Commodities - Relative Backwardation

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

Jan

-82

Mar

-85

May

-88

Jul-

91

Sep

-94

No

v-97

Jan

-01

Mar

-04

May

-07

Jul-

10

Sep

-13

Global Equities - Book to Price

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

Jan

-82

Mar

-85

May

-88

Jul-

91

Sep

-94

No

v-97

Jan

-01

Mar

-04

May

-07

Jul-

10

Sep

-13

Global Equities - Low Volatility

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

Jan

-82

Mar

-85

May

-88

Jul-

91

Sep

-94

No

v-97

Jan

-01

Mar

-04

May

-07

Jul-

10

Sep

-13

Global Bonds - Directional Global Yields

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

Jan

-82

Mar

-85

May

-88

Jul-

91

Sep

-94

No

v-97

Jan

-01

Mar

-04

May

-07

Jul-

10

Sep

-13

Global Bonds - Relative Yield

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

Jan

-82

Mar

-85

May

-88

Jul-

91

Sep

-94

No

v-97

Jan

-01

Mar

-04

May

-07

Jul-

10

Sep

-13

Currencies - Developed Currency Carry

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

Jan

-82

Mar

-85

May

-88

Jul-

91

Sep

-94

No

v-97

Jan

-01

Mar

-04

May

-07

Jul-

10

Sep

-13

Currencies - Relative PPP

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

Jan

-82

Mar

-85

May

-88

Jul-

91

Sep

-94

No

v-97

Jan

-01

Mar

-04

May

-07

Jul-

10

Sep

-13

Volatility - Realized Volatility

Yes

Risk Factor Strategy

16

All Returns Standardized to 10% Volatility

Source for all data on this slide: Robert J. Shiller’s online library with data used in Irrational Exuberance, Princeton University Press, 2015. http://www.econ.yale.edu/~shiller/data.htm. Monthly data is presented from

January 1926 through June 2015 unless otherwise noted. 1 Monthly data from January 1926 through May 2015 is used.

Current Investment Environment PERFORMANCE

US Equity Shiller P/E Levels US 10 Year Bond Yields

Realized Real Returns of S&P vs. Shiller P/E1

0

5

10

15

20

25

30

35

40

45

50

192

6

193

4

194

2

195

0

195

8

196

6

197

4

198

2

199

0

199

8

200

6

201

4

P/E

Ratio

Avg Q2 P/E: 27

(92nd

Percentile) 0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

192

6

193

4

194

2

195

0

195

8

196

6

197

4

198

2

199

0

199

8

200

6

201

4

Yie

ld

Avg Q2 Yield:

2.2%

(4th Percentile)

2015 YTD Avg: 3.0%

0%

2%

4%

6%

8%

10%

12%

14%

192

6

193

4

194

2

195

0

195

8

196

6

197

4

198

2

199

0

199

8

200

6

201

4

Retu

rns

Average

Forecast

Real Return:

5.6%

Expected Real Return of U.S. 60/40 Portfolio

4.5%

-3.4% -4.0% -5%

-4%

-3%

-2%

-1%

0%

1%

2%

3%

4%

5%

1

Retu

rns

P/E Below 90th Percentile P/E Above 90th Percentile

P/E Above 95th Percentile

Average 5-Year Annual Return

17

http://www.econ.yale.edu/~shiller/data.htm

0%

25%

50%

75%

100%

1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015

Th

em

e P

erc

en

tile

Ra

nkin

g

Value Income Insurance Momentum

Risk Factor Valuations

1 This table presents the attractiveness of each of the 16 Exotic Beta premia types as of June 30, 2015. Gross performance for June 2015 is compared to monthly historical performance since 1985 by calculating the

percentile ranking. Backtested performance is used. 2 This chart shows a 12 month rolling average of the average monthly percent rank of performance by Exotic Beta theme through June 2015. This slide presents

the hypothetical, backtested results of a composite simulated portfolio (as further described on the "Backtest Disclosures" page), which is being provided to share additional color on Kepos’ portfolio structuring and not

as any prediction of future returns or performance. In fact, prospective investors should exercise caution in using or relying on any hypothetical or backtested results as being indicative of future performance. 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. You are urged to read all of the additional disclosures provided at the end of this presentation.

PERFORMANCE

Risk Factor Attractiveness, in Percentiles of Valuation Relative to History1

Historical Attractiveness of Risk Factor Themes2

Equity Indices Bonds Currencies Commodities

Value 31% 10% 4% 43%

Income 11% 9% 36% 15%

Insurance 45% 31% 50% 50%

Momentum 42% 98% 29% 45%

18

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

0%

5%

10%

15%

20%

Ap

r-12

Jul-

12

Oct-

12

Jan

-13

Ap

r-13

Jul-

13

Oct-

13

Jan

-14

Ap

r-14

Jul-

14

Oct-

14

Jan

-15

Ap

r-15

KEB Risk Forecast General Spillover Model

Kepos Exotic Beta Risk Experience

1 Our proprietary spillover models are forward-looking measures of the market environment and are z-scores with a mean of 0 and a standard deviation of 1. The General Spillover Model is designed to capture broad

market disruption. A positive level indicates that markets are more disrupted and therefore it is more likely for stress in one area to spill over into another. In the above chart, we show a 21-day moving average for the

ex-ante risk. Data is presented from April 4, 2012 through June 30, 2015. 2 Risk Allocation represents the volatility in each theme divided by the sum of volatilities across all themes using the same risk forecasts from

RiskMetrics. Current risk allocation is as of June 30, 2015.

PERFORMANCE

Daily Risk Forecasts and Spillover Model1

Spill

over

Level

Ex-A

nte

Ris

k

Current Risk Allocation2

Value

Income

19

Value

Currencies

Equity

Indices

Insurance

Momentum

Bonds Income

Commodities






20

IMPLEMENTATION Impact in Institutional Portfolios

Representative 60 / 40 portfolio With 25% of capital reallocated from

equities to a risk factor portfolio

Total Portfolio Risk1

The typical institutional portfolio is dominated by equity risk –

portfolio can help to reduce the imbalance…

an allocation to a risk factor

Equity, 90%

Fixed Income, 10%

Equity, 72%

Fixed Income, 25% Risk Factor Portfolio, 3%

21

The representative 60/40 portfolio is calculated as a weighted sum of equities (MSCI World USD Net Index is used from January 1990 through May 1994. MSCI ACWI IMI Net Total Return Index (USD) is used

thereafter) and bonds (Barclays Global Aggregate). 1 These charts show the marginal contribution to risk (“MCR”) of each portfolio component. The MCR represents the percentage of overall portfolio risk attributable

to each underlying component and is calculated as the variance attributable to each component (including covariances with other components) divided by the total variance in the portfolio. Mathematically, the MCR for

a given component “i” can be represented as: MCR(i) = { sum over j=1 to N [std(i) * corr(i,j) * std(j)] } / var(p) where N is the number of underlying components, std(i) is the standard deviation of component i, corr(i,j) is

the correlation between component i and component j, and var(p) is the variance of the portfolio. The standard deviation of each component is calculated over the period January 1990 through December 2014. Note

that the representative risk factor portfolio uses humbled backtested returns. Please see important disclosures at the end of this presentation that define “humbled.” Prospective investors should exercise caution in

using or relying on any hypothetical or backtested results as being indicative of future performance. 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




Volatility: 9.87%

Volatility: 7.31%

IMPLEMENTATION Impact in Institutional Portfolios (continued)

… while enhancing returns and reducing overall portfolio risk

Average Excess Return 6.99% 7.50% +0.52%

Volatility 9.87% 7.31% -2.56%

IR 0.71 1.03 +0.32

Representative

Portfolio (60 / 40)

Rep. Portfolio + 25% Risk Factors

Performance Impact

22

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. You are urged to read all of the additional disclosures provided at the end of this presentation. Representative 60/40 portfolio is calculated as a weighted sum of

equities (MSCI World USD Net Index is used from January 1990-May 1994. MSCI ACWI IMI Net Total Return Index (USD) is used from thereafter) and bonds (Barclays Global Aggregate).

1990

1992

19

94

1996

1998

2000

2002

2004

2006

2008

2010

20

12

2014

Gro

wth

of $1 (

log S

cale

)

Representative Portfolio (60/40)

Representative Portfolio with 25%allocated to risk factors (from equities)

$1

IMPLEMENTATION Implementation Issues

We are awash in a zoo of proposed factors

A framework is critical

Stories are great, but statistical robustness is even better

To hedge or not to hedge?

Factor Selection

Sizing and Location

Where does this fit in my portfolio?

How much risk should I budget?

How should we benchmark this?

Sourcing Bank products or external managers?

Other

How should investors use their limited capital efficiently?

How much leverage and risk is appropriate?

How much liquidity and transparency do you need?

Will investment committees maintain conviction when the strategy underperforms equities?

23

APPENDIX A

Risk Factor Investing

APPENDIX A Backtest Returns During Stress Periods (Humbled)

* Catastrophe Bond Exotic Risk Premia not available during this period. This slide presents the hypothetical, backtested results of a composite simulated portfolio (as further described on the "Backtest Disclosures"

page), which is being provided to share additional color on Kepos’ portfolio structuring and not as any prediction of future returns or performance. In fact, prospective investors should exercise caution in using or

relying on any hypothetical or backtested results as being indicative of future performance. Please see important disclosures at the end of this presentation that define “humbled backtest” in addition to the indices

used above. 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. You are urged to read all of the additional disclosures provided at the end of this presentation.

25

ACWI and Risk-Factor Total Returns Standardized to 10% Volatility

Is Risk-Factor Investing Becoming Crowded? APPENDIX A

HFRI Hedge Fund Index Risk Decomposition

Although the hedge fund industry has grown in size and complexity, they seem to be

decreasing their exposure to macro factors.

This slide presents the variance decomposition of 5-year rolling monthly returns on the HFRI Hedge Fund Index. The values on each date refer to the end of each 5-year period. Variance components are derived from

simple OLS linear regressions of HFRI on the return on ACWI and ACWI lagged one month (termed “equity beta”), and separately on the returns on ACWI, lagged ACWI and all of the Kepos exotic beta factors. The

variance from “macro factors” is the difference in variance explained in these two regressions. The variance from alpha is 1 minus the variance explained by the second regression.

26

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

20

14

Alpha

Macro Factors

Equity Beta

Equity Factors

How Might We Measure Crowding?

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Rat

io o

f IR

to

Inst

anta

ne

ou

s IR

Implementation Delay (days)

IR Implementation Decay on Equity Index Relative D/P

1985 - 1995

1995 - 2005

2005 - Present

APPENDIX A

This slide presents the realized information ratios (gross of transaction costs)—for various time periods—for the equity index dividend-price-ratio relative-value exotic beta factor on each day following initial factor

formation. It is intended to show how quickly the information of the dividend-price ratio is reflected in relative global equity index pricing.

27

APPENDIX B

Kepos Exotic Beta Fund

Equity Indices

Bonds

Currencies

Commodities

Value

Income

Insurance

Momentum

Kepos Exotic Betas

B/P, E/P, CF/P

Market Segmentation

Low Volatility

Relative Term Premia

Relative Yield

Mean Reversion

Currency Value

Mean Reversion

Commercial Hedging

Pressure

Mean Reversion

D/P

REITS, Utilities

Dividend Futures

Inflation Risk

Term Premium

Developed Carry

Emerging Carry

Directional Futures

Commodity

Backwardation

Volatility Gamma

Volatility Vega

Catastrophe Risk

Credit Default Risk Volatility Gamma

Seasonal Calendar

Spreads

Momentum Momentum Momentum Momentum

Relative Seasonality

At Kepos, we currently implement about 30 exotic beta factors across all major asset classes and

risk premia types.

29

APPENDIX B

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:

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.

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.

Market Indices Used in Presentation:

MSCI ACWI is an index designed to measure the broad equity market performance of developed and emerging markets.

Barclays Global Aggregate provides a broad-based measure of the global investment grade fixed-rate debt markets.

Bloomberg Commodities Index is a broadly diversified index that allows investors to track commodity futures through a single, simple measure; it is composed of futures contracts on

physical commodities.

VIX is a measure of market expectations of near-term volatility conveyed by S&P 500 stock index option prices.

Hedge Fund Indices Used in Presentation:

HFRX Global Hedge Fund Index is designed to be representative of the overall composition of the hedge fund universe; it is comprised of all eligible hedge fund strategies, including but

not limited to convertible arbitrage, distressed securities, equity hedge, equity market neutral, event driven, macro, merger arbitrage and relative value arbitrage; index includes

performance net of fees.

HFRI Global Fund Weighted Composite Index is an equal-weighted index of over 2,000 single-manager funds with at least $50 million under management or a 12-month track record.

Barclays XARP seeks diversified exposure to a variety of risk premia that span a range of asset classes and investment styles.

AQR Delta Fund provides exposure to a portfolio containing a diversified group of hedge fund risk premia constructed based on capturing the systematic components of dynamic trading

strategies traditionally pursued by hedge funds.

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 does not reflect the manner in which a portfolio is constructed; unlike these indices and benchmarks, the 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 most indices. The figures for the indices may reflect the reinvestment of dividends but do not

reflect the deduction of any fees or expenses which would reduce returns.

Disclosures and Additional Notes

Backtest Disclosures

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 the Simulations were Compiled:

• Multiple Concurrent Models. For the exotic beta strategy, we employed many trading models that we utilize in a 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. The inception dates

are: Jan-03 for catastrophe bonds; Jan-90 for commodities, equities, fixed income, currencies and real assets; Oct-93 for indexed credit; and Sep-98 for volatility markets.

• 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 but net of estimated transaction costs.

• 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, expected diversification benefits, and forecasts of relative attractiveness.

• 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 any portfolio/strategy that you consider to be material

or of interest, we can provide additional information on any such constraints.

Humbled returns are total discounted backtested returns estimated by applying a constant haircut to the total returns of the original undiscounted backtested returns. This haircut is

attributed to the underlying asset classes/strategies in proportion to their average contribution to the original undiscounted backtest. The haircut used in this simulation corresponds to a

two-thirds discount on the average gross return of each exotic beta’s original backtest. By shrinking the simulated returns by a fraction of the gross simulated return, estimated transaction

costs are not discounted.

Backtest Disclosures

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 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. 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.

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).

Risk Factor Investing - CFA Montreal · Risk Factor Investing ... responsibility of any prospective...

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