dynamic alpha strategies€¦ · upside participation, downside management dynamic alpha strategies...

Upside Participation, Downside Management

DYNAMIC ALPHA STRATEGIES

One International Place, Suite 1400, Boston, MA 02110

Phone: 617-535-7542

Email: [email protected]

Web: www.julexcapital.com

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Firm Overview

• The Firm • Founded in 2012, employee-owned, registered in Massachusetts

• Quantitative firm aiming to deliver innovative alpha-centric solutions

• Highly experienced team with extensive institutional investment background • Fidelity/Geode, Loomis Sayles, SSGA, Sun Life, Deutsche Bank

• The Organization and Infrastructure

• Service Providers:

• Brokers: Morgan Stanley, Charles Schwab, Interactive Brokers

• Custodians: BNY Mellon, Charles Schwab, Interactive Brokers

• Law Firm: Rich May, P.C.

• Compliance Consultant: Carrington Legal, LLC

• Product Availability For RIAs: Placemark, Schwab, TD AmeriTrade, FilioFn

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Henry Ma, Ph.D., CFA

President

Henry Ma

CIO

Tony Ash

CFA, COO

Brian Phelan

MD, Sales

Frank Zhuang, Ph.D.

Research, IT

TBA

Analyst Intern

Advisory Board

George Xiang, Ph.D.

Research

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Highly Experienced and Multidisciplinary Team

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Team Experience Education

Henry Ma

CFA

Geode Capital /Fidelity – Hedge Fund Manager

Loomis Sayles – Director of Quantitative Research

Fortis Investments - Director of Quantitative Research

Sun Capital Advisers – Senior Vice President/PM John Hancock – Senior Associate Investment Officer

Ph.D., Economics –

Boston University

BA, MA – Peking

University

Tony Ash

CFA

Sun Life Financial – Managing Director, Head of US

Portfolio Management

MBA, BA – Boston

College

Brian Phelan Deutsche Bank – Director, Fixed Income Sales

Paine Webber – First Vice President, Institutional Sales

BA – Boston College

George Xiang

CFA

State Street Global Advisors (SSGA) – Head of

Quantitative Research

Loomis Sayles – Senior Quantitative Analyst

Conseco Capital – Quantitative Research Manager

Ph.D., Mathematics –

Purdue University

BA – Nankai University

Frank Zhuang Ericsson – Senior Engineer

Nortel, Alcatel/Lucent - Senior Research Scientist

Ph.D. Electric Engineer

– University of Maryland

MS – West Virginia

University

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New Investment Paradigm

• Deep Losses in Last Decade Challenge Traditional Investment Wisdom

• A diversified portfolio did not provide enough protection as correlations rose

dramatically

• Benchmark-centric management approach failed to protect the downside

• Many alternative investments were highly correlated with traditional assets

Data Source: Bloomberg, Yahoo, Julex Capital Management

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S&P 500 Index

- 51% -44%

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Investment Philosophy

• There are always some asset classes performing well at any given time

• Consistent returns can be achieved by tactical positioning in the best-

performing asset classes

• Long term outperformance can be achieved by limiting downside risks


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Investment Process

• Unique Three-Step Adaptive Investment Process

• “Adaptive investment approach” to adjust investments quickly to ongoing

market conditions

• Our model integrates the best elements of three investment approaches with

strong economic rationale

Risk Parity – Adaptive Risk

Volatility-weighted Portfolio Construction

Relative Momentum – Adaptive Return

Select Asset Classes /Sectors / Countries

Risk Switch TM - Adaptive Regime

Identify Risk On/Risk Off Regimes

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Risk Switch TM

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Data Sources: Bloomberg and Julex Capital Management, LLC

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Process In Action

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Risk Switch TM

Determine the

market

environment for

risk-taking

Risk On

Risk Off

Relative

Momentum Select asset

classes,

countries and

sectors

Large Cap, 32%

Small Cap, 26%

International, 22%

REITs, 20%

Investment Grade Bonds, 45%

Treasuries,

55%

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S Investment Objectives and Products

• Dynamic Sector: • Tactical US equity sector/bond rotation

• Strives to outperform a moderate benchmark and S&P 500 over a full market cycle

• Dynamic Income: • Tactical rotation across income-generating

assets

• Strives to outperform Barclays US Aggregate Bond Index over a full market cycle

• Dynamic Multi Asset: • Tactical allocation across multiple macro

asset classes

• Strives to outperform Dow Jones Moderate Index over a full market cycle

• Dynamic Real Asset: • Tactical rotation across multiple real asset

classes

• Strives to outperform Barclays US TIPS Index over a full market cycle

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• Dynamic Multi-Asset

• Dynamic Real Asset

• Dynamic Income

• Dynamic Sector

Growth Income

Absolute Return

Inflation

• Absolute Returns: Strives for consistent returns regardless of market conditions

• Strives to outperform benchmarks with lower or similar risks over full market cycle

• Client-centric solutions: benchmark-agnostic

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Julex Dynamic Sector: Portfolio and Performance • Portfolio Components

• S&P Equity Sectors

• Energy: XLE

• Materials: XLB

• Industrials: XLI

• Consumer Discretionary: XLY

• Consumer Staples: XLP

• Healthcare: XLV

• Financials: XLF

• Technology/Information: XLK

• Utilities: XLU

• Style Classifications

• Small Value: IWN

• Small Growth: IWO

• Mid-Cap Value: IWS

• Mid-Cap Growth: IWP

• Large Value: IVE

• Large Growth: IVW

• Less Risky Assets

• US Bonds: AGG

• US TIPS: TIP

• US Treasury: IEF

• US Treasury Long: TLT

• Cash

• Performance *

10%

8%

9%

13%

9% 12%

12%

10%

9%

8%

Portfolio Weights Example

XLB

XLF

XLI

XLK

XLV

XLY

IVW

IWP

IWN

IWO

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec YTD DJMUS

2015 -2.15 4.19 -0.99 -0.60 0.33 2.28

2014 -3.22 4.95 -0.47 -0.32 2.18 2.31 -2.21 4.07 -2.01 2.39 3.04 -0.10 10.72 9.09

2013 5.80 1.71 4.02 1.37 0.20 -0.98 5.77 -3.27 4.78 3.98 3.04 2.72 32.83 19.56

2012 0.68 0.99 1.67 1.90

* Past performance is not indication of future returns. These are unaudited gross returns. The inception date is Nov. 1, 2012. Dow Jones Moderate U.S. Index added as

primary benchmark for Julex Dynamic Factor effective June 30, 2014 and retroactive to since inception to better reflect dynamic risk profile and active stock/bond allocations.

As of August 31, 2014, the Julex Dynamic Factor Composite has been renamed the Julex Dynamic Sector Composite. DJModUS – Dow Jones Moderate U.S. Index

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Julex Dynamic Income: Portfolio and Performance

• Portfolio Components

• Income-Generating Equities

• Dividend Stocks: DVY

• REIT: VNQ

• MLPs: MLPI/AMLP

• Preferred Stock: PFF

• High-Yielding Fixed Income/Loans

• US High Yield: JNK

• Emerging Market Bond: EMB

• Bank Loans: BKLN

• Safe Bonds

• US Bonds: AGG

• US TIPS: TIP


• Cash

• Performance*

8%

6%

9%

9%

15%

8%

21%

8%

8%

8%


DVY

VNQ

MLPI

EMB

JNK

PFF

BKLN

AGG

TIP

IEF

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Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec YTD BAB

2015 2.17 -0.60 -0.09 -0.03 1.44 1.24

2014 0.56 1.17 1.09 1.80 1.76 1.83 -1.35 2.59 -1.96 -1.15 0.28 -0.11 6.59 5.95

2013 1.68 0.80 2.57 1.84 -2.77 -0.59 1.33 -1.46 1.28 2.06 -0.37 -0.10 6.31 -2.02

2012 0.03 0.49 -0.06 0.46 0.22

*Past performance is not indication of future returns. These are unaudited gross returns. The inception date is Oct. 1, 2012

BAB – Barclays Aggregate U.S. Bond Index

PSN “TOP GUNS” PERFORMER IN INTERMEDIATE BOND AND US ETF BOND CATOGERIES

FOR THE YEAR ENDED Q3 2014

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Julex Dynamic Multi-Asset : Portfolio and Performance

• Portfolio Components

• Risk Assets

• US Large Cap: SPY

• US Small Cap: IWM

• International: EFA

• Emerging Markets: VWO

• Real Estate: VNQ

• MLPs: MLPI/AMLP

• Commodity: DJP

• Hybrids

• High Yield Bonds: JNK

• Gold: GLD

• Bonds

• US Bonds: AGG

• US TIPS: TIP


• US Long Term Treasuries: TLT

• Cash

• Performance*

32%

26%

22%

20%


SPY

IWM

EFA

VNQ

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Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec YTD DJM

2015 -0.44 -0.11 -0.70 -0.01 -1.26 3.10

2014 -2.50 3.11 0.27 0.24 2.10 2.53 -1.23 5.08 -3.55 -1.70 1.08 0.29 5.52 5.35

2013 -0.75 4.39 -2.44 4.82 3.08 2.03 1.66 13.28 7.86

* Past performance is not indication of future returns. These are unaudited gross returns. The inception date is June 1, 2013.

DJM – Dow Jones Moderate Index (Global)

PSN “TOP GUNS” PERFORMER IN GLOBAL BALANCED AND ETF GLOBAL BALANCED

CATOGERIES

FOR THE YEAR ENDED Q3 2014

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Julex Real Asset: Portfolio and Performance

• Portfolio Components • Commodity-Related Equities:

• Materials Sector: XLB

• Energy Sector: XLE

• Infrastructure MLP: MLPI/AMLP

• Real Estates:

• US Real Estate: VNQ

• Foreign Real Estate: RWX

• Commodities:

• Gold: GLD

• DJUBS Index: DJP

• Inflation Protection Bond: TIPS

• Cash

• Performance*

12%

8%

4%

10%

10%

11%

35%

10%


DJP

VNQ

RWX

MLPI

XLB

XLE

TIP

Cash

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Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec YTD TIP

2015 3.38 -1.81 -1.16 0.30 0.63 2.18

2014 -0.54 1.06 -0.09 2.07 1.29 2.12 -2.18 2.67 -3.44 -1.20 0.19 -0.57 1.22 3.65

2013 1.29 -3.87 -2.95 0.65 -1.18 1.10 1.33 -0.26 0.42 -3.57 -8.27

* Past performance is not indication of future returns. These are unaudited gross returns. The inception date is April 1, 2013.

TIP – Barclays US TIPS Index

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Management Team Henry Ma, Ph.D., CFA, President and Chief Investment Officer. Dr. Ma has two decades of extensive hands-on and

leadership experience in asset management industry. Prior to founding Julex, he worked as a global macro hedge fund manger

with Geode Capital Management (a Fidelity affiliate). Earlier, he served as Director of Quantitative Research and Financial

Engineering with Loomis Sayles & Co., and Director of Quantitative Research and Risk Management with Fortis Investments.

He led quantitative research and risk management groups to develop investment strategies, portfolio risk analytics and structured

credit strategies. Dr. Ma also worked as Senior Vice President at Sun Life Financial, where he helped manage $30 billion in

fixed income assets. His investment career began with John Hancock Financial Services as a Senior Associate Investment

Officer. He developed investment and derivatives strategies as well as oversaw $3 billion in a multi-asset portfolio. Dr. Ma is a

published author and an industry speaker on the topics of quantitative investing, risk management and structured finance. He

earned a Bachelor and a Master in Economics and Management from Peking University and a Ph.D. in Economics from Boston

University.

Tony Ash, CFA, Managing Director and Chief Operating Officer. Mr. Ash has 30 years of broad experience in asset

allocation and investment risk management. Most recently, he served as Managing Director and Head of US Portfolio

Management at Sun Life Financial. In that role he developed and implemented investment policies, strategies, and mandates for

$37 billion in all asset classes backing the insurance general account. He entered the financial services industry in 1982 as an

Investment Analyst at New England Life and joined the U.S. Operations of Sun Life Financial in 1985. During his tenure at Sun

Life, Tony led the launch of a successful multi-billion dollar captive investment adviser and investment company complex (Sun

Capital Advisers Trust) in 1998 and also served as internal Investment Advisor to the Sun Life U.S. Employees Defined Benefit

and Defined Contribution plans from 1999 to 2009. Tony received his BA in Economics and his MBA in Investments both from

Boston College. Tony has been a member of the ACLI Investment Advisory Council for the SIMS Conference.

Brian Phelan, Managing Director, Sales. Brian brings over thirty years of capital markets experience to Julex Capital

Management. He spent twenty-two years as a First Vice President at PaineWebber Group in institutional fixed income sales

covering major and middle market accounts for investment grade and high yield corporate debt, residential and commercial

mortgage backed securities, asset backed securities and rates. Later, he worked at Deutsche Bank Securities as a Director in the

generalist fixed income securities platform within the Capital Markets Group. Most recently, Brian co-founded MacBride

Partners, a consulting firm to assist it’s clients improve investment performance by implementing the industry best practices.

Brian graduated from the Carroll School of Management at Boston College with a BS in General Management / Marketing and

currently holds his Series 7 and Series 63 licenses.

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Research Team

George Xiang, Ph.D., CFA, Research Consultant and Member of Investment Committee. Dr. Xiang has extensive

experiences in fixed income, quantitative equity, and commodities. His experiences include designing investment products,

creating trading strategies, and developing analytic tools. During his career, George worked as Head of Global Fixed Income

Research at SSGA, a senior quantitative analyst at Loomis Sayles & Company and quantitative research manager at Conseco

Capital Management. George received a Ph.D. in mathematics and MS in computer science from Purdue University, and BS in

mathematics from Nankai University. He is CFA and FRM charter holder. He has numerous publications in finance,

mathematics, and computer science. Also, George has one patent pending that involves investment technology and products.

Frank Zhuang, Ph.D., Research Consultant and Member of Investment Committee. Dr. Zhuang is an expert in machine

learning and artificial intelligence. He has extensive experiences in applying cutting-edge statistical techniques and technologies

to financial modeling and trading in last twenty years. His expertise includes neural networks, signal processing, machine

learning, pattern recognition, artificial intelligence and other advanced statistical analysis. He has published numerous research

in those subjects in the nationally recognized journals. During his career, Dr. Zhuang has served as a Senior Research Scientist

with Nortel, Alcatel/Lucent and other global technology companies. He holds Ph.D. degree in Electrical Engineering from the

University of Maryland, College Park. He also earned M.S. degree in mathematics from West Virginia University.

Advisory Board

Maryam H. Muessel , Advisor. Ms. Muessel has been a senior leader in the financial industry for decades. She was the Chief

Investment Officer for Global Credit at BNP Paribas, a $1 trillion global asset manager. At BNP, she was responsible for

defining and monitoring the management process and the investment strategy implemented by the credit investment teams

across over $250 billion in fixed income mandates globally. Maryam also actively participated in designing and developing the

product range. She joined Fortis Investments in 2004 as the CIO for US Fixed Income & Structured Finance, which was

ultimately acquired by BNP. In 2008 she became COO of Alternatives & Solutions division with a direct responsibility on

Global Credit & Hybrids. Prior to Fortis, she was ACA’s COO and head of Structured Credit and Asset Management Business.

From 1998 to 2004, Maryam held senior positions at Prudential Securities where she was in charge of the CDO business, MBIA

where she was in charge of their Alternative Investment business and at CapMAC where she was in charge of their structured

credit and financial engineering business. She began her career in 1985 at Mellon Bank. Maryam is a graduate in Economics

from University of Southern California and holds a Doctorate/MA in Economics from Georgetown University.

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APPENDIX : SUPPLEMENTAL INFORMATION –

• HOW TO USE JULEX STATEGIES

• MACRO OPPORTUNITIES PRO FORMA PEFORMANCE

• BACK TEST HISTORICAL RESULTS

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Julex Strategies in Your Portfolio

Equity, 35%

Julex Dynamic

Sector 30%

Fixed Income,

35%

Core Portfolio w/30% Weighting in Julex Dynamic Sector

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Equity, 50%

Fixed Income,

50%

Core Portfolio

12/31/1999 -

9/30/2014

50/50 Core

Portfolio

Annual Return 5.3%

Standard Deviation 7.7%

Drawdown -27%

Sharpe Ratio 0.43

12/31/1999 -

9/30/2014

35/35 Core

Portfolio

w/30% Julex

Sector

Annual Return 7.3%

Standard Deviation 7.2%

Drawdown -19%

Sharpe Ratio 0.73

Note: Julex Dynamic Multi Asset Index performance results and total portfolio investment results shown on this slide are HYPOTHETICAL

based on modeled results and are gross before investment management fees. Please see Disclosures for more information.

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Julex Macro Opportunities: Portfolio and Performance

• Portfolio Components • Dynamic Multi Asset

• Dynamic Sector

• Dynamic Income (2X leverage)

• Performance*

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34%

33%

33%

Portfolio Weights

Dynamic Multi Asset

Dynamic Sector

Dynamic Income (2Xleverage)

* Pro Forma performance with the three sub-strategies 1/3 Dynamic Sector + 1/3 Dynamic Income (2X leverage) + 1/3 Dynamic Multi Assets/Focus from November 2012

through June 2014; actual live Macro Opportunities Composite performance starting in July 2014. As of August 31, 2014, the Julex Dynamic Factor Composite has been renamed

the Julex Dynamic Sector Composite. * HFRI GMI – HFRI Global Macro Index

Date Source: Bloomberg, Yahoo, Julex Capital Management, Hedge Fund Research

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec YTD HFRI GMI*

2015 0.38 1.56 -0.49 -0.45 0.99 1.94

2014 -1.56 3.44 0.63 1.14 2.57 2.80 -2.20 4.95 -3.41 0.40 1.60 0.21 10.74 6.37

2013 3.76 1.01 4.09 2.31 -3.59 -1.00 4.24 -2.91 4.02 3.70 1.41 1.36 19.57 -0.45

2012 0.97 1.02 2.00 0.96

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Julex Dynamic Sector – Back Test Results

Note: Julex Dynamic Sector performance results shown on this slide are HYPOTHETICAL based on modeled results and are gross before

investment management fees. However, the month returns since November 2012 are live Julex composite. performance. Performance. Dow Jones

Moderate U.S. Index added as primary benchmark for Julex Dynamic Factor effective June 30, 2014 and retroactive to since inception to better

reflect dynamic risk profile and active stock/bond allocations. As of August 31, 2014, the Julex Dynamic Factor Composite has been renamed the

Julex Dynamic Sector Composite. Please see Disclosures for more information.. Data Source: Bloomberg, Yahoo, Julex Capital Management

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January 2000 December 2014

Julex Dynamic

Sector

Dow Jones Moderate U.S.

Index S&P 500

Index

Annual Return 12.3% 6.6% 4.2% Standard Deviation 9.4% 10.2% 15.3%

Sharpe Ratio (risk free rate = 2%) 1.09 0.45 0.15

Maximum Drawdown -14.2% -33.1% -50.9%

Year

Julex Dynamic

Sector

Dow Jones Moderate U.S. Index

S&P 500 Total

Return Index

2000 -0.1% 4.4% -9.1%

2001 6.5% 0.2% -11.9%

2002 4.2% -10.6% -22.1%

2003 24.0% 24.1% 28.7%

2004 15.6% 11.2% 10.9%

2005 5.3% 6.0% 4.9%

2006 13.9% 10.2% 15.8%

2007 10.4% 4.9% 5.5%

2008 9.5% -22.6% -37.0%

2009 20.2% 22.6% 26.5%

2010 12.0% 15.2% 15.1%

2011 12.2% 3.3% 2.0%

2012 10.9% 11.9% 16.0%

2013 32.8% 19.6% 32.4%

2014 10.7% 9.1% 13.7%

0

1,000

2,000

3,000

4,000

5,000

6,000

Dynamic Sector IndexDow Jones Moderate U.S. IndexS&P 500

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Julex Dynamic Income – Back Test Results

Note: Julex Dynamic Income performance results shown on this slide are HYPOTHETICAL based on modeled results and are gross before

investment management fees. However, the monthly returns since October 2012 are live Julex composite performance. Please see Disclosures for

more information.


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January 2000 – December 2014

Julex Dynamic Income

Barclays Aggregate U.S.

Bond Index

Annual Return 9.6% 5.7%

Standard Deviation 5.9% 3.5%

Max. Drawdown -5.8% -4.1%

Sharpe Ratio (risk free rate = 2%)

1.3 1.1

Year

Julex Dynamic Income

Barclays Aggregate U.S.

Bond Index

2000 9.8% 11.6%

2001 7.9% 8.4%

2002 10.5% 10.3%

2003 17.0% 4.1%

2004 7.8% 4.3%

2005 3.8% 2.4%

2006 9.4% 4.3%

2007 1.2% 7.0%

2008 10.8% 5.2%

2009 18.2% 5.9%

2010 15.4% 6.5%

2011 11.3% 7.8%

2012 8.6% 4.2%

2013 6.3% -2.0%

2014 6.6% 5.9%

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

Dynamic Income Index

Barclays Aggregate U.S. Bond Index

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Julex Dynamic Multi-Asset – Back Test Results

Note: Julex Dynamic Multi Asset performance results shown on this slide are HYPOTHETICAL based on modeled results and are gross before

investment management fees. However, the monthly returns since June 2013 are live Julex Composite performance. Please see Disclosures for more

information.


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Julex Dynamic

Multi-Asset

Dow Jones Moderate

Index


Standard Deviation

10.5% 10.2%

Max. Drawdown -15% -35%


1.2 0.4

Year

Julex Dynamic

Multi-Asset

Dow

Jones

Moderate Index

2000 5.8% -2.2%

2001 4.7% -2.5%

2002 2.8% -7.1%

2003 37.0% 27.2%

2004 3.8% 13.2%

2005 16.3% 7.3%

2006 19.1% 11.9%

2007 22.6% 8.0%

2008 21.2% -24.8%

2009 16.5% 23.8%

2010 19.7% 14.0%

2011 17.0% 0.3%

2012 5.9% 11.2%

2013 24.4% 14.5%

2014 5.5% 5.3%

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

Dynamic Multi-Asset Index

Dow Jones Moderate Index (Global)

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Julex Dynamic Real Asset – Back Test Results

Note: Julex Dynamic Real Asset performance results shown on this slide are HYPOTHETICAL based on modeled results and are gross before

investment management fees. However, the monthly returns since April , 2013 are live Julex Composite performance. Please see Disclosures for

more information.


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Julex Dynamic

Real Asset Barclays TIPS

Index


Standard Deviation

6.6% 6.3%

Max. Drawdown -10% -12%


1.2 0.8

Year

Julex Dynamic

Real Asset Barclays TIPS

Index

2000 10.4% 14.1%

2001 9.2% 10.0%

2002 12.6% 17.5%

2003 21.6% 9.3%

2004 14.8% 10.2%

2005 8.6% 4.5%

2006 9.4% 1.7%

2007 13.4% 12.0%

2008 -1.9% -1.6%

2009 23.7% 10.1%

2010 12.7% 7.8%

2011 12.3% 13.0%

2012 5.1% 6.1%

2013 -0.9% -7.8%

2014 1.2% 3.7%

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

Dynamic Real Asset index

Barclays TIPS Index

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Disclosures

This information in this presentation is for the purpose of information exchange. This is not a solicitation or offer to buy or sell any security. You must do your own due diligence and consult a professional investment advisor before making any investment decisions. The use of a proprietary technique, model or algorithm does not guarantee any specific or profitable results. Past performance is not indicative of future returns. The performance data presented are gross returns.

The risk of loss in trading securities can be substantial. You should therefore carefully consider whether such trading is suitable for you in light of your financial condition. All information posted is believed to come from reliable sources. We do not warrant the accuracy or completeness of information made available and therefore will not be liable for any losses incurred.

The investment performance shown in the Appendix is HYPOTHETICAL. It is based on the back tests of historical data. Hypothetical performance results have many inherent limitations, some of which are described below. No representation is being made that any account will or is likely to achieve profits or losses similar to those shown. In fact, there are frequently sharp differences between hypothetical performance results and the actual results subsequently achieved by any particular trading program.

One of the limitations of hypothetical performance results is that they are generally prepared with the benefit of hindsight. In addition, hypothetical trading does not involve financial risk, and no hypothetical trading record can completely account for the impact of financial risk in actual trading. For example, the ability to withstand losses or adhere to a particular trading program in spite of trading losses are material points which can also adversely affect actual trading results. There are numerous other factors related to the markets in general or to the implementation of any specific trading program which cannot be fully accounted for in the presentation of hypothetical performance results and all of which can adversely affect actual trading results.

The composition of a benchmark index may not reflect the manner in which a Julex portfolio is constructed in relation to expected or achieved returns, investment holdings, portfolio guidelines, restrictions, sectors, correlations, concentrations, volatility, or tracking error targets, all of which are subject to change over time.

No representation or warranty is made to the reasonableness of the assumptions made or that all assumptions used to construct the performance provided have been stated or fully considered.

In the back test, we used the index returns in case the historical returns of the ETFs are not long enough. The ETF returns were approximated by index returns subtracted by their respective expense ratios. Please see “Notes on Data” for more details.

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Notes on Data

In the back test, we used the index returns in case the historical returns of the ETFs are not long enough. The ETF returns were approximated by index returns subtracted by their respective expense ratios.

The following summarizes the detailed calculations:

(1) IWM: Russell 2000 Index - 20bps before 5/31/2000

(2) EFA: MSCI EAFE Index - 34 bps before 8/28/2001

(3) VWO: MSCI EM Index -15 bps before 4/29/2005

(4) VNQ: MSCI US REIT Index - 10 bps before 10/29/2004

(5) MLPI: Alerian MLP Infrastructure Index - 85 bps before 5/28/2010

(6) GLD: London Gold Fixing - 40 bps before 12/31/2004

(7) JNK: Barclays Capital US High Yield Index - 40 bps before 1/31/2008

(8) AGG: Barclays Capital US Aggregate Index - 8 bps before 10/31/2003

(9) IEF: Barclays Capital US Treasury Index - 15 bps before 8/30/2002

(10) TLT: Barclays Capital 20+ year US Treasury Index -15 bps before 8/30/2002

(11) SHV: Three-month T-bill before 02/28/2007

(13) DVY: Dow Jones US Select Dividend Index - 39 bps before 12/31/2003

(14) EMB: JP Morgan EMBI Global Core Index - 60 bps before 1/31/2008

(15) PFF: S&P US Preferred Index - 47 bps before 4/30/2007

(16) BKLN: S&P/LSTA Bank Loan Index -65 bps before 4/29/2011

(17) IVE: S&P 500 Value Index - 18 bps before 6/30/2000

(18) IVW: S&P 500 Growth Index - 18 bps before 6/30/2000

(19) IWS: Russell MidCap Value Index - 25 bps before 9/28/2001

(20) IWP: Russell MidCap Growth Index - 25 bps before 9/28/2001

(21) IWN: Russell SmallCap Value Index - 25 bps before 8/31/2000

(22) IWO: Russell SmallCap Growth Index - 25 bps before 8/31/2000

(23) DJP: Dow Jones UBS Commodity Index - 75 bps before 11/30/2006

(24) RWX: Dow Jone Global Real Estate Index -59 bps before 1/31/2007

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