qCIO Global Macro Hedge Fund Strategy - November 2014

qCIO Global Macro Hedge Fund Strategy November 2014 Q M S Advisors .

This material does not constitute investment advice and should not be viewed as a current or past recommendation or a solicitation of an offer to buy or sell any securities or to adopt any investment strategy.

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qCIO: Capitalizing on Market Inefficiencies Long-Term Returns from Short-Term Dislocations In the long run, markets tend to behave like weighing machines, which value assets rationally on the basis of what they are actually worth. In the short term, however, markets tend to be more like voting machines, which reflect the often erratic desires and fears of fickle publics and willful national governments.

Benjamin Graham

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qCIO: Exploiting Temporary Mispricings qCIO seeks to exploit these constantly evolving economic conditions and the temporary mispricings that result among individual geographies and asset classes, opportunistically adjusting our investment views in response to the changing patterns of risk and reward in the markets. qCIO does this through close quantitative analysis of global pricing trends, business cycles, volatility levels and other macro-economic signals.

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qCIO: A Market Neutral Global Macro Fund Bespoke Tactical Macro Investing qCIO’s returns are driven not by the directional movement of any one market but by exploiting shortterm mispricings among the markets themselves. qCIO’s derived alpha tends to be highly efficient due to the targeted balance of risk and return it achieves across markets. qCIO: a customizable strategy with a consistent return per unit of risk.

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Quantitative Global Macro Hedge Fund Strategy November 2014

This material does not constitute investment advice and should not be viewed as a current or past recommendation or a solicitation of an offer to buy or sell any securities or to adopt any investment strategy.

Table of Contents Portfolio Objectives Asset Classes and Market Coverage Model Overview and Investment Process Overview of Signals Across Investment Strategies Derivation of Relative Return and Risk Expectations Blending: Aggregation and Apportioning of Views Portfolio Construction

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Portfolio Objective Quantitative Global Macro Strategy Focused On Maximizing Risk-adjusted Returns Example: Excess Return over Cash Volatility Sharpe Ratio

Objective 10 - 20% 5 - 10% 2.0

Relative Tactical positions are formulated on an Absolute Return basis To maximize risk-adjusted total return Long or short positions may be taken in any asset classes The portfolio may be implicitly leveraged Trades are implemented with futures, forwards or option contracts Stock-index futures, forwards or options on nine equity markets 10-year government bond futures, forwards or options in seven countries Currency futures, forwards or options on seven currencies Historical simulation does not guarantee future performance of any individually managed account or fund.

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Coverage Asset Classes and Markets A set of Models covering multiple Asset Classes and Markets Markets currently included in the modeling process 9 stock markets: US, Japan, UK, Eurozone, Switzerland, Australia, Canada, Hong Kong and emerging markets 7 bond markets: US, Japan, UK, Germany, Australia, Canada, Switzerland 7 currency markets: USD, EUR, JPY, GBP, CHF, CAD,AUD

A system built around five independent set of models, with nonoverlapping signals and return drivers Risk Premia: Intra-country Relative Value: Inter-country Stock-Bond Stock VS Stock Bond-Cash Bond VS Bond Currency

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Model Overview Global Macro Strategy: Approach An quantitative global macro investment strategy built around five independent sets of models with non-overlapping signals and return drivers Risk Premia Arbitrage Intra-country Systems

Market Spreads Inter-country Systems FX

Stock VS Stock Bond VS Bond

Cash VS Bond

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Investment Process Investment Procedure Outline

Across all sub-systems

Portfolio Implementation

Pairwise Views

Blending

Portfolio Construction

Derive direction and confidence of investment views for all pairs

Derive expected returns for all assets through Bayesian blending

Within each sub-system Signals

Identification of common signals for all pairs

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Portfolio construction: Tactical trades implemented via futures contracts

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Signals

Pairwise Views

Blending

Portfolio Constru.

Investment Process Investment Procedure Outline All recommended strategies of the qCIO Model are based on expected excess returns derived from blending investment views of five independent sub-systems designed for different asset classes and markets; the weights of the views are determined by their relative statistical confidence as well as their dynamic correlations.

Foreign Exch. sub-system Bond-Bond sub-system

qCIO’s Blending Model

Stock-Stock sub-system

Cash-Bond sub-system Bond-Stock sub-system

Expected Excess Returns and Risks

Strategies Q.M.S Advisors

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Signals

Pairwise Views

Blending

Portfolio Constru.

Common Signals And Returns Diversification Across Signals Of Different Nature Across Asset Classes Example (bond versus cash): 7 markets

7 iterations of the model

US Bonds vs. US cash, Japanese Bonds vs Japanese Cash, etc.

At each iteration, the dependent variable is defined as the excess return of bonds over cash, hedged into USD The explanatory variables correspond to the signal associated with the country under consideration A dynamic constant is included, corresponding to a risk premium

Within Asset Classes Example (equity versus equity): 9 markets: Consider each possible pair

36 iterations

Japan vs. US, EU vs. US, EU vs. JP, UK vs. Japan, etc.

The dependent variable is the excess returns of the two stock markets considered (relative to cash), hedged into USD The explanatory variables correspond to the difference in signals between 2 markets Example: Yield Gap for Japan vs. US = YG(USA)-YG(Japan)

No constant (premium) is included as there is no rationale as to why stock markets should outperform one another Q.M.S Advisors

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Signals

Pairwise Views

Blending

Portfolio Constru.

Investment Process Typology of Signals

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Pairwise Views

Signals

Pairwise Views

Blending

Portfolio Constru.

Sequential Derivation of Direction and Confidence of Investment Views for Each Sub-System Each pair of assets is considered in turn The expected excess return of the pair of assets is the dependent variable Hindsight biases are minimized by assuming that all signals work equally and moderately well at inception The relative importance of each signal is determined by Bayesian adaptive regression according to its consistency to performance Direction and confidence of investment views are both expressed as expected relative return and standard error

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Signals

Pairwise Views

Blending

Portfolio Constru.

FX Signals’ Contribution By Type Signals’ Ranking By Current Relative Explanatory Power FX Sub-System: Current Signal Ranks (1 = Best) USD JPY Risk-adjusted Carry 4 5 Yield Trend 5 1 1 Flows 3 Growth 4 1 GDP Revisions 4 4 1 Technicals 3.5 Composite 5 1

EUR 6 6 7 6 4 3.5 3

GBP 3 7 6 7 4 4.5 4

CHF 7 2 4 3 4 6 5

CAD 2 4 2 5 4 5 4

AUD 1 3 5 2 4 4.5 6

FX Sub-System: Last Week's Signal Ranks USD JPY Risk-adjusted Carry 4 3 Yield Trend 2 4 Flows 3 1 Growth 2 3 GDP Revisions 4 4 1.5 4.5 Technicals Composite 2 3

EUR 2 7 7 5 4 5 6

GBP 5 6 6 7 4 1.5 1

CHF 1 5 4 6 4 4.5 4

CAD 6 3 2 4 4 4 5

AUD 7 1 5 1 4 7 7

FX Sub-System: Signals' Weights USD Risk-adjusted Carry 18% Yield Trend 12% Flows 6% Growth 8% GDP Revisions 2% Technicals 55%

EUR 12% 8% 10% 5% 7% 58%

GBP 12% 6% 10% 5% 7% 60%

CHF 12% 10% 8% 7% 6% 57%

CAD 13% 8% 10% 5% 8% 56%

AUD 16% 5% 11% 5% 5% 58%

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JPY 12% 10% 7% 11% 5% 54%

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Signals

Pairwise Views

Blending

Portfolio Constru.

Cash-Bond Signals’ Contrib. By Type Signals’ Ranking By Current Relative Explanatory Power Bond-Cash Sub-System: Signals on scale from -5 to +5 US Jap Eur Value -0.9 -2.3 -3.4 Growth +3.6 +2.2 +2.4 Risk -0.7 +1.3 -2.7 Yield Dynamics +2.8 +2.0 -0.3 Composite Bond-Cash +3.9 +3.2 +2.5

UK +2.7 +3.5 -3.9 +1.4 +3.5

Aus -3.9 +2.3 -0.9 +2.2 +2.9

Can -3.6 +3.1 -4.8 +2.0 +1.6

Swi -2.5 +1.6 -0.4 -1.4 +3.0

Bond-Cash Sub-System: Signals' change from last month US Jap Eur Value -0.5 -0.3 -0.2 Growth -0.2 -0.3 -0.3 Risk +1.4 -0.8 -0.6 Yield Dynamics +5.7 +3.5 +1.5 Composite Bond-Cash +0.7 +0.3 -0.3

UK +0.1 -0.2 -0.9 +3.8 +0.3

Aus +0.7 -0.1 -0.9 +5.8 +2.4

Can +0.0 -0.3 -0.9 +5.8 +1.7

Swi +1.4 -0.8 +3.9 +1.3 +0.6

Bond-Cash Sub-System: Signals' weights US Jap Value 49% 33% Growth 33% 29% Risk 0% 9% Yield Dynamics 18% 29%

UK 30% 57% 0% 13%

Aus 8% 41% 21% 29%

Can 40% 39% 7% 14%

Swi 25% 42% 13% 19%

Eur 34% 66% 0% 0%

Positive (negative) signals recommend long (short) duration

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Signals

Pairwise Views

Blending

Portfolio Constru.

Bond-Bond Signals’ Contrib. By Type Signals’ Ranking By Current Relative Explanatory Power

Bond-Bond Sub-System: Current Signal Ranks (1 = Best) US Jap Eur Yield Dynamics 1 6 3 Value 4 1 7 Composite Bond-Bond 1 5 6

UK 2 6 4

Aus 5 2 3

Can 4 3 2

Swi 7 5 7

Bond-Bond Sub-System: Last Month's Signal Ranks US Jap Eur Yield Dynamics 1 7 3 2 1 7 Value Composite Bond-Bond 1 4 7

UK 2 4 2

Aus 6 6 6

Can 4 3 3

Swi 5 5 5

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Signals

Pairwise Views

Blending

Portfolio Constru.

Bond-Stock Signals’ Contrib. By Type Signals’ Ranking By Current Relative Explanatory Power Stock-Bond Sub-System: Signals on scale from -5 to +5 US Jap Eur Value -4.1 -2.7 -3.1 Business Cycles +3.6 +3.6 +3.7 Composite Stock-Bond -2.5 -4.3 -4.0

UK -3.8 +4.1 -4.6

Aus -2.3 +3.7 -3.5

Can -3.7 +3.7 -3.4

Swi -3.5 +3.6 -2.9

Stock-Bond Sub-System: Signals' Change from Last Month US Jap Eur Value -0.2 -0.1 +0.3 Business Cycles +0.0 -0.1 +0.0 Composite Stock-Bond +0.1 +0.0 -0.2

UK +0.0 +0.0 -0.2

Aus -0.6 +0.0 -0.0

Can +0.0 +0.0 -1.0

Swi -0.2 +0.0 -0.2

Aus 0.6 0.0

Can 0.6 0.0

Swi 0.5 0.0

Stock-Bond Sub-System: Signals' Confidence Level (1 = "Normal") US Jap Eur UK Value 0.7 0.4 0.7 2.3 Business Cycles 0.0 0.0 0.0 0.0

Positive (negative) signals recommend to overweight (underweight) Stocks against Bonds

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Pairwise Views

Signals

Blending

Portfolio Constru.

Stock-Stock Signals’ Contrib. By Type Signals’ Ranking By Current Relative Explanatory Power Stock-Stock Sub-System: Current Signal Ranks (1 = Best) US Jap Eur UK Value 5 8 2 3 Momentum 4 6 3 2 1 9 4 2 Implied Volatility Energy Prices 7 3 4 5 Business Cycles 5 8 9 3 Composite Stock-Stock 2 3 4 5

Aus 9 3 3 6 3 6

Can 7 4 8 9 6 7

Swi 4 6 5 8 7 7

HK 1 9 7 1 1 2

EMF 6 8 6 2 3 9

Stock-Stock Sub-System: Last Month's Signal Ranks US Jap Eur Value 4 8 2 Momentum 5 7 2 Implied Volatility 2 9 3 Energy Prices 7 2 2 Business Cycles 5 6 9 Composite Stock-Stock 2 9 3

Aus 9 2 6 8 3 7

Can 7 4 7 9 8 7

Swi 5 5 4 6 7 6

HK 1 9 8 3 1 2

EMF 6 8 5 3 3 9

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UK 3 3 1 5 3 4

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Signals

Pairwise Views

Blending

Portfolio Constru.

Pairwise Views Process qCIO is based on the sequential analysis of all expected returns and standard error of all asset pairs for each sub-system qCIO is designed so as to ensure an optimal and robust dynamic modeling of all pairs of assets considered by utilizing advanced Bayesian methodologies For each of the five sub-systems, we obtain: The expected excess return for every pair of assets The expected risk for every pair of assets The evolution of the weights for each signal

In total 92 pairs of assets are systematically dynamically modeled and analyzed

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Signals

Pairwise Views

Blending

Portfolio Constru.

Blending Aggregation and Apportioning of Views Currency System USD vs JPY USD vs EUR EUR vs JPY etc.

Stock-Bond System US Stocks vs Bonds Japan Stocks vs Bonds Eurozone Stocks vs Bonds etc

Blending

Stock-Stock System US vs Japan US vs Eurozone Japan vs Eurozone etc.

Expected Returns and Standard Errors

Bond-Bond System US vs Japan US vs Eurozone Japan vs Eurozone etc.

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Bond-Cash System US Bonds vs Cash Japan Bonds vs Cash Euro Bonds vs Cash etc.

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Signals

Pairwise Views

Blending

Portfolio Constru.

Blending Aggregation and Apportioning of Views Bayesian blending mechanism to obtain expected returns based on: Prior expectations: Excess returns are set to 0 in the absence of views, their covariance is estimated historically using exponential decay Views: Expected excess returns obtained from the Bayesian regression Confidence of the views

The expected returns can be interpreted as a weighted average of the equilibrium prior and the tactical views. The weights are determined by the relative confidence that we have in the views and the risk of the assets Expression of the pairwise views: 1% eS 1 − 1 0   E ( RStocks ) 0 1 − 1  E ( R  = 0.5% + e ) Bonds b    1 0 0   E (Cash)  0.75% ec

P Q.M.S Advisors

E(ret) = V + e

Stocks outperform bonds by 1% Bonds outperform cash by 0.5% Stocks will generate a 0.75% return

Σ=diag(cov(e))

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Signals

Pairwise Views

Blending

Portfolio Constru.

Blending Aggregation and Apportioning of Views The conditional expected returns of each asset class is expressed as: E(R) = [ ( τ Ω )-1 + PT Σ-1 P ] -1 . [ ( τ Ω )-1 Π + PT Σ-1 V ] With: Π the vector of equilibrium returns (set to 0) Ω the covariance of returns (based on historical data) P the matrix of views Σ the covariance of the views τ a calibration factor (set to 0.02, no consensus on its value in the literature)

Limiting cases: P=0: No views BL returns= Equilibrium returns (0) Inv(Σ)→∞: No forecast error BL returns = Views

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Signals

Pairwise Views

Blending

Portfolio Constru.

Blending: Systems’ Interdependence Excess Returns Are Derived From Every Sub-Systems The expected excess return of each asset is influenced by views in all five sub-systems. Some of these influences are not surprising: The expected returns of stocks are heavily influenced by views in the stockstock and stock-bond sub-systems The expected returns of bonds are heavily influenced by views in the bondbond and bond-cash sub-systems The expected returns of currencies are heavily influenced by views in the FX sub-system

But because of correlation among assets, a sub-system can influence the expected return of assets that are not directly involved in its own views. For example: The stock-stock sub-system is contributing to higher expected returns for all currencies against the US dollar. This is because the stock-stock sub-system expects the US stock market to out-perform European stock markets in currency-hedged terms, and this is associated with a weaker dollar.

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Signals

Pairwise Views

Blending

Portfolio Constru.

Blending: Systems’ Interdependence Excess Returns Are Derived From Every Sub-Systems Sub-Systems' contributions to expected excess returns of stock markets over cash, % p.a.

US Jap Eur UK Aus Can Swi 0.8% -1.8% -0.2% -0.1% 1.0% 0.5% -1.4% FX Sub-System 1.9% 1.0% 1.9% 1.8% 1.7% 1.5% 1.6% Bond-Cash Sub-System 1.0% 0.2% -0.1% 0.3% 0.6% 0.6% 0.2% Bond-Bond Sub-System -2.9% -2.5% -3.3% -3.4% -3.1% -2.6% -2.9% Stock-Bond Sub-System 1.2% 0.7% 0.8% 0.5% 0.2% -0.9% -0.9% Stock-Stock Sub-System Expected Stock Excess Return 2.0% -2.3% -0.9% -0.9% 0.3% -0.8% -3.4% * All stock markets except HK & EMF are currency-hedged into US dollars

HK -0.7% 2.5% 0.4% -3.3% 4.0% 2.9%

EMF 0.1% 1.6% 0.5% -2.7% -2.2% -2.7%

Sub-Systems' contributions to expected excess returns of bond markets over cash, % p.a.

US Jap Eur UK Aus Can Swi FX Sub-System -0.3% -0.2% -0.3% -0.2% 0.1% -0.3% 0.1% Bond-Cash Sub-System 1.9% 1.5% 1.8% 1.9% 2.1% 1.8% 1.5% Bond-Bond Sub-System 1.3% 0.0% -0.2% 0.3% 0.6% 0.9% -0.4% Stock-Bond Sub-System 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Stock-Stock Sub-System 0.2% 0.1% 0.2% 0.2% 0.1% 0.1% 0.1% Expected Bond Excess Return 3.1% 1.6% 1.6% 2.1% 2.9% 2.5% 1.3% * All bond markets are currency-hedged into US dollars Sub-Systems' contributions to expected excess returns of currencies over USD, % p.a.

JPY EUR GBP AUD CAD FX Sub-System 15.9% 2.5% 1.3% -0.9% -0.2% Bond-Cash Sub-System 0.0% -0.1% 0.0% -0.1% 0.1% Bond-Bond Sub-System -0.1% 0.0% 0.1% -0.1% 0.1% Stock-Bond Sub-System 0.0% 0.2% 0.0% 0.0% 0.0% Stock-Stock Sub-System -0.1% -0.1% 0.0% -0.2% -0.1% 15.8% 2.4% 1.4% -1.2% -0.1% Expected Currency Excess Return

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CHF -0.4% 0.0% 0.0% 0.2% -0.1% -0.3%

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Signals

Pairwise Views

Blending

Portfolio Constru.

Portfolio Construction Model Tradeoff between return and risk is made The objective function is to maximize single-period expected return subject to tracking error target Flexible control of turnover can be achieved by means of a transaction penalty parameter (factor is highest in the first half of the month), and other methods Round-trip transaction costs assumptions are 12 bp, 4 bp, and 8 bp for stocks, bonds and FX Portfolios with different objectives and constraints are constructed using the same set of expected return, ensuring information consistency Q.M.S Advisors

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Signals

Pairwise Views

Blending

Portfolio Constru.

Portfolio Construction Optimal Weights Targeting 5% Risk p.a.

Expected Information Ratio Current 2.28 Five-year average 2.47

* All bond markets and all stock market except Hong Kong and Emerging Markets are currency-hedged into US dollars

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Signals

Pairwise Views

Blending

Portfolio Constru.

Portfolio Backtests Unrestricted Systematic Global Macro Program Realized Performance - 15 years Annual Alpha = 6.30% Tracking Error = 7.77% Information Ratio = 0.81

The unrestricted program is the most accurate reflection of the model’s views.

Where a benchmark is present, no short-selling or leverage is permitted.

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Defining Features qCIOÂŽ Systematic Global Macro Program qCIOÂŽ Global Macro and GTAA Strategies are global, multi asset class strategies, that seek to add alpha through advanced quantitative investment processes. Potential investment opportunities are identified via rigorous and disciplined approaches based on combinations of economic and financial factors.

Generally, investment managers assemble their portfolios based on their long-term views of the performance of a single asset class, usually employing a five-year investment horizon. This traditional approach doesn’t take into account short-term macro events that have the potential to move the market. While these events take place, the resulting mis-valuations provide the opportunity to capture short-term incremental returns that are complementary to the long-term holdings of a traditional portfolio.

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Defining Features qCIO® Systematic Global Macro Program qCIO® seeks to capitalize on numerous sources of alpha by identifying the market’s constantly evolving economic conditions and taking long and short positions in global equity, bond, credit, commodity and currency futures markets. qCIO® views these asset classes on a differential basis, and in accordance with an array of macro-economic events in a number of different geographic markets. qCIO® seeks to generate absolute return that has insignificant to very low correlation to a portfolio’s traditional asset classes, and allow investors to add alpha to their portfolios by exploring short-term sources of return while broadening their investment opportunity set from domestic markets to global markets, and from a single asset class to multiple asset classes.

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Defining Features qCIO® Systematic Global Macro Program Diversification across signals Relying on one type of strategy is likely to fail as a variety of signals drive returns and as their correlation with returns varies over time. In contrast, the qCIO® process analyse markets methodically and focuses on a wide array of market signals to identify opportunities.

These signals are grouped into five broad “investment themes”: equilibrium, value, price dynamics, growth, and risk/sentiment that are consistent with economic intuition and are retained on the basis of their predictive power.

In accordance with this analysis, opportunities for alpha can be grouped under two broad assumptions: Shorter term momentum for the risk and sentiment indicators, and for price dynamics signals Mean reversion for economic indicators (e.g. growth, valuation, and carry) Altogether multiple market views and diverse signals are expected to indicate where any target market stands in relation to its fair value or in relation to other markets.

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