qCIO Global Macro Hedge Fund Strategy - March 2015

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.
Q.M.S Advisors
Av. de la Gare, 1 | 1003, Lausanne | CH
tel: +41 (0)78 922 08 77
e-mail: info@qmsadv.com
website: www.qmsadv.com
qCIO
Global Macro Hedge Fund Strategy
March 2015

Q.M.S Advisors Av. de la Gare. 1 | 1003, Lausanne CH | tel: 078 922 08 77 | e-mail: info@qmsadv.com | website: www.qmsadv.com Page 1Q.M.S Advisors Av. de la Gare. 1 | 1003, Lausanne CH | tel: 078 922 08 77 | e-mail: info@qmsadv.com | website: www.qmsadv.com Page 1
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

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.

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 short-
term 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.

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.
Quantitative Global Macro
Hedge Fund Strategy
March 2015

Q.M.S Advisors Av. de la Gare. 1 | 1003, Lausanne CH | tel: 078 922 08 77 | e-mail: info@qmsadv.com | website: www.qmsadv.com Page 5
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

Historical simulation does not guarantee future performance of any individually managed account or fund.
Example:
Objective
Excess Return over Cash 10 - 20%
Volatility 5 - 10%
Sharpe Ratio 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
Portfolio Objective
Quantitative Global Macro Strategy Focused On
Maximizing Risk-adjusted Returns

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 non-
overlapping signals and return drivers
Risk Premia: Intra-country Relative Value: Inter-country
Stock-Bond Stock VS Stock
Bond-Cash Bond VS Bond
Currency
Coverage
Asset Classes and Markets

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

Identification of
common signals for
all pairs
Derive direction
and confidence of
investment views
for all pairs
Portfolio construction:
Tactical trades
implemented via
futures contracts
Derive expected
returns for all
assets through
Bayesian blending
Signals Pairwise Views Blending Portfolio Construction
Within each sub-system Across all sub-systems Portfolio Implementation
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.
qCIO’s
Blending
Model
Stock-Stock
sub-system
Foreign Exch.
sub-system
Bond-Bond
sub-system
Cash-Bond
sub-system
Bond-Stock
sub-system
Expected Excess
Returns and Risks
Strategies
Investment Process
Investment Procedure Outline
Signals
Pairwise
Views
Portfolio
Constru.
Blending

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
Signals
Pairwise
Views
Portfolio
Constru.
Blending

Investment Process
Typology of Signals
Signals
Pairwise
Views
Portfolio
Constru.
Blending

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
Pairwise Views
Sequential Derivation of Direction and Confidence
of Investment Views for Each Sub-System
Signals
Pairwise
Views
Portfolio
Constru.
Blending

FX Sub-System: Current Signal Ranks (1 = Best)
USD JPY EUR GBP CHF CAD AUD
Risk-adjusted Carry 4 5 6 3 7 2 1
Yield Trend 2 3 5 1 4 7 6
Flows 4 2 1 5 7 6 3
Growth 4 3 6 2 1 7 5
GDP Revisions 4 4 4 4 4 4 4
Technicals 4 6 4.5 3.5 7 1.5 1.5
Composite 4 6 6 3 6 2 1
FX Sub-System: Last Week's Signal Ranks
Risk-adjusted Carry 4 5 6 3 7 2 1
Yield Trend 3 2 5 1 4 7 6
Flows 4 2 1 5 7 6 3
Growth 4 2 6 3 1 7 5
GDP Revisions 4 4 4 4 4 4 4
Technicals 5 6 4 3 7 2 1
Composite 6 6 5 2 4 4 1
FX Sub-System: Signals' Weights
Risk-adjusted Carry 15% 11% 8% 10% 4% 11% 14%
Yield Trend 13% 9% 9% 8% 16% 8% 7%
Flows 7% 7% 8% 10% 8% 10% 11%
Growth 9% 10% 12% 7% 14% 6% 4%
GDP Revisions 2% 5% 5% 6% 4% 8% 5%
Technicals 55% 59% 57% 59% 54% 57% 59%
FX Signals’ Contribution By Type
Signals’ Ranking By Current Relative Explanatory Power
Signals
Pairwise
Views
Portfolio
Constru.
Blending

Bond-Cash Sub-System: Signals on scale from -5 to +5
US Jap Eur UK Aus Can Swi
Value -1.5 +0.9 -3.3 +4.8 -4.0 -4.5 -2.9
Growth +3.1 +1.7 +1.0 +2.7 +3.9 +1.5 -2.4
Risk +1.5 +2.3 +4.9 +3.9 +0.7 +2.2 +4.8
Yield Dynamics -4.9 -3.6 -2.9 -5.0 -3.7 -4.5 -4.1
Composite Bond-Cash +2.6 +3.5 +1.9 +3.3 +2.7 -0.1 +2.2
Bond-Cash Sub-System: Signals' change from last month
Value +1.0 +2.8 +0.7 +1.8 +0.6 +0.1 +0.1
Growth +0.5 +0.5 +0.1 +0.9 +0.1 +0.4 +0.0
Risk +5.6 +2.6 +0.3 +2.5 +5.6 +5.3 +3.2
Yield Dynamics -9.5 -1.9 -4.8 -7.7 -4.8 -9.5 -9.0
Composite Bond-Cash -0.8 +1.7 +0.2 -0.1 +0.2 -3.4 -1.6
Bond-Cash Sub-System: Signals' weights
Value 51% 32% 25% 31% 4% 33% 14%
Growth 40% 28% 71% 60% 41% 51% 51%
Risk 0% 12% 0% 0% 21% 0% 13%
Yield Dynamics 8% 28% 4% 9% 34% 16% 21%
Positive (negative) signals recommend long (short) duration
Cash-Bond Signals’ Contrib. By Type
Signals
Pairwise
Views
Portfolio
Constru.
Blending

Bond-Bond Sub-System: Current Signal Ranks (1 = Best)
Yield Dynamics 1 6 5 2 3 4 7
Value 3 1 5 2 4 6 7
Composite Bond-Bond 1 4 5 2 3 6 7
Bond-Bond Sub-System: Last Month's Signal Ranks
Yield Dynamics 1 6 4 3 2 5 7
Value 4 1 3 7 2 5 6
Composite Bond-Bond 3 1 4 6 2 5 7
Signals
Pairwise
Views
Portfolio
Constru.
Blending
Bond-Bond Signals’ Contrib. By Type

Stock-Bond Sub-System: Signals on scale from -5 to +5
Value -3.7 -4.0 -3.4 -4.0 -2.6 -3.2 -3.3
Business Cycles +3.6 +3.4 +3.7 +4.1 +3.7 +3.7 +3.7
Composite Stock-Bond -2.6 -2.5 -2.5 -4.2 -3.6 -3.6 -3.5
Stock-Bond Sub-System: Signals' Change from Last Month
Value -1.4 -0.8 -1.1 -1.1 -1.5 -1.2 -1.6
Business Cycles +0.1 -0.0 -0.0 -0.0 +0.0 -0.0 -0.0
Composite Stock-Bond -0.3 +1.6 +0.3 -0.0 -0.5 -0.2 -0.2
Stock-Bond Sub-System: Signals' Confidence Level (1 = "Normal")
Value 0.9 0.2 0.4 2.0 0.7 0.9 0.6
Business Cycles 0.0 -6.3 0.0 0.0 0.0 0.0 0.0
Signals
Pairwise
Views
Portfolio
Constru.
Blending
Bond-Stock Signals’ Contrib. By Type
Positive (negative) signals recommend to overweight (underweight) Stocks against Bonds

Stock-Stock Sub-System: Current Signal Ranks (1 = Best)
US Jap Eur UK Aus Can Swi HK EMF
Value 5 8 2 4 9 7 3 1 6
Momentum 5 7 4 5 1 2 4 8 9
Implied Volatility 6 1 9 8 7 2 3 4 5
Energy Prices 8 1 2 5 4 7 9 3 6
Business Cycles 6 9 8 4 3 4 7 1 3
Composite Stock-Stock 5 1 7 8 6 3 3 3 9
Stock-Stock Sub-System: Last Month's Signal Ranks
Value 5 8 2 4 9 7 3 1 6
Momentum 4 7 6 3 1 2 5 8 9
Implied Volatility 5 9 1 2 4 8 7 3 6
Energy Prices 8 3 1 5 4 7 9 2 6
Business Cycles 8 9 7 4 3 4 6 1 3
Composite Stock-Stock 5 6 3 6 4 3 5 3 9
Signals
Pairwise
Views
Portfolio
Constru.
Blending
Stock-Stock Signals’ Contrib. By Type

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
Pairwise Views
Process
Signals
Pairwise
Views
Portfolio
Constru.
Blending

Blending
Aggregation and Apportioning of Views
Stock-Bond System
US Stocks vs Bonds
Japan Stocks vs Bonds
Eurozone Stocks vs Bonds
etc
Bond-Bond System
US vs Japan
US vs Eurozone
Japan vs Eurozone
etc.
Bond-Cash System
US Bonds vs Cash
Japan Bonds vs Cash
Euro Bonds vs Cash
etc.
Stock-Stock System
US vs Japan
US vs Eurozone
Japan vs Eurozone
etc.
Currency System
USD vs JPY
USD vs EUR
EUR vs JPY
etc.
Blending
Expected Returns
and Standard Errors
Signals
Pairwise
Views
Portfolio
Constru.
Blending

c
b
S
Bonds
Stocks
e
e
e
CashE
RE
RE
+=




















−
−
%75.0
%5.0
%1
)(
)(
)(
001
110
011 Stocks outperform bonds by 1%
Bonds outperform cash by 0.5%
Stocks will generate a 0.75% return
P E(ret) = V + e Σ=diag(cov(e))
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:
Blending
Signals
Pairwise
Views
Portfolio
Constru.
Blending

The conditional expected returns of each asset class is expressed as:
E(R) = [ ( ττττ ΩΩΩΩ )-1 + PT ΣΣΣΣ-1 P ] -1 . [ ( ττττ ΩΩΩΩ )-1 ΠΠΠΠ+ PT ΣΣΣΣ-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
Blending
Signals
Pairwise
Views
Portfolio
Constru.
Blending

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 stock-
stock and stock-bond sub-systems
The expected returns of bonds are heavily influenced by views in the bond-
bond 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.
Signals
Pairwise
Views
Portfolio
Constru.
Blending
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.
FX Sub-System 0.8% 3.1% 2.3% 0.9% 1.0% 2.1% 1.4% 3.1% 1.8%
Bond-Cash Sub-System 1.0% 0.5% 1.1% 1.0% 0.9% 0.9% 0.9% 1.4% 0.9%
Bond-Bond Sub-System 1.2% 0.6% 0.3% 1.1% 0.9% 0.4% -0.1% 1.0% 0.8%
Stock-Bond Sub-System -2.7% -1.9% -2.6% -2.9% -2.8% -2.4% -2.7% -3.0% -2.3%
Stock-Stock Sub-System -0.1% 4.4% -1.2% -2.4% -1.0% 1.2% 0.8% -1.0% -4.8%
Expected Stock Excess Return 0.2% 6.7% -0.1% -2.4% -0.9% 2.2% 0.3% 1.5% -3.7%
* All stock markets except HK & EMF are currency-hedged into US dollars
Sub-Systems' contributions to expected excess returns of bond markets over cash, % p.a.
FX Sub-System 0.1% 0.1% 0.0% 0.5% 0.0% 1.0% -0.5%
Bond-Cash Sub-System 1.0% 1.0% 1.0% 1.1% 1.2% 1.0% 0.7%
Bond-Bond Sub-System 1.6% 0.5% 0.3% 1.5% 1.1% -0.1% -1.0%
Stock-Bond Sub-System 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.1%
Stock-Stock Sub-System -0.2% -0.1% -0.4% -0.5% -0.2% -0.1% -0.2%
Expected Bond Excess Return 2.6% 1.6% 0.9% 2.5% 2.0% 1.9% -0.9%
* 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 CHF
FX Sub-System -10.5% -7.6% 4.3% 7.6% 2.6% -8.4%
Bond-Cash Sub-System 0.0% -0.1% 0.0% -0.1% 0.0% 0.1%
Bond-Bond Sub-System -0.2% -0.2% 0.3% -0.3% -0.5% -0.1%
Stock-Bond Sub-System 0.1% 0.4% 0.0% -0.1% -0.1% 0.7%
Stock-Stock Sub-System -0.3% 0.1% 0.2% 0.1% -0.2% 0.1%
Expected Currency Excess Return -10.9% -7.3% 4.8% 7.3% 2.0% -7.7%
Blending: Systems’ Interdependence
Excess Returns Are Derived From Every Sub-Systems
Signals
Pairwise
Views
Portfolio
Constru.
Blending

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
Signals
Pairwise
Views
Portfolio
Constru.
Blending

* All bond markets and all stock market except Hong Kong and Emerging Markets are currency-hedged into US dollars
Optimal Weights Targeting 5% Risk p.a.
Signals
Pairwise
Views
Portfolio
Constru.
Blending
Current 2.28
Five-year average 2.50
Expected Information Ratio

Portfolio Backtests
Unrestricted Systematic Global Macro Program
Signals
Pairwise
Views
Portfolio
Constru.
Blending
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.
Annual Alpha = 5.56%
Tracking Error = 7.50%
Information Ratio = 0.74
Realized Performance - 15 years

qCIO® 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.
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.
Defining Features

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.
Defining Features

qCIO Global Macro Hedge Fund Strategy - March 2015

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