qCIO Global Macro Hedge Fund Strategy - November 2014

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

Q.M.S AdvisorsAv. de la Gare, 1 | 1003, Lausanne | CH

tel: +41 (0)78 922 08 77e-mail: [email protected]

website: www.qmsadv.com

qCIO Global Macro Hedge Fund Strategy

November 2014

Q.M.S Advisors Av. de la Gare. 1 | 1003, Lausanne CH | tel: 078 922 08 77 | e-mail: [email protected] | website: www.qmsadv.comPage 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 orpast recommendation or a solicitation of an offer to buy or sell any securities or to adopt anyinvestment strategy.

Quantitative Global Macro

Hedge Fund StrategyNovember 2014


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 ObjectiveQuantitative 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

CoverageAsset Classes and Markets


An quantitative global macro investment strategy built around five

independent sets of models with non-overlapping signals and return drivers

Model OverviewGlobal 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 ProcessInvestment 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 ProcessInvestment Procedure Outline

SignalsPairwise

Views

Portfolio

Constru.Blending


Common Signals And ReturnsDiversification 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

SignalsPairwise

Views

Portfolio

Constru.Blending


Investment ProcessTypology of Signals

SignalsPairwise

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 ViewsSequential Derivation of Direction and Confidence

of Investment Views for Each Sub-System

SignalsPairwise

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 5 1 6 7 2 4 3

Flows 3 1 7 6 4 2 5

Growth 4 1 6 7 3 5 2

GDP Revisions 4 4 4 4 4 4 4

Technicals 3.5 1 3.5 4.5 6 5 4.5

Composite 5 1 3 4 5 4 6

FX Sub-System: Last Week's Signal Ranks


Risk-adjusted Carry 4 3 2 5 1 6 7

Yield Trend 2 4 7 6 5 3 1

Flows 3 1 7 6 4 2 5

Growth 2 3 5 7 6 4 1

GDP Revisions 4 4 4 4 4 4 4

Technicals 1.5 4.5 5 1.5 4.5 4 7

Composite 2 3 6 1 4 5 7

FX Sub-System: Signals' Weights


Risk-adjusted Carry 18% 12% 12% 12% 12% 13% 16%

Yield Trend 12% 10% 8% 6% 10% 8% 5%

Flows 6% 7% 10% 10% 8% 10% 11%

Growth 8% 11% 5% 5% 7% 5% 5%

GDP Revisions 2% 5% 7% 7% 6% 8% 5%

Technicals 55% 54% 58% 60% 57% 56% 58%


FX Signals’ Contribution By Type Signals’ Ranking By Current Relative Explanatory Power

SignalsPairwise

Views

Portfolio

Constru.Blending

Bond-Cash Sub-System: Signals on scale from -5 to +5

US Jap Eur UK Aus Can Swi

Value -0.9 -2.3 -3.4 +2.7 -3.9 -3.6 -2.5

Growth +3.6 +2.2 +2.4 +3.5 +2.3 +3.1 +1.6

Risk -0.7 +1.3 -2.7 -3.9 -0.9 -4.8 -0.4

Yield Dynamics +2.8 +2.0 -0.3 +1.4 +2.2 +2.0 -1.4

Composite Bond-Cash +3.9 +3.2 +2.5 +3.5 +2.9 +1.6 +3.0

Bond-Cash Sub-System: Signals' change from last month


Value -0.5 -0.3 -0.2 +0.1 +0.7 +0.0 +1.4

Growth -0.2 -0.3 -0.3 -0.2 -0.1 -0.3 -0.8

Risk +1.4 -0.8 -0.6 -0.9 -0.9 -0.9 +3.9

Yield Dynamics +5.7 +3.5 +1.5 +3.8 +5.8 +5.8 +1.3

Composite Bond-Cash +0.7 +0.3 -0.3 +0.3 +2.4 +1.7 +0.6

Bond-Cash Sub-System: Signals' weights


Value 49% 33% 34% 30% 8% 40% 25%

Growth 33% 29% 66% 57% 41% 39% 42%

Risk 0% 9% 0% 0% 21% 7% 13%

Yield Dynamics 18% 29% 0% 13% 29% 14% 19%


Positive (negative) signals recommend long (short) duration

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

SignalsPairwise

Views

Portfolio

Constru.Blending

Bond-Bond Sub-System: Current Signal Ranks (1 = Best)


Yield Dynamics 1 6 3 2 5 4 7

Value 4 1 7 6 2 3 5

Composite Bond-Bond 1 5 6 4 3 2 7

Bond-Bond Sub-System: Last Month's Signal Ranks


Yield Dynamics 1 7 3 2 6 4 5

Value 2 1 7 4 6 3 5

Composite Bond-Bond 1 4 7 2 6 3 5


SignalsPairwise

Views

Portfolio

Constru.Blending

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

Stock-Bond Sub-System: Signals on scale from -5 to +5


Value -4.1 -2.7 -3.1 -3.8 -2.3 -3.7 -3.5

Business Cycles +3.6 +3.6 +3.7 +4.1 +3.7 +3.7 +3.6

Composite Stock-Bond -2.5 -4.3 -4.0 -4.6 -3.5 -3.4 -2.9

Stock-Bond Sub-System: Signals' Change from Last Month


Value -0.2 -0.1 +0.3 +0.0 -0.6 +0.0 -0.2

Business Cycles +0.0 -0.1 +0.0 +0.0 +0.0 +0.0 +0.0

Composite Stock-Bond +0.1 +0.0 -0.2 -0.2 -0.0 -1.0 -0.2

Stock-Bond Sub-System: Signals' Confidence Level (1 = "Normal")


Value 0.7 0.4 0.7 2.3 0.6 0.6 0.5

Business Cycles 0.0 0.0 0.0 0.0 0.0 0.0 0.0


SignalsPairwise

Views

Portfolio

Constru.Blending

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

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 3 9 7 4 1 6

Momentum 4 6 3 2 3 4 6 9 8

Implied Volatility 1 9 4 2 3 8 5 7 6

Energy Prices 7 3 4 5 6 9 8 1 2

Business Cycles 5 8 9 3 3 6 7 1 3

Composite Stock-Stock 2 3 4 5 6 7 7 2 9

Stock-Stock Sub-System: Last Month's Signal Ranks


Value 4 8 2 3 9 7 5 1 6

Momentum 5 7 2 3 2 4 5 9 8

Implied Volatility 2 9 3 1 6 7 4 8 5

Energy Prices 7 2 2 5 8 9 6 3 3

Business Cycles 5 6 9 3 3 8 7 1 3

Composite Stock-Stock 2 9 3 4 7 7 6 2 9


SignalsPairwise

Views

Portfolio

Constru.Blending

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


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 ViewsProcess

SignalsPairwise

Views

Portfolio

Constru.Blending


BlendingAggregation 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

SignalsPairwise

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:


SignalsPairwise

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


SignalsPairwise

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.

SignalsPairwise

Views

Portfolio

Constru.Blending

Blending: Systems’ InterdependenceExcess 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% -1.8% -0.2% -0.1% 1.0% 0.5% -1.4% -0.7% 0.1%

Bond-Cash Sub-System 1.9% 1.0% 1.9% 1.8% 1.7% 1.5% 1.6% 2.5% 1.6%

Bond-Bond Sub-System 1.0% 0.2% -0.1% 0.3% 0.6% 0.6% 0.2% 0.4% 0.5%

Stock-Bond Sub-System -2.9% -2.5% -3.3% -3.4% -3.1% -2.6% -2.9% -3.3% -2.7%

Stock-Stock Sub-System 1.2% 0.7% 0.8% 0.5% 0.2% -0.9% -0.9% 4.0% -2.2%

Expected Stock Excess Return 2.0% -2.3% -0.9% -0.9% 0.3% -0.8% -3.4% 2.9% -2.7%

* 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 -0.3% -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 CHF

FX Sub-System 15.9% 2.5% 1.3% -0.9% -0.2% -0.4%

Bond-Cash Sub-System 0.0% -0.1% 0.0% -0.1% 0.1% 0.0%

Bond-Bond Sub-System -0.1% 0.0% 0.1% -0.1% 0.1% 0.0%

Stock-Bond Sub-System 0.0% 0.2% 0.0% 0.0% 0.0% 0.2%

Stock-Stock Sub-System -0.1% -0.1% 0.0% -0.2% -0.1% -0.1%

Expected Currency Excess Return 15.8% 2.4% 1.4% -1.2% -0.1% -0.3%


Blending: Systems’ InterdependenceExcess Returns Are Derived From Every Sub-Systems

SignalsPairwise

Views

Portfolio

Constru.Blending


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

SignalsPairwise

Views

Portfolio

Constru.Blending


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

Portfolio ConstructionOptimal Weights Targeting 5% Risk p.a.

SignalsPairwise

Views

Portfolio

Constru.Blending

Current 2.28Five-year average 2.47

Expected Information Ratio


Portfolio BacktestsUnrestricted Systematic Global Macro Program

SignalsPairwise

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 = 6.30%Tracking Error = 7.77%Information Ratio = 0.81

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 FeaturesqCIO® 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.




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.


qCIO Global Macro Hedge Fund Strategy - November 2014

Economy & Finance

Transcript of qCIO Global Macro Hedge Fund Strategy - November 2014