Risk Factor Allocation: A New Investment Paradigm? · PDF file24/11/2014 · Head...

Swiss edition

For qualified investors

Thomas Merz

Head UBS ETF Europe

Risk Factor Allocation:

A New Investment Paradigm?

Asset management

UBS ETFs

Lugano Fund Forum, November 24, 2014

1

Indexing experience

• Natural Sciences & Mathematics, University of Zurich

• Business & Economics, University of Basel

• Part-time lecturing position, Zurich University of Applied Sciences

(ZHAW), Banking & Finance Center

• Invited lecturer, University of Zurich, Department of Banking and Finance

• UBS AG, Global Asset Management

UBS Global Asset Management

Source: UBS Global Asset Management, data of Total AuM as at 30 September 2014, data of ETF only as at September 2014

Total AuM ETF only Total AuM Structured Beta & Indexing

AuM

US

Dbn

0

40

80

120

160

200

240

2006 2007 2008 2009 2010 2011 2012 2013 set.14

11'201 12'392

15'870

20'532

64

87

128

145

50

100

150

200

10'000

12'000

14'000

16'000

18'000

20'000

22'000

2011 2012 2013 End of Sep'14

# E

TF

s

Au

M (

US

Dm

)

AuM (USDm) # ETFs (rhs)

2

Three main questions

• Is factor-based investing a new investment style?

• Does factor-based investing work in practice?

• What are some of the implementation challenges with factor-based portfolios?

The basic challenge in investment management

3

Source: UBS Global Asset Management

• Institutional investors typically base their general investment decisions on their view

which of the two scenarios best describe the selected (current) investment framework

(market structure)

• Generally two types of market structures to choose from:

• Equilibrium

• Off-equilibrium

A) Equilibrium structure B) Off-Equilibrium structure

Assumption that markets are not

very information efficient and

that prices depart from their

theoretical values and hence,

active asset management can

add value (alpha) by timing the

market and proofing selection

skills

Assumption that markets are to

a high degree information

efficient and that active asset

management is not able to add

value (alpha) after costs are

taken into account

«Yale»- vs. « Norway » -Model

4

1) As at the end of 2013

Source: UBS Global Asset Management, 2013 The Yale Endowment Annual Report, Ministry of Finance, Norway, 04.04.2014

"Yale-Model" "Norway-Model"

21

830

0

100

200

300

400

500

600

700

800

900

YaleEndowment

Norway Govt .Pension Fund

Global (GPFG)

Asse

ts U

SD

bn

0% 20% 40% 60% 80%

Public Eq.

Pr ivat e Eq.

RE

FI

Nat. Res.

M ult . Asset *

Yale Endowmen t

Norw. Govt . PensionGlobal (GPFG)

* Implemented via Absolut Return strategies

Strategic Asset Allocation1) Asset under Management in USD1)

Strong belief in active management

Direct active management efforts to less efficient

asset classes

Rely on "star managers"

Strong equity orientation

Significant allocation to "Alternatives"

Trade liquidity for return

Strong belief in harvesting risk premia

Equity risk premia

Value premia

Small Cap premia

Momentum premia

Low Volatility premia

Liquidity premia

A brief history of risk factors

5


1930 1940 1950 1960 1970 1980 1990 2000 2010 …

Old Finance

Modern Portfolio Theory

"Core Finance"

"New Finance"

MVA: Markowitz (1952)

CAPM: Sharpe (1964)/ Lintner (1965)/

Mossin (1966)/ Treynor (1961,1962)

APT: Ross (1976)

3-F Model: Fama& French 1992

4-F Model: Jegadeesh&Titman (1993)/

Carhart (1997)

F-portfolio: Grinold& Kahn (2000)

n-F Model: Fung&Hsieh (2004)/ Jensen,

Yechiely& Rotenberg (2005) n-factor-model

1

3

4

.

.

.

n

ttMtiitit rrrr ˆˆˆ

ttititMtiitit HMLSMBrrrr ˆˆˆˆˆ

ttitititMtiitit MOMHMLSMBrrrr ˆˆˆˆˆˆ

tniiitMtiitit rrrr ˆˆ...ˆˆˆˆ21

Alpha and beta being clearly separated

6

Source: UBS Global Asset Management, MSCI

Basic question of Modern Portfolio Theory

(since the work of the Nobel Prize winner Harry Markowitz)

still to be answered:

"How is an optimally diversified portfolio constructed?"

up to 70's 80's 90's since 2000

Risk adj.

Alpha

Strategy-

Beta

Factor-

Beta

Factor-

Beta

Sector-

Beta

Country-

Beta

Regional-

Beta

Alpha

(portfolio return

as TR)

Alpha

Alpha

Beta

Broad

Market-

Beta

What is factor based investing?

7

1) cf. Ang (2014), A Systematic Approach to Factor Investing

Source: UBS Global Asset Management, MSCI

Basic concept:

• Ideally, inventors create portfolios using many components (building blocks) with

independent risks that are individually rewarded by the market for their level of risk

• Theoretically, by mixing these components in a portfolios, such portfolios are better

diversified and show more efficient returns (risk adjusted) than traditionally organized

portfolios

What are factors1)

• Factors should have a solid intellectual foundation and strong support in theoretical

and empirical research

• Have exhibited significant premiums that are expected to persist in the future (at least

in the short to mid term) and are systematic by definition – they arise from risk or

behavioral tendencies which will likely persist

• Historical and data evidence (reward the willingness to suffer losses during bad times)

• Be implementable in liquid, traded instruments

Risk vs. return drivers of factor based strategies

8


Low Volatility

Min. Variance

Max.

Diversification

Equal Risk

Contribution

Equal Weight

Fundamental

Index

Intrinsic Value

High Div. Yield

Momentum

Quality

Reduction of

volatility

Increase of

diversification

Enhancement of

returns

Purification of

factor exposures

Risk

drivers

Return

drivers

Factors represent strategy bets away

from the market-cap weighted index

* The strategies are equally weighted combinations of the MSCI Factor

Indexes shown in parentheses. VW = MSCI World Value Weighted Index,

EW = MSCI World Equal Weighted Index, Qual = MSCI World Quality Index,

Mom = MSCI World Momentum Index, MV = MSCI World Minimum Volatility

Index. All are MSCI Factor Indexes based on the MSCI World Index.

Source: MSCI, reurn and risk, June 1988 to June 2013

* * *

Historical performance of selected risk premia

9

Source: Source: UBS Global Asset Management, S&P Dow Jones Indices, Barclays. Data from December 1995 to December 2013. Calculations based on monthly data.

Premia Volatility Inf. Max.

ann. ann. Ratio Drawdown

β(SMB) 2.7% 11.2% 0.24 -42.4%

neg. β(VOL) 1.5% 11.0% 0.14 -44.8%

β(HML) 2.6% 5.7% 0.46 -18.4%

β(MOM) 4.1% 9.2% 0.45 -23.9%

β(QAL) 4.1% 5.5% 0.75 -14.5%

β(CVE) 8.4% 7.9% 1.06 -18.3%

β(HML) 6.9% 12.6% 0.55 -29.3%

β(MOM) 9.0% 10.7% 0.84 -16.8%

β(CRD) 2.2% 11.1% 0.20 -43.8%

β(TRM) 2.6% 11.2% 0.24 -22.4%

Premia*

Eq.

Comm.

FI

0

100

200

300

400

500

600

Jun

-90

Jun

-91

Jun

-92

Jun

-93

Jun

-94

Jun

-95

Jun

-96

Jun

-97

Jun

-98

Jun

-99

Jun

-00

Jun

-01

Jun

-02

Jun

-03

Jun

-04

Jun

-05

Jun

-06

Jun

-07

Jun

-08

Jun

-09

Jun

-10

Jun

-11

Jun

-12

Jun

-13

Global Fama-French Factors

Low Size (SMB) Value (HML) Momentum (WML)

0

50

100

150

200

250

Barra Global Equity Model (GEM2) Factors

World factor Momentum factor

Negative Volatility factor Value factor

Negative Size Factor + SizeNonlinearity Factor

* In some cases, factor returns have been combined or the sign has been reversed so the magnitudes of the premia are visually comparable. For factor researchers, the difference between the Fama-

French and Barra approaches is not trivial. Fama-French factors are found through a process of sorting and bucketing stocks to build factor-mimicking portfolios while Barra factors are found through cross-

sectional multivariate regression. Menchero (2010) provides a good discussion of the differences in factor estimation methodologies.

Source: MSCI, for Fama-French's cumulative factor returns, data range from June 1990 to August 2013. For the Barra Global Equity Model, data range from December 1996 to December 2013.

* SMB = Small Cap,

VOL=Volatility,

HML=Value,

MOM=Momentum,

QAL=Quality, CVE=

Curve, CRD= Credit,

TRM=Term

* *

Different objectives of factor based strategies

10

Smart (Alternative) Eq. Beta Strategies Objective Strategy Biggest Factor Exposures

Equal-Weighted

Equal Risk Contribution

Divesity-Weighted

Fundamentally Weighted

Dividend Weighted

Momentum Tilt Momentum factor tilt Momentum strategy β(MOM)

Minimum-Variance

Non-Optimized Low VolatilityReduce portfolio volatility Low-volatility strategy** β(MKT), neg. β(VOL)

Diversification strategyReduce idiosyncratic risks β(SMB), β(LIQ), β(HML)

Value factor tilt Value strategy* β(HML), β(SMB)

* Value strategies:

Stock selection:

FTSE RAFI Index: The companies with the largest RAFI fundamental

values from the all-cap stock universe

MACI Value-Weighted Index: All constituents of the relevant MSCI

standard index (large- and mid-caps)

S&P Pure Value Index: Value companies from the relevant S&P parent

index

Weighting:

FTSE RAFI Index: Sales, Cash Flow, Book Value, and Dividends

MACI Value-Weighted Index: Sales, Cash Flow, Book Value, and Earnings

S&P Pure Value Index: Value Score derived from three value factors:

Book Value to Price, Earnings to Price, and Sales to Price

** Low-volatility strategies:

Portfolio construction:

Min-Variance: Mean-variance optimization assuming same expected

returns for all stocks

Non-Opt. Low Volatility: Low volatility stocks weighted by the inverse of

their historical volatilities

Strategy inputs:

Min-Variance: Volatilities and correlations (typically estimated using multi-

factor risk model)

Non-Opt. Low Volatility: Historical volatilities

Portfolio constraints:

Min-Variance: Typically impose various optimization constraints, e.g. short-

selling restrictions

Non-Opt. Low Volatility: Typically none

Complexity:

Min-Variance: More complex, due to the use of optimization and risk model

Non-Opt. Low Volatility: Low

Portfolio Volatility:

Min-Variance: 20-30% volatility reduction

Non-Opt. Low Volatility: 20-30% volatility reduction

Source: S&P Dow Jones Indices 2012

Comparing factor based strategies

11

Source: S&P Dow Jones Indices 2012

Note: For the three "diversification strategies" the analysis goes back to January 31, 2006 as this is the earliest date where index performance data is available for all examined indices. Data include S&P

Dow Jones Indices, FTSE, Bloomberg. Data from January 31, 2006 to October 31, 2011.The Equal-Weight Strategy is represented by S&P 500 Equal Weight Index; the Diversity-Weighted Strategy

is simulated using the S&P 500 stock universe, following the methodology in Fernholz, Garvy, and Hannon (1998) and a parameter ρ of 0.76; the Equal Risk Contribution Strategy is represented by

Lyxor SmartIX ERC USA Equity Index. Some of the data reflected in this chart may reflect hypothetical historical performance.

For the three "value strategies" S&P Dow Jones Indices, FTSE, MSCI. Data from June 30, 1995 to October 31, 2011. The analysis starts from June 30, 1995, as this is the earliest date for which

performance data is available for all the examined indices. The Fundamental Index Strategy is represented by FTSE RAFI US 1000 Index, the Value Weighted Strategy is represented by MSCI

USA Value Weighted Index, and the Pure-Value Strategy is represented by S&P 500 Pure Value Index. Some of the S&P 500 Pure Value Index data reflected in this chart may reflect hypothetical

historical performance

For the two "low volatility strategies" data includes S&P Dow Jones Indices, MSCI. Data from December 31, 1998 to October 31, 2011. The analysis goes back to December 31, 1998 as this is the

earliest date where index performance data is available for all examined indices. The Non-Optimized Low Volatility Strategy is represented by the S&P 500 Low Volatility Index and the Minimum

Variance Strategy is represented by the MSCI USA Minimum Volatility Index. Some of the S&P 500 Low Volatility Index data reflected in this chart may reflect hypothetical historical performance.

Alphas are geometrically averaged and annualized figures. A P-Value of below 5% or 1% corresponds respectively to statistical significance at 5% or 1% level.

Smart Beta Strategies Index Name Sharpe Information Alpha p-value

Ratio Ratio ann. (alpha)

Equal-Weighted S/&P 500 Equal Weight Index 0.08 0.31 1.40% 13%

Diversity-Weighted Simulation (Fernholz, Farvy, Hannon 1998) 0.03 0.49 0.44% 10%

Equal Risk Contribution SmartIX ERC USA Equity Index 0.08 0.58 0.76% 40%

Fundamental Index Strategy FTSE RAFI US 1000 Index 0.41 0.47 1.28% 14%

Value Weighted Strategy MSCI USA Value Weighted Index 0.29 0.14 -0.03% 95%

Pure Value Strategy S&P 500 Pure Value Index 2.40 0.18 -1.31% 51%

Non-Optimized Low Volatility S&P 500 Low Volatility Index 0.36 0.42 3.01% 7%

Minimum-Variance Strategy MSCI USA Minimum Volatility Index 0.12 0.28 1.06% 30%

Equity factors reflected via factor (smart beta)-indices

12


* MSCI: Foundation of Factor Investing, Dezember 2013

• Factor Investing – investment in a portfolio of securities grouped together in view of

pre-defined characteristic (fundamental and/or statistical)

• Based on extensive academic work, MSCI puts together six Systematic Factors*

Value A stock that tends to trade at a lower price relative to its fundamentals (i.e. earnings,

sales, book value, etc.) and thus considered undervalued by a value investor

Quality

Quality stock is identified based on a set of clearly defined fundamental criteria that

seek to identify companies with outstanding quality characteristics (typically defined

through balance sheets checks)

Momentum Momentum stock assumes to capitalize on the continuance of currently existing

upward trend in its market valuation

Volatility The main goal is to devolatilize the portfolio and provide a tilt towards lower risk

stocks (as measured by amount of variation in a market valuation around average)

Size

Different market capitalization segments perform in a different way across the

business cycle, and smaller segments have typically more variation in asset prices but

also upside potential in good times

Yield Yield factor aims to capture excess return of stock due to higher-than-average dividend

yield, high dividend income adds to total on top of capital appreciation

All systematic factors are cyclical

13

Source: UBS Global Asset Management, MSCI. Cumulative relative returns. Data from June 1988 to June 2013. The data show that each of the factor indexes has experienced at a minimum a

consecutive two-to-three year period of underperformance. Some factors historically have undergone even longer periods; the Small Cap or Low Size factor (captured by the MSCI World

Equal Weighted Index) went through a six-year period of underperformance in the 1990s

• Systematic risk

factors

• Strategy specific

risks

• Relative

performance risk

(vs. market-cap

weighted

benchmark)

Factor strategies

are exposed to the

three risk sources:

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1994-05-31 2000-05-31 2006-05-31 2012-05-31

EqW_MinVol

EqW_Value

EqW_RW

EqW_Qual

EqW_HDiv

MinVol_Value

MinVol_RW

MinVol_Qual

MinVol_HDiv

RW_Qual

RW_HDiv

Relative risk of factor (smart) beta strategies

14

* The table summarizes the maximum relative drawdown numbers with respect to the S&P 500 Index. Maximum relative drawdown is the maximum drawdown of the long-short index whose return is

given by the fractional change in the ratio of strategy index to the benchmark index. Daily total return data for the period 23 December 2002 to 31 December 2012 has been used for the analysis as this

is the earliest date since data for all indices are available.

** Returns data has been downloaded from Datastream

*** Scientific Beta USA Flagship Indices, data has been downloaded from www.scientificbeta.com.

Source: EDHEC 2013, Daily total return data for the period 23 December 2002 to 31 December 2012 has been used for the analysis as this is the earliest date since data for all indices are available.

Index Provider Smart Beta Indices Max. Rel.

Drawdown*

Recovery Time

(days)

FTSE RAFI U.S. 1000 Index 12.71% 439

FTSE EDHEC Risk Efficient U.S. Index 8.72% 46

MSCI USA Minimum Volatility Index 12.82% 371

S&P 500 Equal Weight Index 13.72% 453

High Liquidity Max Decorrelation 14.27% 104

High Liquidity Max Deconcentration 15.53% 110

High Liquidity Efficient Max Sharpe 10.14% 103

High Liquidity Efficient Min Volatility 6.17% 507

High Liquidity Diversified Risk Parity 8.81% 108

Low Max Deconcentration 7.75% 222

Value Max Deconcentration 14.44% 44

Growth Max Deconcentration 17.84% 109

High Dividend Yield Max Deconcentration 11.55% 122

Traditional**

Specialist***

http://www.scientificbeta.com/

15




• What are some of the implementation challenges with factor-based portfolios?

16

Tilt towards low volatility

0.00%

0.20%

0.40%

0.60%

0.80%

1.00%

10% 20% 30% 40% 50% 60% 70% 80%

Ind

ex

We

igh

t

Volatility

Inverse Volatility Weighting

MSCI ACWI MSCI ACWI Risk WeightedSource: Bloomberg, MSCI, UBS Global Asset Management. Data as of October 2014, volatility calculation is based on May 2014 rebalancing

Past performance of investments is not necessarily an indicator of future results.

Volatility

+ -

Investable risk weighted factor strategies

17

Strategy Ann. Return Ann. Volatility* Max Draw dow n Sharpe Ratio"" Beta # Stocks

ACWI 4.7 16.6 58.4 0.24 1.0 2449

ACWI Risk Weighted 9.7 16.1 55.2 0.55 0.9 2449

ACWI Minimum Volatility (USD) 7.6 11.2 43.4 0.55 0.6 336

Source: MSCI, UBS Global Asset Management, as of 30 September 2014

* 2 Based on monthly net returns data; * * Based on BBA LIBOR 1M

0.00%

0.10%

0.20%

0.30%

0.40%

0.50%

0.60%

0.70%

0.80%

10% 20% 30% 40% 50% 60% 70% 80%

Ind

ex

We

igh

t

Volat ilityACWI ACWI Risk Weighted

Method Inputs

Construct a portfolio by

outweighing lower volatility

securities.

Computation of individual volatilities

(standard deviations).

Construct a portfolio by

minimizing the total volatility of

the portfolio (mean-variance).

Computation of individual volatilities as

well as all pair-wise correlations.

Construct a portfolio by targeting

the total volatility of the portfolio.

Computation of signal for

(de)leveraging equity exposure and

going cash; and vice versa.

Determination of leverage.

0

50

100

150

200

250

300

350

400

Dec-00 Dec-01 Dec-02 Dec-03 Dec-04 Dec-05 Dec-06 Dec-07 Dec-08 Dec-09 Dec-10 Dec-11 Dec-12 Dec-13

MSCI ACWI MSCI ACWI Risk Weighted MSCI ACWI Minimum Volatility (USD) MSCI ACWI Risk Control 15%

* Based on monthly returns

"" Based on BBA LIBOR 1M

Source: MSCI, UBS Global Asset Management, data as of Oktober 2014, MSCI, MSCI ACWI Risk Control 15% is constructed combining 95% of the MSCI World Risk Control 15% with 5% of the

MSCI EM Risk Control 15%.

18

Low volatility does not equal low drawdown

Paradox: "Inverse Volatility Risk Premium"

-60%

-45%

-30%

-15%

0%0

100

200

300

400

Dec-00 Dec-02 Dec-04 Dec-06 Dec-08 Dec-10 Dec-12

Drawdown

Performance

Drawdown MSCI ACWI (Net, USD) Drawdown MSCI ACWI Risk Weighted (Net, USD)

MSCI ACWI (Net, USD) MSCI ACWI Risk Weighted (Net, USD)

189

364

Source: Bloomberg, MSCI, UBS Global Asset Management


19

Low volatility factor: Sources of performance

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

Jun-99 Jun-01 Jun-03 Jun-05 Jun-07 Jun-09 Jun-11 Jun-13

Region exposure

EMERGING MARKETS EUROPE & MIDDLE EAST

NORTH AMERICA PACIFIC

-20%

-15%

-10%

-5%

0%

5%

10%

15%

Sector exposure

Current Max Min Avg-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8Factor exposure

Current Max Min Avg

0%

2%

4%

6%

8%

10%

12%

14%

16%

Jun-99 Jun-01 Jun-03 Jun-05 Jun-07 Jun-09 Jun-11 Jun-13

Top 10 security weight (%)

MSCI ACWI Index MSCI ACWI Risk Weighted Index

Source: Bloomberg, MSCI, UBS Global Asset Management


20




• What are some of the implementation challenges with factor-based

portfolios?

Combining factor (smart) beta exposures

21

* Calculated as Rsmart – Rcore

** Calculated as TE, annualized

Source: S&P Dow Jones Indices, FTSE, AQR Capital Management LLC. Data from December 31, 1990 to October 31, 2011. The Fundamentally Weighted Strategy is represented by MSCI USA Value

Weighted Index; the Equal-Weighted Strategy is represented by S&P 500 Equal Weight Index, the Low-Volatility Strategy is represented by S&P 500 Low Volatility Index, and the Momentum

Strategy is represented by AQR US Large Cap Momentum Index. The Alternative Beta Composite Strategy is simulated by equally weighting the four alternative beta strategies on a monthly

basis. Some of the S&P 500 Equal Weight Index and S&P 500 Low Volatility Index data reflected in this table may reflect hypothetical historical performance.

Total Total Sharpe Active Active Inf.

Return Risk Ratio Return Risk Ratio

Core Strategy

US Equity 8.80% 15.10% 0.35 - - -

Smart Beta Strategies

Fundamentally Weighted 9.60% 15.60% 0.40 0.80% 4.00% 0.20

Equal-Weighted 11.30% 16.90% 0.47 2.50% 5.70% 0.44

Low-Volatility 10.20% 11.40% 0.60 1.40% 9.90% 0.14

Momentum 10.60% 18.20% 0.39 1.70% 9.10% 0.19

Eq-weighted Portfolio

Smart Beta Composite 10.70% 14.20% 0.51 1.90% 3.40% 0.55

Factors β(MKT) (SMB) β(HML) β(MOM) β(VOL)

β(MKT) 1 0.25 -0.16 -0.16 0.65

(SMB) 1 -0.2 -0.06 0.66

β(HML) 1 -0.59 -0.38

β(MOM) 1 -0.11

β(VOL) 1Implementation:

Multi-factor strategies ≠

combination of single

factor strategies

Avoid unintended factor

exposure or overlaps

Crossing of trades

during rebalancing may

reduce turnover

* **

Challenges with regards to orthogonality

22

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

1995-03-06 2001-03-06 2007-03-06 2013-03-06

EqW_MinVol

EqW_Value

EqW_RW

EqW_Qual

EqW_HDiv

MinVol_Value

MinVol_RW

MinVol_Qual

MinVol_HDiv

RW_Qual

RW_HDiv

Source: MSCI, UBS Global Asset Management, data from May 1994 to May 2014, daily return data, pairwise correlations are calculated using rolling 200 day windows


Challenges in portfolio construction

23

Global Bonds40%

Equity USA40%

Equity World ex USA20%

Dev Eq Risk10%

Value10%

Size10%

EM Eq Risk10%

HY Spread10%

Default10%

Duration10%

Real Rates10%

Inflation10%

Volatility10%

Historical Risk/ Return Statistics Traditional 60/40 Eq Weight Factor Portf.

Return p.a. 2.52% 6.74%

Volatility p.a. 13.75% 6.84%

Variance p.a. 189.18 46.74

Return p.a. 6.01% 5.97%


Variance p.a. 124.06 33.48

Return p.a. 5.98% 4.75%


Variance p.a. 118.83 33.77

5 yrs

10 yrs

15 yrs

Traditional (60/40) Equal Weights

Source: Podkaminer (2013), data based on MSCI Barclays, CBOE, periods ending as of 31 December 2011, factor returns based on 60, 120, and 180 monthly data

MVO vs. Equal Weights and Traditional

24

Size3%

EM Eq Risk19%

Duration6%

Real Rates68%

Volatility4%

Dev Eq Risk3% Size

15%

EM Eq Risk30%

Duration2%

Real Rates45%

Volatility3%

Dev Eq Risk15%

Real Rates76%

Volatility8%

5 yrs Historical Data 10 yrs Historical Data

15 yrs Historical Data Historical Risk/ Return Statistics MVO Factor Portf. Eq Weight Factor Portf. Traditional 60/40

Return p.a. 9.10% 6.74% 2.52%

Volatility p.a. 6.84% 6.84% 13.75%

Variance p.a. 46.79 46.74 189.18

Return p.a. 8.86% 5.97% 6.01%

Volatility p.a. 5.79% 5.79% 11.14%

Variance p.a. 33.52 33.48 124.06

Return p.a. 7.57% 4.75% 5.98%

Volatility p.a. 5.81% 5.81% 10.90%

Variance p.a. 33.76 33.77 118.83

5 yrs

10 yrs

15 yrs

Source: Podkaminer (2013), data based on MSCI Barclays, CBOE, periods ending as of 31 December 2011, factor returns based on 60, 120, and 180 monthly data

Concluding remarks

25

• According to the academic literature, higher average returns can be expected for the

passive portfolio over the long run with very little strategy risk

• Ultimately, investing in factor (smart) beta springs from the same logic and the same

approach as choosing an active manager

• Many implementation challenges for factor (smart) beta returns due to:

• High uncertainty in expected returns

• No consensus on the proper way of weighting the single risk premia

• Complex portfolio construction with shorting and leverage

• High transaction costs due to complex and regular rebalancing

• Factor based indices are useful tools in asset allocation and portfolio construction:

simplicity, transparency should be key criteria for evaluating smart beta and risk premia

benchmarks

Disclaimer

26

For marketing and information purposes by UBS. For qualified investors only. The information and opinions contained in this document have been compiled or

arrived at based upon information obtained from sources believed to be reliable and in good faith, but is not guaranteed as being accurate, nor is it a complete

statement or summary of the securities, markets or developments referred to in the document. UBS AG and / or other members of the UBS Group may have a

position in and may make a purchase and / or sale of any of the securities or other financial instruments mentioned in this document.

UBS funds under Swiss, Luxembourg and Irish law.

Before investing in a product please read the latest prospectus carefully and thoroughly. Units of UBS funds mentioned herein may not be eligible for sale in all

jurisdictions or to certain categories of investors and may not offered, sold or delivered in the United States. The information mentioned herein is not intended to be

construed as a solicitation or an offer to buy or sell any securities or related financial instruments. Past performance is not a reliable indicator of future results. The

performance shown does not take account of any commissions and costs charged when subscribing to and redeeming units. Commissions and costs have a

negative impact on performance. If the currency of a financial product or financial service is different from your reference currency, the return can increase or

decrease as a result of currency fluctuations. This information pays no regard to the specific or future investment objectives, financial or tax situation or particular

needs of any specific recipient. The details and opinions contained in this document are provided by UBS without any guarantee or warranty and are for the

recipient's personal use and information purposes only. This document may not be reproduced, redistributed or republished for any purpose without the written

permission of UBS AG.

This document contains statements that constitute “forward-looking statements”, including, but not limited to, statements relating to our future business development.

While these forward-looking statements represent our judgments and future expectations concerning the development of our business, a number of risks,

uncertainties and other important factors could cause actual developments and results to differ materially from our expectations.

Representative in Switzerland for UBS funds established under foreign law: UBS Fund Management (Switzerland) AG, P.O. Box, CH-4002 Basel. Paying agent:

UBS AG. Prospectuses, simplified prospectuses or Key investor information, the articles of association or the management regulations as well as annual and semi-

annual reports of UBS funds are available free of charge from UBS AG, P.O. Box, CH-4002 Basel or from UBS Fund Management (Switzerland) AG, P.O. Box, CH-

4002 Basel.

Source for all data and charts (if not indicated otherwise): UBS Global Asset Management

© UBS 2014. The key symbol and UBS are among the registered and unregistered trademarks of UBS. All rights reserved. E33

Risk Factor Allocation: A New Investment Paradigm? · PDF file24/11/2014 · Head...

Documents

Transcript of Risk Factor Allocation: A New Investment Paradigm? · PDF file24/11/2014 · Head...