risk factor allocation: a new investment paradigm? · pdf file24/11/2014 · head...
TRANSCRIPT
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
Source: UBS Global Asset Management
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
Source: UBS Global Asset Management
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
Source: UBS Global Asset Management
* 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***
15
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?
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
Past performance of investments is not necessarily an indicator of future results.
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
Past performance of investments is not necessarily an indicator of future results.
20
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?
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
Past performance of investments is not necessarily an indicator of future results.
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%
Volatility p.a. 11.14% 5.79%
Variance p.a. 124.06 33.48
Return p.a. 5.98% 4.75%
Volatility p.a. 10.90% 5.81%
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.
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