Download smart beta in the limelight

WhiteCapability
MFS
Paper Series
Focus
Month
September
20122016
®
Authors
SMART BETA IN THE LIMELIGHT
Factor construction framework and market update
IN BRIEF
Noah C. Rumpf
Director of Quantitative
Equity Research
Christopher C. Callahan
Regional Head
North American Institutional
R. Dino Davis, CFA
Institutional Portfolio Manager
• Adoption and sophistication increases — Investors are increasingly
incorporating factor-based strategies into their portfolio mix and becoming
more sophisticated — e.g., by employing multiple smart beta strategies and
expressing interest in multifactor applications — in part because single
factors have had periods of underperformance.
• Building factor-based portfolios — Combination factor portfolios
usually outperform single factors. The simple combination of factor-based
strategies in the equity market, i.e., an asset allocation approach on a topdown sector or portfolio basis, can add value by reducing portfolio volatility
and increasing average returns. Combining those factors cross-sectionally,
i.e., building the portfolio from the bottom up, creates portfolios of stocks
that score well on multiple factors, further improves portfolio performance
relative to the asset allocation approach.
• Optimized factor combinations — Some form of optimization can
provide additional value when factors are combined, again primarily through
volatility reduction. Here also, cross-sectional combinations (bottom-up) of
factors have provided higher return with lower volatility than asset allocation
(top-down) type approaches.
• Active management — Smart beta strategies can be combined, or
better yet, based on a signal composed of several factors. As they progress
along this spectrum, they begin to look more like quantitatively based active
investment strategies that may have a more developed underlying approach
and proven track records.
Asset owners are increasingly using smart beta strategies,
also known as factors or factor-based strategies, in their equity
investment portfolios. In doing so, they are looking beyond asset
classes to examine factors — the broad, persistent drivers of risk
SEPTEMBER 2016 / SMART BETA IN THE LIMELIGHT
and return that underlie all assets — and
determine how best to capture the risk premia
associated with them.1
These last two return-enhancing steps, combining factors in
the cross section and via optimization-based methods, begin to
blur the line between smart beta and quantitative investing, in
which well-developed investment processes and long track
records are the norm.
In thinking about factors such as value, quality and size, one
might consider the following analogy: In the same way that
protein, fat and carbohydrates are the underlying constituents
of a hamburger and fries, factors are the building blocks of
asset classes. Even if we are focused on the protein and
carbohydrates in the hamburger and fries (the asset class), the
fat (the factor) is still part of the meal. Factors are part and
parcel of what drives risk and returns in investment strategies,
whether they are explicitly identified or not.
To provide some context before delving into the key
points mentioned above, we have included some data
on the current adoption of equity factor strategies among
institutional investors.
Factors: State of play
Investors in securities essentially take two forms of risk:
systematic factor risk and idiosyncratic security-specific risk.
It is the former that can be isolated and targeted in specific
investment strategies. While some individual factors have
shown persistence and produced positive returns over time,
they have also been shown to have periods of relative
underperformance. This has led to an interest in understanding
the merits of employing multiple factor strategies.
In this paper, we present research focused on factor portfolios
invested in global equities, and make four key points. The first
point is that factor investing continues to gain momentum.
The second is that multifactor models tend to outperform
single factors over time. The third is that when combining
factors, it is generally more efficient, both in terms of average
return and risk-adjusted return, to do so cross-sectionally
through a multifactor model, rather than via asset allocation.
An asset allocation approach makes portfolio allocations
individually to each of the factors, while a cross-sectional
approach allocates based on an aggregation of factor rankings.
For example, in this case, the cross-sectional forms its portfolio
by ranking the universe by each factor, then combining those
ranks and taking the top 20% of the average rank. The asset
allocation approach forms separate factor portfolios and then
combines the portfolios each month. The fourth is that using
optimization-based tools that weight factors in a composite
based on those factors’ returns, correlations and volatilities can
help improve the investment returns of the composite, mainly
by reducing the volatility of its returns. (See the Methodology
on page 8 for further details.)
Investing in equity factors is gaining traction across the globe
and particularly in Europe. Almost three-quarters (72%) of over
250 asset owners in Europe, North America and Asia surveyed
in a recently published FTSE Russell study indicated they have
implemented or are actively evaluating equity factor strategies,
up from 44% last year and double the percentage reported in
2014 (see Exhibit 1).2 The proportion of asset owners reporting
they have not evaluated factor investing has dropped from
40% in 2014 to 12% in 2016.
Exhibit 1: Usage of factor-based strategies
25%
17%
7%
5%
16%
36%
15%
18%
15%
13%
32%
2014
15%
23%
26%
2015
Do not anticipate evaluating
smart beta in the next 18 months
Anticipate evaluating smart
beta in the next 18 months
Currently evaluating smart beta;
no existing smart beta allocation
Evaluated and decided
not to implement
36%
Have smart beta allocation
2016
Source: FTSE Russell: Smart Beta: 2016 Global Survey Findings from Asset
Owners.
While growth has been flat over the past two years, just under
half of asset owners with $10 billion or more in assets under
management (AUM) have a factor allocation. Growth in factor
adoption has been strongest among asset owners with under
$1 billion in AUM: In 2016, just over a quarter have a factor
allocation, up from 9% in 2014.
—2—
SEPTEMBER 2016 / SMART BETA IN THE LIMELIGHT
Europe continues to lead North America and Asia in adopting
equity factor strategies (see Exhibit 2). Among adopters, the
share of equity portfolio AUM devoted to factor strategies has
increased: Those with over 20% of assets in factor strategies
has grown from 18% in 2014 to 39% in 2016 (see Exhibit 3).
Exhibit 4: Number of factor strategies used
21%
12%
14%
Exhibit 2: Factor adoption by region
2015
2014
2016
22%
5 or more
4
3
2
1
31%
52%
40%
40%
Europe North
America
38%
33%
Source: FTSE Russell: Smart Beta: 2016 Global Survey Findings from Asset
Owners.
28%
21%
24%
2016
Europe North Asia
America Pacific
Europe North Asia
America Pacific
Source: FTSE Russell: Smart Beta: 2016 Global Survey Findings from Asset
Owners.
Having established the trend toward greater use and
sophistication vis-à-vis smart beta investing, we delve into
our first two points: 1) combination factor portfolios usually
outperform single factors; and 2) combining factors in the cross
section has historically been more effective than combining
factors through asset allocation.
Exhibit 3: Proportion of equity portfolio invested
in factors
Technical definitions
18%
20%
11%
9%
22%
22%
13%
Over 20%
14%
6%
22%
19%
40%
2014
Factors — Broad, persistent drivers of risk and return that
underlie all assets.
39%
24%
22%
2015
2016
Smart beta — Typically, rules-based strategies that screen a
broad group of securities to target one or more factors in an
attempt to deliver higher returns or reduced risk relative to a
traditional index.
16%–20%
11%–15%
6%–10%
0%–5%
Asset allocation approach — In this context, an asset
allocation approach makes portfolio allocations individually to
each of the factors on a top-down equal-weighted basis or
based on some other fixed weighting schema.
Cross-sectional approach — Adopts a security-level
bottom-up approach and makes portfolio allocations based
on an aggregation of factor rankings.
Source: FTSE Russell: Smart Beta: 2016 Global Survey Findings from Asset
Owners.
There is a diversity of opinion and practices in factor investing.
Among institutional investors employing equity factor-based
strategies, more than two-thirds are using two or more factor
strategies (see Exhibit 4). However, usage is dispersed with fat
tails: 31% use a single strategy, while 21% use five or more.3
On average, four strategies are being evaluated by asset
owners (among both users’ factor strategies and those who
do not use them).4
Portfolio optimization — A formal mathematical approach
to making investment decisions across a collection of financial
instruments or assets in such a way as to make the portfolio
better than any other according to some criterion.
—3—
SEPTEMBER 2016 / SMART BETA IN THE LIMELIGHT
Combining smart beta strategies
Exhibit 5: Smart beta strategy returns
To examine the merits of combining factors, we looked at the
historical returns of six common smart beta strategies, as well
as the best possible equal-weighted combinations of those
strategies.5 The strategies are: high price momentum
(momentum), low volatility, high return on equity (ROE), low
price-to-book or high book-to-price (B/P), high dividend yield
(yield) and low price-to-earnings or high earnings yield (E/P).
Mean return/ Monthly return
Ann.
Month
volatility
Sharpe Ratio
Factor
We represent these smart beta strategies using the MSCI World
Universe.6 For each strategy, at each month-end, beginning
December 31, 1997,7 we ranked all the stocks in the universe
by the factor in question and then formed an equal-weighted
portfolio from the top 20% of stocks. We held this portfolio
for a month, observed its return, and then rebalanced. For
more on the details of this process, please refer to the
Methodology section at the end of the paper.
Exhibit 5 shows that these strategies have had average returns
of between 79 bps/month (low volatility) and 100 bps per
month (E/P). The volatility of monthly returns has varied
considerably, ranging from 3.32% to 6.47%, the standard
deviation calculated for the low volatility and book-to-price
(B/P) portfolios, respectively.
Momentum
0.88
4.82
0.15
Low Volatility
0.79
3.32
0.11
ROE
0.85
4.99
0.12
B/P
0.97
6.47
0.16
Yield
0.97
5.69
0.18
E/P
1.00
6.13
0.19
Source: MSCI World universe, 31 December 1997–31 December 2015; percent
returns to top-quintile portfolios, rebalanced monthly.
To illustrate the benefits of combining strategies, in Exhibit 6
below we summarize the monthly returns to combinations of
different numbers of these smart beta strategies, starting with
a combination of two strategies, then looking at three
strategies, and so forth.
For each number of factors used, we chose the equal-weighted
combination of factors with the highest ratio of mean monthly
return to volatility of return. For example, of the 15 possible
combinations of two of the six factors, the one with the best
average return relative to volatility of return was momentum
plus low volatility.
Exhibit 6 – Combining factors
Equal-weighted combination of factors
Best two factors
Best three factors
Best four factors
Best five factors
Six factors
Momentum+Low Volatility XS
Mean return/Month
Monthly return
volatility
Ann. Sharpe Ratio
0.93
3.49
0.25
Momentum+Low Volatility AA
0.84
3.85
0.14
Momentum+ROE+Low Volatility XS
0.99
3.73
0.29
Momentum+Yield+Low Volatility AA
0.88
4.31
0.16
Momentum+B/P+ROE+Low Volatility XS
0.98
3.72
0.28
Momentum+Yield+ROE+Low Volatility AA
0.87
4.44
0.15
Momentum+B/P+ROE+Yield+Low Volatility XS
0.99
4.02
0.27
Momentum+E/P+ROE+Yield+Low Volatility AA
0.90
4.74
0.16
All Factors Equal Wtd XS
0.98
4.49
0.24
All Factors Equal Wtd AA
0.91
4.96
0.16
XS = cross-sectional combination of factors, i.e., a multifactor model that builds a portfolio via bottom-up stock selection.
AA = an asset allocation type combination of factors or time series approach that builds a portfolio via a top-down factor approach.
Source: MSCI World universe, 31 December 1997–31 December 2015; one-month forward returns to top-quintile portfolios, rebalanced monthly; individual stock returns
capped at +/-75%; all returns in percent.
—4—
SEPTEMBER 2016 / SMART BETA IN THE LIMELIGHT
We show returns to two types of factor combinations. The
first, labeled “XS” for cross-sectional (bottom-up), forms
its portfolio by ranking the universe of individual stocks by
each factor and then combining those ranks and taking the
top 20% of the average rank. The second, labeled “AA” for
asset allocation (top-down), forms separate factor portfolios
and then combines the portfolios each month. For each
approach to factor combination, we chose the set of factors
with the best mean return to volatility ratio, i.e., the best XS
and AA combination.
Factor correlation structure
We can make a few statements about the results in Exhibits 5
and 6:
These correlations, along with information about factors’
average returns and their return volatility, can be important
inputs in deciding how to weight factors in a composite model,
or how to weight factor portfolios in a combined portfolio.
Exhibits 7 and 8 below summarize the time series and average
cross-sectional correlations of these factors.
The six factors shown in Exhibit 5 are not independent. For
example, book-to-price and earnings-to-price have historically
been positively correlated: Stocks that tend to trade cheap
relative to their book value also tend to trade cheap to their
earnings. Both factors tend to be negatively correlated with
price momentum, since stocks that have been outperforming
their peers are often more expensive on a valuation basis than
those peers. It is also true that over time different portfolios
designed to capture these factors will exhibit different behavior.
1) Combination factor portfolios outperform single
factors — Investing in a combination of factors yields an
improved return profile, relative to single factors, due to
diversification. For example, the asset allocation combination
of momentum and low volatility has returned, on average,
84 bps per month, the average of the two individual
strategies’ returns of 79 and 88 bps per month. However,
the volatility of the combined portfolio is 3.85%, which
is less than the average of the two strategies’ volatility,
i.e., 4.07%.
Exhibit 7: Average cross-sectional factor correlations
over time*
Momentum
ROE
B/P
Yield
E/P
0.10
0.14
-0.28
-0.11
-0.09
0.20
-0.08
0.29
0.18
-0.56
0.11
0.53
0.21
0.19
Low Volatility
2) Bottom-up/cross-sectional outperformed top-down/
asset allocation — For each number of factors combined,
a multifactor model or cross-sectional combination of factors
has been more effective than an asset allocation approach.
In each multifactor combination, the XS row has a higher
mean return and higher return per unit of risk than the
AA row.8
ROE
B/P
Yield
0.37
Source: MSCI World universe, 31 December 1997–31 December 2015.
*Factor correlations are estimated from pooled cross-sectional data, i.e.,
monthly cross sections of MSCI World data are aggregated and correlations
are estimated from that aggregate’s dataset.
3) No single number of factors outperform — While
there are clear benefits to combining multiple strategies,
there is no single number of factors that is clearly more
beneficial than the others (at least among these six factors).
For example, the best three-factor model’s top quintile
portfolio has about the same mean return and return-to-risk
ratio as the best five-factor model. This is partly because
these factor combinations are equal-weighted and do not
take advantage of factor correlation structure.
The exhibits illustrate the value added over time of making
simple combinations of smart beta strategies, particularly at
the security level (as opposed to the factor portfolio level). The
next section of the paper looks at the benefit of accounting for
relationships between factors in such combinations.
Low
Volatility
Exhibit 8: Time series correlations of top quintile less
universe portfolio returns**
Momentum
Low Volatility
ROE
B/P
Yield
Low
Volatility
ROE
B/P
Yield
E/P
0.29
0.32
-0.68
-0.44
-0.49
0.14
-0.44
-0.01
-0.33
-0.55
0.01
0.14
0.50
0.59
0.78
Source: MSCI World universe, 31 December 1997–31 December 2015.
**Time series correlations are the correlations of monthly returns to a portfolio
formed from the top 20% of stocks, scored by the factor in question, minus
the return of the equally-weighted universe portfolio.
—5—
SEPTEMBER 2016 / SMART BETA IN THE LIMELIGHT
Exhibit 7 confirms that book/price and earnings/price have
historically been positively correlated, i.e., stocks that trade
cheap to book have also traded cheap to earnings. Both factors
are also positively correlated with dividend yield. Momentum is
negatively correlated with all three factors, reflecting the fact
that high-momentum stocks are often not value stocks. One
can observe other relationships as well. For example, the fact
that historically book/price has been negatively correlated with
high return on equity (ROE).
The time series return correlations in Exhibit 8 show that these
factors often have return dynamics similar to their factor
correlations, as one might expect. Momentum, for example, is
negatively correlated with book-to-price, dividend yield and
earnings-to-price, indicating that the momentum factor has
outperformed when value factors have underperformed and
vice versa.
Value of optimization
These correlation structures, combined with the differing
factor signal strengths and return volatilities shown in Exhibit 5,
indicate that it is possible to improve on the average and
risk-adjusted returns of the equally weighted models
shown in Exhibit 6. We illustrate this in Exhibit 9 below,
which summarizes the returns to equal-weighted and
optimized asset allocation and cross-sectional models. To give
the optimization methodologies the most opportunity to add
value, we focus on models that combine all six factors.
Exhibit 9: Equal-weighted and optimized model returns
Mean return/
Month
Monthly
return
volatility
Ann. Sharpe
Ratio
All factors optimized XS
1.01
4.02
0.28
All factors optimized AA
0.88
4.29
0.24
All factors equal-wtd XS
0.98
4.49
0.16
All factors equal-wtd AA
0.91
4.96
0.16
Model
volatility of those returns, and their return correlation. All else
being equal, it will allocate more weight to factors with better
signal strength, lower return volatility and returns that are less
correlated with other factors.
In the case of the optimized cross-sectional model, we use panel
data regression. This approach sets factor weights based on
each factor’s historical signal strength and its cross-sectional
correlation with other factors. All else being equal, it will allocate
more weight to factors with better signal strength and lower
correlation with other factors. Please refer to the Methodology
at the end of the paper for further details on these techniques.
We chose these two methods to indicate the potential value
added of optimization because each uses the information
readily available to those employing both the cross-sectional,
multifactor modeling approach and the asset allocation, time
series approach. When building a portfolio based on a
multifactor ranking of characteristics, two key pieces of
available data are the historical signal strength of the factors
and their cross-sectional correlations over time. When building
a time series model for allocating to factor portfolios, key data
available are the average returns of each factor, their volatilities
and return correlations. While we use different optimization
methodologies for the cross sectional and asset allocation
approaches, our results are broadly the same if we apply the
same set of optimized weights (for example the mean-variance
weights) to the asset allocation and cross sectional approaches.
It should be noted that the optimized results are in-sample and
thus reflect the upper bound of the potential value added by
optimizing weights.9
In both the cross-sectional and time series cases, optimized
models achieve better risk adjusted returns than their equal
weighted counterparts, primarily by reducing return volatility.
This reflects the benefits of combining factors with an eye to
their correlations and volatilities. The optimized cross-sectional
model outperforms both its equal-weighted peer and both
time series models, primarily by virtue of combining factors at
the security, rather than the portfolio, level.
XS = cross-sectional combination of factors (a multifactor model)
AA = an asset allocation type combination of factors or time
series approach
Source: MSCI World universe, 31 December 1997–31 December 2015.
In the case of the optimized asset allocation model, we use
mean-variance optimization. This approach allocates weight to
factor portfolios based on their average historical return, the
—6—
SEPTEMBER 2016 / SMART BETA IN THE LIMELIGHT
$1 invested on 31 December 1997
Exhibit 10: Cumulative total returns to top quintile portfolios
8
All factors optimized XS
7
All factors optimized AA
6
All factors equal-wtd XS
All factors equal-wtd AA
5
4
3
2
1
0
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
Source: MFS research, 31 December 1997–31 December 2015.
XS = cross-sectional combination of factors (a multifactor model)
AA = an asset allocation type combination of factors or time series approach
While the summary table in Exhibit 9 can provide an overview
to differences in model performance, a chart of compounded
returns can show the implication of these differences over
time. Exhibit 10 shows that the cross-sectional portfolios have
had materially larger compounded returns over time. For
example, a $100 million invested at the end of 1997
compounds to $728 million based on the cross-sectional
optimized model, while for the optimized asset allocation
model that value is $542 million, a difference of $186 million.
Given the points made, it is little surprise that multifactor
combination strategies are on many investors’ radar. Just under
half of the respondents in the FTSE Russell 2016 study are
evaluating multifactor smart beta strategies, while close to
40% are evaluating low volatility strategies (see Exhibit 11).
In our view, it can be helpful to complement factor-based
investment approaches with fundamental inputs as a way to
capture qualitative analysis in a way that purely quantitative
methodologies are not always able to do as effectively. Adding
a complementary independent source of information may also
improve risk-adjusted returns by increasing diversification.
Exhibit 11: Factor strategies being evaluated
46%
39%
26%
24%
23%
23%
19%
16%
15%
14%
13%
9%
Multifactor Low volatility
combination
Value
Fundamental Momentum
High quality
Maximum
Minimum
diversification variance
Source: FTSE Russell: Smart Beta: 2016 Global Survey Findings from Asset Owners.
—7—
Dividend/ Equal weight
Income/Yield
Risk parity
Defensive
SEPTEMBER 2016 / SMART BETA IN THE LIMELIGHT
Conclusion
In summary
Our research shows that combination factor portfolios
outperform single factor strategies. Moreover, bottom-up
multifactor models have outperformed a top-down asset
allocation approach. Optimized models have achieved better
risk-adjusted returns than their equal-weighted counterparts,
primarily by reducing return volatility.
1. Interest in factor investing is growing in the market along
with a greater level of sophistication.
2. Multifactor portfolios have outperformed single-factor
portfolios over time.
In our view, the analysis presented shows that harnessing
systematic factor returns over time requires the ability to
employ a multifactor approach along with other portfolio
management tools such as optimization. As more sophisticated
smart beta approaches are used, the more they begin to look
like quantitatively-based active investment strategies that, like
MFS, typically have a well-developed underlying approach and
proven track records.
When investment strategies are predicated on more complex
quantitative models, it is important to bear in mind that models
have limitations, and these need to be thoughtfully taken into
account. We suggest that complementing a factor-based
approach with a fundamental signal — what we term a
“blended” approach — may be beneficial in some cases.
—8—
3. In multifactor portfolios, a cross-sectional bottom-up
approach is more efficient than a top-down assetallocation approach.
4. Optimized portfolios have achieved better risk-adjusted
returns than their equal-weighted counterparts, primarily
by reducing return volatility.
5. As they become more sophisticated, multifactor portfolios
begin to look like active quantitative strategies, which
have incorporated multifactor capabilities for several
decades.
6. Fundamental analysis may provide a complementary
independent investment signal to quantitative portfolio
construction methods, which by design look to the past to
predict the future, improving diversification and potentially
improving risk-adjusted returns in the process.
SEPTEMBER 2016 / SMART BETA IN THE LIMELIGHT
Methodology notes
Universe: constituents of the MSCI World Index, observed monthly from 31 December 1997 through 31 December 2015.
Data: Fundamental data, e.g., earnings per share or return on equity, is sourced from Compustat for US stocks, and the Worldscope
database for international stocks. All fundamental data is lagged by three months to reflect a reporting lag. Pricing data, used to
calculate price momentum, stock level volatility and forward returns, is provided by FactSet Historical Prices. Forward returns are
trimmed at +/- 75% to limit the influence of outliers.
Factor definitions:
• Momentum: Trailing 12 month total return, less the most recent month’s return
• Volatility (used for the low volatility factor): The standard deviation of the last two years’ monthly returns. The underlying monthly
returns are capped at +/- 100% to limit the influence of outliers
• Return on Equity: Trailing twelve months net income divided by average total equity
• Book/price: Most recent book value per share divided by price per share
• Earnings/price: Trailing twelve months earnings per share/price
• Yield: For US stocks, dividend yield is primarily a projected annual per share dividend payment, based on the most recent dividend
paid (excluding extra or special dividends). For international stocks, where reporting frequencies are more variable, dividend yield
is primarily based on dividends paid over the last twelve months
• The Sharpe Ratio is a risk-adjusted measure calculated to determine reward per unit of risk. It uses a standard deviation and
excess return. The higher the Sharpe Ratio, the better the portfolio’s historical risk-adjusted performance
Optimization:
• Mean-variance optimization: Factor portfolio weights are optimized to maximize an objective function that seeks to maximize the
weighted return of the factor portfolios and minimize the weighted covariance of the factor portfolios. The returns underlying
the optimization are the total returns of factor top quintile portfolios. Weights are constrained to be greater than or equal to
zero. The factor covariance matrix is “shrunk,” in that sample correlations and volatilities are regressed toward the mean
correlation and volatility for the set of factors. This shrinking is done to limit the effect of noise and create more intuitive weights
• Panel data regression: The dataset used in this study is a history of monthly cross sections. Each monthly dataset can be
envisioned as a spreadsheet of data, with individual stocks in each row, and characteristics or factor exposures (e.g., momentum,
book/price) in each column. To create the independent variables used in the regression, each month’s factor exposures are
stacked on top of each other, creating one matrix of six columns and several hundred thousand rows (roughly: 12 months x 18
years x 1500 stocks). Similarly, each month’s forward return data is stacked to create a vector of the same length. Forward returns
are then regressed onto the factor exposures. The resulting set of betas is scaled so that it sums to 100 to provide the crosssectional model weights.
—9—
Endnotes
1
In this paper, factor-based strategies and smart beta are used synonymously, referring to rules-based strategies that screen a broad group of securities to target one or more
factors in an attempt to deliver higher returns or reduced risk relative to a traditional index. Risk premium is the return in excess of the risk-free rate of return that an investment
is expected to yield.
2
F TSE Russell: Smart Beta: 2016 Global Survey Findings from Asset Owners. This is the third year FTSE Russell has conducted this study. The number of asset owners participating in
the study was: 181 in 2014; 214 in 2015; and 253 in 2016. It was conducted in Jan and Feb 2016.
3
Ibid.
4
FTSE Russell Insights: Combining Factors, February 2016.
5
hile the insight that multifactor models have outperformed an asset allocation approach is something we have seen in our own research and portfolio management experience, in
W
writing this paper we acknowledge the contributions of Roger Clarke, Harindra de Silva, and Steven Thorley, “Factor Portfolios and Efficient Factor Investing”, June 30, 2015.
6
MSCI World Index captures large and mid cap representation across 23 developed market countries. It currently has 1,645 constituents, covering approximately 85% of the
The
free float-adjusted market capitalization in each country.
7
Data for the analysis was available from this date.
8
F or examples of use of similar smart beta strategies in recent literature see: “Alternatively Weighted and Factor Indexes”, FTSE Russell, February 2016; “Smart Beta: 2016 Global
Survey Findings from Asset Owners”, FTSE Russell, 2016 (cited elsewhere in paper); Rob Arnott, Noah Beck, Vitali Kalesnik, Ph.D., and John West, CFA, “How Can ‘Smart Beta’ Go
Horribly Wrong?”, Research Affiliates, February 2016.
9
In both approaches, one can definitely improve over the simple, backward-looking averages used to fit these models. For example, one can develop forward-looking forecasts for
factor returns, applicable in either the multifactor modeling or asset allocation approach. However, the two approaches used here are equally simplified given their input data. It is
also important to point out that the returns to these optimized models are completely in-sample. In other words, model returns are calculated over the same sample set for which
models are fit. In this sense, these model returns represent the best-case improvement in model performance possible through optimization. In reality, researchers commonly see
that when used out-of-sample, i.e., applied to new data, models do not perform as well as they do over the data to which they are fit.
The views expressed are those of the author(s) and are subject to change at any time. These views are for informational purposes only and should not be
relied upon as a recommendation to purchase any security or as a solicitation or investment advice from the Advisor.
Unless otherwise indicated, logos and product and service names are trademarks of MFS® and its affiliates and may be registered in certain countries.
Issued in the United States by MFS Institutional Advisors, Inc. (“MFSI”) and MFS Investment Management. Issued in Canada by MFS Investment Management
Canada Limited. No securities commission or similar regulatory authority in Canada has reviewed this communication. Issued in the United Kingdom by MFS
International (U.K.) Limited (“MIL UK”), a private limited company registered in England and Wales with the company number 03062718, and authorized and
regulated in the conduct of investment business by the U.K. Financial Conduct Authority. MIL UK, an indirect subsidiary of MFS, has its registered office at
One Carter Lane, London, EC4V 5ER UK and provides products and investment services to institutional investors globally. This material shall not be circulated
or distributed to any person other than to professional investors (as permitted by local regulations) and should not be relied upon or distributed to persons
where such reliance or distribution would be contrary to local regulation. Issued in Hong Kong by MFS International (Hong Kong) Limited (“MIL HK”),
a private limited company licensed and regulated by the Hong Kong Securities and Futures Commission (the “SFC”). MIL HK is a wholly-owned, indirect
subsidiary of Massachusetts Financial Services Company, a U.S.-based investment advisor and fund sponsor registered with the U.S. Securities and Exchange
Commission. MIL HK is approved to engage in dealing in securities and asset management-regulated activities and may provide certain investment services
to “professional investors” as defined in the Securities and Futures Ordinance (“SFO”). Issued in Singapore by MFS International Singapore Pte. Ltd., a private
limited company registered in Singapore with the company number 201228809M, and further licensed and regulated by the Monetary Authority of Singapore.
Issued in Latin America by MFS International Ltd. For investors in Australia: MFSI and MIL UK are exempt from the requirement to hold an Australian financial
services licence under the Corporations Act 2001 in respect of the financial services they provide to Australian wholesale investors. MFS International
Australia Pty Ltd (“MFS Australia”) holds an Australian financial services licence number 485343. In Australia and New Zealand: MFSI is regulated by the
SEC under US laws and MIL UK is regulated by the UK Financial Conduct Authority under UK laws, which differ from Australian and New Zealand laws.
MFS Australia is regulated by the Australian Securities and Investments Commission.
MFSE-SMBETA-WP-9/16
36250.5