IQ INSIGHTS
Dynamic Timing of Smart
Beta Strategies: Is it Possible?
by Ric Thomas, CFA and Rob Shapiro, CFA, CAIA
Investing in smart beta strategies
has evolved from a niche concept
into an established investment belief
among many institutional investors.
The widespread acceptance of
transparent, rules-based strategies
that seek to achieve active-like
performance by capturing specific
risk premiums in the market is
confirmed by various studies and
surveys, along with our own
experience in working with asset
owners. We welcome this trend — it
is a topic we have long championed.
Initially, investors’ main consideration centered on which of the
smart beta equity premiums — such as size (focused on the
added premium provided by small-cap stocks), value (provided
by stocks currently out of favor despite strong fundamentals),
quality, or low volatility — made the most strategic sense in a
diversified investment plan. But more recently, investors have
started to ask whether they can dynamically rotate the timing
of their exposure to these factors over the long term.
The timing question is an important one, because missing from
the smart beta conversation so far has been a discussion of the
valuation of these factor portfolios. When investors seek to alter
a strategic allocation to equities, high yield bonds, or any other
asset class, they naturally look to valuation for guidance. A
sharply rising P/E ratio on equities, for example, is often
impetus for some investors to reduce their equity allocation in
favor of a cheaper growth asset class. Similarly, if we think of
smart beta portfolios as refined asset classes, it makes sense to
attempt to measure their valuation over time.
Precision and Timing:
A Winning Combo for
Implementing Smart Beta
The issue of timing has long been part of the discussion
surrounding smart beta strategies. Investors often become
interested in smart beta because of a view they have about a
particular risk premium in the market-for example
gravitating toward a low-volatility-tilted factor portfolio out
of a concern about the exposure of their existing holdings to
tail-risk volatility. The challenge with this approach is that a
factor that may be compelling during one market regime may
be less so in another. So some investors opt for a multi-factor
portfolio, which combines multiple factors in one portfolio to
take advantage of the potential diversification that a
combination of risk premiums provides over time. This
method cuts down on the cyclicality of choosing one factor
and may reduce turnover and transaction costs, offering a
kind of set-it-and-forget-it approach to implementing smart
beta across a portfolio.
While the multi-factor approach offers much in the way of
convenience, it provides for a somewhat limited level of
precision and performance attribution. As such, many
investors prefer a third approach in which they separate
smart beta factors into various distinct component portfolios,
each organized around and tilted toward a single attribute.
That way, if investors become more bullish on valuation and
less so on low volatility, they can dynamically re-weight the
overall portfolio in the same way that they might alter their
equity-to-fixed income allocation.
The model we develop here for timing smart beta factors can
be a particularly good complement to those investors who
favor segmenting their smart beta allocations.
IQ Insights | Dynamic Timing of Smart Beta Strategies: Is it Possible?
Realizing the usefulness of this information for investors, we
developed a simple method to track the valuation of smart beta
factor portfolios. We also confirmed that historically smart beta
premiums are positively related to these valuations, meaning
the method can help forecast strategy performance. This
analysis can, thus, help investors make a more informed
prediction of the long-term prospects for their smart beta
portfolios and provide guidance for dynamic rebalancing of
portfolio weights.
A Valuation Model for Smart Beta Timing
Various studies show that a valuation-based approach provides
reasonably accurate long-term forecasts for asset prices. In
particular, Campbell and Shiller (1998)1 showed that simple
aggregate valuation ratios, such as price-to-earnings, dividendto-price and book-value-to-price, could accurately predict
long-term equity market returns. They argued that P/E multiples
are relatively constant in the long-run, always reverting back to
a historical norm. Therefore, a high P/E ratio necessitates that
either the numerator (P) must fall or the denominator (E) must
rise in order to bring the ratio back into equilibrium. In fact,
they found it was price, not earnings that adjusted to restore
balance, dealing a blow to believers in market efficiency, who
would have predicted that higher prices reflect a perfect sharing
of information about the prospects for earnings growth (but, as
we know, the efficient market hypothesis does have its holes).
This finding raises important questions for adherents of
factor-based investing. Can valuation ratios also help estimate
the returns on smart beta portfolios? Are returns for low
volatility equity portfolios (or for quality, small-cap or value, for
that matter) poor when valuations for those kinds of stocks get
expensive? Can we measure this valuation and is it possible to
create a rebalancing rule that favors attractively valued smart
beta portfolios over time? The answer to all these questions, as
we suggest above, is yes.
Figure 1: Valuation Spreads of Smart Beta Attributes
Value — Median B/P Spread
Size — Median B/P Spread
2.0
1.0
Attractive
1.6
0.8
1.2
0.6
0.8
0.4
0.4
0.0
0.2
Expensive
Jan
1987
— Spread
1994
— Average Spread
2001
— 1 Std Dev Above
2008
Jul
2015
— 1 Std Dev Below
Low Volatility — Median B/P Spread
0.0
Expensive
Jan
1987
— Spread
1994
— Average Spread
2001
— 1 Std Dev Above
2008
Jul
2015
— 1 Std Dev Below
Quality — Median B/P Spread
Attractive
0.4
Attractive
0.0
0.2
Attractive
-0.2
0.0
-0.4
-0.2
-0.6
-0.4
-0.6
-0.8
-0.8
Expensive
Jan
1987
— Spread
1994
— Average Spread
2001
— 1 Std Dev Above
2008
Jul
2015
— 1 Std Dev Below
-1.0
Expensive
Jan
1987
— Spread
1994
— Average Spread
2001
— 1 Std Dev Above
2008
Jul
2015
— 1 Std Dev Below
Source: SSGA, MSCI, As of June 30, 2015.
Past performance is not a guarantee of future results.
Standard deviation is a historical measure of the volatility of returns. If a portfolio has a high standard deviation, its returns have been volatile; a low standard deviation indicates
returns have been less volatile. Standard Deviation is normally shown over a time period of 36 months, but the illustrations noted in this material may reflect a shorter time frame.
This may not depict a true historical measure, and shouldn’t be relied upon as an accurate assessment of volatility.
State Street Global Advisors
2
IQ Insights | Dynamic Timing of Smart Beta Strategies: Is it Possible?
Analysis
Measuring the valuation of the various attributes themselves
we believe is straightforward. We simply divide the universe of
stocks in the MSCI World Index into four different key
attributes — quality, low volatility, size and value. Within each
attribute, we determine the top and bottom quintiles (i.e., the
20% highest quality and lowest quality stocks, 20% least and
most volatile, etc.) and then calculate the median book-to-price
ratios for each pair of quintiles. Finally, we take the difference
between these two ratios. A large spread between the ratios for
the top and bottom quintiles implies that the attribute is
attractively priced, and a low number suggests that the
attribute is expensive. Figure 1 plots these valuation ratios for
the four distinct smart beta attributes over time.
Consistent with Campbell and Shiller, the chart shows that the
valuation ratios are relatively constant. An extreme level of
cheapness (such as found in size in 1999) tends to correct itself
and reverts back to a more normal ratio (such as found in size in
2004). This observation leads to the question as to whether it is
prices, or book values, that adjust to restore this equilibrium.
A simple plot of the year-end valuation spreads relative to
forward subsequent returns indicates that, as with equities
as a whole, it is prices that adjust, and valuation spreads can
help estimate smart beta returns. In Figure 2, each point
represents the book-to-price spread as of June 30, 2015 for each
of the various factors, between 1993 and 2011. The Y-axis shows
the subsequent three-year excess return over the cap-weighted
index of a long-only smart beta portfolio organized around that
factor. The figure suggests that the return premiums to smart
beta portfolios are time-varying but predictable.
Figure 2: Valuation Spreads Versus Future Excess Returns
Size — Subsequent 3-Yr Excess Returns (%)
Value — Subsequent 3-Yr Excess Returns (%)
9
10
5
6
0
3
-5
0
-10
-3
0.0
0.5
B/P Spreads
1.0
1.5
Managed Volatility — Subsequent 3-Yr Excess Returns (%)
-15
0
0.2
0.4
B/P Spreads
0.6
0.8
Quality — Subsequent 3-Yr Excess Returns (%)
20
6
4
10
2
0
0
-10
-20
-2
-0.5
-0.4
-0.2
B/P Spreads
0.0
0.2
-4
-0.8
-0.6
-0.4
B/P Spreads
-0.2
0.0
Source: SSGA, MSCI, As of June 30, 2015. Data is from 01/01/1993 through 12/31/2014
The calculation method for value added returns may show rounding differences.
Past Performance is not a guarantee of future results.
Source: State Street Global Advisors. Data is from 01/01/1993 through 12/31/2014
The data displayed is a hypothetical example of back-tested performance for illustrative purposes only and is not indicative of the past or future performance of any SSGA product.
Back-tested performance does not represent the results of actual trading but is achieved by means of the retroactive application of a model designed with the benefit of hindsight.
Actual performance results could differ substantially, and there is the potential for loss as well as profit. The performance may not take into account material economic and market
factors that would impact the adviser’s actual decision-making. The performance does not reflect management fees, transaction costs, and other fees expenses a client would have
to pay, which would reduce returns. Please reference the disclosure section for the model methodology and other important disclosures.
State Street Global Advisors
3
IQ Insights | Dynamic Timing of Smart Beta Strategies: Is it Possible?
While the plots confirm a positive relationship between
valuation spreads and subsequent returns, two challenges arise
from fully implementing a strategy based on this finding. First,
the coefficient of determination, or “R-squared,” of these four
relationships varies between 0.15 and 0.35. You can see this
intuitively by observing the relatively wide dispersion of the
points around the lines in Figure 2. Hence, while the smart beta
portfolios may be predicable, there is certainly some margin for
error. Second, for each of the factors, the majority of the data
points lie above 0% on the Y-Axis. In other words, for many
investors, a simple buy and hold methodology with periodic
static rebalancing may be enough, since in the long run many of
these smart beta portfolios tend to perform well, even if the
starting point of valuation isn’t optimal.
The Results?
Let’s now test one simple rule that illustrates how an investor
might apply this analysis to a dynamic rebalancing method.
Of course, armed with our analysis, there are many different
approaches an investor can take, and the method shown here
is but one of many possibilities.
The benchmark is a static equal-weighted portfolio of four
smart beta component portfolios — size, valuation, quality and
low volatility. For a possible implementation of a timing
strategy, an investor could simply divide a portfolio initially
into the same four equal weights. The investor would rebalance
the overall portfolio monthly, and continue to allocate to each
component equally, unless the book-to-price spread declines
by such an amount that it reaches a standard deviation of -1
relative to the average spread (i.e. becoming too expensive).
In that case, the timing method completely sells out of the
expensive portfolio and reallocates the capital equally to the
remaining three. Additionally, in order to mimic a “long-term
mindset,” once a component portfolio is sold it cannot be
repurchased for three years unless at some point the book-toprice ratio improves to a standard deviation of +1 relative to
the average spread (i.e., becoming cheap again).
Figure 3 shows the backtested performance of this
methodology. The chart shows that the timing method would
have added value over a purely equal-weighted method fairly
consistently over long periods of time. The example
methodology we outline here does not have overly high
turnover and transaction costs, and we believe that the added
value should still be significant when such costs are accounted
for. This finding provides some hope to investors wishing to
maximize the return premiums of their rules-based equity
portfolios. It also highlights the importance, in general, of
understanding the valuation characteristics of factor portfolios
before making a long-term investment in them.
State Street Global Advisors
Figure 3: A Dynamic Smart-Beta Methodology
12
10
8
6
4
2
0
Jan
1993
— MSCI World Index
1998
2003
— Static Weights
2009
Dec
2014
— Dynamic Weights
Source: SSGA, MSCI, As of June 30, 2015. Data is from 01/01/1993 through
12/31/2014
Past performance is not a guarantee of future results.
Index returns are unmanaged and do not reflect the deduction of any fees or expenses.
Index returns reflect all items of income, gain and loss and the reinvestment of
dividends and other income.
Source: State Street Global Advisors. Data is from 01/01/1993 through 12/31/2014
The data displayed is a hypothetical example of back-tested performance for
illustrative purposes only and is not indicative of the past or future performance of
any SSGA product. Back-tested performance does not represent the results of actual
trading but is achieved by means of the retroactive application of a model designed
with the benefit of hindsight. Actual performance results could differ substantially,
and there is the potential for loss as well as profit. The performance may not take
into account material economic and market factors that would impact the adviser’s
actual decision-making. The performance does not reflect management fees,
transaction costs, and other fees expenses a client would have to pay, which would
reduce returns. Please reference the disclosure section for the model methodology
and other important disclosures.
The Importance of Timing and Valuation
It makes sense to have a good understanding of valuation before
making long-term allocations to any asset class, and valuation
estimates can be applied to factor portfolios as well as they can
to traditional asset classes. For those who because of turnover
or other considerations do not wish to implement a dynamic
re-balancing process, the valuation methodology we present
can be used to at least time their entry into the factor, or factors,
of their choosing or help them decide which to access when.
Given the long-term better risk-adjusted performance of smart
beta factors generally, we believe the important thing may be
merely to be invested in them. However, valuation does matter
in the future performance of smart beta portfolios over time,
and implementing a simple rules-based dynamic rebalancing
method based on valuation indeed offers the potential for
enhanced returns.
1
Campbell, J. Y., and R. J. Shiller. “Valuation Ratios and the Long-Run Stock Market Outlook.”
Journal of Portfolio Management 24.2 (1998): 11–26.
4
IQ Insights | Dynamic Timing of Smart Beta Strategies: Is it Possible?
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The Backtested data shown is from January 1987 through December 2014. The
forecasting results utilize Backtested performance for hypothetical Advanced Beta
factor portfolios. The performance for these hypothetical factor portfolios was
created based on the holdings of a model paper portfolio created by the Investment
Solutions Group using Factset Research Systems. Results shown are hypothetical
(“Backtested”) and are not the result of actual trading using client assets and were
achieved by means of management of a model paper portfolio. The model portfolio
reflects decision-making contemporaneous with each performance period presented.
The impact of economic, market, and other factors is limited to the simulation period,
and future portfolio holdings may change as the portfolio management team’s
assumptions for economic conditions change. Actual performance may differ
substantially from the backtested results. Backtested results have inherent
limitations because they do not reflect actual trading by SSGA during the period
described. SSGA was not managing money in this strategy during the period shown.
The views expressed in this material are the views of Ric Thomas and Rob Shapiro
through the period ended August 6, 2015 and are subject to change based on market
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