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Designing a Smoother RideBalancing Risk and Return Using Dynamic Asset Allocation
n Identifying changes in the risk/
reward trade-off as they occur
n Judging when adjustments to
asset allocation are warranted
n Smoothing portfolio volatility and
limiting the severity of losses
January 2010
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Table of Contents
1Key Research Conclusions
2Introduction
Why Do Long-Term Investors Care About Short-Term Risks?
7How Much Risk Do You Really Have?
Measuring Volatility and Diversification Potential
14How Much Return Is Enough?
Measuring the Opportunity
21Achieving More Consistent Outcomes
The Portfolio Impact of Dynamic Asset Allocation
25Notes
26Glossary
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Dynamic Asset Allocation 1
Key Research Conclusions
A well-designed long-term asset allocation is crucial to the
success of any investment program. But even a thoroughly
diversified portfolio is vulnerable to large losses, particularly
when a financial-market shock occurs. We have developed
dynamic tools that can be used to adjust an asset-allocation
strategy systematically as market conditions change.
Our dynamic asset-allocation research seeks to measure
short-term risks and returns more accurately in order to rein in
volatility and cut down on extreme outcomes, without giving up
return potential. We believe such an approach can deliver a
more consistent investment experience, regardless of the
capital-markets environment. Some of the key conclusions from
our research are:
n By focusing on controlling risk, and being skeptical about
making changes to portfolio weights based solely on expected
returns, dynamic asset allocation can smooth out volatility and
mitigate extreme outcomes, without sacrificing performance
in the long run.
nMarket risks can be more reliably forecast than returns, largely
because volatility trends tend to persist for extended periods
across all major asset classes.
nContrary to popular belief, periods of high volatility are often
not followed by large gains. Its crucial for investors to
measure how well they are being compensated for accepting
more risk.
n Return forecasting can help to indicate when markets are
most vulnerable and when they are likely to be most reward-
ing. Factors such as valuations, levels of corporate profitability,
the level and direction of interest rates, and credit spreads can
provide early warning signals.
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2 AllianceBernstein.com
IntroductionWhy Do Long-Term Investors Care About Short-Term Risks?
The past 10 years have offered a stark reminder of just how
volatile the capital markets can be. Over this period, global
equities have twice suffered peak-to-trough falls of more than
45%, followed by sharp recoveries.1 Global investment-grade
corporate bonds underperformed government bonds by almost
17% in 2008, only to beat them by 15% in the first nine
months of 2009.2 Central banks have adjusted monetary policy
rates dramatically on several occasions, and commodity price
movements have been unprecedented, with oil fluctuating
between US$20 and US$150 a barrel in the past decade.
This type of volatility can be extremely unsettling to investors
and may even cause lasting damage to the growth of their
portfolios. The traditional way of mitigating these types of
violent capital-markets swings is portfolio diversification, in the
form of a well-balanced long-term asset allocation. Spreading
assets across a wide array of weakly correlated investments can
reduce the short-term volatility of returns without giving up
much performance in the long run. Many investors have
adopted such an approach, adding a wide array of asset types
and strategies to their mixnot only global stocks and fixed
income but also real estate, commodities and alternative
investments such as hedge funds.
A well-diversified long-term asset-allocation strategy is one of
the most important decisions an investor is ever likely to make.
But even a thoroughly diversified long-term strategy is vulnera-
ble to unusually large losses. During extreme and unexpected
financial-market shocks (sometimes referred to as tail events),
equity volatility soars and correlations between assets can
increase rapidly, making diversification less effective just when
investors need it most. The top chart in Display 1 shows how,
over the past decade, a portfolio invested in a balanced mix of
60% equities and 40% fixed income would have suffered large
fluctuations while generating hardly any real growth.
And diversifying by adding other asset classes would not have
made much difference to the outcome. Few asset classes
provided a safe haven during the technology, media and
telecommunications (TMT) collapse, and only government
bonds offered any protection during the credit crisis (Display 1,
bottom).
A Balanced Allocation Can Behave in Different Ways
The discomfort that investors suffer during market downturns
illustrates a broader problem: the tendency for the risk profile of
any fixed asset mix to stray materially from investors expecta-
tions. Over the past four decades, the 60/40 portfolioan asset
allocation designed to suit an investor with a moderate
tolerance for riskhas at times displayed the volatility of an
all-bond portfolio and at other times been as volatile as an
all-equity portfolio (Display 2).
Any major shift in volatility alters the range of returns that an
investor is likely to experience. For example, a portfolio with an
expected return of 7% and an expected volatility of 9% (which
is how the 60/40 mix behaves over the long run) should
generate returns somewhere between a gain of 25% and a loss
of 11% in a given year, only exceeding an 11% loss about once
in 40 years.
If volatility shot up to 15%, this would substantially increase
both the upside and downside potential of the portfolio, to a
gain of 37% on one hand and a loss of 23% on the other. 3 Its
unlikely that an investor with a moderate profile would be
comfortable with that degree of uncertainty.
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Dynamic Asset Allocation 3
Display 2
The Volatility of a Balanced Account Has Fluctuated Widely
Portfolio Volatility: 60% Global Stocks / 40% Global Government Bonds**
Percent
0
5
10
15
20
70 73 76 79 82 85 88 91 94 97 00 03 06 09
Long-Term Average
Equity-Like Volatility
Bond-Like Volatility
Through September 30, 2009Past performance is not indicative of future results.*Refers to 60% in the MSCI All Country World Index and 40% in the Barclays Global Aggregate Index, rebalanced monthly. Growth of US$1 is calculated on an inflation-adjusted basis. Returns to high-yield and investment-grade credit refer to excess returns over comparable-dated government bonds. See notes on page 25 for asset class definitions.**Throughout this paper, unless otherwise noted, global bonds refer to government bonds and global stocks refer to developed-country equities, with returns hedged into US dollars.Source: Barclays Capital, Bloomberg, FTSE NAREIT, Global Financial Data, MJK Associates, MSCI, Thomson Reuters and AllianceBernstein
Display 1
A Balanced Allocation Has Had a Bumpy Ride over the Past Decade
Growth of US$1 of a Global 60% Stock / 40% Bond Asset Allocation*
TMT Collapse: Cumulative Returns Mar 00Sep 02
GlobalEquities
Emerging-Market
Equities
REITs High-Yield
Credit
Investment-Grade
Credit
Govt.Bonds
CommodityFutures
ForeignCurrency
(47)%
12%
(27)%
(3)%
22%
2%
(7)%
(44)%
Credit Crisis: Cumulative Returns Oct 07Feb 09
US$1.01
US
Dollars
(54)%(67)%
(35)%
(17)%
9%
(19)%(11)%
(61)%
GlobalEquities
Emerging-Market
Equities
REITs High-Yield
Credit
Investment-Grade
Credit
Govt.Bonds
CommodityFutures
ForeignCurrency
0.50
0.75
1.00
1.25
00 01 02 03 04 05 06 07 08 09
(29)%
(36)%
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4 AllianceBernstein.com
It seems counterintuitive that a balanced portfolio should
behave in such different ways. The fact is that even though a
60/40 mix is balanced in terms of asset allocation, it is concen-
trated in terms of risk. One asset classequitiesdrives the
lions share of portfolio volatility. Since stocks are three to four
times more volatile than bonds, they generate an average of
about 90% of the performance variability of the typical 60/40
portfolio. So, when equity-market volatility ebbs and flows, it
tends to take the whole portfolio along with it, (See Equities
Drive Portfolio Volatility, page 5).
Mitigating the threat of a disconnect between investors
expectations and actual portfolio outcomes is most important
during periods of high or rising volatility, which usually coincide
with bear markets. At these times, investors are likely to be
feeling severe pressure in other areas that affect their invest-
ment plans. For example:
n Individuals are more likely to lose their jobs and income;
n Foundations and endowments are likely to face declines in
charitable contributions;
n Pension funds are likely to see their funding capacity decline
as public plans are hit by falling tax revenues and private plans
face underfunding due to declining corporate profits; and
n Assets become illiquid and access to credit dries up.
In short, equity-market misery is often compounded by other
factors that can make a bear market even more painful. These
realities argue for a more flexible approach to asset alloca-
tionone that can enhance a long-term strategy by providing a
smoother pattern of returns.
Dynamic Asset Allocation: Responding to PrevailingMarket ConditionsThe goal of our research was to find a systematic and durable
way to monitor changes in the market environment in order to
find a better balance between changes in market risk structure
and changes in return potential (Display 3).
This is not a new idea: as long as capital markets have existed,
investors have been seeking systems for buying low and selling
high. Unfortunately, the results of such systems have been
inconsistent at best. In our view, this is because most strategies
focus almost exclusively on returns, even though it is extremely
challenging to predict short-term turns in the markets with a
high degree of accuracy.
In the course of our research, we started to question whether
the focus on predicting market returns was too one-sided. After
all, risks can change significantly as well.
We found that risk could be forecast with considerably moreconfidence, and that improvements in forecasting could have a
significant impact on the efficacy of a dynamic strategy. This is
where our approach really diverges from traditional tactical asset
allocationit seeks to improve the risk/reward trade-off
primarily by mitigating risk, rather than by reaching for higher
returns.
Our tools measure the expected risks of a portfolio (by estimat-
ing asset volatilities and correlations) and the expected returns
available so that, when the risk environment changes, we can
determine whether investors are being paid enough to maintain
or increase their exposure.
Display 3
Weighing Risk and Return
Fundamental Oversight
Market Risk
VolatilitiesCorrelations
SentimentValuations
Interest RatesCredit Spreads
Return Potential
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Dynamic Asset Allocation 5
Equities Drive the Volatility of a Balanced Account
Contribution to Portfolio Volatility60% Equities / 40% Government Bonds
Percent
(5)
0
5
10
15
20
69 74 79 84 89 94 99 04 09
Equities Bonds
0.7%
8.3%
Long-Term Average
Equities
(92% of risk)
Bonds
(8% of risk)
9.0%
Through September 30, 2009Source: Barclays Capital, MSCI and AllianceBernstein
Understanding where a portfolios risk is coming from is
crucial in order for investors to manage portfolio fluctuations
effectively. Equities are three to four times more volatile than
government bonds, which means that they tend to play a
bigger part in overall portfolio volatility than their nominal
value suggests.
The display below shows how much of a 60/40 portfoliosperformance variability has been driven by equities and how
much has been driven by bonds over the past 40 years. Over
the period that we studied, equities contributed an average
of 92% of the overall risk8.3% out of the 9.0% total
and bond volatility accounted for the remaining 8%.
Contributions to risk can fluctuate depending on each asset
classs volatility at the time, and the extent to which the
performance patterns of the asset classes are correlated. For
example, in the 1970s, bonds contributed more to portfolio
volatility than usual because concerns about inflation
undermined the fixed payments of bonds and made their
prices very volatile. And, because stocks were also adversely
affected by inflation fears, the correlation between the two
asset classes rose above its normal level. In that inflationary
context, bonds not only became riskier; they also provided
less portfolio diversification.
During the 2000s, the opposite scenario has unfolded.Government bond volatility has been extremely low, and
stocks and bonds have displayed strong negative correla-
tions as concerns about the sustainability of real economic
growth and the risk of deflation have loomed larger than
worries about inflation. As a result of this negative correla-
tion, in recent years bonds have acted as a powerful
diversifier against equity risk. But in cases of extreme equity
volatility, such as the escalation of the credit crisis in late
2008, a portfolio would have needed a much larger
weighting in bonds and much less in equities in order to rein
in overall portfolio volatility. n
Equities Drive Portfolio Volatility
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6 AllianceBernstein.com
When applied in a systematic way over time, we believe that
dynamic asset allocation will produce measurable benefits,
namely:
n Less portfolio volatility;
n Fewer extreme negative outcomes, reducing the probability of
large losses; and
n Comparable long-term returns.
A reduction in tail events both mitigates outsize losses and
reduces outsize gains (Display 4). This tends to result in outper-
formance in bear markets and underperformance in recoveries.
In the sections that follow, we discuss the building blocks of our
approach: how we analyze market risks, how we assess
potential returns and our mechanism for integrating these
forecasts into asset-allocation recommendations.
Display 4
Dynamic Allocation Seeks to Improve Distribution of Returns
Dynamic Asset Allocation
Conventional Asset Mix
Returns
FewerLargeLosses
FewerLargeGains
Less Volatility
Lower volatility
Fewer tail events
Comparable returns
n Even a well-diversified long-term asset allocation can suffer high volatility and heavy losses.
n In the short term, shifting market risks can cause portfolio outcomes to disconnect alarmingly from what long-term averages
might suggest.
n Our dynamic asset-allocation approach responds to short-term market changes, with the goal of providing a smoother
pattern of returns.
Chapter Highlights
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Dynamic Asset Allocation 7
How Much Risk Do You Really Have?Measuring Volatility and Diversification Potential
The goal of our dynamic risk tools is to identify shifts in
volatilities and correlations in their early stages so that we can
adjust portfolios in time to mitigate damage or respond to
changing diversification opportunities.
For example, as the technology, media and telecommunications
(TMT) bubble was collapsing in April 2000, simulation results
suggest that our forecasts would have pointed to high equity
volatility, while our correlation model would have indicated a
good opportunity to diversify risk, with both real estate
investment trusts (REITs) and bonds showing below- average
correlations with equities (Display 5). All else being equal, this
would have called for less exposure to equities and increased
exposure to bonds and REITs.
In September 2008, as the credit crisis escalated, our volatility
forecasts likely would have signaled high risk in the equity and
real estate markets. The forecast correlation between REITs and
equities, at +0.76, would have signaled a below-average
diversification opportunity from real estate.
By contrast, the simulation results suggest that our model
would have highlighted the exceptional diversification benefits
available from government bonds at the timeas shown by the
strong negative correlation of (0.34) between bonds and
equities. All else being equal, this would have called for lower
equity and real estate exposure and higher exposure to bonds.
Volatility Is Easier to Forecast than ReturnsGiven that volatility fluctuates so much, how can we forecast it
with any confidence?
Surprisingly, our research shows that volatility can be predicted
with reasonable accuracy. We found that over the past 40 years,
the short-term volatility factors in our model could have
explained 30%50% of the variability in global equity, bond,
currency, commodity futures and real estate volatility. This is a
very good fit: in the world of return forecasting, a model that
explains more than 10% of future returns is considered quite
powerful.
The main reason why we believe that volatility can be more
accurately forecast than returns is that recent levels of volatility
tend to persist for extended periods before slowly trending back
toward their long-term averages.
The finding that volatility trends persist makes intuitive sense.
For example, if investors expect an economic shock to cause a
Display 5
Risk Forecasting Provides Early Warning Signals
TMT CollapseApr 2000
+0.55
Correlation withEquities
+0.76+0.37Global REITs
+0.15 (0.34)+0.12Global Bonds
Volatility
14% 26%19%Global Equities15% 28%15%Global REITs
5% 5%4%Global Bonds
Long-Term Avg.Credit CrisisSep 2008
12Month Forecast
Through September 30, 2009. Source: Barclays Capital, FTSE NAREIT, GlobalFinancial Data, MSCI and AllianceBernstein
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8 AllianceBernstein.com
recession, they are likely to have doubts about corporate cash
flows and the health of companies balance sheets. Asset types
that are most affected by the turmoil, such as equities, corpo-
rate bonds and industrial commodities, are likely to become
more volatile as a result. Monetary, fiscal or regulatory authori-
ties may step in with measures to address the fallout, but it is
often not immediately clear to the market how well these
measures will work. Volatility is unlikely to ease back toward its
long-term average until investors gain confidence about the
economic outlook and its impact on the value of their invest-
mentsand this process takes time.
The S&P 500, the equity index for which we have the longest
series of historical data, shows how sticky volatility has been
since 1929. We ranked past levels of annualized stock-market
volatility4 from highest to lowest and then calculated the future
levels of volatility over the subsequent month, quarter and year
(Display 6). Following the 20% of periods when volatility was at
its highestabout 35% on averageit was still well above its
long-run average a year later. Likewise, in the quintile of periods
when volatility was at its lowest, volatility was slow to climb
back toward its long-term average.
We found that the same pattern occurred, with remarkable
consistency, across a range of global asset classes and countries.
In other words, recent history is typically a good indicator of the
level of volatility we are likely to experience in the months
ahead. We studied numerous methods for incorporating recent
market data in developing our shorter-term risk forecasts. Forexample, there are short-term volatility forecasts that are traded
in the markets, such as the Chicago Board Options Exchange
Volatility Index (VIX). This represents expectations of US equity
market volatility over the next 30 days, implied by the pricing of
options on the S&P 500 futures contract. However, market-
based forecasts are not available for most asset classes, do not
have long histories and can become distorted during crises just
when they are most needed. We believe that a model based on
Display 6
Volatility Is Sticky Across Asset Classes
Annualized Volatility: Top and Bottom Quintiles
S&P 500
19292009Global Stocks
19702009
Months Forward
0
10
20
30
40
0 3 6 9 120
10
20
30
0 3 6 9 12
Currencies
19742009Commodities
19702009Global Fixed Income
19702009
0
2
4
6
8
10
0 3 6 9 125
7
9
11
13
15
0 3 6 9 120
5
10
15
20
25
0 3 6 9 12
Percent
Past (Realized) Volatility Forward Volatility Average
Through June 30, 2009Each observation is ranked by past volatility at the end of the month and sorted by quintile (in-sample) except currencies, which are sorted by tercile. Past volatility is anexponentially weighted average using daily data with a three-week half-life (5% decay per day). All metrics are annualized.Source: Barclays Capital, Bloomberg, FTSE NAREIT, Global F inancial Data, MJK Associates, MSCI, S&P, Thomson Reuters and AllianceBernstein
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Dynamic Asset Allocation 9
daily data and driven by recent volatilities is the most robust and
flexible. It can be applied over a wide range of asset classes and
countries and can be used to forecast volatilities over different
time horizons.
Its worth noting that the aim of our volatility forecasting is not
to predict shocks before they happena difficult task at the
best of timesbut to give us fair warning when the market risk
structure starts to change.
One analogy is hurricanes: Even the best meteorologists havetrouble predicting the exact number of hurricanes that will
happen in a given year, but once a storm begins, it is possible to
measure how it is building and changing. It is rare that a
hurricane escalates from a category one to a category five
overnight, so there is often time to take cover.
Simulation results for the period from 1970 to 2009 suggested
that our volatility forecasts would have been quite sensitive to
changing risk environments, displaying the ability to capture
extremes. For example, our equity volatility forecasts ranged
from 9.8% to 33.7% and our bond forecasts ranged from
2.2% to 12.1% (Display 7, left).
Most importantly, we found that our volatility forecasts were
quite accurate. The bar charts to the right in Display 7 isolate
the 20% of cases when volatility was forecast to be highest and
the 20% of cases when volatility was forecast to be lowest,
comparing our 12-month forecasts with the actual levels of
volatility that occurred. There was a good fit between theforecast and realized levels. Simulations showed that, in the
lowest-volatility periods, realized global equity volatility aver-
aged about 12%, compared with an average forecast of 11%
by our model. In the highest-volatility periods, realized volatility
was about 18%, compared with forecast volatility of 20%.
For more on our volatility forecasting techniques, see Stormy
Weather: How Our Volatility Model Works, page 10.
Display 7
Our Forecasts Are Built to Capture Changing Risks
Global Volatility: One-Year Forecasts19702009
70 73 76 79 82 85 88 91 94 97 00 03 06 09
0
10
20
30
40 Global Stocks
Global Bonds
Lowest-VolatilityQuintile
18%20%
12%11%
Highest-VolatilityQuintile
6%6%
3%3%
Lowest-VolatilityQuintile
Highest-VolatilityQuintile
RealizedForecast
RealizedForecast
Nifty50 1982
Recession
BlackMonday
Savings& LoanCrisis
CreditCrisis
TMTBubbleAsian
Crisis
Global
Stocks
Global
Bonds
Percent
As of September 30, 2009Source: Barclays Capital, Global Financial Data, MSCI and AllianceBernstein
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10 AllianceBernstein.com
We use an adaptive risk modeling framework to forecast
volatility and the correlations between asset classes. First we
estimate the volatilities and correlations of the major market
risk factors, such as global equity price movements and
interest rates. We can then estimate any assets sensitivity to
those risk factors, and any residualthe amount of volatility
and correlation that is unexplained by those market risk
factors.
When modeling each component, we consider a combina-
tion of its recent realized volatility and correlations, as well
as its long-term averages, so that we can best capture the
changing nature of the risks as capital-markets conditions
evolve.
Short-Term Risk: This measure helps us gauge very recent
changes in market sentiment so that our forecasts can pick
up sudden shifts in market risk. Volatility observations in the
past three weeks count for half the weight in our short-term
measure.
Medium-Term Risk: Our medium-term factor has a slightly
longer look back period, with the goal of understanding
whether we are operating in a generally high- or low-risk
environment. This measure is important since the short-term
risk factor can at times become quite volatile, swinging
above and below the long-term average.
Long-Term Risk: This factor in our model is based on our
analysis of very long-term capital-markets return data from
each asset class. The long-term measure captures the
tendency of all asset classes to revert toward their long-term
averages over time.
To illustrate how these different measures work in concert to
form our dynamic risk forecasts, we sampled four time
periods. The display on the right breaks out the readings for
each of the three factorsshort-, medium- and long-term
riskand shows our resulting one-year global equity-market
volatility forecasts.
The Credit Crisis, 20072009The credit crisis, which started in mid-2007, gathered momen-
tum in February and March 2008, around the time of the
collapse of the investment bank Bear Stearns. In historical
simulations for this period, our short-term volatility readings
rose above 20%.
But soon after the Bear Stearns failure, short-term equity-market volatility began to fade, falling below its average.
Nevertheless, the initial pickup in volatility sent our medium-
term measure upward, highlighting the fact that we were in an
environment of high investor anxiety.
As a result, our volatility forecast remained at or above the
long-term average throughout the credit crisis. Once volatility
began to escalate again in September with the collapse of
Lehman Brothers, our short-term volatility measure quickly
registered the spike in market anxiety and caused our forecast
to shoot up simultaneously.
As the markets began to recover in 2009, volatility edged back
down toward its long-term norm. But by the end of September
2009, our tools were still counseling caution as volatility
remained above normal.
The TMT Bubble, 19982002The technology, media and telecommunications (TMT) bubble
peaked in March 2000. Its subsequent collapse was punctuated
by a series of shocks to the financial markets, including the
September 11, 2001, terrorist attacks, the bankruptcy of Enron
in December 2001, the collapse of WorldCom in July 2002 and
the demise of Arthur Andersen, Enrons audit firm, in August of
that year.
In simulations during this period, the short-term volatility
measure in our model fluctuated significantly, spiking and
subsiding several times. In concert, the factors in our model
worked well, highlighting the elevated risks and the need for
caution.
Stormy Weather: How Our Volatility Model Works
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Dynamic Asset Allocation 11
The 1990s Bull MarketThe mid-1990suntil the collapse of the Thai baht marked the
start of the Asian crisis in July 1997was a period of below-av-
erage global equity volatility. Historical simulations showed that
this lull would have been reflected in the short- and medium-
term volatility factors in our model. But our long-term metric
would have indicated that volatility was likely to move back up
somewhat toward its average. The resulting forecast would
have allowed for increased risk-taking given the low-volatility
environment, but would have sounded a note of caution giventhat depressed volatility was unlikely to persist indefinitely.
The 1987 Stock-Market CrashBlack Monday, when global stock markets plunged on
October 19, 1987, was not successfully signaled by our model
in the historical simulation, although our risk forecasts adapted
reasonably well to the sharp rally that followed. The crash
was preceded by a period of low volatility. The simulation
showed that our risk forecasts for the period would have
been moderate, reflecting reasonably low short- and
medium-term volatility, qualified by the assumption that
below-average volatility was likely to correct upward over
time.
The suddenness of the stock-market crash meant that our
risk forecasts would not have given early warning of thespike. However, we found that our return forecasts were
well below normal, reflecting increasingly expensive
valuations and rising interest rates, resulting in an under-
weight in equities. This illustrates the importance of
incorporating additional tools besides volatility forecasting in
measuring the risk/return trade-off. n
Volatility Forecasting: Global Equity Case Studies
Credit Crisis TMT Bubble
Mid-1990s Bull Market and Asian Crisis 1987 Market Crash
Short-Term VolatilityMedium-Term VolatilityLong-Term Volatility AllianceBernstein One-Year Forecast Volatility
Percent
Percent
Percent
Percent
0
20
40
60
Jan 08 May 08 Sep 08 Jan 09 May 09 Sep 09
0
10
20
30
40
Jan 99 Jan 00 Jan 01 Jan 02 Jan 03 Jan 04
0
20
40
60
Jan 87 May 87 Sep 87 Jan 88 May 88 Sep 88 Jan 89
0
6
12
18
24
Jan 95 Jul 95 Jan 96 Jul 96 Jan 97 Jul 97 Jan 98
Through September 30, 2009At times, our one-year forecast can be greater than its three components. This is mainly due to an adjustment for autocorrelation in daily returns.Source: Global Financial Data, MSCI and AllianceBernstein
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12 AllianceBernstein.com
Measuring the Diversification OpportunityVolatility is not the only aspect of market risk. Correlations can
also shift dramatically over time, either reducing or increasing
the diversification opportunities available across a portfolio.
For example, the correlations between equities and other asset
classes have historically moved in a surprisingly large range, as
illustrated by the three-year rolling correlations between equities
and various other assets since the 1970s (Display 8).
Changing correlations can leave investors exposed to more riskthan they realize if volatilities and correlations rise at the same
time. For example, a common rationale for holding foreign
currencies and commodities is their near-zero long-term average
correlations with global equities (0.07 and 0.04, respectively).
Adding them to a portfolio is typically not perceived as adding
much in overall risk. But if correlations riseas might happen if
fears of a global recession caused investors to flee all economi-
cally sensitive assetsthen benefits from diversification might
disappear, causing portfolio risk to increase.
On the other hand, there are times when correlations fall or
even turn negative, making the diversification opportunity
better than average.
By far the most important correlation for investors is the
relationship between global equities and interest ratesthe two
primary sources of risk in most portfolios. In the long run these
assets are very weakly correlated, but their relationship has at
times been both strongly positive and strongly negative.
For example, during the 1970s, when inflation was thedominant concern, the correlation between stocks and bonds
was well above its long-term average. This is typical of periods
of high inflation, when the fixed payments of bonds become
less attractive and the future cash flows from stocks less
valuable, simultaneously putting pressure on both asset classes.
When we back-tested our model by simulating how it would
behave in this environment, we found that during the 1970s it
would have indicated rising correlations and a diminished
diversification benefit from holding bonds (Display 9).
Display 8
Diversification Benefits Have Fluctuated Widely
Rolling Three-Year Correlations with US Equities
(1.0)
(0.5)
0.0
0.5
1.0
High
Low
Average
0.60
0.65 0.62
0.17
0.48
0.04 0.07
Non-USEquities
Emerging-MarketEquities
REITs USTreasuryBonds
Investment-GradeCredit
Commo-dity
Futures
ForeignCurrency
As of September 30, 2009. Periods under observation are: non-US equities, USTreasury bonds, commodity futures and foreign currencysince 1972; REITssince1976; emerging-market stockssince 1990; and investment-grade creditsince1991. See notes on page 25 for asset class definitions.Source: Barclays Capital, Bloomberg, FTSE NAREIT, Global Financial Data,MJK Associates, MSCI, Thomson Reuters and AllianceBernstein
Display 9
Capturing Changing Diversification Opportunities
Forecast Correlations: Global Bonds and Global Equities
69 73 76 79 83 86 89 93 96 99 03 06 09
(0.6)
(0.3)
0.0
0.3
0.6 Inflation Concerns
Economic Growth Concerns
Through September 30, 2009Source: Barclays Capital, Global Financial Data, MSCI and AllianceBernstein
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Dynamic Asset Allocation 13
In a bear market, when concerns about economic growth and
deflation are running high, equities and bonds tend to move in
opposite directions, with investors stampeding out of equities
into the safety of the fixed payments of government bonds. For
example, during the Russian crisis and the collapse of Long Term
Capital Management (LTCM) in 1998, the TMT crash of 2000
2002 and the credit crisis of 20072009, back-testing showed
that our model would have picked up signals of increasingly
negative correlations between stocks and government bonds,
indicating that bonds were offering a better hedge than usual.
At the same time, rising positive correlations between stocks
and other risky assets, such as REITs, would have signaled that
economically sensitive assets offered fewer diversification
benefits.5
Chapter Highlights
n Volatility fluctuates, but its trends tend to persist, allowing us to predict future volatility with a reasonable level of confi-
dence. In most situations, this helps us to identify and react to changing volatilities in time to adjust portfolio risk exposure.
n Different asset classes offer different degrees of diversification over time. By identifying when those changes are most likely
to happen, we can increase or decrease diversifiers to smooth out fluctuations in the portfolio.
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14 AllianceBernstein.com
How Much Return Is Enough?Measuring the Opportunity
While it is important to have effective measures of risk, in order
to make informed asset-allocation decisions, it is also crucial to
have a well-researched perspective on future returns. Even
when volatility is low, return forecasts can provide early warning
of building market pressures. And even when volatility is high,
return forecasts may be signaling improving conditions. The
question is, how much return is enough?
To accept more risk, an investor must have the potential to earn
more in return. Interestingly, the additional compensation
required is not linear. This is because investors are loss
aversethe pain they feel from incurring a large loss will
typically outweigh the pleasure they get from generating a
comparable portfolio gain (Display 10, left). So expected returns
need to increase at a faster rate than expected risks for investors
to be content to maintain their long-term asset-allocation
strategy. The chart to the right in Display 10 illustrates the
return requirement for an investor with 60% of his or her
wealth in an investment that has a long-run expected volatility
of 10% and an expected return over cash of 3% (a Sharpe
ratioin other words, extra return per unit of riskof 0.3).
If an investor is determined to keep a 60% weighting in the
asset, what risk/return trade-off is required if expected volatility
rises from 10% to 20%? With twice the volatility, the potential
for large losses is high, so the investor would need to generate
Display 10
Investors Are Averse to Large Losses...
Loss Gain
Pleasure
Small Pleasure
Big Pain
Pain
...so They Should Ask More in Return for Taking Large Risks
Risk/Return Trade-Off Required to Maintain Long-Term Allocation
0
3
6
9
12
15
5 10 15 20 25
Expected Volatility (Percent)
Expected
Excess
Re
turn
(Percent)
2 Risk
4 Return
Source: Amos Tversky and Daniel Kahneman, Advances in Prospect Theory: Cumulative Representations of Uncertainty, Journal of Risk and Uncertainty (1992)
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Dynamic Asset Allocation 15
four times the amount of returna 12% excess return
requirement, or a Sharpe ratio of 0.6.6
If the investor does not believe that such high returns are likely,
he or she should reduce risk by cutting the allocation to
equities, or look for more attractive investment alternatives
elsewhere.
Are investors justified in assuming that a rise in volatility will be
accompanied by an even greater rise in return? Market theory
suggests that when risk rises, assets should reprice immediatelyso expected returns adjust upward. But our research shows that
this is not something that can be taken for granted. Historically,
there is little evidence to support the idea that returns go up
following a phase of high volatility.
We looked at the major asset classes to see what returns were
generated in the 12 months after periods when volatility was at
its highest and its lowest (Display 11). For example, in the equity
market, we found that, on average, investors received less,
rather than more, compensation for exposure to a high-volatility
environment.
Historically, in the 20% of occasions when the S&P 500 Index
was at its most volatile, its average annualized volatility was
about 35%. In the 20% of occasions when the index was least
volatile, it averaged 8%. In the subsequent 12-month periods,
the average excess return over cash following low volatility was
7.6% while the return after high volatility was only 5.3%.
Investors received less compensation following periods of high
riskthe opposite of what theory would predict.
The same pattern emerged in most other asset classes. Govern-
ment bonds were the only asset class where low volatility wasfollowed by weaker returns and high volatility resulted in slightly
above-average returns. But even there, the increase in return
was far less than the increase in volatility. The same analysis over
three- and five-year periods showed no basis for believing that
investors should expect to get paid more for persevering
through periods of high volatility.7
In other words, investors would have done better to start with
the assumption that they should reduce exposure when volatility
is high and add exposure when volatility is low. Of course, its
not that simple. There are times when the odds point to higher
Display 11
High Volatility Is Not Necessarily Followed by High Returns
34.5
8.0
23.9
6.8
8.7
2.1
13.1
7.0
21.8
6.2
S&P 500
Global Equities
Global Government Bonds
Foreign Currency
Commodity Futures
5.3
7.6
4.2
6.5
2.4
1.0
1.5
2.0
3.74.6
Past Volatility (Percent) 12-Month Forward Excess Returns (Percent)
As of June 30, 2009. Past volatilities are sorted on a monthly basis into quintiles (terciles in the case of foreign currency); the bars refer to the highest and lowest quintiles (orterciles). Past volatility is an exponentially weighted average using daily data with a three-week half-life (5% decay per day). Excess returns refer to returns over cash.Periods are since: S&P 5001928; global equities1970; global government bonds1970; foreign currency1974; commodity futures1970. See notes on page 25 for assetclass definitions. Source: Barclays Capit al, Bloomberg, FTSE NAREIT, Global Financial Data, MJK Associates, MSCI, S&P, Thomson Reuters and AllianceBernstein
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16 AllianceBernstein.com
returns and investors are more likely to be rewarded for taking
risk. The aim of our dynamic asset-allocation tools is to identify
these occasions and determine how much of an adjustment to
the asset allocation is warranted.
In order to decide whether the return opportunity calls for a
change in portfolio exposure, other factors, such as valuation,
need to be taken into account. As in the previous example,
Display 12 shows excess returns to the S&P 500 Index, but
focuses just on the excess returns that were generated in the 12
months after periods when volatility was at its highest.
While the average excess return to equities following the
highest-volatility periods was about 5.3%, sorting these
observations by earnings to price (the markets earnings yield)
showed considerable variation. When valuations were least
expensive (earnings were high relative to the market price),
investors were well rewarded over the following year, earning
an average of 15%. But when volatility was high and valuations
were not already low, exposure to equities resulted in losses of
5.5% on average.
A Multilayered Approach to Return ForecastingValuation is just one of many factors that should be considered
when trying to predict returns for any given risk level. In
constructing our return forecasts, we believe it is important to
take a multifaceted view of the opportunity, taking into account
the markets current view of expected returns, historical
risk/return relationships, and an array of market and economic
indicators that help to explain the likely path of future returns.
We begin by assessing the markets view of the return opportu-
nity. This is a useful starting point because market pricingincorporates a large amount of information about investors
views on the attractiveness of various asset classes. When the
market value of an asset class moves higher (or lower), investors
are voting with their wallets, indicating that, all else being
equal, they expect future returns to be higher (or lower).
To arrive at an estimate of the markets expected return for each
asset class, we gather data on the market values of all publicly
traded assets (this gives us each assets weight in the market
portfolio), the volatility and correlations of the assets, and an
estimate of the average investors risk profile. Based on this
information, we estimate the returns that the market requires in
order to be comfortable holding its current portfolio.
Of course, the market is not always right about future returns. It
has a tendency to get ahead of itself at times, becoming overly
optimistic or pessimistic. We try to improve on the market view
by counterbalancing it with two additional components of our
expected return model: an estimate of the typical compensa-
tion for risk available for each asset class and a multifactor
model of returns.
Our estimate of the typical compensation for risk takes account
of long-term risk/return relationships and current volatilities and
correlations in order to provide an estimate of returns at each
point in time. The purpose of this estimate is to pull back our
forecasts toward long-term averages at times when the market
seems to be overly optimistic or pessimistic about an asset class.
So, when the market seems to be pricing in extremely high
expectations about the likely reward per unit of risk, we inject a
level of skepticism into our forecast. Likewise, when the market
seems overly conservative in its expectations, our forecast is
likely to be more optimistic.
Display 12
Risk-Taking Is Better Rewarded When Valuations Are Low
S&P 500 12-Month Forward Excess Returns
During the 20% of Most Volatile Periods19282009
Quintile of Earnings to Price
(5.5)%
5.3%
15.0%
Most Expensive
Average
Least Expensive
Through June 30, 2009Source: S&P and AllianceBernstein
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Dynamic Asset Allocation 17
While historical risk/reward relationships sharpen the focus of
our forecasts, they do not tell the whole story. We use a
multifactor return model to refine our expected return forecasts
further, capturing a range of important factors from across the
global capital markets. We incorporate information from the
equity markets on valuations, corporate profitability, earnings
quality and market momentum; we look at bond-market
information, such as interest rates and the shape of government
bond yield curves; and we include information from other
markets, such as credit spreads, real estate valuations and key
economic indicators.
For each asset class, we then seek to determine which factors
will be most important in explaining future returns and the
relative weight of each factor in our model. The more extreme
the value of a particular indicator becomes, the greater its
influence will be on our return expectations. This allows the
main controversies of the day to be expressed in our forecasts.
Display 13 shows the most decisive factors in our expected
return model for equities, bonds and REITs, at five-year intervals
since 1980. Weve found that most asset classes are sensitive to
factors from multiple markets. For example, when forecasting
equity and REIT returns, we not only incorporate equity and REIT
factors, but we also include bond-market variablessuch as
short-term interest rates and recent trends in 10-year govern-
ment bond yields. By the same token, our fixed-income
forecasts consider stock-market and economic indicators, which
provide additional perspectives on future economic growth and
inflation.
Some factors influence multiple asset classes, but in different
directions. For example, very low short-term interest rates tend
to signal the bottom of an economic cycle, which implies that
their next move is upward as accelerating growth and inflation
pressures trigger central bank interest-rate increases. This
scenario tends to be bad for government bonds but good for
stocks and REITs. The opposite applies when short-term interest
rates are unusually high.
Display 13
Key Drivers of Asset Return Forecasts Vary over Time
Largest Values in Our Expected Return Calculation
Yield Momentum REIT Valuation REIT Valuation Yield Momentum REIT Momentum REIT Momentum
REIT Valuation
REITs
Return on EquityEquity Valuation Equity Valuation Return on EquityYield MomentumReturn on Equity
REIT Valuation
Yield Momentum Equity Valuation Equity Valuation Equity Valuation
Equities
SlopeInflationEquity MomentumShort RatesShort RatesSlope
Equity MomentumSlopeShort Rates Slope Slope Short Rates
Yield Momentum REIT Momentum Real Short Rates Real Short Rates
Bonds
200520001995199019851980
Positive for Expected Returns Negative for Expected Returns
Yield MomentumYield Momentum
Credit Spread
Return on Equity
Credit Spread
Slope
Short Rates
REIT Valuation
2009
As of September 30, 2009. Factors are as follows: equity valuationprice/book, price/earnings and dividend yield; equity momentumequity total return over past year; returnon equityreported trailing earnings/book value; credit spreaddifference in yield between non-government bonds and government bonds of comparable maturity; slopedifferencebetween three-month and 10-year yields; short ratesthree-month yields; real short ratesthree-month yields adjusted for inflation; yield momentumrecent trend in 10-year
government bond yields; inflationconsumer price index; REIT valuationdividend yield; REIT momentumrecent trend in REIT prices relative to equities.Source: Barclays Capital, FTSE NAREIT, Global Financial Data, MSCI and AllianceBernstein
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Dynamic Asset Allocation 19
market that was speaking particularly loudly was credit. This
was not surprising given that credit had been an early victim of
the subprime mortgage crisis in 2007. Spreads on corporate
bonds had moved sharply wider as credit sold off, preceding the
most dramatic of the falls in equity valuations and interest rates,
and acting as a potential warning signal of a large disconnect in
the markets.
The fact that credit-market factors had become such a domi-
nant number in our results meant that our return forecasts were1.7% lower than they might otherwise have been. Accordingly,
driven more by our return expectations than by our volatility
forecasts, our dynamic asset-allocation tool set would have
recommended an underweight in equities in August 2008.
By August 2009, six months after the low point in the credit
crisis, our return forecast was reflecting a different picture.
Corporate bond spreads had tightened following a strong
credit-market rally, equity valuations had risen, central bank
policy rates remained extraordinarily low and long-dated
government bond yields were below average. Collectively,
these factors significantly increased our estimate of the
excess return opportunity in equities, to 7.6%.
Given the increase in expected returns, we would have beenadding to our equity exposure by this stage. But, with risk
levels still well above their historical norms, our tool set
would still have been calling for a modest underweight in
this case. n
Return Forecasts Seek to Identify Gathering Storms and Improving Conditions
Forecasts for Global Equity Excess Returns
2.1%
7.9%
3.2%
7.6%
5.2% 5.2% 5.2% 5.2%Long-Term Equity Risk Premium
+1.5 +1.8 +0.8 +1.0Global Market Sentiment
+0.1 +0.1 +0.0 +0.5Typical Risk/Return Relationship
(3.4) +0.6 (0.9) +1.6Equity Factors
(0.3) (0.3) (1.7) (1.2)Credit Factors
(1.0) +0.5 (0.3) +0.5Government Bond Factors
2.1 7.9 3.2 7.6Total Excess Return
15.8% 21.8% 17.0% 19.6%Volatility Forecast
February 2000 March 2003 August 2008 August 2009
TMT Collapse Credit Crisis
As of September 30, 2009Source: Barclays Capital, Global Financial Data, MSCI and AllianceBernstein
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20 AllianceBernstein.com
Models that rely exclusively on past market patterns often fail. Our forecasts take account of historical market events, stress-
tested across a wide array of global markets and periods, but also give weight to fundamental insights and market theory. In
our portfolio construction, we limit the aggressiveness of our forecasts given the difficulty of predicting market returns with
great certainty. We also ensure consistency by making sure that return forecasts for highly correlated assets are linked. These
features help us to express our insights without making overly aggressive changes to our asset-allocation recommendations. n
Risk and Return Forecasts Must Be IntegratedSo far, we have argued that asset allocation based solely on the
return opportunity ignores crucial information about risk.
Similarly, focusing solely on volatility ignores important signals
embedded in return forecasts. We believe the key is to frame
the question in terms of return per unit of risk. In other words,
when the risks change, are we adequately compensated?
Sometimes the answer to this question is obvious. When risk is
low and expected returns are high, it makes sense to add
exposure to an asset. We found that in most cases where riskswere below average and expected returns were above average,
our framework would have called for an overweight in equities
(Display 14). In high-risk, low-return situations, the decision is
often equally clear-cut: reduce equity exposure.9
But, more commonly, the risk/return trade-off is a complex one.
Expected returns may be rising, but may not be high enough to
justify increasing exposure to an asset. This would have been
the case in late 2008, when our return expectations would have
been above average but the risks would have outweighed the
return opportunity. In high-risk, high-return environments, we
would have been overweight only 47% of the time.
Similarly, low expected returns do not necessarily call for a
reduction in equity exposure. There are times when our equity
return forecasts are below average, but so are the risks, so that
an overweight in equities might provide the best trade-off. One
such period was 20032005, when bargains were increasingly
scarce as equities recovered from the TMT bubble, but equity-
market volatility was also very low by historical standards. Our
tool set would have called for an overweight 37% of the time
when both risks and expected returns were below average.
Display 14
Opportunities Must Be Weighed Against Prevailing Risks
Monthly Global Equity Positioning (19702009)
Expected Volatility
ExpectedReturn
Low Risk, High Return
Low Risk, Low Return
High Risk, High Return
High Risk, Low Return
Overweight Underweight
10%
90%
47%53%
37%
63%
18%
82%
Through September 30, 2009Source: MSCI and AllianceBernstein
n Its not safe to assume that times of exceptional volatility offer exceptional return opportunities. Historically, the highest-
volatility periods have generally not yielded the best returns.
n On their own, there is only so much that forecasts of risks or returns can tell us. The key is to integrate the two perspectives,
framing the opportunity in terms of return per unit of risk.
Chapter Highlights
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Dynamic Asset Allocation 21
Achieving More Consistent OutcomesThe Portfolio Impact of Dynamic Asset Allocation
We know that a well-designed long-term asset allocation is
crucial to the success of any investment program. We also know
that market risk structures and return opportunities are
constantly changing. So how can we use this insight to enhance
a given long-term asset-allocation strategy?
Smoothing VolatilityOur research suggests that dynamic asset allocation may be
helpful in reducing portfolio volatility. The simulation results in
Display 15 show the volatility of a dynamically managed
portfolio that could invest in global stocks, REITs, bonds and
cash, and the volatility of a static portfolio that had a long-run
average of 55% stocks, 35% bonds, 10% REITs and 0% cash,
rebalanced monthly. At times of moderate volatility, the dynamic
strategy behaved in much the same way as the rebalanced static
allocation, but at times of high volatility there were significant
differences. The static portfolio was susceptible to large
fluctuations during each of the bear markets of the last 40
years, whereas the dynamic approach was more stable.
As a result of their different behavior in high-volatility periods,
the dynamic strategys long-term annualized volatility was
considerably lower than that of the static portfolio7.8%
compared with 9.2%.
Display 15
Dynamic Asset Allocation Can Result in a Smoother Ride over Time
12-Month Rolling Volatility
Static Rebalanced Dynamic Allocation
Long-Term Average
7.8%9.2%
Static Dynamic0
5
10
15
20
70 73 76 79 82 85 88 91 94 97 00 03 06 09
Percent
Through September 30, 2009The performance depicted above is hypothetical and is derived from a back-tested simulation. Please read Note on Simulation Results on page 30 for importantadditional information.Static portfolio results are based on a portfolio that is 55% MSCI World Index, 35% Barclays Global Aggregate Index (as adjusted to reflect duration only) and 10% FTSENAREIT, rebalanced halfway back to target when weights become +/5% from their long-term target. For physical securi ty positions, we assume one- way transaction costs of 0.6% for equities and bonds and 1.0% for REITs. For equity and bond derivatives, we assume total one-way transaction costs and cost of financing of 0.5%.Source: Barclays Capital, MSCI and AllianceBernstein
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22 AllianceBernstein.com
Since the dynamic approach tends to scale back exposure
during periods of elevated volatility, investors can experience a
smoother, more consistent pattern of returns. The bumpier ride
provided by the static allocation reflects more frequent discon-
nects from the investors desired returns and risk profile because
current market forces are not taken into account.
Trimming the TailsWe argued at the beginning of this paper that long-term
investors are justified in worrying about short-term risks because
of the damage done by extreme (tail) events. Display 16shows simulated total returns generated by the dynamic asset-
allocation approach during bear-market periods and in the
recoveries that followed.
During the bear markets in our study, the dynamic approach
would have significantly outperformed the static allocation,
mainly by reducing the portfolios exposure to high volatility.
Dynamic allocation would have outperformed in all five of the
major bear markets of the past 40 years.
For example, in the simulation the dynamic approach lost 23%
during the credit crisis from 2007 to early 2009, compared with
34% for the static allocation. And during the TMT collapse, the
dynamically managed portfolio lost 11%, compared with 18%
for the static allocation. On average, when the markets fell, the
loss suffered by the dynamic strategy was 20% less severe than
that of the static allocation.
Of course, there is no such thing as a free lunch. In the 12-
month periods following bear-market troughs, the dynamic
approach underperformed the static allocation, typically because
still-elevated volatility and correlation forecasts were calling for
more cautious exposure to the markets. This meant that not asmuch of the upside was captured. For example, as the markets
rallied between the beginning of March and the end of
September 2009, the simulated dynamic portfolio returned
21% compared with the static allocations 30%. And in the
recovery following the Black Monday crash in 1987, the
dynamic approach lagged the static portfolio by about 3%.
But while the dynamic strategy tended to lag initially during
recoveries, over the entire simulation history we found that it
performed nearly as well in rising markets, capturing more than
90% of the gains that a static mix would have achieved. In
other words, the dynamic portfolios ability to keep more of its
Display 16
Dynamic Approach Can Help Outperform in Bear Markets
(23)
(11)(18)Jan 00Sep 02
(12)(18)
(7)(15)
(34)
14
22
19
30
13
19
16
21Oct 07Feb 09
Jan 90Sep 90
Sep 87Nov 87
DynamicAllocation Relative
StaticRebalanced
7
8
11
6
Dec 87Nov 88
Oct 02Sep 03
Mar 09Sep 09
Oct 90Sep 91
DynamicAllocation Relative
StaticRebalanced
(1)
(23)(27)Jan 73Sep 74 22 214 Oct 74Sep 75 (1)
(3)
(9)
(3)
Simulated Performance During Bear MarketsPercent
Simulated Performance During Recoveries (Year After Decline)Percent
Through September 30, 2009The performance depicted above is hypothetical and is derived from a back-tested simulation. Please read Note on Simulation Results on page 30 for importantadditional information.Static portfolio results are based on a portfolio that is 55% MSCI World Index, 35% Barclays Global Aggregate Index (as adjusted to reflect duration only) and 10% FTSENAREIT, rebalanced halfway back to target when weights become +/5% from their long-term target. For physical securi ty positions, we assume one- way transaction costs of 0.6% for equities and bonds and 1.0% for REITs. For equity and bond derivatives, we assume total one-way transaction costs and cost of financing of 0.5%.Source: Barclays Capital, FTSE NAREIT, Global Financial Data, MSCI and AllianceBernstein
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Dynamic Asset Allocation 23
capital intact during downturns, and its greater flexibility in
consistently exploiting day-to-day return opportunities, largely
compensated for its modest underperformance in recoveries.
Display 17shows how the distribution of returns of a static
portfolio compared with the returns of a dynamic allocation
approach over the past four decades. We found that more of
the dynamic strategys returns would have fallen within the
(10)%20% range, reflecting lower average portfolio volatility.
Tail events were also less frequent. The incidence of extreme
gains was lower, but so was the frequency of extreme losses,
with annual losses of more than 20% reduced from eight
occurrences to just one.
Enhancing Risk-Adjusted ReturnsAlthough boosting returns is not a primary aim of our dynamic
asset-allocation tool set, simulations showed that a dynamic
approach would have generated slightly higher average total
returns than a conventional balanced strategy since 1970. Much
of the time, these extra returns were picked up during risk-
reduction periods, but at other times they were generated by
exploiting return opportunities during periods of normal
volatility.
Compared with the rebalanced 55% equity, 35% bond,10%
REIT portfolio discussed above, we found that a dynamic
approach significantly enhanced risk-adjusted total returns,
resulting in a Sharpe ratio of 0.46 compared with 0.36 for the
static allocation (Display 18, page 24). We found that including
more asset classes in the asset-allocation decision could improve
risk-adjusted returns even further.
Its worth noting that these results were achieved without
dramatic changes in portfolio weights. For example, the most
turbulent phase of the credit crisis in late 2008 would have
called for a weighting of about 61% in bonds, while the lowest
bond allocation, in 1978, was about 15%. We found that the
bond allocation would have been within 20% of the long-term
target for roughly 90% of the time.
Display 17
Dynamic Allocation May Reduce the Frequency of Extreme Losses
Frequency of Rolling 12-Month Returns19702009 (Simulated)
73
64
8
19
1
15
366
386
Below (20)% (20)%(10)% (10)%20% Above 20%
Static Rebalanced
Dynamic Allocation
Through September 30, 2009The performance depicted above is hypothetical and is derived from a back-tested simulation. Please read Note on Simulation Results on page 30 for importantadditional information.Static portfolio results are based on a portfolio that is 55% MSCI World Index, 35% Barclays Global Aggregate Index (as adjusted to reflect duration only) and 10% FTSE
NAREIT, rebalanced halfway back to target when weights become +/5% from their long-term target. For physical securi ty positions, we assume one- way transaction costs of 0.6% for equities and bonds and 1.0% for REITs. For equity and bond derivatives, we assume total one-way transaction costs and cost of financing of 0.5%.Source: Barclays Capital, FTSE NAREIT, Global Financial Data, MSCI and AllianceBernstein
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24 AllianceBernstein.com
ConclusionThe long-term asset-allocation decision is one of the most
important decisions an investor is ever likely to make, but we
believe that complementing the long-term allocation with a
dynamic asset-allocation strategy can add further value by
making portfolio positioning more sensitive to short- and
medium-term fluctuations in forecast risk and return.
While much of the existing body of research on dynamic asset
allocation focuses on boosting investment returns, we believe
that the strategy has more to offer from a risk-management
perspective. We believe that a dynamic approach can create a
more consistent fit between investor objectives and portfolio
outcomes, smoothing volatility and reducing the incidence of
outsize losses, without necessarily sacrificing return potential.
Display 18Dynamic Allocation Can Enhance the Risk/Return Trade-Off
Total Return
Volatility
Sharpe Ratio
9.1%
9.2%
0.36
Static Rebalanced
9.5%
7.8%
0.46
Dynamic Allocation
+0.4%
(1.4)%
+0.1
Change
Historical Simulation: Asset Allocations
0
25
50
75
100
70 73 76 79 82 85 88 91 94 97 00 03 06 09
Cash Equities REITs Bonds
Percent
Through September 30, 2009The performance depicted above is hypothetical and is derived from a back-tested simulation. Please read Note on Simulation Results on page 30 for importantadditional information.
Static portfolio results are based on a portfolio that is 55% MSCI World Index, 35% Barclays Global Aggregate Index (as adjusted to reflect duration only) and 10% FTSENAREIT, rebalanced halfway back to target when weights become +/5% from their long-term target. For physical security positions, we assume one- way transaction costs of 0.6% for equities and bonds and 1.0% for REITs. For equity and bond derivatives, we assume total one-way transaction costs and cost of financing of 0.5%.Source: Barclays Capital, FTSE NAREIT, Global Financial Data, MSCI and AllianceBernstein
Chapter Highlights
n Dynamic asset allocation can make the investment experience less turbulent in times of market upheaval by smoothing out
volatility and reducing extreme outcomes.
n In back-testing, the dynamic approach tended to outperform in bear markets, while lagging somewhat in recoveries.
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Dynamic Asset Allocation 25
Notes
1 Global equity returns refer to the MSCI All Country World Index after November 2000 and a market-weighted combination of the MSCI
World and MSCI Emerging Markets indices before that.
2 Investment-grade corporate bond outperformance refers to the excess returns relative to government bonds of the Barclays Global AggregateCorporate Index, hedged into US dollars.
3 In this example, if volatility fell to 5%, that would reduce both the upside and downside risk, to a gain of 17% and a loss of 3%. This assumes a
95% level of confidence. In statistical terms, assuming returns occur in a normal distribution, if an investment has an annual expected return of
7% and a volatility of 9%, there is a 68% probability of generating returns in a range of 7% plus or minus 9%, in other words (2)%16% (a one-
standard-deviation event). There is a 95% probability of generating returns in a range of 7% plus or minus 2 9%, in other words (11)%25%
(a two-standard-deviation event).
4 As a measure of past volatility, we used an exponentially weighted average using daily data with a three-week half-life (5% decay per day).
5 While correlations vary significantly over time, we will tend to forecast somewhat slower shifts in correlations than volatilities. This is because
trends in correlation are more difficult to discern from very recent events. Consider a time when volatility rises for stocks and bonds. This
could mean one of two things for correlations: either they will increase (typically when inflation is a concern) or they will decrease (typically
when deflation or economic growth is a concern). Until it is clear which source is driving the volatility, we cant draw any conclusions about
future correlations. Short-term measures can be noisy, so we rely more heavily on medium-term measures, which we find to be more
effective in determining which factor is likely to be driving correlations and their likely value in the future.
6 This assumes an unconstrained investor with a portfolio consisting of one risky asset class and cash.
7 For example, S&P 500 excess returns following low volatility averaged 7.6% after one year, 7.4% after three years and 7.1% after five years.
After high volatility, the average was 5.3% after one year, 6.2% after three years and 6.6% after five years. For global equities, the results were
6.5%, 4.2% and 5.3% after high volatility and 4.2%, 3.4% and 3.0% after low volatility. Excess returns for fixed income were 1.0%, 0.7% and
1.0% versus 2.4%, 2.8% and 2.9%; for currencies 2.0%, 1.9% and 1.6% versus 1.5%, 1.4% and 1.5%; and for commodities 4.6%, 6.9% and 5.2%
versus 3.7%, 4.1% and 4.6%.
8 The display does not show the contribution of economic factors, because during the two periods under discussion they were not a material
driver of expected returns in our models.
9 As shown in the display, in isolated cases our framework would have been overweight when risks were above average and returns were below
average, or underweight in the opposite scenario. The most likely cause for this is correlation considerations; for example, when the diversifica-
tion opportunity offered by bonds was unusually attractive or unattractive.
Asset class definitions are as follows: global equitiesMSCI All Country World Index or MSCI Developed World Index; emerging-
market equitiesMSCI Emerging Markets Index; Non-US equitiesMSCI Europe, Australasia and Far East (EAFE) Index; REITs
FTSE NAREIT Global Real Estate Index; high-yield creditBarclays Capital US High Yield Index; investment-grade creditBarclays
Capital US Investment Grade Index; global government bondsBarclays Capital Global Aggregate Treasury Index; US Treasury
bondsBarclays Capital US Treasury Index; commodity futuresproprietary composite; foreign currencyGDP-weighted basket of
currency returns relative to the US dollar.
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26 AllianceBernstein.com
Glossary
BenchmarkA standard barometer against which investments can be
measured in terms of performance, characteristics, construction
and similar criteria. Sometimes widely recognized instruments
(e.g., US Treasuries) or interest rates (e.g., the US fed funds rate
or LIBOR) serve as benchmarks. More commonly, a benchmark is
composed of an unmanaged group of securities with the same
general characteristics as the portfolio being measured against
it. Stock indices such as the S&P 500, the FTSE 100 and the
Nikkei 225 are commonly used for equities, while indices like
the Barclays Global Aggregate are often used in fixed income.
BondA security that pays interest. The issuer agrees to pay the
bondholder a regular set sum based on the amount borrowed
and the bonds coupon, and to repay the principal amount of
the loan at a future date. Many variations exist on this basic
format, including bonds with no coupon and with variable
coupons. The price of a bond is quoted assuming a par value of
100; thus, if a bond price is quoted as $90 and the principal
value of the actual holding is $1,000, that holding is valued at
$900. A bond selling above 100 is said to be trading at a
premium; at 100, at par; and below 100, at a discount. The
price varies over the life of the bond as interest rates, perceived
credit quality and other factors fluctuate, and as the bond
approaches maturity. A bonds price is inversely related to its
yield: it rises when the bonds yield falls and declines when the
yield rises.
CorrelationA statistical measure of the relationship between two variables.
Possible correlations range from +1 to (1). A zero correlation
indicates that there is no relationship between the variables; in
other words, a change in one variable will be matched by a
totally random change in the other. A correlation of (1) indicates
a perfect negative correlation, meaning that if one variable rises
relative to its own average, the other always falls relative to its
own average. A correlation of +1 indicates a perfect positive
correlation, meaning that if one variable rises relative to its
average, the other variable does the same.
DurationA measure of a bonds price sensitivity to changes in interest
rates, expressed in years. Duration approximates how much a
bonds price will change if interest rates change by a given
amount. For each year of duration, a bonds price will fall (or
rise) roughly one percentage point for each one-percentage-
point increase (or decrease) in yield. Thus, a bond with a longer
duration will perform worse when rates rise than a bond with a
shorter duration; conversely, it will perform better when rates
fall. Technically, duration is the weighted average term to
maturity of the bonds cash flows. Thus, it is shorter than
maturity for coupon-bearing bonds, which make annual or
semiannual payments throughout the life of the bond. Duration
is a good approximation of price sensitivity when interest-rate
changes are small, but less so when interest-rate changes are
large.
EquityOwnership of a company in the form of shares that represent a
claim on the corporations earnings and assets. Common-stock
holders have the right to vote on directors and other key
matters. Preferred-stock holders do not have voting rights, but
have priority when it comes to dividend payments. A firm can
authorize additional classes of stock, each with its own set of
contractual rights.
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Dynamic Asset Allocation 27
Equity Risk PremiumA forward-looking estimate of how much equities are likely to
outperform bonds. Equity investors typically demand a higher
return due to their greater risk of not receiving cash flows for
their investment.
Excess ReturnThe difference between returns, which may be applied to
managers or sectors. When referring to a manager or portfolio,
the excess return is typically the same as the active returnthe
difference between the managers or portfolios return and thatof the benchmark. A fixed-income sectors excess return is the
difference between its return and that of a comparable-duration
government bond: if short-term corporate debt returns 6% and
a short-term government security returns 4%, the excess return
is 2%. (See Risk-Free Rate.)
Information RatioThe ratio of a portfolios excess return, or premium, to its
tracking error, or the standard deviation of the premium over
the period being measured. It is designed to measure how much
excess return a manager delivers for each unit of risk. A higher
number indicates a more favorable balance of reward to risk
than a lower number. A positive information ratio indicates that
the portfolio outperformed, and a negative number indicates
that the portfolio underperformed. (See Excess Return and
Sharpe Ratio.)
Market ValueThe current price of a security in the market, as reflected by the
last reported price on an exchange, or the current bid-ask
spread if the security is traded over the counter.
Normal DistributionThe frequency distribution of a set of data that follows a bell-
shaped curve. The most frequent values are clustered around
the mean and fall off smoothly on either side of it. Extremely
large values and extremely small values are rare and occur near
the tail ends. In a normal distribution, 68% of observations fall
one standard deviation above or below the mean, while 95% of
observations fall two standard deviations above or below the
mean and 99.8% fall within three standard deviations. There
are other kinds of distribution. For example, in a fat tailed
distribution, the extremities are larger than those of a normal
distribution, implying a higher probability of experiencing
extreme values. (See Standard Deviation and Tail Event.)
Return on Equity (ROE)A measure of how much profit a company is able to generate
with the capital provided by shareholders. This measure is
calculated by dividing after-tax income for a specified time
period (e.g., trailing 12 months, trailing five years, forward 12
months) by the book value. Return on equity is expressed as a
percentage.
RiskIn common parlance, the chance of loss or of something bad
occurring. In financial parlance, it usually means the uncertainty
of outcomes due to one or many causes; it can be positive as
well as negative. Risk is usually measured by the standard
deviation of returnsin other words, the extent to which
returns may vary from the norm. Volatile assets tend to have a
wider range of possible returns and thus are said to be higher-
risk.
Risk-Free Rate
An investment with a predictable rate of return. An example is a
short-term government bond. A short-term government bond
has the explicit backing of a government, and the time period
before the bond matures is short enough to minimize the risks
of inflation and market interest-rate changes. Its yield is
therefore considered risk-free.
Sharpe RatioA measure of the risk-adjusted return of a financial security (or
asset or portfolio). It compares the excess return of an asset
against the return of a risk-free asset such as cash or govern-
ment bonds and divides that by the volatility of the excess
return. (See Information Ratio.)
SpreadThe difference between two variables, such as a securitys bid
and ask prices (bid-ask spread). In the corporate bond market,
the yield spread is the difference in yield between two bonds,
most often between the yield of a corporate bond and a
benchmark, such as a government bond, of comparable
maturity. Valuation spreads measure the difference between
expensive and cheap segments of the market.
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28 AllianceBernstein.com
Standard DeviationA statistical measure of risk that shows how aligned or at
variance the returns of an asset, industry or fund are relative to
their historical performance.
Tail EventExtremely large values and extremely small values, which are
rare and occur near the tail ends of a frequency distribution.
(See Normal Distribution.)
Transaction CostsThe costs incurred when buying or selling an asset security, such
as commission, fees and any indirect taxes.
VolatilityThe extent to which the price of a financial asset or market
fluctuates, measured by the standard deviation of its returns.
Volatility is a commonly cited risk measure.
YieldA component of the return on an investment. A shares dividend
yield is its annual dividend payment as a percentage of its
market price. A bonds yield is its annual interest payment as a
percentage of its market price. Measures of yield include current
yield, which considers only coupon interest, and yield to
maturity, which is the rate that equates the present value of the
bonds expected cash flows with its market price.
Yield Curve
A line connecting the yields of bonds from one end of thematurity spectrum to the other. Because yields typically rise
sharply at the short end of the spectrum and rise more gradually
at longer maturities, the plotted line usually forms a curve.
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Dynamic Asset Allocation 29
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30 AllianceBernstein.com
Note on Simulation Results
The asset-allocation framework discussed in this paper is
a new strategy for which actual data are not yet avail-
able. The portfolios and their performance are hypotheti-
cal and do not represent the investment performance or
the actual accounts of any investors. The securities in
these hypothetical portfolios were selected with the full
benefit of hindsight, after their performance over the
period shown was known. The results achieved in our
simulations do not guarantee future investment results.
The model performance information in this presentation is based
on the back-tested performance of hypothetical investments over
the time periods indicated. Back-testing is a process of
objectively simulating historical investment returns by applying a
set of rules for buying and selling securities, and other assets,
backward in time, testing those rules, and hypothetically
investing in the securities and other assets that are chosen.
Back-testing is designed to allow investors to understand and
evaluate certain strategies by seeing how they would have
performed hypothetically during certain time periods.
It is possible that the markets will perform better or worse than
shown in the projections; that the actual results of an investor
who invests in the manner these projections suggest will be
better or worse than the projections; and that an investor may
lose money by investing in the manner the projections suggest.
The projections assume the reinvestment of dividends and
include transaction costs of 0.6% for purchases and sales of
equities and bonds and 1.0% for real estate investment trusts
(REITs). For equity and bond derivatives, we assume total
one-way transaction costs and cost of financing of 0.5%. We
assume no deduction for advisory fees, and that assets are
allocated in the manner the projections suggest for nearly 40
years and are rebalanced monthly.
Although the information contained herein has been obtained
from sources believed to be reliable, its accuracy and complete-
ness cannot be guaranteed. While back-testing results reflect
the rigorous application of the investment strategy selected,
back-tested results have certain limitations and should not be
considered indicative of future results. In particular, they do not
reflect actual trading in an account, so there is no guarantee
that an actual account would have achieved the results shown.
Back-tested results also assume that asset allocations would not
have changed over time and in response t