April 2012
Schroders
How flexible do we need to be?
Greg Cooper, Simon Doyle, Simon Stevenson and Chris Durack
Executive summary
In our recent paper on “Why strategic asset allocation is flawed”1, we questioned the ability of the
fixed asset allocation approach common to our industry to deliver consistent investment outcomes in
line with objectives typically outlined to individuals.
That paper concluded that real returns of 4% to 5% p.a. were achievable historically from capital
markets, however achieving return objectives over very long term time horizons is simply not enough
to meet individuals’ requirements. Investors need greater comfort that their return objectives will be
delivered over more meaningful time horizons. In short, fixed strategic asset allocations have
historically generated higher longer term volatility which makes that approach unsuitable to achieving
these objectives consistently.
Our analysis shows that in order to achieve the real return objective over a 5 to 10 year time frame a
relatively unconstrained asset allocation is required. Moreover, the asset allocation flexibility required
is significant which we believe will require the industry to recast its approach to return forecasting and
asset allocation. Importantly, there are relatively straightforward approaches which can be used to
significantly improve forecasting accuracy and consequently form the basis for delivering better
outcomes to individuals. However, again, significant flexibility in asset allocation will be required.
Introduction
In a prior paper we considered the extent to which the fixed strategic asset allocation approach - which is the
mainstay of the investment portfolio construction of most investors - was able to meet required investment
objectives and to understand the real risks introduced from such an approach. We analysed a ‘stylised’
traditional balanced fund using asset class data commencing in 1900.
We showed that historically, while in the long run, average return objectives have been met, the time horizons
required are significant. This is largely due to the fact that equity markets in particular have delivered real
returns in long term cycles or ‘regimes’. This also has important implications for the money weighted returns
earned by individuals, as individuals don’t earn the ‘average’ as their investment capital is not constant.
In summary if the industry is to achieve the investment objectives it communicates to individuals, a substantial
rethink of the approach is required. Fixed strategic asset allocations generate significant medium term volatility
of outcomes, making them unsuitable for consistently achieving objectives. In the trade-off between delivering
real returns over constant time frames with a fixed strategic asset allocation, something has to give. It is in this
context that we demonstrated that fixed strategic asset allocations don’t work.
However, if fixed asset allocations fall short, it begs the question as to what level of flexibility is required in the
asset allocation process in order to consistently achieve investment objectives. Accordingly in this paper we
have analysed:
The degree of asset allocation flexibility that would have been required historically to achieve a typical investors’
investment objectives (in this case taken as CPI+4.5% p.a. over rolling 5- 10 year periods). On a prospective
basis, given a reasonable framework for estimating returns from different asset classes, the degree of asset
allocation flexibility required to meet investment objectives on a rolling 5-10 year time frame.
1
“Why Strategic Asset Allocation is flawed”; Cooper, Durack, Doyle and Stevenson, Schroder Investment
Management Australia Limited, March 2012
April 2012
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Analysis and results
Historical analysis
To understand the degree to which the constraints of a traditional asset allocation strategy would need to
be unwound in order to achieve a more consistent pattern of returns, we analysed the historical dataset on
a decade by decade basis. The analysis was initially conducted assuming perfect foresight on the returns,
risks, and correlations between the asset classes.
In this case we determined the optimal portfolio for a real return of 4.5%p.a. based on the minimisation of
downside risk. However, such an analysis would have the potential to result in “corner solutions”, where
the whole portfolio is invested substantially in one asset class for the decade. Consequently, in order to
make the analysis more “realistic” and to reduce the reliance on the requirement for perfect foresight, we
ensured a minimum level of diversification by utilising a ‘diversity factor’ to provide optimal portfolios
slightly below the efficient frontier (the portfolio with the best return for a given level of volatility) but much
more diversified. The diversity factor is based on two elements: a measure of concentration, in this case
the Herfindahl Index2, which is basically the sum of the squares of the portfolio weights (i.e. extremes are
magnified); and a multiplier that determines how much influence the concentration measure has on the
optimisation output. A pure optimised portfolio assumes the underlying forecasts are 100% correct; using
the diversity factor reduces the impact of forecasting errors on the portfolio. Chart 1 below shows the
results of our analysis.
Chart 1: Optimal portfolios with diversity factor - decade by decade
Asset Allocation and Real Returns by Decade ‐ Diversified
10.0%
100%
8.2%
90%
7.8%
8.1%
8.0%
80%
6.3%
6.2%
6.0%
70%
4.5%
4.4%
4.6%
4.6%
4.5%
60%
4.0%
50%
2.0%
40%
30%
Global Equities
0.0%
‐1.5%
Aust. Equities
20%
Bonds
‐2.0%
Cash
10%
Real Return
‐4.0%
0%
1900‐09
1910‐19
1920‐29
1930‐39
1940‐49
1950‐59
1960‐69
1970‐79
1980‐89
1990‐99
2000‐09
Source: Schroders, SMART, Global Financial Data
The analysis shows that achieving a real return objective of 4.5%p.a. over shorter (10 year) timeframes is
possible but requires substantial flexibility in the asset allocation. We can see that the portfolios outlined
above meet the rolling time constraints set by funds with much higher frequency than a fixed SAA portfolio,
except for one important exception (the 1970’s).
Chart 2 summarises our findings in respect of the degree of asset allocation flexibility required on a decade
by decade basis relative to a traditional 60/40 balanced fund.
2
The Herfindahl Index (also known as the Herfindal-Hirschman Index or HHI) is commonly used as a measure
of the size of firms in relation to the industry and an indicator of the amount of competition among them. In this
case we use the measure to reduce the dominance of any one asset class in a portfolio.
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Chart 2: Difference in asset allocation required to meet investment objective
Difference in Asset Allocation from Standard 60/40
50%
40%
More Defensive
30%
20%
10%
0%
-10%
-20%
-30%
More Growth -40%
-50%
1900-09
1910-19
1920-29
1930-39
1940-49
1950-59
1960-69
1970-79
1980-89
1990-99
2000-09
Source: Schroders
It is clear from the above that the asset allocations ranges required to consistently deliver objectives are
very wide – and certainly much wider than the ranges that would be more commonly in use in the industry.
Chart 3 below compares the decade by decade returns of the variable asset allocation portfolio with the
fixed strategic asset allocation (SAA) portfolio against the target real return objective of 4.5% p.a. We can
see that the flexible asset allocation portfolio captures much of the upside in good decades, but also does
not experience the poor performance of the traditional fixed strategic asset allocation (SAA) portfolio in
challenging decades. Average real returns over the period are 5.3% p.a. for the fixed SAA portfolio and
5.4% p.a. for the unconstrained portfolio.
Chart 3: Optimal portfolios performance - decade by decade
10.0%
Fixed SAA
8.0%
Variable AA
4.5% target
6.0%
4.0%
2.0%
0.0%
‐2.0%
‐4.0%
1900‐09 1910‐19 1920‐29 1930‐39 1940‐49 1950‐59 1960‐69 1970‐79 1980‐89 1990‐99 2000‐09
Source: Schroders, SMART, Global Financial Data
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The above analysis shows that it would generally have been possible historically to meet rolling real return
targets – with at least some level of foresight with respect to future asset class returns. We now explore to what
degree future market returns are predictable on a systematic basis that would have enabled such portfolios to
be constructed on a forward looking basis with a higher degree of certainty.
Forecasting returns
In a typical fixed asset allocation process the return assumptions could be described as long run equilibrium
returns. What we ideally require is a more accurate forecast of future market returns, particularly equity market
returns, utilising a systematic process that requires no qualitative judgement. That is, could we construct, using
only currently available data, a more accurate return assumption for the coming decade?
Research by Campbell and Shiller has shown that simple analysis based on cyclical adjusted PEs can provide
relatively accurate forecasts over 7 years or longer time horizons. The quality of this simple relationship is
shown powerfully in charts 4 and 5, which plots the actual 10 year returns versus a predicted 10 year return
based purely on Shiller PE (real price divided by real 10 year rolling earnings) for the US equity and Australian
equity markets
Campbell and Shiller used this relationship in their writings of the late 1990s and early 2000s to argue that the
outlook for the US equity market in the 2000s was very poor, which with the benefit of hindsight, was correct. In
addition, work by John Bogle provides a framework to forecast a wide variety of asset classes over a 7 to 10
year period. Bogle showed that by decomposing asset classes into their basic elements and assuming mean
reversion of these elements, relatively accurate long term forecasts can be made.
Chart 4: US Equity Market – Actual and Forecast Returns Based on Shiller PE
20%
15%
10%
5%
0%
Actual 10 year return
Forecast 10 year return
‐5%
Jan‐20
Jan‐17
Jan‐14
Jan‐11
Jan‐08
Jan‐05
Jan‐02
Jan‐99
Jan‐96
Jan‐93
Jan‐90
Jan‐87
Jan‐84
Jan‐81
Jan‐78
Jan‐75
Jan‐72
Jan‐69
Jan‐66
Jan‐63
Jan‐60
Jan‐57
Jan‐54
Jan‐51
‐10%
Source: Schroders, Datastream, Predicted return is calculated using inverted Shiller PE, Annual data
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Chart 5: Australian Equity Market – Actual and Forecast Returns Based on Shiller PE
25%
Actual 10 year return
20%
Forecast 10 year return
15%
10%
5%
0%
2021
2020
2019
2018
2016
2015
2014
2013
2012
2011
2009
2008
2007
2006
2005
2004
2002
2001
2000
1999
1998
1997
1995
1994
1993
1992
1991
1990
‐5%
Source: Schroders, Datastream, Predicted return is calculated using inverted Shiller PE, Monthly data
As shown above, it is possible to estimate future returns significantly more accurately than assuming equilibrium
returns using relatively simple techniques based on publically available information. Most importantly these
approaches are intuitively sensible in that they recognise that the valuation of an asset class (represented by
the price earnings ratio) is a key determinant of expected future returns.
Having shown that even a reasonably straightforward forecast methodology can produce successful outcomes,
and such forecasting results in a significant divergence of expected returns from traditionally utilised equilibrium
returns, we contend that investors should be making use of this information to develop asset allocations that are
more likely to meet investment objectives. Such portfolios are likely to result in a much wider divergence of
asset allocations than traditional fixed strategic asset allocations. In the same way that most investors would
never expect to hold a fixed allocation to a particular stock in an equity portfolio irrespective of price, we see no
reason why investors should be comfortable doing the same with their asset class exposure.
Where to from here?
On the understanding that we can make better return forecasts and recognising that significant uncertainties
currently exist with the global economic outlook, we now consider what asset allocation is required to meet
investment objectives on a forward looking basis.
Clearly at the present time, in light of the uncertainty around the global economic outlook (e.g. inflation vs
deflation), there is a high degree of uncertainty attached to any potential outcome. Where there is a choice
between portfolios that emphasise return maximisation versus those that target certainty of achievement of
objectives, we would argue that in the current environment the latter is significantly lower risk (to members).
To this end, even prior to undertaking any portfolio optimisation, it would be our view that utilising a portfolio with
a very heavy skew to a risky asset class (e.g. equities), is a far more uncertain proposition than at any time in
the recent past. As such, to the extent that return objectives can be achieved with greater certainty, this should
be the structure of choice.
In Schroders’ multi-asset team’s forward looking base case we assume the aftermath of the global financial
crisis and the deleveraging process continues for a total of 7 years, consistent with historical experience. The
base case analysis provides returns for equity markets that at first look very high. However, while the
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assumptions underlying the forecasts are relatively conservative, they are consistent with analysis based on the
Shiller PE, and they are not that different from the long term performance of these markets (since 1900 13%p.a.
for Australian equities and 11%p.a. for unhedged global equities).
The key return assumptions are set out in table 1 below.
Table 1: Prospective 7-10 Year Return Forecasts
Asset Class
Growth Assets
Australian Equities
Global Equities
Australian REITs
Diversifying Assets
High Yield Bonds
High Yield Floating Rate
Defensive Assets
Australian Bonds
Index Linked Bonds
Cash
Base Scenario
12.5%
11.5%
6.5%
6.0%
7.0%
5.5%
5.0%
5.5%
Source: Schroders
Portfolio modelling scenario
The return distribution assumptions were optimised in Schroder Multi-Asset Risk Technology (SMART) relative
to inflation, to provide a portfolio consistent with a real 4.5%p.a. real return objective. The optimised portfolio is
outlined in table 2 below. We have compared this portfolio relative to the superannuation industry average asset
allocation of default strategies as published by APRA3.
Table 3: Portfolio comparison – Base Case Portfolio vs Industry Average
Asset Class
Forward Looking Industry Average
Base
Growth Assets
Australian Equities
7.9%
29.0%
Global Equities
12.9%
24.0%
Australian REITs
4.1%
10.0%
Diversifying Assets
High Yield Bonds
5.3%
14.0%*
High Yield Floating Rate
18.5%
Defensive Assets
Australian Bonds
1.0%
15.0%
Index Linked Bonds
15.8%
Cash
34.4%
8.0%
Statistics
Real Return
4.6%
6.4%
Volatility
4.9%
8.5%
Prob. Of loss
7.5%
12.9%
95% VaR
-1.5%
-6.2%
95% CVaR
-5.3%
-12.9%
99% Stress VaR
-11.6%
-21.5%
Source: Schroders, SMART VaR, *High yield used as a proxy for other assets (e.g. unlisted)
3
APRA Annual Statistics, June 2011
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There are a number of key observations about the portfolios to note.
1.
The base case gives a very different asset allocation to the typical industry portfolio. This is largely a
function of the base case portfolio emphasising objectives over return maximisation. Given the wide
distribution of possible outcomes embedded in the forecasts, a portfolio with greater certainty is favoured.
This can be achieved with a much lower exposure to riskier assets.
2.
The base case portfolio has a very high weighting to cash. This is driven by the strong risk-return nature of
cash, where in Australia we expect a relatively high real return while the volatility of cash is very low.
3.
The base case portfolio has a bias towards index-linked bonds over nominal bonds. This is not surprising
given the portfolio was optimised against inflation and reflects the better relationship between index-linked
bonds and inflation relative to nominal bonds.
4.
The base case portfolio has a relatively low exposure to equities and a relatively high exposure to high
yielding credit. Given expected returns from credit securities are relatively high based on the elevated level
of credit spreads, this provides the ability to access the corporate risk premium but do so in a risk controlled
manner, by being higher up the capital structure.
5. While the industry average portfolio has a higher expected return, this comes with considerable downside
risk. The VaR and Conditional VaR results show the potential for significant negative return outcomes (not
inconsistent with what was observed in 2008).
In considering this latter point we should have regard to the issue of path dependency. By this we mean
considering that while the longer term average outcomes may end up being good, the nature of the journey to
achieve those outcomes can and does have a very big influence on individual outcomes. We will address this
issue in more detail in a forthcoming paper.
Conclusions
We had previously conducted analysis to support the proposition that real returns from capital markets are
consistent with the objectives of the typical investment approach of up to 5% p.a.. However, the historical
evidence did not provide as much support for real returns consistent with many individuals’ objectives to be
delivered reliably within the required timeframes for fixed asset allocation portfolios. Instead fixed asset
allocation portfolios require a very long term time horizon, given that equity markets in particular have delivered
real returns in long term cycles or ‘regimes’.
If the industry is to achieve the investment objectives it communicates to individuals, a substantial rethink of the
approach is required. Fixed strategic asset allocations generate significant medium term volatility of outcomes,
making them unsuitable for consistently achieving objectives.
The historical evidence provides more support for less constrained asset allocation approaches when the
required pattern of real return delivery is taken into consideration. Credible forward looking analytical
frameworks exist that provide reliable estimates of future capital market performance. Such frameworks require
the explicit incorporation of relative and absolute value.
In particular, we observe that there are three key portfolio management capabilities required for a plan to
achieve a real return objective of circa 4-5% p.a. over a defined 5-10 year time frame:
1.
The breadth of asset allocation ranges needs to be wide, with our analysis suggesting that unconstrained
ranges are most likely to be required. This is the only way to be reasonably assured of the potential to
achieve the real return objective over the time frame stipulated. Narrower asset allocation ranges will
require the time frame over which to achieve the real return objective to be lengthened substantially.
2.
Some capability around the forecasting of asset class distributions over the objective time frame will be
required. While academic literature suggests this is possible, a robust process will have to be developed to
successfully use the wider asset allocation ranges. One thing to note: this does not suggest the ability to
forecast markets with pinpoint accuracy. Just taking Campbell and Shiller’s analysis in the late 1990s,
would have allowed a plan to steer away from international equities and the subsequent poor performance.
3.
Ability to change asset allocation when required. Frequency of asset allocation changes will not be high in
managing to the 5 to 10 year part of the time frame, given the long run regime nature of financial markets.
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However, managing to a shorter time period would involve managing the cyclical nature of financial
markets.
Disclaimer
Opinions, estimates and projections in this report constitute the current judgement of the author as of the date of this article.
They do not necessarily reflect the opinions of Schroder Investment Management Australia Limited, ABN 22 000 443 274,
AFS Licence 226473 ("SIMAL") or any member of the Schroders Group and are subject to change without notice.
In preparing this document, we have relied upon and assumed, without independent verification, the accuracy and
completeness of all information available from public sources or which was otherwise reviewed by us.
SIMAL does not give any warranty as to the accuracy, reliability or completeness of information which is contained in this
article. Except insofar as liability under any statute cannot be excluded, Schroders and its directors, employees, consultants
or any company in the Schroders Group do not accept any liability (whether arising in contract, in tort or negligence or
otherwise) or any error or omission in this article or for any resulting loss or damage (whether direct, indirect, consequential
or otherwise) suffered by the recipient of this article or any other person.
This document does not contain, and should not be relied on as containing any investment, accounting, legal or tax advice.
Past performance is not a reliable indicator of future performance. Unless otherwise stated the source for all graphs and
tables contained in this document is SIMAL. For security purposes telephone calls may be taped.
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