Schroders
What price complexity?
Rethinking Australian Equity multi-manager portfolios
Greg Cooper
Chief Executive Officer
Schroder Investment Management Australia Limited
April 2009
What price complexity?
For professional investors and advisers only
Introduction
Over the years I have become a firm subscriber to the KISS school of investing. This is not some vague
reference to a quantitative trading scheme developed by Paul Stanley and Gene Simmons et al, but rather
the notion that investment is not a complicated game. The mantra of “Keep it Simple Stupid” applies
probably as much to successful investing than any other paid endeavour.
Investing is not complicated. That’s not to say it isn’t hard, and doesn’t require skill, but it is not
complicated. Unfortunately our industry – and those that surround it - earns a good deal from making it just
so.
Prima facia examples of this include the whole “structured debt” class of securities (take stuff you can buy
already, repackage it, charge a fee and sell it), hedge funds and especially hedge fund of funds (charging a
lot for what in aggregate hasn’t looked any better than a conservative balanced fund), and private equity
(charging you a multiple to leverage what you could already buy – and probably already owned).
Outside of these more obvious examples the complexity theme has worked its way into fundamentally
quite simple structures. The whole “segregation and specialisation” of the traditional asset classes and the
move away from simple balanced structures ultimately represents a move to greater complexity. While
sometimes complexity can bring benefits, more often than not in our view it increases costs and can hide
risks. There are often many stakeholders who can benefit from complexity, but not always the end client.
Within Australian equities the last 6 years has witnessed a doubling in the number of fund managers (and
a multi-fold increase in the number of products and strategies offered). At the same time we have seen
more managers appointed to increasingly smaller and more specialised mandates (e.g. boutiques).
The net result has been higher fees paid to an industry where talent has become increasingly dispersed.
We would argue that this is not in aggregate a positive result for end clients.
The purpose of this paper is to stimulate some greater debate within the industry by reviewing the actual
reported experience of Australian equity managers and, on the basis of some realistic assumptions, set out
some alternatives for the industry to consider in the construction of Australian equity multi-manager
portfolios. While some of this analysis may be relevant to other parts of the portfolio (and again at the
minimum we would hope stimulates some further analysis and debate) given the nature of the Australian
equity market we would be a little careful in making broad assertions that these results can be more
generally applied.
We would also be early to point out that as a large manager of “core” Australian equity portfolios some of
this analysis may come across as self serving. We would make no apologies for that – the data is what the
data is. In addition, after many years of hearing the “benefits” of more specialised structures we hope this
goes someway to offering an alternative point of view.
Data
There are a number of issues that should be addressed up front in analysing the data for reported
performance of Australian equity managers. Firstly, there is no truly comprehensive source for data on
manager performance in Australian equities. We have chosen to use the Mercer MPA Australian Shares
universe but would be careful to point out in advance that this is not a full data set.
Secondly, in looking to analyse the performance of aggregated multi-manager portfolios we would also
point out that a single comprehensive source of data on these portfolios doesn’t exist (note that in referring
to multi-manager portfolios in this document we are considering any institutional combination of Australian
equity managers with the aim of outperforming the index. This encompasses retail-multi-manager funds,
implemented consultants, superannuation funds and other large institutional investors).
There are two very important points here that are worth making:
1. While the main surveyors attempt to remove selection bias from their data, it is not possible to
completely remove it. (You can’t force a manager to report data, and if they are on their way out
they will simply stop reporting. Likewise, managers choose which products to add into the survey,
and when they add them). For example, the Mercer Survey had 59 long only managers in
December 1999. In December 2004 there were 45 managers with a 5 year track record and in
December 2008 only 34 managers with a 10 year track record. While the demise of some of these
managers is captured in the data it is not fully captured, and in any case a 50%-60% survivorship
rate implies some not insignificant costs in rotating away from “failed” managers.
What price complexity?
For professional investors and advisors only
2. The reported data shows percentage value add, not what really counts which is dollars of value
add. ie. A manager with $100mn receives the same performance weight as a manager with $5bn
– yet the economic consequences of the two are quite different.
In fact the point that the survey’s are not in aggregate asset weighted could be a part explanation for why
the industry overall appears to outperform the index – something the passive side of the industry would
argue is impossible over time (it’s a negative sum game after fees for all participants).
The simple model
Fundamentally, the simplest model for constructing an Australian equity portfolio would be to replicate the
index - assuming, and this is in our view a big assumption, you believe the lowest “risk” portfolio is the
index portfolio. Leaving that debate aside for another day, we will take the low risk/low cost option as
being the index portfolio.
However, for those of the view that 1) active management can add value (after fees), or 2) charge an
“active” fee to their end clients and therefore feel compelled to choose at least some active managers,
consideration needs to be given to how to combine one or a number of managers to generate post fee
alpha. We would set out that the “objective” of most multi-manager Australian equity combinations is:
“to deliver the highest post-fee alpha for a given level of relative (to the index) risk”.
In particular, the level of post-fee alpha targeted has to be at least sufficient to cover the multi-manager’s
own internal costs or work involved in constructing the portfolio. Risk in this metric is determined to mean
“variability” of the alpha, particularly negative variability.
Given the need (or desire) for a certain minimum level of alpha and the consequence that to deliver this
some level of risk must be taken, the question can then be split into:
1. What factors contribute to maximising post-fee (including tax and transactions costs) alpha; and
2. What factors contribute to minimising negative variability.
Prior research
Before conducting our own analysis it is worthwhile commenting on some of the available published
reports on construction of multi-manager portfolios.
–
Gallagher and Gardener (2005)1 make the point that in a study of US managers “We document
increased difficulties for blended portfolios in their attempts to outperform the market. This arises
due to significant erosion in the active bets of stocks held in blended portfolios. Our research also
identifies a potentially significant efficiency problem for blended portfolios, where up to 15 percent
of stock trading in any quarter is executed between common equity fund managers for a 10 fund
portfolio case. As expected, this effect is amplified when an analysis is performed at the industry
level. Improved efficiencies in active portfolio design would be most likely achieved where a single
agent is permitted to construct and maintain the portfolio structure in a manner which does not
erode the active investment opportunities offered by selected institutions.”
–
Eggins (2005) reviews the Gallagher and Gardner (2005) Australian study and makes the point –
that blending managers reduces risk, but with decreasing economies of scale. In addition the case
is made that alpha does not decrease as the number of managers increase. This is shown in the
chart below. We would agree with the first point (and as noted there are a number of other studies
that point to the same effect, including Brands and Gallagher (2004)4). However in respect of the
second point while the no loss in average alpha will be the case before fees, we would counter that
given that most managers scale fees down with increasing assets the post-fee alpha will decline as
more managers are appointed to smaller mandates. Eggins also makes the point to which we
would agree that “Compared to a single manager, however, multi-managers do not incur any
additional transactions costs.”
1
2
3
Gallagher, D.R. and Gardner, P., (2005), “The implications of blending specialist active equity management”,
University of NSW, School of Banking and Finance
2
Eggins, J, “Understanding the ingredients in the multi-manager portfolio pot”, JASSA Issue 2, 2007
3
Gallagher, D.R. and Gardner, P., (2005), ‘Portfolio Design and Challenges Inherent in Multiple Manager Structures’,
JASSA (The Journal of Finsia — Financial Services Institute of Australasia), Summer(4), pp 20–25.
4
Brands, S. and Gallagher, D.R. (2004), “Risk and Return properties of fund-of-funds”. JASSA Issue 1, Autumn 2004.
Page 3
What price complexity?
For professional investors and advisors only
Source: Eggins (2005)
– Eggins and Parish (2007)5 make the point that there is a “positive relationship between excess
returns and tracking error” based on the 5 year period to June 2006. They also state that “this
result is not unique to this time period or asset class”. While that appears to be the case in some
periods our results contradict this somewhat for later periods. In particular our results show that to
the extent there is some pick-up in alpha with tracking error this is not sufficient to offset the higher
fees.
The studies above and numerous others which measure the relative differences between boutiques and
“institutions” or satellites and cores all broadly point to a number of outcomes with which we would
intuitively agree – that beyond a point (say 5 or 6 managers) adding additional managers will not materially
improve the risk return trade-off. However we would also point out that all of these studies focus on pre-fee
analysis. Given the likely scale down in fees with asset size we would contend that the picture changes
somewhat on a post-fee basis. We explore this further below.
The Risk/Return trade-off
One of the more common assumptions embedded in multi-manager portfolio construction is that when
managers take risk, this risk will ultimately be rewarded (if not, your objective should be risk and fee
minimisation). However, the question should be asked as to what extent this is correct. We have witnessed
a strong rise in recent years in the demand for high-alpha (or should we say high “expected” alpha)
mandates – which usually entail significantly greater concentration of stock ideas (and often higher
turnover).
Part of the argument for such structures revolves around “capturing best ideas” and not “paying for an
index” portfolio. While it sounds attractive in theory we would offer two thoughts for consideration:
1. Superannuation funds (and most institutional pools) are long term investors. The greatest
arbitrages in active asset management are typically time horizon arbitrages (taking a long term
view will be rewarded). Higher turnover implies a demand for shorter-term returns. If you’re time
horizon is 5+ years, this implies turnover of 20%. Many “high alpha” mandates have 100%+
turnover implying time horizons of less than 1 year. This is inconsistent with the notion of being a
long term investor and trying to minimise total costs and taxes.
2. In aggregate the superannuation industry in Australia (assuming an average 30% holding in
Australian equities) represents over half the market capitalisation of the Australian share market.
5
Eggins, J, and Parish, S. “Two much of a Good Thing? The fallacy of over diversification” Russell, March 2007.
Page 4
For professional investors and advisors only
What price complexity?
The notion that everyone can be in a concentrated, best ideas portfolio and have significant nonindex positions is mathematically impossible.
Ignoring fees, there are three basic possibilities that the risk/return trade-off could take:
–
–
–
increasing returns to risk;
stable returns to risk;
decreasing returns to risk.
These payoffs are represented graphically below.
Alpha
Alpha
Risk
Alpha
Risk
Risk
One could also postulate that you get a combination of these as you move along the risk axis (i.e. Initially
increasing returns to risk, then stable, then declining). This would be our proposition, but firstly lets review
the empirical evidence.
Empirical evidence
Analysing the actual pre-fee, pre tax results for active Australian equity managers from the Mercer MPA
database reveals some interesting results.
The charts below show for three different snapshots in time the actual risk / reward trade-off for all
enhanced index and long only managers in the Mercer MPA Australian shares universe.
Risk, in these examples, has been calculated as the “average over 3 years of the rolling 3 year annualised
tracking errors” (or whatever longest period is available, subject to a minimum of 3 years). The attempt
here is to get some reasonably realistic measure of relative actual risk through time. Alternative measures
were also considered but didn’t lead to materially different results.
We have shown annualised excess returns over 3 and 5 year periods to 31 January 2000, 2005 and 2009.
(Cleary our results in each case exclude managers with less than 3 or 5 year track records, which may in
itself involve some (upward or downward) selection bias) – The appendix shows the individual manager
results for each period over 3 and 5 years.
Page 5
For professional investors and advisors only
What price complexity?
3 Years- Average Excess Return
4.5%
Excess Return (%p.a.)
4.0%
3.5%
2000
3.0%
2005
2009
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
-0.5%
1%
2%
3%
4%
5%
6%
Actual Tracking Error
5 Years- Average Excess Return
4.5%
Excess Return (%p.a.)
4.0%
2000
3.5%
2005
3.0%
2009
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
1%
2%
3%
4%
5%
6%
Actual Tracking Error
Source: Data - Mercer MPA, January 2009, Schroders calculations
We can observe from the charts above that for the periods to 2005 there appeared to be a positive
relationship between actual portfolio risk and return (especially in the 3 year data – albeit there are only 4
observations for that data point), this did not necessarily hold for the other periods. In fact, for the periods
to 2009 returns initially improved to risk but then appeared to decrease as tracking errors rose above 4%.
This would to us appear to be a more intuitive result from a practitioners’ perspective. Given the relative
narrowness of the Australian equity market (financials & resources making up close to 60%) while some
increasing returns to risk are possible, as risk is increased substantially the degree of concentration in
portfolios required (at either a sector level or a stock level) is such that portfolios become significantly more
prone to individual stock or sector specifics, which are somewhat harder to forecast and therefore add to
volatility but not necessarily to returns.
In addition, if we look at the performance of smaller cap stocks relative to large cap stocks (shown below)
there appears to be some correlation with the relative performance of higher risk managers – both did
better in the period to 2005 and correspondingly worse in the periods to 2000 and 2009. This is again not
surprising as part of the increase in risk is likely to come through larger allocations to smaller cap stocks
Page 6
For professional investors and advisors only
What price complexity?
(one could argue “beta dressed up as alpha”). Albeit that the equal and opposite point could be made
about high-risk manager performance in the other periods and therefore these are not valid points for
comparison either, our rebuttal would be that if it is all down to small cap then why not just isolate this
effect (and pay for it) separately. In any case, the reasons for the performance differentials could be the
subject for future research.
Relative Outperformance of Small Caps
(periods to 31 January)
6.0%
Annualised Excess Return (%p.a.)
3 years
4.0%
5 years
2.0%
0.0%
-2.0%
-4.0%
-6.0%
-8.0%
2000
2005
2009
Source: Datastream
Another way to consider this analysis is to look at the average reward per unit of risk taken – or the
information ratio for managers over the same periods. This is shown in the charts below.
3 Years- Average Information Ratio
0.9
0.8
2000
Information Ratio
0.7
2005
0.6
2009
0.5
0.4
0.3
0.2
0.1
0.0
-0.1
1%
2%
3%
4%
Actual Tracking Error
Page 7
5%
6%
For professional investors and advisors only
What price complexity?
5 Years- Average Information Ratio
0.9
0.8
2000
Information Ratio
0.7
2005
0.6
2009
0.5
0.4
0.3
0.2
0.1
0.0
1%
2%
3%
4%
5%
6%
Actual Tracking Error
Again, with the exception of the period to 2005 we can observe the tail-off in reward to risk as risk
increases above 4% actual tracking error.
So what does this mean from a portfolio construction perspective? The above results show that, in general,
there is some reward to risk (in particular the average information ratio’s are positive) on a pre-fee basis,
however this appears to tail-off as progressively more risk is taken in the portfolio. At the same time it
would be our observation that fee’s for higher risk portfolios are greater than those for lower risk portfolios.
We now consider the impact of fees on the overall portfolio.
Post fee analysis
In order to understand the likely post-fee impact of alternative manager configurations it is necessary to
make some broad assumptions about likely fee levels for different mandate types.
For the purpose of this analysis we have made the following assumptions with respect to average fee
levels. These fee levels are based on our own expectations and discussion with industry participants.
Most critically, it is not so much the absolute levels that are important but the relativities between each type.
We would agree that there is likely to be some dispersion around these levels from manager to manager,
but for a combination of managers it comes ultimately comes back to the average fee for that group and
the relativity between groups.
Manager
type
Expected Alpha
Average Fee in bp for size of mandate in $mn
Assumed
Actual
50
100
500
1,000
Index
0.10%
0.10%
10
8
5
2
Enhanced
Index
0.50%
0.25%
18
15
14
12
Core
1.80%
1.15%
55
45
35
30
High Alpha
2.50%
1.65%*
80
70
50
n/a
*Assuming you include the period to 2005, else this number will be closer to the core figure.
We have shown above two “expected alpha” scales. The first represents what would probably be the
realistic expectation for managers of different types – and what could be argued represents a better than
average actual outcome based on the value-add from manager research.
The second “actual alpha” expectation is based on the average alpha for different managers that has been
historically produced as per the data presented in the charts above.
Page 8
For professional investors and advisors only
What price complexity?
One area that makes this analysis potentially more difficult is the use of performance fees. At the single
manager level it could be argued that the use of performance fees will potentially mean that higher risk
structures can be put in place at limited overall cost to the client as if the performance is not delivered then
it will not be paid for. This is something we plan on addressing more broadly in a forthcoming paper,
however we would make the following comments:
–
Properly structured, performance fees can help in achieving better post fee outcomes. However,
this will typically involve lower percentage participation than is commonly the case, the use of high
water marks (which is common in institutional mandates) and the need to retain managers for a
significant length of time to allow the fee “benefit” from underperforming managers to offset the fee
increase from managers who outperform. As underperforming managers are rotated out of the
structure this potential future fee benefit is lost and aggregate fee’s rise.
–
When an increasing number of managers are used (as is common in higher alpha structures for
Australian equities) setting each manager with a performance fee generally results in aggregate
fees rising – much the same as it would if you paid a single manager a performance fee for their
correct individual stock calls but not their incorrect ones!
We have suggested in the fee table above a relative “flat fee” comparison for high-alpha managers.
Clearly as fund sizes increase, given the fall off in average fees, different combinations of manager lineups will result in different aggregate fee levels. The chart below shows the total fee for different
combinations of managers at different total fund sizes.
Average Fees by Mandate Size
0.60%
0.50%
Average Fee in %
Passive
Enhanced
0.40%
2 Core
0.30%
4 Core
50% Passive 4 High alpha
0.20%
50% Passive 6 High alpha
50% Passive 8 High alpha
0.10%
0.00%
100
250
500
1000
2500
5000
Size in $mn
We can observe from the above that as mandate size grows, average fees (should) fall even in the more
complicated structures. However, it is also worth pointing out that as size grows a “multi-core” combination
is cheaper, or at worst broadly similar, to the passive + high alpha combinations in terms of total fees. This
would change to the extent that:
1.
The passive component is reduced or replaced with enhanced passive;
2.
More “high-alpha” satellites are employed.
The charts below now take our assumed “alpha” expectations for each manager above and overlay this fee
scale to generate a post fee alpha (although notably still pre-tax) for various combinations of passive,
enhanced passive, core and passive + high alpha combinations plotted by changing fund size.
Page 9
For professional investors and advisors only
What price complexity?
Post Fee Alpha
Assumed Alpha and IR
1.60%
Post Fee Alpha (%p.a.)
1.40%
Passive
1.20%
Enhanced
1.00%
2 Core
4 Core
0.80%
6 Core
0.60%
50% Passive 4 High alpha
0.40%
50% Passive 6 High alpha
50% Passive 8 High alpha
0.20%
0.00%
100
250
500
1000
2500
5000
Size $mn
It is clear from the chart above that the post-fee outcomes now tilt significantly in favour of the option of
using blended “core” managers rather than a passive+satellite (high alpha) approach. In fact, based on
these fee differentials, the conditions required to make the multi-core approach similar to the
passive+satellite approach are that the expected return of the core manager would have to be less than
(by at least the fee differential) half of that of the high alpha manager. This is clearly not supported by
empirical evidence.
While the results for total fees are probably not that earth-shattering (or shouldn’t be), what is of more
interest to multi-manager portfolio constructors is what happens to total portfolio risk and post-fee returns
as you employ alternative manager structuring arrangements.
Analysing risk and return
In order to analyse the risk and return of alternative combinations of managers we need to make a number
of assumptions about the likely risk of those managers. We have assumed the following:
Manager
type
Index
Expected Alpha
Assumed
Actual
Tracking
Error
Information Ratio
Assumed
Actual
0.10%
0.10%
0.10%
1.00
1.00
Enhanced
Index
0.50%*
0.25%
0.50%
1.00
0.50
Core
1.80%*
1.15%
3.00%
0.60
0.38
High Alpha
2.50%*
1.65%
6.00%
0.42
0.28
*It is clear that these numbers are better than historical averages, however we have (rightly or wrongly)
allowed some benefit towards skill in manager selection.
In addition to this we have made the assumptions that:
– Alpha’s are normally distributed (albeit in practice they appear to have slight positive skew and
higher kurtosis); and
– The alpha of any two passive managers is 100% correlated, any two enhanced passive managers
is 50% correlated and any two core or high alpha managers is 0% correlated. In respect of this
last correlation estimate we would note that 0% appears to us a low figure for any two managers
Page 10
For professional investors and advisors only
What price complexity?
consistently through time. We would content that while the actual data shows most managers
alpha’s between -60% and 60% correlated6 given the limited breadth of the Australian market, as
you increase the number of managers it will become increasingly difficult to find larger groups with
low correlations. In any case, it is the difference between the core and high-alpha correlations that
will matter not the absolute figures.
The charts below show the combined risk and reward of alternative manager combinations.
Post Fee Alpha/Risk
($2.5bn portfolio - Assumed IR)
1.60%
2 Core
4 Core
Post Fee Alpha (%p.a.)
1.40%
6 Core
1.20%
1.00%
50% Passive 4 High alpha
50% Passive 6 High alpha
0.80%
0.60%
0.40%
Enhanced
0.20%
Passive
0.00%
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
Risk (%p.a.)
6
Eggins J and Warren G, “Clustering with style: piecing together the Australian Equity manager jigsaw”, Russell,
May 2007
Page 11
For professional investors and advisors only
What price complexity?
Post Fee Alpha/Risk
($2.5bn portfolio - Actual IR)
0.90%
2 Core
4 Core
0.80%
Post Fee Alpha (%p.a.)
6 Core
0.70%
0.60%
50% Passive 4 High alpha
50% Passive 6 High alpha
0.50%
0.40%
0.30%
0.20%
Enhanced
0.10%
Passive
0.00%
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
Risk (%p.a.)
As we can see from the charts above there appears to be no clear risk and return benefit to selecting the
“passive+satellite” approaches common in multi-manager portfolios today. In fact there are distinct benefits
to selecting a smaller number of larger core managers. If we were to then consider the effect on tax from
the higher turnover satellites and the increased governance required to monitor and research this larger
group of higher risk managers, the benefits to selecting a smaller number of core managers appear to us
to be relatively clear.
Conclusions
Australian equities is probably one of the “simpler” asset classes – certainly in terms of the access to data,
managers and our familiarity with it. The concept of “keeping it simple” applies here as much as it does
anywhere else.
However, despite the evidence, it is our observation that complexity and irrationality have crept their way
into the structuring of Australian equity multi-manager portfolios much the same way they have in other
asset classes and products. As outlined in this paper, we believe there are some simple approaches that
could be taken to maximise the post-fee relative returns from this asset at minimal risk. We would note the
following:
– We have observed a sharp rise in the demand for higher tracking error/higher fee mandates
combined with lower fee/lower cost enhanced index and passive mandates;
– Such structures would make sense if the risk/reward curves were linear or increasing with risk,
however the evidence shows that this is not the case;
– Our observations show that it is direct fees and indirect costs of turnover and taxes that increase
with the level of risk taken, further reducing the benefits to “high alpha” structures;
– The information ratio has generally been maximised in the 2-4% “tracking error” range – a space
that curiously has been reduced in recent years at the “expense” of these lower tracking error cores
and higher alpha/fee mandates;
To this end, appointing fewer managers, to larger more diversified mandates with an after tax focus, at a
point on the cost curve where average fees are minimised would result in better after tax and fee returns at
lower aggregate risk. Such structures are simple, require less governance and lower monitoring costs and
will make it easier for internal investment teams to aggregate and monitor total portfolio risk.
Complexity is over-rated.
Page 12
For professional investors and advisors only
What price complexity?
Appendix
The charts below show the results for each individual manager over 3 and 5 years to the respective period from the
Mercer MPA Australian Shares universe as at January 2009.
5 years to Jan 2000
3 years to Jan 2000
8%
15%
6%
Excess Return (%p.a.)
Excess Return (%p.a.)
10%
5%
0%
-5%
-10%
4%
2%
0%
-2%
-4%
-6%
-8%
-15%
0%
2%
4%
6%
8%
0%
10%
2%
4%
6%
8%
10%
Actual Tr ack ing Error
Actual Tracking Error
5 years to Jan 2005
3 Years to Jan 2005
8%
15%
6%
Excess Return (%p.a.)
Excess Return (%p.a.)
10%
5%
0%
-5%
-10%
4%
2%
0%
-2%
-4%
-6%
-8%
-15%
0%
2%
4%
6%
8%
0%
10%
2%
4%
6%
8%
10%
Actual Tracking Error
Actual Track ing Error
5 years to Jan 2009
3 Years to Jan 2009
8%
15%
6%
Excess Return (%p.a.)
Excess Return (%p.a.)
10%
5%
0%
-5%
-10%
4%
2%
0%
-2%
-4%
-6%
-8%
-15%
0%
2%
4%
6%
8%
10%
0%
Actual Tr acking Err or
Source: Data - Mercer MPA, January 2009, Schroders calculations
Page 13
2%
4%
6%
Actual Track ing Error
8%
10%
What price complexity?
For professional investors and advisors only
Important information
The views and opinions contained herein are those of Greg Cooper, Chief Executive Officer, and do not necessarily
represent Schroder Investment Management Limited’s house view.
For professional investors and advisers only. This document is not suitable for retail clients.
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.
Page 14