White Papers

The holy grail in asset allocation

18/02/2013

Greg Cooper

Greg Cooper

CEO Schroders Australia / Global Head of Institutional

Background

For individual investors it is money weighted returns that matter not long term average rates of return. Unfortunately the investment and superannuation industry continues to emphasise time weighted average returns as the basis for reporting of longer term performance (in a large part because the alternative is complicated) but also as the basis for longer term portfolio construction via a strategic asset allocation framework. As we have discussed in numerous papers over the years1  this approach can and does result in quite large volatility of outcomes for individuals in both pre and post retirement.

Partly as a result of the volatility in markets over the last few years, there appears to us to be an increasing acceptance in the industry that the historical approaches to portfolio construction need to change, and in particular greater reference needs to be given to delivering more consistent returns against underlying objectives. Such objectives are usually framed around CPI.

However, as nature abhors a vacuum, the investment industry abhors a lack of complicated solutions to an old problem. As such, we have witnessed an increasing number of “solutions” being rolled out to combat the problem of volatility of outcomes.

In this paper we review a number of those solutions and suggest a simpler framework that funds could adopt – in whole or in part – to delivering outcomes more consistent with objectives.

In particular, we consider that incorporating strategies that address the valuation characteristics of asset classes over the medium term (so called “objective based strategies”) can significantly improve the behaviour of portfolios that continue to operate their overall structure driven by a Strategic Asset Allocation (SAA) framework.

Why are we here?

We have described in numerous previous papers how traditional approaches to portfolio construction can result in a wide range of outcomes for individuals. In particular, the long term volatility inherent in asset markets, especially equities, can mean that the typical asset allocation framework can result in very sub-optimal outcomes for large cohorts of members over time.

We showed that over the last 110 years a traditional balanced type portfolio with a fixed SAA has generated real returns of around 5.3% p.a. However:

  1. The medium term volatility of the results was significant, with the best 5 decades accounting for an average real return of 9.1% p.a. and the remaining 6 decades giving an average real return of only 1.5% p.a.
  2. There were substantial periods of drawdown relative to inflation with one such period lasting as long as 12 years and resulting in a loss of value relative to inflation of over 45%.
  3. Substantial flexibility is required in asset allocation in order to dampen the volatility sufficiently to achieve individual investment objectives consistently (or at least over rolling 5 to 10 year time frames).

The medium term volatility of the fixed strategic asset allocation based approach is highlighted in the following chart.

1See Appendix for a detailed list of papers referred to in this document

Stylised balanced fund (fixed SAA) – 10 Year Rolling Real Returns

Source: Schroders, Global Financial Data, Balanced fund is 30% global equity, 30% Australian equity, 30% Australian bonds, 10% cash.

Indeed the result of this significant volatility in return outcomes is that to be confident in delivery of investment objectives the time horizons required are significantly longer than most would realise (or accept).

The chart below shows what level of real return would have been achieved 80% and 90% of the time for a given time horizon.

Historical probability of achieving real return outcomes

The above chart shows that even with a 40 year time horizon we would have achieved a rolling real return outcome of CPI+4.3.% or better only 80% of the time. If our time horizon was 20 years and we required a 90% probability the return target was only CPI+2.2%. That is, one in every 10 individuals over 20 years would have achieved less than CPI+2.2%.

Historically if our target was CPI+4% we would have required a time horizon of 53 years to achieve this with 90% probability. For a CPI+5% target with 90% probability 85 years would be required. This also suggests that in fact the achievement of these returns is not so much a statistical probability that will occur given enough time, but rather a statistical anomaly in that we have only achieved these returns from this asset allocation because the return on equities (particularly Australian equities) has been very high.

While the 110 year return from a 60/40 portfolio has been circa 5.3% p.a., to have a reasonable degree of certainty of achieving our desired outcome the time horizons required exceed most investors’ lifetime accumulation periods.

Controlling medium term volatility becomes even more important when we take into consideration the accumulation and decumulation process and the resultant impact on money weighted returns. Again in other papers we have shown that due to the pattern of saving and dis-saving, achieving return objectives over very long term time horizons is simply not enough to meet that individual’s investment requirements.

For example, using the same modelling as in our long term time-weighted analysis above, but overlaying a simple de-cumulation strategy, the chart below shows the percentage of final salary that could have been paid for 20 years assuming retirement happened at the start of any year from 1900 to 1990.

Percentage of final salary that could be paid for 20 years

Source: Schroders, Global Financial Data. Income stream results based on retirement with 8 x final salary as a lump sum and setting income stream such that it lasts 20 years using a balanced investment strategy for retirement from 1900 to 1990

Individuals receive money weighted returns not time weighted returns. This necessitates controlling medium term volatility of investment outcomes to a much greater degree than is recognised by the industry. The fixed strategic asset allocation process common in our industry contributes to this volatility and so runs the risk of delivering poorer investment outcomes. If we are to meet individual investment objectives consistently for different age groups and savings patterns through time a greater focus on delivering more consistent medium term investment returns is required.

This will necessitate some change in how we manage portfolios vis a vis the outcomes we are aiming to achieve. But how?

Evaluating Solutions

If the source of the problem is the fixed nature of asset class weights and the inherent volatility this introduces, one way to analyse the problem is to consider the degree of flexibility that would have been required to deliver a more consistent outcome. The chart below 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.

Difference in Asset Allocation required to meet Investment Objective

Source: Schroders

It is clear from the above that the asset allocation 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.

Consequently, if the problem is one of asset allocation flexibility (or lack thereof), then there are multiple ways that we can address this problem. In particular, the industry has variously promoted solutions to this issue. Four such solutions that we consider are:

  1. Diversification
  2. Dynamic asset allocation approaches
  3. Life cycle solutions
  4. Risk Parity strategies

The interesting thing about each of these solutions is that they profess to approach the solution from a different angle.

Diversification

Our analysis throughout this paper (and prior papers) has largely been predicated on the premise of four major asset groups – Australian equities, global equities, fixed income and cash. This is largely a function of these asset groupings being the ones with the greatest available track record.

Clearly to the extent that we can add assets into a portfolio that have different characteristics to the existing assets and consequently where the price moves are not perfectly correlated with those existing assets, this will have an overall benefit on the risk and return characteristics of the portfolio.

In this regard we need to separate out shorter term diversification benefits from those over the longer term. Given that the volatility we are trying to control for is a medium to longer term volatility, such diversification benefits need to operate across a longer time horizon to be effective (e.g. a diversification benefit that results largely from valuation timing as is the case with listed vs unlisted assets is unlikely to be persistent through time). However, in sharp down markets even short term diversification becomes quite critical. That is, the path asset returns take is as important as the actual return in these environments.

True diversification can only come from an exposure to underlying cash flows of a security that are less correlated with other securities. Given the broad based exposure of traditional listed asset portfolios, it is our contention (see prior papers on the use of alternatives and complexity in portfolio construction) that true medium term diversification is difficult outside of the mainstream asset classes (albeit there could be benefits in reconstructing listed assets along different categorisations).

As the chart below highlights, correlations of different assets can have a habit of converging towards 1 at the time you most want the diversification benefits to operate.

Rolling Two Year Asset Class Correlations

Source: DataStream, Schroders to 31 January 2013. Index data used: Equities – S&P 500 TR Index, Corporate US Bonds – ML US Corporates 5-10 Year TR Index, Commodities – GSCI TR Index TIPS – Barclays Global Inflation US 10 Year TR Index

Dynamic Asset Allocation

In the case of dynamic asset allocation approaches the cyclical volatility of asset markets is addressed by adjusted the strategic asset allocation to some degree as a result of valuation or other views on the market.

Again, while dynamic asset allocation processes can (and are) used to adjust strategic asset allocations at the margin, such approaches are typically performed:

  1. With limited range of movement, – say +/- 10% at most, and more typically +/- 5% to an individual asset class; and
  2. Within a relative framework against the strategic asset allocation process.

As highlighted in the chart above on the degree of historical flexibility required in asset allocation, relatively small moves away from a fundamentally wrong strategic asset allocation are unlikely to alter the overall portfolio characteristics sufficiently to meet the objective.

Lifecycle Solutions

There are a myriad of potential lifecycle solutions that we have observed. They generally range from fixed “glidepath” approaches which reduce the exposure to growth (i.e. equity) assets through time based on age for certain cohorts, to more sophisticated approaches that dynamically adjust the exposure to growth or defensive assets based on a target balance or income requirement.

Given the range of potential solutions that fit under this banner it is difficult (and unfair) to make a sweeping conclusion. However two points we would highlight are:

  1. Any process that is independent of valuation levels of assets over the medium term is unlikely to remove the potential volatility of outcomes (unless the individual or sponsor is prepared to accept considerable volatility of contribution rate).
  2. The more individually tailored the solution the greater the degree of administrative and communication complexity involved. They also require the individual to be “locked” into such strategies for a large part of the lifecycle to be effective.

Funds should consider carefully their value proposition to members (which are usually framed around size, scale and standardisation) before embarking on overly complicated, individually tailored approaches.

In the case of more generic glidepath solutions, we have again highlighted in previous papers how these do not achieve outcomes any better than a simple balanced fund.

The chart below shows the outcome over time for individuals who commence saving at different points in time for a balanced fund vs a typical glidepath approach.

Source: Schroders, Global Financial Data

While these results can be shifted around by changing the glidepath parameters, if the outcome is so variable based on small changes in glidepath pattern, then that ultimately dictates that such an approach is unlikely to be robust through time.

Risk Parity Strategies

Risk Parity strategies have been gaining popularity in recent years given their strong performance and an increasing degree of literature supporting the concept.

While there are considerable variants on the theme, in general risk parity portfolios are constructed on the basis that the contribution to risk from different asset classes is equal (which is different to the actual exposure to those asset classes). While the number of asset classes included and the names of those can vary, at its simplest a typical risk parity strategy involves a substantially lower equity exposure than a traditional portfolio and a much higher exposure to bonds and sometimes other alternatives such as commodities. Such portfolios are then leveraged to increase the expected return on the portfolio which would otherwise be lower than a traditional portfolio (given the higher exposure to lower return asset classes). For a more complete view on how such strategies work refer to our recent paper on the topic.

While we agree broadly with the theory that risk of a traditional portfolio is dominated by equities and that domination can be inappropriate for long periods of time, simply redefining the asset mix and leveraging it with no regard for expected return (and risk) can introduce other biases into the portfolio that are just as damaging at other points in time.

More generally, simple risk parity strategies have a risk profile more consistent with leveraged bond portfolios. With yields converging on a very low absolute number, we would caution against the expectation that such portfolios will provide a degree of diversification should bond yields rise substantially (or, more importantly should inflation rise substantially when the objective is framed around an inflation relative target).

We show in the chart below rolling 10 year performance of a simplified Australian risk parity portfolio versus a traditional balanced portfolio.

Comparison of Rolling Real Returns of Risk Parity vs Balanced

Source: Schroders, Global Financial Data, Australian risk parity portfolio vs Global balanced portfolio.

It is clear from the above that in the context of delivering returns relative to objectives, it is difficult to argue that a risk parity portfolio is any better than a traditional portfolio (and neither are that good in our view). Note that we would acknowledge that many investors’ recent experience of risk parity portfolios may be better than shown in the above chart, however, there are very few strategies with 10 years of history and none to our knowledge built from an Australian asset class frame of reference.

In particular, the historical probability through time of a risk parity portfolio meeting a CPI+4.5% p.a. target on a rolling 10 year basis was 58% versus 57% for a traditional portfolio – hardly a materially better outcome. At the same time, the potential drawdown on a risk parity approach was a lot worse than that for a traditional approach, at -8.5% p.a. for the 10 years to 1974 versus a traditional portfolio of -3.7% p.a. for the same 10 year period.

Evaluating Solutions

In our view there are two key considerations for investors when evaluating investment strategies that target more consistent outcomes. The first is to make an assessment of the robustness of the conceptual investment framework, people and process applied to achieve the stated investment objectives. The second is to determine when such a portfolio might properly be allocated within a broader (say multi-manager) investment framework. The answer to the second of these considerations will, in our view, depend on an evaluation of the following:

- Can the characteristics of an objective based strategy be sufficiently identified?
- Are they fundamentally different to other asset classes?

If the answers to the above questions are yes, then there are strong arguments for an objective based strategy to be treated as a separate “asset class” for the purposes of determining an allocation. The funding of such an allocation would then be determined as part of an overall portfolio that is most efficient in meeting the defined objective for the overall portfolio.

The common thread in the broad solutions that are outlined above is that they are generally predicated on:

  1. an underlying framework that assumes asset class returns will be logically consistent (equities > bonds > cash) over the relevant time horizon; and
  2. an indifference between time weighted returns and money weighted returns in an environment where capital is being constantly added to or subtracted from.

Put simply, we could plot these solutions on a traditional investment framework as set out below.

Traditional Investment Framework

Source: Schroders, Rf = Risk free rate, P = Optimal portfolio of risky assets

However, it is clear historically that over reasonably long periods of time, such relationships do not always hold. We can see this in a selection of historical 10 year efficient frontiers shown below.

Historical efficient frontiers

Source: Schroders, Global Financial Data, Based on Australian equities and bonds for 10 year period shown. Monthly data.

Conditionality of Return Distributions

One of the principal issues that we return to is that any investment process that is to be robust through time must have regard to the expected risk and return characteristics of its components. Generally the more static investment processes outlined above assume that those risk and return characteristics are reasonably stable and largely independent of prior risk and return results.

Rather than assume the long run equilibrium expected return and risk characteristics, a more robust approach will assume that markets are always in disequilibrium – or in an adjustment process – relative to long run return expectations. When viewed this way, the views on long run are conditioned on important observable information such as valuation, cycle and liquidity variables to determine expectations for the forward period.

In the table below, we show the behaviour of the US equity market on an unconditional basis versus a conditional basis over the period from 31 December 1899 through to 31 July 2012. In showing the conditional distribution for equities over this period, the factor used to condition the data was a simple valuation measure, price earnings (PE) multiples using reported earnings. Over the period we examined observed monthly PE ratios and then examined the three year equity market return that immediately followed in each case. By doing this we were able to disaggregate the unconditional equity distribution (entire sample period), into three conditional equity distributions:

- one distribution of returns based on conditional “cheap” valuations (i.e. when PE ratios were less than 10 times),
- one distribution of returns when equities were in a range of “mid” valuation (i.e. when PE ratios ranged between 10 times and 20 times), and
- on a distribution of returns conditional on valuations that were “expensive”.

Unconditional versus conditional equity distributions. Rolling three year returns. US equity market December 1899 to July 2012.

  Unconditional Conditional
PE<10x 10x<PE<20x PE>20x
Number of observations 1316 278 880 158
Average 3 year return 9.8% p.a. 16.4% p.a. 8.8% p.a. 4.3% p.a.
Max 3 year return 42.4% p.a. 36.5% p.a. 42.4% p.a. 29.2
Min 3 year return -42.7% p.a. 0.5% p.a. -42.7% p.a. -34.7% p.a.
Frequency < 0% p.a. 16% 0% 17% 35%
Frequency between 0% p.a. and 10% p.a. 34% 28% 37% 25%
Frequency > 10% p.a. 51% 72% 46% 40%
Return range 85.1% 36.1% 85.1% 63.9%

*Maximum 3 year return p.a. less minimum 3 year return p.a. based on monthly data
Source: GFD, Schroders

We can observe from the above that the characteristics of the conditional distributions are fundamentally different when compared with the entire (unconditional sample). Most notably the average 3 year return in the “expensive” market was 4.3% p.a.. When PE ratios were in the “mid-range” the distribution of rolling 3 year returns showed an average return of around double the expensive sample at 8.8%. This average return then roughly doubled again when conditioned on “cheap” market valuations.

Again, in terms of range of return outcomes, each distribution showed marked difference. Of note here was the absence of a negative three year return when the starting PE ratio for the market was less than 10 times. The overall range of returns for both the “cheap” and “expensive” conditional distributions were less than the unconditional and “mid-range” conditional distributions indicating a higher degree of consistency in return outcomes in both cases. Positive for the cheap market, but negative for the expensive market.

Where to from here?

It is our contention that a more appropriate strategy for funds where achieving some consistency in the money weighted outcomes for individuals is important, and such funds receive and disburse varying capital contributions from different member cohorts through time, will be one that emphasises medium term stability of real return outcomes. In short, funds need to build in “objective based strategies” into their portfolio construction process. The degree to which strategies are incorporated will be a function of several external constraints.

  1. Peer based objectives

    While a CPI+4% to 5% return objective is very common as a primary growth portfolio objective, risk objectives can often be defined in terms of a peer group. It also remains reasonably common to observe investor objectives for (say) growth portfolios to target returns above the median manager/fund in nominated peer surveys. For investors where these objectives are in place (or implied), the path by which returns are generated becomes highly sensitive. To diversify too far away from the peer group’s asset allocation will involve taking higher levels of risk relative to the peer objective. This “peer relative” problem in the industry has led to an incumbency bias towards the use of reasonably static portfolios predicated on unconditional asset class distributions at the expense of more efficient portfolios that process and incorporate more available information.
  2. Return maximisation

    In a number of cases, the objectives set for an investor’s portfolio may be to target a return in excess of a target real rate of return. For example, a portfolio objective may be set to achieve a return in excess of CPI+5% p.a.. However given the aggressive nature of the objective, this target would usually be associated with a defined tolerance for downside outcomes including negative returns. In such cases, for reasons of diversification, it is rare to see such portfolios allocated solely to equities asset classes to address the potential downside outcomes. Again, due to its fundamentally different return drivers, an objective based portfolio when included in such a configuration to meet a (constrained) return maximisation portfolio will increase the efficiency of these portfolios relative to their objectives.
  3. Diversification of approach

    In the case of a multi-manager portfolio framework, it is currently commonplace to observe “growth” portfolios with real return targets of CPI+ 4% to 5% invested (say) 70% in growth assets (shares, property and growth alternatives), and 30% in defensive assets (bonds, cash and defensive alternatives). If an objective based strategy competes for an allocation in such a growth portfolio, based on its long term characteristics, a mean-variance optimiser will allocate close to 100% of the preferred (i.e. risk adjusted) weight to the objective based portfolio. However, if a multi-manager structure is required it may simply not be possible to identify a suitable number of objective based managers to given the required level of “manager diversification” for a high allocation to objective based strategies. In any case, regardless of the unambiguous efficiency improvement based on the optimised results, even a constrained allocation of say 20% would add meaningfully to the efficiency of such an overall portfolio. This improvement may be seen as being incorporated without changing the overall portfolio construction approach and execution using a diversified panel of investment managers. Furthermore, the approach may be seen as diversified (rather than just the mix of asset classes) because the drivers of returns in the objective based portfolio are fundamentally differentiated for the reasons discussed above.

More broadly, if funds are deliver better money weighted outcomes across different cohorts it is imperative that assets be managed against objectives rather than via arbitrary long term strategic asset allocations.

This will require funds to:

  1. Define the investment objective they are trying to achieve for members and measure outcomes specifically against this objective;
  2. Develop an objective based asset allocation framework taking into account the conditional distributions of asset class returns;
  3. Develop more absolute risk metrics; and
  4. For those implementing through multi-manager frameworks, allow for much greater flexibility in mandate design, the introduction of multi-asset mandates or much greater use of derivatives.

Appendix

Bibliography of Prior Research Pieces on Objective Based Investment Strategies (and related topics) from Schroder Investment Management Australia Limited referred to in this paper. Copies available from Schroders:

2007, 2008, 2009, “CPI+5 White Paper”;
January 2009, “It’s about risk, not return”;
April 2009, “What price complexity”;
August 2009, “Keeping it simple, back to the future for Asset Allocation”
February 2011, “Complexity Adding Value”
August 2011, “Post Retirement – Time to Focus on the Endgame”
September 2011, “Life Cycle Funds – Just Marketing Spin”
March 2012, “Why SAA is Flawed”
April 2012, “Asset Allocation - How flexible do we need to be?”
May 2012, “Understanding the Journey to Retirement”
October 2012, “Risk Parity – No Free Lunch”
November 2012, “Avoiding the valuation traps in Strategic Asset Allocation”

Data Sources Used in this Paper

Data sourced from Global Financial Data:

Australia Consumer Price Index
Australia Total Return Bills Index
Australia 10-year Government Bond Return Index
GFD World Return Index
Australia S&P/ASX 200 Accumulation Index
S&P 500 Total Return Index (w/GFD extension)

Disclaimer

Opinions, estimates and projections in this article 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 ("Schroders") 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. Schroders 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) for 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.

For individual investors it is money weighted returns that matter not long term average rates of return. Unfortunately the investment and superannuation industry continues to emphasise time weighted average returns as the basis for reporting of longer term performance (in a large part because the alternative is complicated) but also as the basis for longer term portfolio construction via a strategic asset allocation framework. As we have discussed in numerous papers over the years[1], this approach can and does result in quite large volatility of outcomes for individuals in both pre and post retirement.

Partly as a result of the volatility in markets over the last few years, there appears to us to be an increasing acceptance in the industry that the historical approaches to portfolio construction need to change, and in particular greater reference needs to be given to delivering more consistent returns against underlying objectives. Such objectives are usually framed around CPI.

However, as nature abhors a vacuum, the investment industry abhors a lack of complicated solutions to an old problem. As such, we have witnessed an increasing number of “solutions” being rolled out to combat the problem of volatility of outcomes. 

In this paper we review a number of those solutions and suggest a simpler framework that funds could adopt – in whole or in part – to delivering outcomes more consistent with objectives.

In particular, we consider that incorporating strategies that address the valuation characteristics of asset classes over the medium term (so called “objective based strategies”) can significantly improve the behaviour of portfolios that continue to operate their overall structure driven by a Strategic Asset Allocation (SAA) framework. 


[1] See Appendix for a detailed list of papers referred to in this document

Important Information:
Opinions, estimates and projections in this article 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 ("Schroders") 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. Schroders 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) for 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. Schroders may record and monitor telephone calls for security, training and compliance purposes.