Your Future, Your Super: Constructing Portfolios to Pass the New Performance Test

Asset classes that have a low average after-fee alpha or a large volatility of that alpha may see an increasing threat from passive substitutes.

The Australian Government’s Your Future, Your Super (YFYS) reforms came into effect on 1 July 2021 and aim to improve the efficiency, transparency and accountability of the superannuation industry. A central element of the YFYS legislation is the new performance test, which measures product performance versus legislated benchmarks. This performance test introduces an additional constraint into the increasingly complex process of investment decision-making for superannuation funds.

Our analysis suggests that large deviations from benchmark exposures would require a material increase in expected outperformance to balance the incremental risk relative to the benchmark. Accordingly, we expect the benefit of active management to be the highest in asset classes where deviation from benchmark risk exposures is small and alpha is most reliable.

Robust portfolio construction and innovative investment solutions should help to achieve benchmark-like risk exposures with a high probability of outperformance over the medium term. At the same time, alpha sources that are uncorrelated (or negatively correlated) to the total portfolio alpha become increasingly valuable.

What does the performance test mean for superannuation funds?

The Australian Prudential Regulation Authority (APRA) will assess the returns of superannuation funds by calculating outperformance at the asset class level using a list of legislated benchmarks. In a nutshell, APRA will aggregate the over- or underperformance at the asset class level (measured over a rolling period of eight years) to calculate the super fund’s overall weighted performance. Hence, value created or destroyed through strategic asset allocation (SAA) decisions will be irrelevant to the performance test as the focus is on performance within each asset class.

A super fund is considered to fail the test if its product underperforms the benchmark (after fees and tax) by more than 50 basis points (bps) per year. The consequences for failure are material. The underperforming super fund must notify members that the product has performed poorly and suggest they consider moving their money into a different superannuation product. If a super fund underperforms for two consecutive years, the current version of the Treasury Laws Amendment suggests that the fund cannot accept new default members.

How might the legislation affect super funds’ investment objectives?

Investors generally optimise portfolio allocations to maximise the expected return for a given level of expected risk. Under YFYS, super funds will need to consider an additional constraint: imposing a maximum level of relative risk versus the legislated benchmarks. As a result, it may be prudent to identify investments within each asset class that have a high probability of positive alpha against the legislated benchmark while having low alpha volatility (tracking error). This objective can loosely be summarised as maximising the information ratio, which divides alpha by tracking error.

Of course, super funds have additional objectives outside the focus of YFYS, such as ESG targets, performance relative to peers, member outcomes more broadly, fees, liquidity, or downside risk management. It has always been part of the job of asset allocators to balance competing and interacting objectives, but this is becoming increasingly complex in today’s environment.

What are the implications of YFYS for major asset classes?

We assessed risk and return potential for equities, bonds and alternatives relative to legislated benchmarks. It turns out that the expected alpha and alpha volatility vary materially between the three asset classes. This may have implications for a super fund’s decision to invest actively or passively, or to consider innovative solutions that can combine the best of both worlds.

Global equities

It is well known that the majority of active equity funds do not beat their benchmarks. As an example, 86% and 93% of active global large cap funds have underperformed passive providers over the last five and 10 years, respectively (based on Morningstar data of EMEA-domiciled funds as of 31 December 2020).

The relevant YFYS benchmark for global equities is the MSCI All Country World ex Australia Index. Our analysis of popular Australian-domiciled global equity funds estimates a tracking error of around 6%-7% against this benchmark. This can be higher for funds that are concentrated in just a few stocks or have geographical focus areas that deviate from the benchmark.

The largest driver of relative risk for truly active funds is often idiosyncratic in nature, which can lead to some diversification benefits when combining multiple managers in an equity portfolio. Idiosyncratic risk can come from stock selection, timing decisions, frequent allocation changes, or off-benchmark allocations. However, depending on the management style, factor tilts like low-beta, value, small cap, or currency exposures may also contribute to the tracking error.

Without high conviction in an equity manager’s ability to generate sustainable alpha while keeping risk characteristics broadly in line with the legislated benchmark, it may be difficult for super funds to justify a large proportion of active equity investments.

Given the historical underperformance of the average active equity manager and the material tracking error, many investors may be seeking more reliable sources of excess returns. One solution could be the concept of “portable alpha”. This allows investors to gain passive equity exposure to the desired index efficiently through derivatives with low margin requirements. The remaining funds could then be invested in high quality, active portfolios that may offer a meaningful return potential over cash without significant correlation to equities. Interested readers can find more information about PIMCO’s approach to generating cost-effective alpha in equity allocations here.


In our 2017 research paper, Bonds Are Different, our analysis showed that unlike their equity counterparts, active bond funds have tended to largely outperform their median passive peers after fees. A few plausible reasons are the large proportion of non-economic bond investors like central banks, benchmark rebalancing and turnover, structural tilts, liquid derivatives strategies, the value of security-level research, new issue concessions, and generally lower informational efficiency.

The relevant benchmarks for global and domestic bonds are the Bloomberg Barclays Global Aggregate Index (AUD hedged) and Bloomberg Ausbond Composite Index, respectively. As Figure 1 shows, the main driver of risk in these benchmarks is interest rate duration, while credit risk factors act as modest diversifiers.

Figure 1: Volatility decomposition of benchmarks and tracking error of representative bond investments

Investors could further increase the return potential over the Global Aggregate Index with modest allocations to credit strategies. However, this can increase the risk of underperformance over the short term.

When comparing bonds and equities, not only is the absolute level of volatility in core bonds lower than in equities, but also the relative risk tends to be materially lower in active bond funds compared with active equity funds. Based on PIMCO’s benchmark-aware core bond strategies, we would suggest a tracking error of around 2% over the long term is realistic. This is less than half the tracking error of the average active equity fund.

As a result, active bonds tend to score better on the numerator and the denominator of the information ratio compared with equities. A higher expected outperformance paired with lower variation in the outperformance should be beneficial for the performance test.

However, we would note that not all bond strategies are the same. For example, absolute return bond strategies – strategies without benchmark-like duration and credit risk – can introduce considerable tracking error against the international or domestic core bond benchmarks.


The risk assessment in the “other” bucket is complex because it captures many investment strategies with materially different risk factor exposures such as commodities, private market assets and hedge funds. Listed and unlisted infrastructure and property investments have their own dedicated benchmarks and do not fall into the “other” bucket. The legislated benchmark is 50% Bloomberg Barclays Global Aggregate Index, 25% MSCI All Country World ex Australia Index AUD hedged, and 25% MSCI All Country World ex Australia Index AUD unhedged.

Private credit may offer return potential in the high single digits and is fundamentally exposed to fixed income and equity risk. This results in a high likelihood of outperformance. The tracking error will depend on the level of risk in any particular private debt solution but would generally be well below 10%.

An allocation to traditional absolute return, alternative risk premia and hedge fund strategies, which tend to have low correlations to market indices, may benefit the overall super fund portfolio. But the advantage they offer in terms of diversification comes at the cost of deviation from the legislated benchmark, which can be a disadvantage under the YFYS performance test. In addition, it is unlikely that defensive alternatives with return targets in the low single digits would beat the legislated benchmark over the long term.

Although some defensive alternatives may appear unattractive on a standalone basis, they can play a role in a broader portfolio context. While YFYS creates an incentive to hug the benchmark, investors should consider lofty valuations in public equity and bond markets may mean low returns and larger left tails. A modest allocation to true diversifiers could reduce the risk embedded in public markets and therefore help a portfolio to outperform the legislated benchmarks.

One approach could be to combine an active overlay (portable alpha) with synthetic passive exposures to global equities and global bonds to match the legislated benchmark. The passive exposures could hedge the risk of material underperformance of the chosen benchmarks. At the same time, the active overlay could be tailored to seek consistent outperformance uncorrelated with equity and bond markets. Examples of such overlays could range from high quality enhanced cash portfolios, to flexible, absolute-return-oriented bond alpha strategies, or even full-blown hedge funds.

What drives the risk of underperformance to legislated benchmarks?

As risk factors are the drivers of returns within asset classes, it is important to understand the risk factor exposures of the legislated benchmarks as well as the factor exposures of the underlying investment strategies.

Figure 1 above highlights the components of the tracking error of a representative global bond strategy and Australian short-term credit strategy against the benchmarks. The active risk factors in the global bond example are well diversified across interest rates, currencies, and various credit exposures. The tracking error for high quality short-dated domestic credit, however, is higher and more concentrated, mainly due to the duration underweight.

When combining various investment strategies within an asset class bucket, portfolio theory suggests to diversify risk factors. As an example, if all the fixed income strategies structurally overweight credit risk and underweight duration risk relative to the legislated benchmark, then the overall tracking error will be higher than in a scenario where the relative risk factor exposures of underlying investments diversify each other.

The tracking error is helpful in estimating the unconditional probability of underperformance but imposes the assumption of a normal distribution. Because asset returns do not necessarily follow a normal distribution, super funds are also interested in understanding the tail risks of their investments relative to the legislated benchmarks.

To help illustrate these tail risks, we estimated the relative performance of four alternative assets in historical scenarios of rising rates (2013 “taper tantrum”) and falling equity prices (2020 COVID-19 drawdown). Figure 2 shows the estimated benchmark return along with the returns of alternative strategies relative to the benchmark. It also highlights the risk factor contributions.

Private equity, private corporate credit, diversified fund of hedge funds, and defensive alternative risk premia (ARP) are proxied by PIMCO’s proprietary models that aim to represent the true economic (mark-to-market) risk embedded in alternative strategies. This differs from the reported volatility, which, due to the well-known smoothening bias of reported returns in illiquid assets, is generally lower than implied by the risk factor exposures. As an example, the economic risk in private equity is estimated with risk factors that represent a leveraged exposure to small cap stocks with a value tilt and liquidity risk.

Figure 2: Stress testing alternative assets: historical scenario analysis

We estimate the 50/50 equity/bond benchmark would return +6% and −16% in the rising rate and pandemic shocks, respectively. The performance for the COVID-19 shock is driven by a negative contribution from equity risk while interest rates and the exposure to foreign currencies act as diversifiers.

In our analysis, the COVID shock leads to a mark-to-market underperformance of more than 20% for private equity relative to the benchmark. This is driven by the materially higher equity beta, style tilts, liquidity risk and the lack of interest rate duration. The other alternative assets demonstrate swings of lower magnitude around the benchmark return. Private credit is expected to modestly outperform in the historical rates shock and modestly underperform in the COVID scenario.

Super funds may be in a position to add value through a thoughtful optimisation of the alternatives bucket. In summary, the objective is to build an efficient portfolio of alternatives that has a high probability of outperformance with moderate tracking error and tail risk against the legislated benchmark. Our modelling suggests that a core allocation to private credit with satellite allocations to hedge funds, defensive ARP, and private equity has potential to materially outperform the blended benchmark with relatively modest tracking error and tolerable tail risk. This is represented by the alternatives portfolio in Figure 2.

The superior risk/return trade-off of an optimised alternatives portfolio demonstrates the advantage of taking a portfolio approach to beat the legislated benchmark as opposed to assessing each investment strategy on a standalone basis against its benchmark.

Taking a total portfolio approach will be beneficial for the performance test

In our view, super funds can benefit from a total portfolio perspective because the legislation implies that outperformance in one asset class can offset underperformance in another. Beta exposures should be selected with reference to the respective asset class benchmarks and alpha sources should be sufficiently well diversified from other alpha sources across the total portfolio. An alpha stream that is uncorrelated (or even negatively correlated) with the fund-level alpha is more valuable than highly correlated alpha.

As an example, consider two scenarios for a 60/40 portfolio of stocks and bonds, assuming a negative correlation between the equity and interest rate factor.

  • In scenario 1, the equity benchmark is perfectly replicated except for an equity beta overweight of 0.1, while the fixed income benchmark is perfectly replicated except for an interest rate duration underweight of 2 years. For this portfolio, we would estimate the tracking error to be 1.28%.
  • In scenario 2, the equity portion remains the same but the bond portion has an interest rate duration overweight of 2 years. In this case, the portfolio-level tracking error would be 0.79%.

The 38% reduction in tracking error between scenarios 1 and 2 is a direct result of increasing the diversification of relative risk between the two asset classes.

Asset classes that have a low average after-fee alpha or a large volatility of that alpha, and hence a low information ratio, may see an increasing threat from passive substitutes. Given a history of reliable alpha in global bonds, active, benchmark-aware fixed income strategies may be comparatively well positioned for the performance test.

Innovative concepts such as combining passive market betas with active and more reliable sources of alpha may deliver more consistent performance outcomes. One example would be strategies that source cost-efficient equity beta exposure and add more reliable fixed income alpha as an overlay. These “stocks-plus” strategies are particularly appealing under YFYS, where the sustainability of alpha is important.

The alternatives bucket may also offer opportunities for outperformance without too much deviation from the legislated benchmark’s risk characteristics. Strategies that have embedded equity and fixed income exposures, such as private credit, may be a sweet spot. In this complex space, we believe it’s important to partner with our clients to discuss customised solutions tailored to meet particular needs and objectives.

To read more of PIMCO’s research articles and viewpoints designed for professional investors, please visit our Research page.

[1] While the concept of maximising the information ratio can be an appropriate guidance, the reality is more nuanced due to the non-linear nature of the performance test. The non-linearity is introduced by the binary outcome of the performance test (pass/fail), the periodic assessment frequency with a finite assessment horizon, and inherent path dependency. For example, the objective of maximising the information ratio may be less relevant for a fund that comfortably outperformed over the past eight years and more relevant for funds that are close to the 50 bps underperformance cutoff.

[2] Source: Morningstar, performance of EMEA domiciled active funds against a composite of passive peers in the Global Large Blend category including all share classes.

[3] The currency hedged or unhedged version of the index is chosen based on the nature of hedging in the underlying investments.

[4] This is based on the five largest funds as of 31 August 2021 with track record from 1 July 2014 to 30 June 2021 in the Morningstar Australia OE Equity World Large Blend category.

[5] See our 2013 research paper, Asset Allocation: Risk Models for Alternative Investments, for more details.

[6] This is based on a return volatility of 14.88% for an equity beta of 1, a return volatility of 0.73% for one year of interest rate duration exposure and a correlation of −0.49 between the two factors.

The Author

Robert Mead

Head of Australia, Co-head of Asia-Pacific Portfolio Management

Fabian Dienemann

Quantitative Research Analyst



PIMCO Australia Pty Ltd
ABN 54 084 280 508
AFS Licence 246862
Level 19, 5 Martin Place
Sydney, NSW 2000

PIMCO Australia Pty Ltd ABN 54 084 280 508, AFSL 246862. This publication has been prepared without taking into account the objectives, financial situation or needs of investors. Before making an investment decision, investors should obtain professional advice and consider whether the information contained herein is appropriate having regard to their objectives, financial situation and needs.

Alpha represents a portfolio’s risk-adjusted performance (the difference between a portfolio’s actual returns and the expected performance, given the portfolio’s level of risk as measured by beta). It is possible that during any timeframe, the alpha of a portfolio can be positive while the actual total return performance of the portfolio is negative.

Beta is a measure of price sensitivity to market movements. Market beta is 1.

Charts and data have been provided for illustrative purposes and are not indicative of the past or future performance of any PIMCO product. Charts may not be to scale and users should take this into consideration when conducting analysis.

Correlation is a statistical measure of how two securities move in relation to each other. The correlation of various indexes or securities against one another or against inflation is based upon data over a certain time period. These correlations may vary substantially in the future or over different time periods that can result in greater volatility.

The time period referenced throughout the presentation as pre-COVID-19 refers to the period leading up to Q1 2020. Post-COVID-19 refers to a forward-looking time period following market volatility in Q1 2020 and early Q2 2020.

Asset class proxies are based on publically available indexes or combinations thereof. Custom benchmarks may have been modeled in circumstances where illiquid / alternative asset class benchmarks are not available. In instances where a public index does not provide security level detail, we run regression analysis and replicate the index using risk factors. Custom benchmarks have been created by or in consultation with the user.

There is no guarantee that these investment strategies will work under all market conditions and each investor should evaluate their ability to invest for a long-term especially during periods of downturn in the market. No representation is being made that any account, product, or strategy will or is likely to achieve profits, losses, or results similar to those shown.

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Investing in the bond market is subject to risks, including market, interest rate, issuer, credit, inflation risk, and liquidity risk. The value of most bonds and bond strategies are impacted by changes in interest rates. Bonds and bond strategies with longer durations tend to be more sensitive and volatile than those with shorter durations; bond prices generally fall as interest rates rise, and the current low interest rate environment increases this risk. Current reductions in bond counterparty capacity may contribute to decreased market liquidity and increased price volatility. Bond investments may be worth more or less than the original cost when redeemed. Commodities contain heightened risk, including market, political, regulatory and natural conditions, and may not be suitable for all investors. Currency rates may fluctuate significantly over short periods of time and may reduce the returns of a portfolio. Derivatives may involve certain costs and risks, such as liquidity, interest rate, market, credit, management and the risk that a position could not be closed when most advantageous. Investing in derivatives could lose more than the amount invested. Equities may decline in value due to both real and perceived general market, economic and industry conditions. Investing in foreign-denominated and/or -domiciled securities may involve heightened risk due to currency fluctuations, and economic and political risks, which may be enhanced in emerging markets. Sovereign securities are generally backed by the issuing government. Obligations of US government agencies and authorities are supported by varying degrees, but are generally not backed by the full faith of the US government. Portfolios that invest in such securities are not guaranteed and will fluctuate in value. High yield, lower-rated securities involve greater risk than higher-rated securities; portfolios that invest in them may be subject to greater levels of credit and liquidity risk than portfolios that do not. Mortgage- and asset-backed securities may be sensitive to changes in interest rates, subject to early repayment risk, and while generally supported by a government, government-agency or private guarantor, there is no assurance that the guarantor will meet its obligations. Income from municipal bonds may be subject to state and local taxes and at times the alternative minimum tax. Swaps are a type of derivative; swaps are increasingly subject to central clearing and exchange-trading. Swaps that are not centrally cleared and exchange-traded may be less liquid than exchange-traded instruments. Inflation-linked bonds (ILBs) issued by a government are fixed income securities whose principal value is periodically adjusted according to the rate of inflation; ILBs decline in value when real interest rates rise. Treasury Inflation-Protected Securities (TIPS) are ILBs issued by the US government. Certain US government securities are backed by the full faith of the government. Obligations of US government agencies and authorities are supported by varying degrees but are generally not backed by the full faith of the US government. Portfolios that invest in such securities are not guaranteed and will fluctuate in value.

PIMCO has historically used factor based stress analyses that estimate portfolio return sensitivity to various risk factors. Essentially, portfolios are decomposed into different risk factors and shocks are applied to those factors to estimate portfolio responses. Because of limitations of these modeling techniques, we make no representation that use of these models will actually reflect future results, or that any investment actually will achieve results similar to those shown. Hypothetical or simulated performance modeling techniques have inherent limitations. These techniques do not predict future actual performance and are limited by assumptions that future market events will behave similarly to historical time periods or theoretical models. Future events very often occur to causal relationships not anticipated by such models, and it should be expected that sharp differences will often occur between the results of these models and actual investment results.

Stress testing involves asset or portfolio modeling techniques that attempt to simulate possible performance outcomes using historical data and/or hypothetical performance modeling events. These methodologies can include among other things, use of historical data modeling, various factor or market change assumptions, different valuation models and subjective judgments.

PIRANHA employs a block bootstrap methodology to calculate volatilities and tracking errors. It starts by computing historical factor returns that underlie each asset class proxy from January 1997 through the present date. It then draws a set of specified monthly returns within the data set to come up with an annual return number. This process is repeated 25,000 times to have a return series with the same number of annualised returns. The standard deviation of these annual returns is used to model the volatility for each factor. PIRANHA then uses the same return series for each factor to compute covariance between factors. Finally, volatility of each asset class proxy is calculated as the sum of variances and covariance of factors that underlie that particular proxy.