Optimality is in the eye of the beholder
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Having the right ingredients for equity investing is mission-critical for superannuation funds. On average, APRA-regulated funds invest almost half of members' capital (47%) into listed equities portfolios. Much research exists on the ingredients funds can use to construct a physical equity portfolio-matters like the attractiveness of investing onshore or offshore (in developed versus emerging markets), whether to adopt an active or passive management philosophy, the exploitation of factor risks, different weighting strategies, how to define risk and manage risk budgets, measuring trading costs and the impact of tax on equity investing. Far less research exists on how to combine these singularly important ingredients into the right mix from a taxable superannuation fund investor's perspective.

In this paper we argue that how these ingredients are combined matters. We consider a hypothetical superannuation fund with the following global equity ingredients at its disposal:

  • A set of investment beliefs
  • A passively managed core portfolio
  • Four actively managed, diversified satellite portfolios
  • A tax-management option
  • A risk budget
  • A fee budget
How would a fund combine these ingredients in an optimal way? In the theoretical, frictionless world of 'best ideas', the task is simple: Funds that believe strongly in efficient markets and the 'free lunch' of diversification would maximise allocations to a core passive portfolio, while funds that believe in active management would maximise allocations to the multi-manager satellites. This task is much more complex in the real world, where funds are confronted with nuances like their level of conviction in active management, how much benchmark-relative and peer risk is appropriate to pursue excess returns (alpha) with conviction, and trading off the certain costs of taxes, brokerage and commissions with the uncertainty of alpha. Overlaying this will be the fund's ultimate reality check: a sensible fee budget for pursuing its equity portfolio objectives.

For our hypothetical fund, we adopt a core-satellite structure and show how the fund would identify an optimal combination of an 80% allocation to a passive core and 20% allocation across the satellite active managers, spanning developed and emerging-market equities. This allocation provides the highest potential to maximise returns on a risk-adjusted basis, post-tax, while requiring only that the active managers be moderately skilled and the passive core be tax-managed to provide some damage control over the tax impacts of active management.

This optimal blend keeps overall tracking error (to our hypothetical fund's benchmark) well below 2% and overall fees well below 0.5%-our fund's annual risk and fee constraints-which we believe is very competitive for a multi-manager all-countries equity portfolio with active-investment and tax-management components. A key aspect of our research is showing how our hypothetical fund then steps back from this analysis-what we call the science-to add the art, or the wider lens of judgement, industry awareness and member-centricity, to ultimately settle on an optimal equity portfolio design.

Because optimality is in the eye of the beholder, how well these findings guide superannuation funds will depend on whether they relate to the investment beliefs, objectives and sensitivities of our hypothetical fund. While our fund profile is realistic, even funds that consider themselves very different (for example, not peer-sensitive, not fee-constrained or completely indifferent to tax) will find useful insights.

First, we demonstrate the importance of articulating investment beliefs and reflecting these, as far as practical, in the way equity portfolios are constructed. Second, any fund can use the model we follow in this paper to help blend particular equity portfolio ingredients in an optimal way. A third application is to deepen funds' understanding about how much tax can eat away at alpha programmes-something obscured in the pre-tax performance reporting many funds use-and how a simple device like a tax-managed core portfolio can help. Fourth, this research questions the wisdom of funds setting fee and risk budgets as hard, rather than soft, constraints-many of the optimal portfolio possibilities we identify are simply off the table for funds investing under such strictures.

Defining 'optimal' for our hypothetical fund

Our research cannot feasibly identify a single, universally optimal combination of equity managers and strategies for superannuation funds because investment philosophies differ from fund to fund. What is an optimal mix of equity portfolio ingredients for a fund depends on how that particular fund defines optimal. A fund could view its equity portfolio design as optimal by delivering a portfolio with the greatest potential to maximise any of:

  • Pre-tax returns or excess returns (alpha)
  • Returns or alpha post-tax
  • Risk-adjusted returns (Sharpe or information ratios, pre- or post-tax)
A fund could also use a dashboard approach, which looks at how well a set of these investment objectives are achieved in combination.

Our hypothetical superannuation fund believes in active management and the power of diversification (while being wary of over-diversification) and that it can pick moderately skilled active managers. It is concerned with balancing the risk and return dimensions of its portfolio and thinks of risk primarily as tracking error to a market-cap-weighted benchmark. The fund wishes to align itself with what matters to members-after-tax returns-and therefore considers taxes in the design and management of the equity portfolio. With an eye to future RG 97-type obligations, the fund also views favourably styles that more naturally limit transaction costs. The fund is large enough to access direct accounts and does not invest via pooled funds. Finally and importantly, our hypothetical fund allocates annually a total equity portfolio risk budget (tracking error) of 2% and fee budget of 50 basis points. The fund recognises that limiting tracking error is also helpful in mitigating peer risk.

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