So many of the old investment orthodoxies are now looking tenuous: the US dollar as global reserve currency; the forward march of free trade; even the commitment of some Western democracies (you’ll never guess which one we’ve got in mind) to the rule of law.

Amid this uncertainty, advice from investors increasingly constitutes a list of asset classes and geographies to avoid. Institutional investors contemplating how to deal with high volatility and increasing uncertainty have good reasons to join the ranks of those already employing a total portfolio approach (TPA).

There are, however, challenges for funds seeking to move to TPA, not least in data management, as practitioners like Aware Super and Brighter Super can attest to.

TPA arguably requires a higher level of data and technology maturity than the traditional strategic asset allocation (SAA) approach to investing. Given the arguments in favour of TPA, and evolving data management requirements in investment management more generally, it is worth asset owners considering what these are.

Most practitioners seem to be at pains to emphasise that there is no formal agreed definition of TPA. It’s more vibe than rulebook. Nonetheless, there is something of a consensus that TPA denotes an approach to investing that:

• Considers the overall return and risk objectives of the fund when making individual investment decisions.
• Seeks to avoid a false sense of diversification by ensuring low correlations between individual investments rather than relying on allocation across multiple asset classes as a proxy for diversification.
• Emphasises collaboration across the investment team, as opposed to disparate asset class teams competing for capital.
• Necessitates a more CIO-centric process.
• Results in greater dynamism and responsiveness, particularly in periods of greater risk and volatility.

TPA is juxtaposed with the strategic asset allocation (SAA) approach, in which the primary goal is maintaining consistent allocations to specific asset classes, such as equities, fixed income, property, and alternatives. A 2024 study by WTW’s Thinking Ahead Institute of 26 large asset owners found that those leveraging TPA outperformed their SAA peers by an average of 1.8 per cent p.a. over 10 years — a substantial margin.

The data management requirements of TPA

Data requirements in the investment management world are changing. In the past we’ve written about how regulatory scrutiny, climate related financial disclosure requirements and geopolitical instability are making look through and drill down capability more important. We’ve also touched on the necessity of collecting and processing data on private market assets. Adopting TPA will require asset owners to develop both of these capabilities and more.

Aspect	Strategic Asset Allocation (SAA)	Total Portfolio Approach (TPA)
Data structure	Siloed by asset class	Unified across asset classes
Data timeliness	Periodic updates aligned to reporting cycles	Real-time processing
Analytical tools	Basic performance reporting, regulatory compliance	Advanced analytics, predictive modelling, scenario analysis
Asset coverage	Primarily traditional, public markets	Inclusive of public, private and alternative markets
Decision-making	Historical, rebalancing-driven	Dynamic, data-driven
Operational complexity	Lower, with standardised processes	Higher, requiring sophisticated integration
Source: Novigi

Many of the goals and operating principles of TPA translate directly into a need to overhaul core platforms. Aware Super recently completed the first phase of “Project Odin”. The project was originally conceived as an uplift of Aware Super’s overall investment platform to support a move to TPA, including the rollout of a new enterprise data management platform for investments.

The new platform will need to be able to support the following aspects of TPA.

Centralisation and collaboration

TPAs are CIO-centric, contrasting SAA which can be characterised as board-centric. In TPA, the CIO takes a greater, more dynamic role in determining the overall asset allocation of the fund. To do this, they need to be armed with timely and accurate data on investments across all asset classes, delivered in a format that enables comparisons and decisions to be made rapidly.

Likewise, in TPA, investment teams need to work in a more collaborative way, where each team member knows what the rest of the team is doing, furthering the need for data. This data aggregation and business intelligence capability is a key requirement for an investment data management platform.

Correlations

Since Harry Markowitz introduced Modern Portfolio Theory (MPT) in the 1950s, the importance of diversification to reduce risk has become a given in the investment world. SAA seeks to achieve diversity by allocating capital across asset classes.

But if we look back at Markowitz’s formulation of MPT, we’re reminded that what matters is not what asset classes we’re invested in, but that there is low or negative correlation between individual investments. TPA aims to make individual investment decisions that avoid a false sense of diversification.

Doing this often requires richer data on investments to facilitate both quantitative and qualitative estimations of correlations between investments than asset owners would usually possess. And once they have the data, funds need to be able to be able to analyse these correlations frequently and at scale.

Common standards and definitions

To enable frequent and consistent comparisons of investments, TPA depends on common standards and definitions of key concepts. Environmental, social and governance (ESG) factors are a case in point.

Having common standards and definitions around ESG to screen individual investments against is key to enabling an entire investment team to make decisions that align with funds’ overall objectives. Translating these into common data attributes and models can further empower the team by automating some or all of this work involved in conducting this screening.

Dynamism

TPA aspires to real-time monitoring and dynamic changes in investment strategy to reflect evolving market conditions. Unlike SAA, which typically rebalances to a target mix on a periodic basis, TPA requires the ability to make continuous, data-driven adjustments at the total portfolio level.

Achieving this dynamism demands not only near real-time data across all investments, but also advanced analytics, risk models and decision-support tools that enable rapid assessment of new information and its impact on portfolio positioning.

Building the technical and organisational infrastructure to support this — including investment data platforms, flexible governance processes, and empowered investment teams — is critical to making the aspiration of true dynamism a reality.

A total portfolio approach can’t offer certainty in an uncertain world, but it potentially offers a smarter way to navigate it. Making TPA work, however, demands better, faster and deeper data than most funds have today. Those who can build the right foundations will be far better equipped to act – not just react – as the world keeps shifting.

Kevin Fernandez is general manager of investment technology at Novigi.