Building Backwards: Delivering Total Portfolio Attribution, Analytics, and Liquidity

At its core, the Total Portfolio Approach (TPA) demands that firms consistently generate three outcomes: total portfolio attribution, integrated analytics, and forward-looking, total portfolio level liquidity forecasting. Together, these form the backbone of informed, portfolio-wide decision-making. Yet many asset owners struggle to produce these outputs in a way that is timely, coherent, and scalable across their diversified portfolios.

This paper approaches the problem from the end state backward. In How to Get Big Things Done, Bent Flyvbjerg and Dan Gardner describe a “right-to-left” approach, starting with the desired outcomes and building the structure required to deliver them. Applying that lens here, the focus is not on how TPA should work in theory, but on what must be true operationally to consistently produce its core outputs.

The second paper in Meradia’s Total Portfolio Approach series, Architecting for a Total Portfolio Approach, introduces the importance of top-down calculation mechanisms, highlighting the need to complement traditional bottom-up views with portfolio-level adjustments. Building on that foundation, this paper focuses on what firms must build to reliably produce total portfolio attribution, integrated analytics, and liquidity forecasts in environments defined by incomplete, asynchronous, and evolving data.

Total Portfolio Performance and Risk Attribution

All firms implementing a Total Portfolio Approach move through a common sequence of decisions:

1. What is our long-term view of the market?
2. Based on that view, how should capital be allocated across market segments?
3. Given those allocations, how do we invest within each segment?

Through this process, portfolio-level views are progressively translated into asset class exposures and ultimately into individual investments. Each step narrows the scope of decision-making, moving from broad market assumptions to specific investment actions.

However, as decisions move downstream, the context that informed them is often lost. By the time capital is deployed into individual investments, the portfolio-level rationale, including why capital was allocated to a given segment and what role that segment was intended to play, is no longer visible in traditional performance, risk, and attribution frameworks.

As a result, attributing performance and risk at only the investment or asset class level provides an incomplete picture. It explains what performed, but not whether the underlying portfolio-level decisions were correct. Total portfolio attribution requires reconnecting outcomes to the full decision chain, linking individual investment results back to the top-down views and allocation choices that shaped them. Total portfolio attribution based on this decision chain becomes the desired output.

Notional Portfolios

If total portfolio attribution is defined by the full decision chain, then each step in that chain must be explicitly represented and preserved. In practice, this is achieved using notional portfolios aligned to each stage of the investment process.

Rather than allowing portfolio-level decisions to disappear as capital is deployed, notional portfolios create a parallel structure that captures what was decided, independent of how it was ultimately implemented.

Using the three core decisions referenced above, three notional portfolios would be created:

First, a reference or market view portfolio, representing the firm’s long-term view of the investment landscape. This portfolio encodes assumptions about expected returns, risks, and relationships across market segments. It answers the question: what is our long-term view of the market?

Second, a policy portfolio, which translates that market view into target allocations. This reflects how the firm chooses to position itself given its objectives, constraints, and risk tolerance. It answers the question: how should capital be allocated across market segments?

Third, an implementation portfolio, which reflects how those allocations are expressed in practice. This includes decisions such as manager selection and instrument choice. It answers the question: how do we invest within each segment?

Attribution is then performed by comparing performance across these portfolios. Conceptually, each comparison isolates the impact of a specific decision by holding the prior decision constant.

At the top level, the comparison between the market view portfolio and the policy portfolio isolates the impact of allocation decisions. The market view portfolio represents the return of the opportunity set as defined by the firm’s assumptions, while the policy portfolio reflects the return after applying the firm’s chosen allocations. The difference between the two answers a fundamental question: did our allocation decisions add value relative to our own view of the market?

This same logic extends down the decision chain. With this structure in place, attribution is no longer limited to explaining what performed. Instead, it becomes a mechanism for evaluating decisions at each level of the portfolio construction process.

Integrated Analytics

While total portfolio attribution supports the backward-looking evaluation of decisions, integrated analytics enables the real time decision making required for a successful Total Portfolio Approach. It is within this context that CIOs receive the most immediate and consequential questions, often under significant time pressure.

Consider a sudden market dislocation driven by a macroeconomic event impacting energy investments within a specific region. The CIO is immediately asked: what are our largest positions in this sector, what is our total exposure across public and private markets, and what is the expected impact on the overall portfolio?

Taking a ‘building backwards’ approach, the requirement is not perfect data, but clear and timely answers to these questions.

Most asset owners can answer them within individual asset classes, but extending this across the total portfolio is far more difficult. Data that is consistent within asset classes becomes fragmented at the total level, arriving at different times, with varying lags and levels of completeness. The result is a delayed or incomplete view when it is most needed.

The objective, therefore, is a decision-ready view of the total portfolio at any point in time. Achieving this requires intentionally filling gaps in the data. As described in Meradia’s second paper, this is done through best efforts metrics, combining available data with reasonable assumptions and models to produce a continuous view of the portfolio.

At minimum, total portfolio analytics must answer three core questions:

1. What do we own?
2. What risks are we exposed to?
3. How is the portfolio expected to behave?

Stacked, Reinforced Views

If these three questions define the requirement, then each must be explicitly constructed within a single framework.

First, a holdings view, representing a complete and current picture of the portfolio. In the example, this means identifying all energy related positions across public equities, private investments, and indirect exposures, aggregated and normalized across systems. This answers the question: what do we own?

Second, a risk view, which translates those holdings into consistent exposures. Knowing the list of investments is not sufficient. The CIO needs total exposure to energy. Public exposures can be measured directly, but private assets cannot. Relying purely on reported values understates risk.

To address this, private assets are mapped to public market proxies and updated using recent market movements. This produces a current estimate of total portfolio exposure that reflects actual conditions rather than stale valuations. This answers the question: what risks are we exposed to?

Third, a behavior view, which links exposures to outcomes. In this case, the portfolio is stressed against the observed market move to estimate the impact on total performance and identify where losses are concentrated. This answers the question: how is the portfolio expected to behave?

Each view builds on the prior one. Holdings feed risk, and risk feeds expected behavior. Taking a working backwards approach ensures that gaps are addressed at each stage, rather than carried through to the final output.

The distinction is practical. Using stale data may suggest limited exposure and no action. Using an updated estimate may show the portfolio is materially overexposed, prompting a rebalance or hedge. This is the core principle. Integrated analytics is not about perfect measurement. It is about providing a timely, consistent, and decision-relevant view of the total portfolio. A directionally accurate answer today is more valuable than a perfect answer delivered too late.

Liquidity Forecasting

If integrated analytics provides a view of the portfolio today, liquidity forecasting extends that view forward. As with attribution and analytics, liquidity management within a Total Portfolio Approach (TPA) must begin with the core drivers of the overall portfolio.

GIC and PGIM’s Harnessing the Potential of Private Assets: A Framework for Institutional Portfolio Construction frames TPA liquidity management through three key questions for CIOs:

1. How can an illiquid asset cash flow strategy balance target exposure with the uncertainty and timing of capital calls and distributions?
2. How should an asset allocation glide path be constructed to improve the funding ratio while minimizing its volatility over time?
3. How do changes in strategy, market conditions, or assumptions affect portfolio liquidity, funding, and overall performance?

In response, GIC and PGIM propose a portfolio management framework to support the liquidity needs of asset owners pursuing a TPA. These needs span pension liabilities, capital calls, debt obligations, and rebalancing requirements. Together, they form an implicit total portfolio investment policy statement (IPS), within which asset owners must operate and manage risk.

Taking a “build backwards” approach, the required output is clear: a forward looking, seamlessly integrated, and decision ready view of total portfolio liquidity that enables efficient portfolio management within these systemic boundaries. Building on that foundation, this section proposes an operating model to support these requirements at the total portfolio level, an area where many firms encounter structural gaps.

Ex-Post Foundation isn’t Sufficient

Individual teams often maintain a clear view of their own cash flows: public markets teams can project income and settlements with relative certainty, while private markets teams model capital calls and distributions based on GP timelines and fund level expectations. In isolation, liquidity appears manageable. In aggregate, however, these views are rarely integrated. Cash flows are modeled across disparate systems, with inconsistent assumptions and misaligned time horizons. As a result, while each team may be directionally correct, the CIO and treasury function lack a complete and timely view of portfolio wide liquidity.

Within a TPA, the boundaries between treasury, risk, and ex ante performance begin to blur. These functions can no longer operate in isolation, and their traditional mandates are stretched significantly. Operational boundaries must give way to aligned and normalized modeling across functions, something that is conceptually straightforward but difficult to execute in practice, particularly in a dynamic investment environment.

In this context, the traditional Performance Book of Record (PBOR) may no longer be sufficient, even in an augmented form. While prior discussion has focused on the data required for ex post performance measurement, liquidity management introduces additional demands tied to ex ante modeling. Most asset owners can calculate historical performance with ease, but many struggle to perform ex ante scenario analysis efficiently and accurately. Liquidity management presents a similar challenge: assessing current liquidity and funding ratios may be straightforward, but forward-looking analysis is significantly more complex.

This becomes particularly evident when attempting to bridge fundamentally different investment lifecycles. For example, how does one coherently model the multi-year, uncertain lifecycle of a private equity fund alongside the fixed, contractual cash flows of a bond portfolio to inform a single liquidity glide path? These instruments operate on entirely different timelines, assumptions, and risk profiles. Without a normalized framework to reconcile these differences, aggregation becomes superficial, limiting the ability to generate meaningful insights.

Public market cash flows tend to be stable and predictable, while private market cash flows are irregular and dependent on GP timelines, pacing assumptions, and estimation techniques. However, simply centralizing this data is not enough. Many firms find that even when data resides within a single platform, it cannot be easily applied to answer more complex, cross-portfolio questions. Data remains a necessary condition for TPA but is not sufficient without the core capabilities that sit on top of it. Data does not equate to decision readiness.

Sliced, Modeling Capabilities on Integrated Data

To address this, firms must move beyond aggregation and toward true integration by combining both known and modeled cash flows within a unified framework. This includes categorizing and ranking assets by liquidity to understand how and when capital can be accessed, while also aligning modeling assumptions across asset classes and functions. Only then can bottom-up cash flow projections be meaningfully connected to top-down portfolio requirements. Critically, this same integrated foundation enables ex ante performance analysis, allowing firms to evaluate how liquidity decisions, commitment pacing, and market scenarios translate into expected returns and funding outcomes over time. Without this linkage, liquidity, performance, and risk remain disconnected, constraining the ability to make fully informed portfolio decisions.

When done effectively, this enables stronger alignment between investment activity and funding needs, improving commitment strategies and reducing the risk of liquidity shortfalls. The result is a clearer view of funding ratio variability over time, supported by a dynamic liquidity glide path that reflects both expected and stressed conditions.

Conclusion

The Total Portfolio Approach is an investment philosophy, but without a sound operational structure, it might be inefficient at best and incorrect at worst. Delivering on it requires firms to consistently produce total portfolio attribution, integrated analytics, and forward-looking liquidity forecasts. While these outputs define the destination, the path to achieving them varies. The final paper in this series explores how different asset owners navigate that journey, and the practical choices they made in moving towards a fully operational TPA model.

Download Thought Leadership ArticleServices: Solution Design, Strategy and RoadmapExpertises: All Meradia, Performance, Risk & AnalyticsClients: Asset Owner, PensionAuthors: Jose Michaelraj, Piers Hansen