The complex science of integrating impact into portfolio design

Optimizing portfolio design within a risk/return framework has been a challenge for industry and academics for many decades. Embedding impact within this framework creates additional dimensionality, greatly increasing the complexity of the portfolio design challenge. This article explores the technical challenge of navigating the 3-D investment framework.

An Introduction to Modern Portfolio Theory – The 2-D Challenge

The foundation of modern portfolio theory is the work of Nobel laureates Harry Markowitz, James Tobin and Bill Sharpe. There are various interpretations of the framework, and it is sometimes unfairly criticized. Markowitz defines a framework for creating an efficient frontier (mean-variance) of portfolio opportunities. Tobin and Sharpe build on this foundation to demonstrate how to improve risk/return efficiency by blending cash or leverage with the portfolio that has maximum (expected) excess return over variance (the “risk portfolio”). market”).

Modern portfolio theory presents many challenges. These range from the subjectivity of inputs, return distributions for different investments, how different investments mix, and liquidity. The large takeout: being efficient with risk when looking for a return.

Interestingly, some institutional investors are in a better position to effectively manage the risk/return trade-off.

An example is pension fund leverage: Australian pension funds are not allowed to apply portfolio leverage directly, while pension funds in the United States, Canada and some parts of Europe have fewer restrictions and have built leverage into their portfolios.

Integrating a third dimension: impact

Accounting for impact creates a fascinating but complex challenge. Impact represents a third dimension in the portfolio decision-making framework. To allow us to explore this framework, we make two important assumptions:
1. That sustainability and ESG can be aggregated into a single variable: impact.
2. This impact can be measured continuously (i.e. there is an infinite, not discrete, set of impact measures).
A third portfolio variable introduces significant complexity. Where there used to be a trade-off to consider (return versus risk), there are now three: return versus risk, return versus impact, and risk versus impact. Instead of finding the optimal portfolio on a two-dimensional risk/return frontier, there is now a three-dimensional risk/return/impact surface on which to locate the optimal portfolio. We illustrate this in Figure 1.

Figure 1: A three-dimensional risk (volatility) / return / impact surface.

Based on Figure 1, we make a few important observations:

1. Higher risk, measured by volatility in our simplified framework, can be applied to increased expected returns and impact (or both).

2. We consider the possibility that there is a trade-off between impact and return.

3. If we ignore the impact, the framework collapses into the familiar efficient frontier of modern portfolio theory.

4. For each degree of impact, there is a risk/reward boundary.

Accounting for Active Ownership and Commitment

The opportunity for active ownership and engagement activities to improve impact without effecting returns is timely (here and here). We model commitment to Universal Investor activities as incurring a fixed cost (setting up a team, systems, etc.) and providing a fixed expected benefit. Therefore, the result is a fixed increase in impact.

This is reflected in Figure 2.

Figure 2: Illustration of the effect of undertaking a program of active ownership and engagement activities.

Figure 2 shows two surfaces. The first (shaded yellow/red) repeats Figure 1. The second (green/blue) is further along the impact axis by the degree of net impact expected from program implementation active ownership and engagement activities.

Identification of the optimal portfolio

Identifying the optimal solution becomes more difficult as new dimensions are added to any problem (econometrician John Rust has described it as the “curse of dimensionality”).

For academics, utility functions remain the cornerstone of goal setting, aided by ever-increasing computing power. Utility functions seem less applied in industry, perhaps due to their complexity.

A utility function evaluates the distribution of possible outcomes of the portfolio. In doing so, it implicitly establishes trade-offs between different dimensions. While traditionally the dimensions have been risk and return, the utility function approach has the ability to provide objectivity in the face of the 3-D challenge created by incorporating impact. Note that trade-offs are not necessarily linear. Exploring and formalizing these trade-offs would be a wonderful learning experience for pension fund boards.

Two alternative approaches are more likely to be applied, at least in the short term, as the industry adopts 3D investment frameworks.

Approach 1: targeted impact

The first approach is to target a specific degree of impact. This effectively converts the 3D surface into a single 2D investment frontier (the thick black line), detailed in Figure 3. This brings the portfolio decision back to the familiar 2D investment frontier problem.

Figure 3: Targeting a fixed degree of impact creates a single risk/reward boundary.

Approach 2: Minimum Impact Threshold

The second alternative approach is to set a minimum impact threshold. This effectively splits the 3D surface into two segments, both of which are 3D. In Figure 4, portfolios on the yellow/red surface do not meet a minimum impact threshold, while portfolio managers are free to search the blue/green surface for the best portfolio outcome.

The advantage of this approach over approach 1 is that it allows portfolio managers to further explore the trade-off between impact and return. For example, an additional impact could have been achieved, beyond the fixed threshold applied in Approach 1, with little deterioration in yields.

Figure 4: Setting a minimum impact threshold divides the three-dimensional risk/return/impact surface into two segments.

Challenges in practice

3D investing presents significant practical challenges in practice, which go beyond the known difficulties of 2D investing (forecasting return, risk, etc.). These include:

  • Measuring impact, where the availability of many different datasets creates confusion. A particularly difficult component is the challenge of integrating ESG into a single impact measure.
  • Match timing of risk, return and impact forecasts.

For these reasons, the construction of pension fund portfolios in a 3D world is unlikely to become purely systematized any time soon. For 3D investing to address systemization, these issues would need to be addressed while determining how to set a goal – perhaps utility functions will come to the fore.

It will be important to apply some of the science of portfolio management described: plan participants and regulators will at some point want to understand how portfolio trade-offs were determined. Many of the major pension funds and asset managers are already well advanced on the technical aspects. But the art of decision-making will be an important complement to the science of decision-making.

A final observation is that the role of the portfolio manager will become even more important: portfolio managers will need to understand the range of possible portfolio outcomes and lead their respective boards through a process of determining an appropriate portfolio. Education and communication remain essential skills.

David Bell is Executive Director of the Conexus Institute

Abdul J. Gaspar