Green efficient frontiers. Part 2: Practical considerations in constructing sustainability portfolios

On the surface, constructing a sustainable portfolio or index may seem relatively straightforward. After all, it’s just about excluding and reweighting – or is it? In a previous whitepaper published in April, we looked at how using an optimization technique can improve the sustainability profile, while at the same time limit active risk, compared to a more heuristic approach.

In the second and latest white paper in the series, we delve a bit deeper into that trade-off, and outline some practical considerations for investment managers. Our findings have been published by The Journal of Impact & ESG Investing. We ran over 350 different optimizations, maximizing exposures to commonly used sustainability metrics from ISS, Sustainalytics and the SDI AOP. These metrics included, but were not limited to: ESG Risk Score, SDI Revenue Percentage, Total Carbon Emissions Intensity and SDG Impact. The parent benchmark we used in all cases was the STOXX^® Developed World Index.

In our analysis, we set out to: 

1. Understand the trade-off between tracking error and sustainability as well as the impact of constraining industry exposures
2. Determine whether focusing on one particular metric resulted in improvement or deterioration in other sustainability metrics
3. Demonstrate that it is possible to add a secondary metric to further increase the portfolio’s exposure to it

Our conclusions

#1: Trade-off between tracking error, sustainability and active industry exposures

Optimizing exposure to one sustainability metric generally yields favorable active exposure to other metrics. However, this exposure is less than that predicted by the linear relationship between the metrics. In addition, desired tracking error can be achieved in various ways (e.g., with higher exposure to the sustainability metric or looser industry constraints).

#2: Impact of focusing on one metric

Maximizing exposure to one variable can also result in positive exposures to other variables. Generally, the exposures to the non-optimized variables tend to be small, but it is rare to see a negative exposure. 

#3: Adding a secondary sustainability metric

Including a secondary metric in the optimization objective generally yields active exposure to this metric that is greater than that suggested by the cross-sectional relationship between the metrics. Despite low correlations between sustainability metrics, constructing portfolios with positive active exposure to multiple sustainability metrics is achievable.

Balancing multiple objectives and constraints with an optimizer

As the transition to sustainable investing is truly underway, it’s important for portfolio managers to have the best tools and know-how to achieve their objectives. To that end, we believe using an optimizer – like the Axioma Portfolio Optimizer – is the best way for portfolios managers to achieve maximum factor exposure for a given level of risk, or alternately minimize risk for a given level of exposure. 

Green Efficient Frontiers: Practical Considerations in Constructing Sustainability Portfolios

Read the full paper to learn more about: 

• The optimization frontiers for selected sustainability metrics (tracking error vs. normalized exposure to metric)
• Which metric(s) improve as you go up the tracking error scale?
• What happens if you optimize for Carbon Risk Rating?
• Which pair of sustainability metrics do not have a symmetric relationship?
• Which correlations between metrics are the strongest? 

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