Blog Posts — November 19, 2021

When it comes to sustainability, you can accentuate the positive, not just eliminate the negative

by Melissa Brown and Adrian Zymolka

We have written recently about the overlap—or lack thereof—between different kinds of portfolios that can be loosely grouped in a category called “sustainability”. Some strategies seek mainly to avoid the “bad”, other focus on embracing the “good”. Many investors are now turning to the Sustainable Development Investments Asset Owner Platform, which seeks to identify whether companies contribute to Sustainable Development Goals (SDGs) as defined by the United Nations. A major difference between the SDGs and other methodologies is that the SDGs focus exclusively on characteristics expected to have a positive impact. For example, a company will be assumed to meet SDG 3 if revenues from its products “ensure healthy lives and promote well-being for all at all ages”. A company that does not meet this goal will simply not be rated.

This contrasts with some other methods for constructing sustainable portfolios. In many cases, the main objective is to avoid the worst companies. Other methods also seek to reward best-in-class “actors” but may use different, sector-relative metrics in their definitions. And some newer products focus on multiple ways of defining sustainability, including incorporating the SDGs. See our recent article in Responsible Investor for a description of one such approach.

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For those still looking for ways to incorporate SDGs into their process, one frequent question is whether and how an exposure to a single goal or multiple goals can be improved.

In this post we employed a “fact-finding” approach (see box for description1) to examine the issue of how much exposure to a single SDG a portfolio can potentially achieve, and how that exposure is related to active risk. For this analysis we used the Axioma Worldwide Fundamental Equity Factor Risk Model – Medium-Horizon and the SDG contribution from the SDI AOP data as of July 1, 2021. We believe portfolio managers and asset owners can clearly benefit more from a forward-looking analysis that provides some guidance on how the SDG profile of a portfolio could potentially be improved and what level of tracking error is needed to get there.

While the constructed portfolios may not be suitable candidates for implementation, such insights on SDG potential can help guide portfolio managers when deciding on which SDGs may be best to focus on, whether a particular impact goal can truly be met, and what, at best, to expect from an attempted SDG profile improvement. The maximum combined SDG contribution can also be a good reference when adding back other constraints to see how much they impact potential SDG profile improvements.

Co-author Adrian Zymolka has devised an approach that uses portfolio optimizations to explore the (theoretical) potential of SDG profile improvements. Benchmark assets form the investible universe to create portfolios that maximize SDG contributions subject to only two conditions: 1) staying long-only and fully invested, and 2) limiting the tracking error to the benchmark. In this implementation, the optimizations maximize a single SDG’s contribution (and are done for all SDGs individually), which shows how far the contribution to each individual SDG could (theoretically) be pushed, within the above stated conditions, while also providing information about just how high an exposure can be and what level of tracking error is needed to get there. Note that a company with 10% or more revenue coming from an SDI is assumed to contribute to that SDI.

Optimizing risk vs. SDG

For the study, we ran a series of optimizations with the Axioma Portfolio Optimizer with the objective of maximizing exposure to each SDG, one at a time, while varying the targeted level of tracking error. We ran the tests for three levels of tracking error—1%, 3% and 5%—and for six different country or regional STOXX indices. The metric we used to evaluate the potential was the percent of the portfolio’s market capitalization that would achieve the specific goal2. For example, with a 5% tracking error target, the optimizer found a portfolio that had 100% of its market capitalization in companies that met SDG 3. 

The indices we studied had very little SDG exposure to start with. Our tests for every region, at each level of tracking error, showed that one could attain a substantial improvement in exposure to each of the SDGs3, with the weights increasing at each level of tracking error. There were only a few instances in which a portfolio could have 100% of its weight devoted to a given SDG, and in most of those cases it could only do so at 5% tracking error. On the flip side, even a 1% tracking error portfolio was able to achieve substantially higher exposures to each of the SDGs, as compared with the underlying benchmark, suggesting one does not need to take on a lot of extra risk to get an improvement in a portfolio’s sustainability impact.

In Exhibit 2 we see that some goals are “easier” to attain than others. For example, companies contributing to SDG 3 (Good Health and Well Being) make up 19% of the STOXX Europe 600, the highest weight of any of the SDGs (as was the case across the indices in this study). A portfolio designed to track the European index with just 1% tracking error could boost that exposure to more than 46%, and at 5% the entire portfolio weight could satisfy that SDG. In contrast, SDG 4 (Quality Education) comprises less than 1% of the European index, and even at 5% tracking error could only reach a bit more than a still substantial 50% of the index.

Exhibit 1. Sustainable Development Goals included

Exhibit 2. Maximum achievable level of each SDG by tracking error, defined as weight in index, for selected STOXX Indices

Source: SDI AOP, Qontigo

Higher exposures come from fishing in a bigger pond

The maximum achievable weight in any SDG is obviously a function of each index company’s exposure, but it also depends on the breadth of the index. With more names to choose from, the higher the likelihood that more names will have exposure, and therefore the higher the potential weight. A third requirement is whether the goal is tied to viable products. For example, few companies achieve a majority or decisive ranking on SDG 13, Climate Action. This is partly explained by the underlying methodology, which assesses companies for their contribution to “Strengthen resilience and adaptive capacity to climate-related hazards (…)”, including via infrastructure and insurance. While there are a lot of ways to eliminate companies that hurt the environment, fewer companies produce products that actually help it in ways defined by SDI AOP. As such, we also note that SDGs are geared much more toward country rather than company requirements, as evidenced by SDG 13 in particular.

We see the importance of index breadth clearly in Exhibit 3, where we show the maximum achievable SDG weight at 3% tracking error by region. For all but one SDG, the highest achievable weight is in the STOXX Global 1800. Only SDG 7, Affordable and Clean Energy, gets a slightly higher representation in Japan. At the same time, the achievable weights were lowest in Asia-Pacific ex-Japan, which has far fewer names than the other indices.

Exhibit 3. Maximum achievable SDG weight at 3% tracking error by region, optimized one SDG at a time

Source: SDI AOP, Qontigo

Individual company SDG scores are not mutually exclusive

Not surprisingly, there is some correlation between the various SDGs, hence a company can satisfy the requirements for two or more goals. Therefore, tilting on one SDG may also lead to exposures to others. In Exhibit 4 we show the exposures to all other SDGs, along with the one that was targeted in the optimization, for the STOXX Global 1800. Here we chose the 1% target, to illustrate how even a low tracking error portfolio could lead to meaningful SDG exposures, even other than what was targeted. In the set of bars labeled SDG02 in the chart, the first bar shows the weight in SDG 2 (the targeted SDG, per the x-axis label) of 33%. But that portfolio also has about a 23% weight in SDG 3, a 10% weight in SDG 7, etc. The highest bars are always the targeted SDG, but others clearly come along with it4.

Exhibit 4: Other SDG exposures, STOXX Global 1800

Source: SDI AOP, Qontigo


One option for an investor looking to increase investment exposure for the purpose of driving company impact is to target exposure to SDGs. The more one is willing to stray from the market, the higher the impact in the portfolio. Not all goals are created equal, however, and some may be harder to achieve in investments because they are more difficult to achieve in real life.

Of course, most investors will have constraints beyond just the level of active risk they can assume, so these exposures represent upper boundaries. But they also suggest that, for many of the SDGs, an investor can garner significant exposure that could lead to the portfolio making a real impact.

[1] For more information on other potential applications of this approach, particularly for active portfolios, please contact your Qontigo representative.

[2] Attaining the goal means that a company derives at least 10% of its revenue from the particular SDG (that is, its SDI designation is either “Decisive – 10% to 50% of revenue coming from the SDG” or “Majority – SDG accounts for 50% or more revenue”. Of course, one could limit the analysis to only the biggest contributors, i.e., Majority, defined as the SDG contributing more than 50%.

[3] Note that the analysis only includes companies for which SDG contribution comes through products and services, not those with contributions through operations and conduct.

[4] Note that this analysis does not proportionally allocate the SDG weight by percent of revenue, so there is some double counting here.

Qontigo is a leading global provider of innovative index, analytics and risk solutions that optimize investment impact. As the shift toward sustainable investing accelerates, Qontigo enables its clients—financial-products issuers, asset owners and asset managers—to deliver sophisticated and targeted solutions at scale to meet the increasingly demanding and unique sustainability goals of investors worldwide.

Qontigo’s solutions are enhanced by both our collaborative, customer-centric culture, which allows us to create tailored solutions for our clients, and our open architecture and modern technology that efficiently integrate with our clients’ processes.

Part of the Deutsche Börse Group, Qontigo was created in 2019 through the combination of Axioma, DAX and STOXX. Headquartered in Eschborn, Germany, Qontigo’s global presence includes offices in New York, London, Zug and Hong Kong.