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? 

Download >

We sat down with James Leaton, Research Director of the SDI AOP, to discuss the latest platform developments led by its asset-owner led community.

Can you outline what’s new for this September release?

There are four areas to highlight:

1. Thematic tagging: We know that investors are thinking about key themes such as climate, biodiversity and the circular economy, so we have made it easier to identify revenues linked to these themes with our new tagging approach. This gives investors another lens with which to view their portfolio, rather than just the UN SDG lens.
2. The circular economy: Our framework already captures many circular products/services, such as recycling activities, sustainable product alternatives and Product-as-a-Service offerings. Our enhanced approach ensures that emerging revenue streams from new approaches such as closed-loop production systems, will also be captured as they become material for companies in our universe.
3. Emerging market banks: Data on the exposure of banks is notoriously difficult to find. The first area we are addressing is to assess the loanbook exposure of emerging market banks dedicated to serving micro, small and medium enterprises and underserved groups. This activity plays a critical role in the economic development of these countries.
4. Real Estate: Over the last three years, we have expanded coverage across equities and fixed income, and now we are adding publicly listed Real Estate Investment Trusts (REITs). The initial data relates to social aspects, identifying those REITs which are aligned to Healthcare (SDG 3) and Education (SDG4). 

What’s keeping asset owners up at night, and how is the data helping to alleviate those concerns?

The SDI AOP is not only a data provider. We have built a community of asset owners and asset managers and have regular sessions where users can share their challenges and contribute to the development of the solutions. Greenwashing and Greenhushing concerns are top of mind for many, as they seek to ensure the validity of their sustainable investment approach and effectively communicate to beneficiaries on how they are delivering for them. The credibility of the framework, the quality of the processes applied, and the transparency of the raw data all help users capably demonstrate they are doing what they say they are.

Making data truly useful requires a lot of research and development. Can you outline some of the hurdles you’ve come across and your approach to this latest release?

Investors always want to be ahead of the game, but it can be difficult to predict areas of growth. However, working with the asset owners who are active across asset classes gives us insights we can cross-fertilize in the SDI data. For example, new sustainable technologies may appear first in speciality private companies, and we will want to capture these in our revenue data, as larger companies enter those markets or acquire the private companies.

This also feeds into defining nascent technologies we want to include in our SDI Innovation Outlook data, which identifies the companies that are filing patents in innovative sustainability solutions. The thinking behind this is that the resulting data gives an indication of how a company’s own R&D will contribute to the UN SDGs through future offerings.

Since the launch of the SDI AOP three years ago, are you seeing a shift in the way your subscribers are using the data – or thinking about the data?

We see huge variety in how people are using the data, which is reflective of the flexibility we provide  to meet a user’s needs. Now that we have several years of data, people can see where the growth is, or where transitions are occurring in companies over time. The use cases range from company engagement to index construction to universe definition to reporting.

What can we expect to see next?

The SDI taxonomy continues to iterate. It is not a static framework but evolves to capture the latest thinking and acts as the backbone of what we are doing. We are looking at more ways to apply it, utilizing the artificial intelligence capabilities at our disposal. For example, our users want to have more granularity in terms of the markets being served by products and services, the types of products and the outcomes resulting from them. This is an area of focus for us to meet the future needs of the investor community.

Want more information? STOXX is the exclusive distributor of the SDI AOP data and further information can be shared by contacting us.

A common first step for investors in this journey is to remove investee companies deemed undesirable from an ethical or sustainability standpoint.  

In the first of a series of whitepapers¹ that explore the construction of sustainability portfolios, Qontigo analysts Melissa Brown and Rob Stubbs look into the risk implications of a basic negative screening strategy. Importantly, they show that the use of an optimizer and a risk model in the process can help reduce active risk, freeing up more of the risk budget to increase the allocation to sustainable holdings or to those expected to generate better returns. 

Brown and Stubbs analyze the risk profile of a portfolio derived from the STOXX^® Developed World with four exclusionary screens: 

• involvement in tobacco 
• involvement in controversial weapons
• companies with highly controversial assets 
• companies in breach of the United Nations Global Compact. 

They then create an exclusions-only portfolio that simply excludes stocks and reweighs the remaining constituents, and an optimized portfolio that removes the same stocks and, through the Axioma optimizer and an Axioma worldwide risk model, allocates weights differently to minimize tracking error to the parent index.

In the test, the optimized portfolio had just 18 basis points of predicted active risk, compared with 29 basis points for the exclusions-only version (Exhibit 1). 

Exhibit 1: Tracking error of exclusions portfolios

_{Source: Qontigo, Sustainalytics. Data as of Dec. 31, 2022. From ‘Green efficient frontiers. Part 1: Minimizing the risk impact of exclusions,’ Qontigo, March 2023.}

The study’s backtesting analysis also shows that realized tracking error is consistently lower for the optimized portfolio over time. Furthermore, the analysts also found a more consistent stream of active returns in the optimized portfolio relative to the standard one. 

How does the optimizer work?

The authors then explore how the optimizer is able to achieve a lower tracking error and what the resulting portfolio looks like. The study shows that the optimized portfolio ends up with fewer constituents and a slightly lower representation of the parent index’s weights than does the standard exclusions-only portfolio. These differences, however, were small considering the improvement in active risk.

As they explain, the optimizer exploits correlations between different risk model components (such as specific industries) and excluded stocks. These correlations create “risk substitutes” for the disallowed companies. For example, a number of risk model factors (mainly industries, but also some countries, currencies and style factors) were found to be highly correlated with tobacco stocks (Figure 2). The optimized portfolio is thus able to offset the risk of not owning tobacco companies with, in this case, an overweight in the Food Products sector. 

Exhibit 2: The 20 factors with the highest correlation to Tobacco

_{Source: Qontigo, Sustainalytics. From ‘Green efficient frontiers. Part 1: Minimizing the risk impact of exclusions,’ Qontigo, March 2023.}

“Use of the optimizer, in conjunction with a risk model, recognizes that individual risk factors such as industries or style factors are correlated with other such factors, and that diversification can therefore be improved by taking advantage of these correlations,” the authors write. “Typical exclusions often introduce tracking error through tilts towards or away from risk factors. Replacing excluded stocks, either with highly correlated securities with the same factor characteristics or with securities in correlated factors, mitigates this.” 

Crucial consideration

Active risk is an important consideration for sustainable strategies, and, as the authors note, it only becomes more relevant as the list of excluded activities grows and as investors and asset owners raise their ESG goals beyond initial exclusions. With both client objectives and regulation of sustainability-labeled indices and funds increasing, the optimal allocation of a portfolio’s risk budget will become only more crucial with time.   

To download the whitepaper and find out more about the active risk, active variance analysis and comparative returns of an optimized exclusions portfolio, click here. 

¹ ‘Green efficient frontiers. Part 1: Minimizing the risk impact of exclusions,’ Qontigo, March 2023.

Traditional ESG and climate metrics tend to be risk-avoidance-focused. But as sustainable investments continue to evolve, more and more investors are looking to identify positive investment opportunities in sectors that can benefit from related policy. It is clear that in order to achieve societal goals around health, environmental quality, transport and energy provision, major investments in related technology and infrastructure will be required. The broad scope of the United Nations Sustainable Development Goals (UN SDGs) means the SDI framework can be used to align portfolios for different investors with widely diverse investment objectives. 

To translate goals into actionable investments, investors can work with the SDI Classification – a robust dataset measuring contributions from companies’ products and services that align to the SDGs – for implementation, tracking and stakeholder engagement.

SDI AOP overview

The SDI AOP is an asset owner-led platform that is developing a family of data products centered around using artificial intelligence (AI) to assess company contributions (both positive and negative) to the UN SDGs. Building on the established product (the SDI Classification) that evaluates revenue streams, the newest offering (the SDI Innovation Outlook) analyses forward-looking potential based on firms’ patents.

Exhibit 1 – The UN SDGs captured through the SDI AOP

The SDI AOP was conceived to facilitate the implementation of thematic investing in sustainability, which historically has lacked robust industry standards and quality granular data. Unlike most ESG and sustainability ratings and datasets, the SDI AOP is designed and maintained by asset owners for asset owners. The platform was introduced in 2020 by a consortium made up of APG, AustralianSuper, British Columbia Investment Management Corporation and PGGM. Qontigo is the SDI AOP’s exclusive distribution partner.

Real-world use cases

Although every investor implements a ‘positive contribution’ analysis differently, below are representative examples of how one can leverage the SDGs and the SDI AOP taxonomy for goal-setting, strategy implementation, portfolio reporting and engagement. 

Use case #1
Set goals and define strategies

Select sustainability themes that align with your stakeholders’ interests. For all relevant SDGs, distill strategic goals that are achievable and outcomes that are measurable.

Philips Pensioenfonds, a current SDI AOP user, narrowed its sustainable development focus to four sustainability themes (Good Health and Well-Being, Sustainable Cities and Communities, Responsible Consumption and Production, and Climate Action – Exhibit 2), all of which are goals that Philips’ beneficiaries support and expect to shift capital towards. When the pension manager implemented its sustainable investment policy for its equity investments, it established an SDG benchmark using SDI AOP data. This resulted in an increase from 15% to 35% in capital invested in companies with revenues that contribute to the selected SDGs, that match the required themes. 

Exhibit 2 –SDGs selected by Philips Pensioenfonds

Use case #2
Select metrics and set targets

Based on the desired goals, define individual objectives. 

Users can leverage the SDI AOP’s granular and verified financial metrics and revenues (which are broken down by company, product category, and SDG) as key outputs and proxy metrics for target measurement. One of PGGM’s clients uses the SDGs as a guide in its new investment policy, whose objective is to double the measured impact on four focus areas — climate, water, food and health — and measure the results and impact of those capital allocations across seven related SDGs (Exhibit 3). 

Exhibit 3 – Measuring the SDG-impact of investments at PGGM

_{Source: Integrated report 2021, PGGM, page 29}

The SDI AOP contains transparent information to make reporting to key stakeholders more efficient and reliable (e.g., actual revenue percentages linked to each SDG, warning flags for controversies, confidence scores, and an explanation of how the result has been derived). This enables reporting by investment theme or SDG and extends across a large equity and fixed income universe. The SDI AOP is currently working with its partners to enhance the ability to assess the related outputs and outcomes associated with the revenue streams identified.

Use case #3
Manager and company engagement

Rely on fair and transparent data to engage with asset managers and investee companies. 

The SDI AOP data is used to track the evolution of positive and negative developments of portfolio managers and portfolio companies on priority sustainability themes. Asset owners can use the data to track the velocity and direction of change toward fulfilling their selected goals and outcomes. This data, in turn, can be sourced as key input for feedback with external managers and investee companies. Because the dataset is updated on a quarterly basis, it can reflect the changes in underlying benchmarks.

Australian Super has integrated SDI AOP data analysis into its stewardship program to inform and progress how it engages with companies. This enables the pension plan to have deeper conversations with companies about strategic themes, ultimately leading to more transparency and better investment outcomes for stakeholders. The SDI data products provide a number of insights that can be used to explore sustainable investment opportunities. Firstly the revenue data provides another lens to assess how a company’s business model is aligned with themes such as climate change or circular economy. The product focus looks beyond traditional sectors to understand who contributes to the value chain for areas such as clean energy systems, efficiency, or flood control.

Exhibit 4 – Australian Super’s engagement snapshot 

_{Source: Australian Super FY22 Annual Report, page 44.}

Australian Super also uses the new SDI Outlook to inform engagement with companies on whether they are investing in the future solutions for these themes. The AI-driven global patent analysis measures the quantity and quality of research activity in relevant technologies. For example, this includes comparison across new and emerging areas relevant to the climate theme, such as storage, hydrogen and fusion in the energy space.

Asset-owner network effect

Through an asset-owner-first approach, the SDI AOP Design Authority’s goal is to develop a community of institutional investors who are jointly focused on advancing a credible standard for investing in sustainability themes, using the SDGs as a globally accepted framework. The taxonomy and data are designed to enhance engagement with portfolio companies, managers, regulators and other key stakeholders such as board members or trustees. The impact of a network of like-minded asset owners and managers promoting the adoption of SDG-aligned impact investing will help to spur positive momentum in sustainable investments.  

Interested in learning more? Email sdi@qontigo.com.

Her follow-up white paper² digs deeper into the sources of risk driving portfolios that have been optimized to ‘load’ on specific SDGs at a given point in time. Here, Melissa used end-2021 data from the Sustainable Development Investments Asset Owner Platform (SDI-AOP) to run simple optimizations that had the objective of maximizing exposure (defined by the percentage of revenue) to one, two or all SDGs. The STOXX^® Global 1800 Index was the investment universe and benchmark. The only constraints employed were to be fully invested with a 3% target tracking error.

Melissa created four active portfolios, which respectively maximize exposure to all SDGs, SDG-3 (Good Health and Well Being), SDG-7 (Affordable and Clean Energy), and a combination of SDG-6 and SDG-13 (Clean Water and Sanitation + Climate Action).

In this article, we take the analysis further and put the same optimized SDG portfolios through the ROOF methodology. Qontigo’s ROOF (Risk-On/Risk-OFF) scores were created to quantify investors’ risk appetite, using investor behavior to determine whether they are risk-tolerant (bullish), neutral or risk-averse (bearish). The scores use a bottom-up methodology to gauge the implied sentiment of the average investor in the market, as well as the active sentiment of any portfolio against its benchmark.

Our aim is to compute the portfolios’ ROOF scores and determine the implied risk tolerance of an investor holding them as opposed to the STOXX Global 1800 parent. Note that the following analysis was done at a single point in time — December 31, 2021— but that the ROOF methodology is adaptive to changes in the portfolio’s active exposures to both style and sectors. 

Active ROOF scores

Figure 1 replicates the style factor exposures of the four optimized portfolios from Melissa’s second paper. All portfolios show a positive exposure to the Market Sensitivity and Volatility factors — both risk-tolerant characteristics according to the Style ROOF classification, since they attempt to take on more market risk than the average stock. They also have negative exposures to the risk-averse Dividend Yield, Profitability and Size factors. Low Dividend Yield and low Profitability are risk-tolerant characteristics, as is negative Size (small-cap tilt) since smaller companies tend to be more economically sensitive than their larger peers. We also note that the portfolio targeting a combination of SDG-6 plus SDG-13 (Clean Water and Sanitation + Climate Action), has a very large positive exposure to Leverage — another risk-tolerant characteristic since investing in companies with a lot of debt is much riskier than investing in debt-free companies. 

Figure 1: Active style factor exposures

_{Source: ‘Want to incorporate SDG exposures into your portfolios? There’s no such thing as a (risk) free lunch, but here’s a way to do it…’, Qontigo, May 2022. Style exposures (vertical axis) are stated in standard deviations, so an exposure of -0.2 indicates the aggregate active exposure is 0.2 standard deviations below that of the benchmark. DY=Dividend yield, GR=Growth, LE=Leverage, MS=Market Sensitivity, PR=Profitability, SI=Size, VA=Value, VO=Volatility.}

Not surprisingly, the above active style factor exposures translate into mostly (very) risk-tolerant ROOF Scores for our optimized SDG portfolios. Figure 2 shows the implied amount of active risk tolerance an investor would need to justify owning these portfolios versus the risk tolerance for holding the benchmark. Except for SDG-3 (Good Health and Well Being), which is almost neutral (+22%) in terms of risk appetite versus the benchmark³, holding all other portfolios implies a much higher risk appetite. In other words, the style factor exposures (i.e., high beta, volatile, unprofitable, small caps) of these portfolios are more reliant on a bullish outlook on the economy for them to outperform the benchmark. 

Figure 2: Active style ROOF scores

_{Source: Qontigo.}

Most of the difference in implied risk tolerance levels between SDG-3 (Good Health and Well Being) and SDG-6 + SDG-13 (Clean Water and Sanitation + Climate Action) can be explained by looking at their respective active exposures to the Leverage (a risk-tolerant style) and Value (a risk-averse style) factors. While the SDG-3 portfolio invests in companies that are undervalued and have less leveraged balance sheets, the SDG-6 + SDG-13 portfolio invests in businesses that are higher priced relative to book value, have lots of debt to repay and are also currently unprofitable. This helps explain the much higher implied risk tolerance of an investor holding the latter portfolio.

Conclusion

As Melissa mentioned in the title of her report, there is no free (risk) lunch in achieving SDG exposure, and the ROOF Scores of SDG-optimized portfolios confirm this. But this is also intuitive. Our current development trajectory isn’t sustainable, and reverting that will require the introduction of new and innovative technologies. Some of these technologies will be disruptive in many ways to existing business models, and it is not surprising to see most of them are being developed by new, small, yet-unprofitable companies, which at the same time may have had to borrow a lot of capital to finance their R&D efforts. This is confirmed by the style factor exposure of portfolios optimized for each SDG. These styles traditionally command a certain risk premium, which is precisely what the ROOF methodology is designed to quantify. It is, therefore, not surprising to see that betting on disruptive technology to solve some of our biggest sustainability issues requires a somewhat contrarian investment mind, and a higher risk appetite. So, if you are hungry for sustainability, you may need to sprinkle a little more risk tolerance on your portfolio.

* Olivier d’Assier is Head of Applied Research for Asia/Pacific at Qontigo.

¹ “When it comes to sustainability, you can accentuate the positive, not just eliminate the negative,” Qontigo, November 2021.
² ‘Want to incorporate SDG exposures into your portfolios? There’s no such thing as a (risk) free lunch, but here’s a way to do it…’, Qontigo, May 2022.
³ A ROOF Score between +/- 20% from the benchmark is considered neutral.

Watch the whole panel from Wealth Management EDGE 2022 here: