Case study by DWS
- Signatory type: Investment manager
- Region of operation: Global
- Assets under management: €700 billion
Integrating ESG criteria into investment portfolios is one of the most important trends in asset management right now – and one we take very seriously at DWS. While ESG integration has long been a key feature of our Investment Platform, we have intensified efforts to ensure we apply stricter criteria across all the companies we invest in, including a greater focus on human rights, climate transition risks and those that violate international norms.
In this case study we will outline how DWS enables investors to achieve their own ESG goals when building a portfolio.
Why investors should combine their ESG goals with a strategic asset allocation
Strategic asset allocation (SAA) is a very important investment decision that investors make because it has the largest impact on long-term returns. If investors are serious about ESG integration, it is particularly important that they consider how ESG integration affect long-term investment returns. Risk plays a central role for investments with a long time horizon. Many empirical studies show the advantages of ESG integration on the risks characteristics of individual asset classes.
At DWS, we always consider risks holistically for the entire portfolio. In this example, we are referring to a multi-asset portfolio with liquid asset classes.
A simple and transparent three-step approach enables investors to combine their ESG goals with a strategic asset allocation (SAA) and to recognise the resulting changes.
- Calculate an SAA which settles long-term yield targets.
Many studies show that future returns on multi-asset portfolios are mainly determined by the SAA. We construct the long-term SAA using proprietary expected return and risk metrics (DWS Long View).
- Explain the different ESG facets that are used to integrate customer-specific goals into an asset allocation
The ESG integration takes place at the portfolio construction phase of the SAA process. We use a proprietary software system, the DWS ESG Engine, to analyse the ESG profiles of the asset class indices used in the SAA calculation. The ESG Engine is a data aggregation and consolidation device, which allows an objective data driven ESG analysis based on the expertise from leading ESG vendors (MSCI, ISS-ESG, Morningstar Sustainalytics, S&P TruCost and Arabesque S-Ray).
- Understand the influence of ESG screening from the entire allocation or individual asset classes that are included in the allocation
Our analyses of the possible ESG screens show that the different ESG risks result in very different shifts in country and sector weights in the asset classes.
How our methodologies can build a portfolio linked to your ESG goals
At DWS, we have established two appropriated models based on robust data, designed to build portfolios across different asset classes and with the ability to consider ESG risks
- Our SAA methodology
The input for the SAA calculation is our in-house methodology to calculate expected returns on an index basis. Returns for equity markets are derived from a multi-factor model, which considers dividend yield, inflation, earnings growth and valuation for the return prediction. For fixed income returns, we adjust the yield to maturity for roll-down effects, rating migration, valuation effects and defaults. The expected returns represent the input for the optimisation process. As an example, we forecast 6.8% annual return for equities and 2.9% for US corporates over the next 10 years. Our optimisation process is based on a group risk parity approach, which allocates risks equally to groups which are sensitive to similar risk factors. This approach aims to minimise concentrations risks.
- Our ESG data methodology
The ESG Engine – our proprietary ESG research data tool – generates ESG Scores and Ratings based on three core pillars:
- A controversial sectors screen identifying revenue exposures to sectors such as gambling or military defence.
- Norm violations, defined as reconfirmed violation of the United Nations Global Compact rule framework for corporate behaviour. From an SAA implementation perspective these two pillars may lead to exclusions from the indices so that the eligible investment universe becomes more limited.
- Our DWS SynRating, the best-in-class ESG rating approach to identify ESG leaders and laggards within a given peer group; this is also assessed by three external rating agencies. The SynRating is “sector-neutral”, meaning that the output shows similar rating distributions within each sector, but can also be used for excluding ESG laggards. To ensure a level playing field in the DWS investment universe, the SynRating can also be used to overweight ESG leaders and underweight laggards. Furthermore, it enables us to consider risks and opportunities from the transition to a carbon-free world when implementing an allocation. This “climate transition risk rating” consolidates qualitative and quantitative data from different vendors to identify carbon-intensive companies as well as true climate solution providers.
These ESG aspects are then evaluated within the context of our portfolio construction (security selection) process. To integrate ESG goals within the portfolio process based on the ESG criteria outlined above we need to apply a filter. For this reason, we have developed a wide range of client-specific sustainability filters that we can apply in our process. Our dedicated ESG Advisory team not only helps investors to define the optimal filter to achieve their ESG goals but to also align the filter with the optimal strategic asset allocation.
Example: Implementing ESG filters
The integration of ESG criteria can create new investment universes. Clear transparency is the key to making correct allocation decisions and efficiently mapping individual asset classes
In this example, we will highlight the impact of implementing ESG filters using the DWS SynRating and Climate Transition Risk Rating.
We start with the overall ESG rating and compare the MSCI World Index with a MSCI ESG Index. In the graphic below we see the distribution of companies in the different rating classes. In our own classification we use an A rating for an ESG leader and an F rating for an ESG laggard.
As expected, the ESG index – shown with transparent bars – differs between the two. Our rating indicates a higher proportion of ESG leaders and a lower proportion of ESG laggards compared to the traditional MSCI World Index. Overall, the DWS Synscore for the MSCI World is 62.3 (C-Rating) and for the MSCI World ESG Index it is 72.9 (C-Rating).
The distribution of the SynRating, being a relative best-in-class approach, is similar to a normal distribution across most sectors. Therefore, an investor can improve the SynRating of the allocation significantly by overweighting leaders and underweighting laggards without excluding titles or even whole sectors from the investment universe.
Another aspect of the overall ESG rating is the difference in rating between regions. The lowest values of ESG quality can be observed in emerging markets investments with a DWS Synscore of 52.2. The quality of the rating improves for American and Asian companies and the highest values can be observed for European indices with a DWS Synscore of 79 (B-Rating).
Most importantly, the Synrating is able to keep the sector allocation almost neutral while improving the ESG profile, which also helps to reduce the changes in the risk and return expectations for the calculated strategic asset allocation.
By analysing the breakdown of overall ratings for the different sectors, we do not see any major shift between the rating classes.
The following table shows the relative DWS Climate Transition Risk Rating distribution within each of the MSCI GICS (Global Industry Classification Standard) sectors across the A to F ratings, and based on the constituent’s weight of the MSCI World.
The analysis shows asymmetric distribution of the Climate Transition Risk Rating as companies within a sector are evaluated on an absolute basis. Consequently, climate transition risks are particularly prevalent in specific sectors such as Energy or Utilities.
Therefore, companies looking to improve their climate transition risk profile, not only need to reallocate but to also make rigorous exclusions of certain sectors. These strong new sector tilts significantly alter the risk and return expectations of the new investment universe and require a deeper analysis as well as a recalculation of the strategic asset allocation.
In general, the short comparison shows that the integration of ESG criteria influences the properties of asset classes to different degrees. The simplest part is to increase the ESG quality of the entire portfolio. There are many instruments available to investors to do this and only country-specific differences need to be compensated for. The consideration of individual ESG risks, like climate risk requires a much more precise analysis. Here, solutions must be found to generate the desired goals through stock selection and by experienced managers adjusting the imbalances in the sectors and other risk factors.
In general, slight changes in the sector and country shifts can be compensated for by portfolio managers and their title selection. The individual exposure of the companies is calculated and the influence of ESG criteria is balanced.
An example of this is the reduced weight in emerging markets. In other words, allocating to shares in developed countries with high ESG quality (measured with the DWS ESG Synscore) that generate significant revenue in emerging markets.
However, significant changes from implementing ESG criteria can also have an impact on the asset allocation.
- A reduced weight in small cap equities is offset by a higher allocation weight in small caps with high ESG quality at the expense of blue chips. In the end, the sensitivity to small caps should be the same in the portfolio as before the consideration of ESG criteria.
- Integration of ESG criteria can lead to strong sectoral changes, such as the consideration of climate transition risk or contributions to the United Nation’s Sustainable Development Goals, which can also impact the expected returns. Since our predictions for stock returns are based on a multiple factor model, different factors are adjusted accordingly. One example of this is the dividend yield, which can be changed significantly by excluding sectors with high climate transition risks. As described in the case study, this relationship is currently part of our research for long-term equity returns.