Integrating ESG Preferences in Asset Allocation

We introduced an asset allocation framework for incorporating ESG preferences systematically in an earlier paper¹. It used hypothetical examples to illustrate how to add a third dimension to the traditional two-dimensional efficient frontier of portfolio returns and risk, allowing investors to take into consideration their ESG risk tolerance - in addition to return and risk objectives - when constructing a multi-asset portfolio.

This paper is a natural extension to the first whereby the focus of the paper is on optimization around specific ESG pillars. We rely primarily on our proprietary Responsible Investment Indicator Model (RIIM), which develops an environmental, social and governance profile for corporate, sovereign, municipal and securitized securities using both qualitative and quantitative measures.

It provides a systematic framework for measuring and comparing the ESG characteristics of over 15,000 corporate securities in addition to sovereign, securitized and municipal issuers. Because this model sets a common language for our investors to evaluate ESG risks across asset classes, the ESG scores from RIIM can be used to help drive the asset allocation of ESG-aware portfolios, with the ability to focus on specific sustainability topics.

More specifically, RIIM produces scores for each of the three pillars of ESG – environmental, social and governance. Instead of utilizing aggregate ESG risk scores, we can focus on specific environmental, social, or governance aspects in building the overall score for each asset class. Disaggregating the ratings allows us to consider the elements within ESG that are the more relevant for each investor or strategy.

Before delving into the details, it is worth noting that while RIIM is our preferred ESG risk rating framework, the same asset allocation approach works for other ESG scoring systems too. What is important is that ratings across asset classes should be based on a single source of ESG risk scores so that they can be compared consistently.

For illustration purposes, we have constructed a series of multi-asset portfolios that focus on environmental risks. The same approach can be applied to ESG social and governance risks as well.

Investment Assumptions and Constraints

Our starting point is a balanced portfolio consisting of 60% global equities and 40% global bonds. We then apply the following assumptions and constraints to the portfolio design:

• The investment universe is comprised of major regional equity building blocks (i.e., the U.S., Europe, Japan, and Emerging Markets) and fixed income sectors (i.e., Global Aggregate, Global High Yield, and Emerging Markets Bonds) in order to construct a diversified global portfolio.
• Return forecasts for different asset classes are based on our 5-year Capital Markets Assumptions (CMAs). Volatilities are constructed using historical return data over the past 5 years to reflect the most recent market environment (See Figures 1 and 2).
• Environmental risk scores are aggregated scores of individual securities at the asset class level based on our RIIM. The higher the score is, the more environmental risk the asset class carries.
• The allocation design of the initial portfolio is illustrated in Figure 3, in which regional equity allocations are based on country weights in the MSCI All Country World Index (ACWI) and fixed income sector allocations are based on the experience of a typical global institutional investor.
• In the optimization analysis (Figure 4 on the next page), we set a 3.0% limit for the tracking error to the initial portfolio for two reasons: 1) it is an investment constraint common to institutional clients, so it mirrors real world experience; 2) it anchors the portfolio design to the benchmark thereby removing extreme solutions from the optimization process.
• We set the constraint of a minimum single holding allocation of 3% and a maximum single holding allocation of 30% to ensure portfolio diversification and to avoid corner solutions.
• We did not constrain the equity/ fixed income mix in order to leave more room for the optimizer to find allocations with better risk-adjusted returns and mitigated environmental risk.

Optimization Results

Figure 4 above summarizes the optimization results of asset allocation across a range of different environmental risk tolerance levels. Starting from Portfolio 6 – the portfolio with no consideration given to environmental risk and which hence has the highest environmental risk score, a standard mean-variance optimizer would assign a significant overweight to Emerging Markets Bonds and Global High Yield Bonds. The reasons behind this are two-fold: 1) The two asset classes have attractive risk-adjusted returns base on our 5-year CMAs; 2) These two credit asset classes have high correlations to equity assets and thus act as equity substitutes in the optimized portfolio.

As we decrease our tolerance for environmental risk moving toward the left-hand side of Figure 4, we observe reallocations from higher environmental risk assets, such as Emerging Markets Bond and Global High Yield Bond, to lower environmental risk assets like European and Japanese Equities. Interestingly, the allocation to U.S. Equity, which has higher environmental risk than its developed market peers, starts to increase as we further reduce environmental risk tolerance. This is likely due to the optimizer’s attempt to use U.S. equity exposure to substitute for Emerging Markets Bond allocation to lower the overall environmental risk of the portfolio. The impact on the risk and return, however, does not follow a linear pattern.

It turns out that this can be explained by their sector exposure differences to a large extent. As shown in Figure 5, compared to Global Equity, Global High Yield tends to overweight sectors with higher environmental risks, such as Consumer Discretionary and Energy, while underweighting sectors such as Information Technology and Health Care, which tend to be associated with lower environment risks.

Conclusion

In this simple illustrative exercise, we developed a framework to help investors adjust asset allocation and incorporate their ESG preferences in a consistent manner.