Take a Consistent Approach to Custom ESG Investing

A Consistent Approach to Custom ESG Investing 3

Several features make SASB’s framework attractive. To begin with, SASB is an independent organization, limiting governance and

conflict-of-interest concerns. SASB’s framework is clean, focusing solely on material issues that are important within certain industries. It

is also highly transparent in terms of decision-making. The method SASB uses to determine the importance of each ESG issue is clearly

delineated, so asset owners know exactly what they are investing in. Lastly, it is globally relevant, providing intuitive exposures to investors

in all regions around the world. This framework does not reflect a predominance of views from policy-makers or standard-setters in any

one region.

While ratings providers use readily observable attributes to evaluate stock attractiveness from an ESG perspective, we believe that a

company’s stock price largely reflects the benefits of such attributes. Instead, following the SASB approach, QMA uses industry-specific,

financially-relevant attributes in the evaluation process to identify attributes that have more likely been undervalued by the market.

We also use a combination of effort and action-based metrics in our evaluation process to minimize exposure to ESG traps and the

performance drag that is associated with them. Consider a company that claims to encourage diversity and inclusion. This could simply

be achieved through the existence of a Diversity and Inclusion (D&I) policy. But do they “walk the talk?” In our process we also look

at action metrics, such as the percentage of the workforce that is female. The combination of these metrics helps us to avoid ESG

window dressing.

We first map each of SASB’s material items to raw, objective ESG data. The use of objective data helps maintain the transparency of

the final ESG score and removes any ambiguity in the evaluation of attributes. We do not introduce analyst views or opinions. The

combination of these metrics also helps us to avoid ESG greenwashing.

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We are confident that these ESG scores are transparent, alpha neutral, and can facilitate customization into any equity investment solution.

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EXPANDING ESG INSIGHTS

Frustratingly, ESG data is still voluntarily disclosed and incredibly sporadic. Some items, mainly about companies’ own practices and

performance (e.g., emissions, diversity), can be difficult or impossible to determine. The median firm in the S&P 500 Index discloses

only a dozen different ESG data items. In global markets, the picture worsens, and becomes practically non-existent in emerging markets,

with only a trivial number of ESG items disclosed.

QMA uses a proprietary data completion methodology to expand ESG insights. Stock-return similarity helps us identify additional firms

that are “good” or “bad” from an ESG perspective. Our research shows that the performance of firms we infer to have positive or negative

ESG characteristics is comparable— if not stronger— than it is for firms with actual ESG data. This technique can also be applied to make

inferences on individual ESG issues (e.g., carbon emissions), which helps us address varying ESG preferences investors may have.

We are actively researching approaches to supplement our data completion methods, such as Natural Language Processing (NLP)

techniques, which also allow us to extract insights from various forms of unstructured data. Our approach to using NLP is not

straightforward machine learning. It is built, rather, around the development of rules, which are used to guide the search process to

extract relevant information. We can create custom rules to search for insights relating to any ESG issue—another method of expanding

ESG insights for client customization.

ESG INTEGRATION

Exclusion is the most common form of integration among ESG funds, and yet, it can also be detrimental to asset owners, increasing risk

and tracking error when entire sectors/industries are removed from investment consideration.

Many ESG indexes exclude a large portion of the index they represent. For some investors this may be satisfactory. However, ESG

indexes have built in disadvantages. The goal of an ESG index is to deliver the return of the parent index. They do this frequently

through what we call smart beta techniques (such as reweighting stocks in proportion to the ESG score), or simple optimization

techniques (maximizing exposure to ESG for a given level of tracking error). These methodologies may limit both the investment strategy

and potential ESG exposure. They may also restrict the ability of investors to meet their individual risk-return needs.

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In our initial research, we analyzed performance from December 2008 through Decemb

er 2015 for companies with ESG scores in the Russell 3000

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and S&P 500 indices. The results

showed that companies with positive ESG metrics according to material items showed a strong tendency to outperform the “bad” companies, with an average excess return of 1.7%

(vs. -1.4% for companies with bad ESG metrics).

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With our ESG evaluation methodology, we continue to look at ways to evolve. If all firms strive for better ESG, will there be any real variation in ESG standards? This can be thought of

as ESG compression. How does an evaluation methodology solve this problem? Related, we are also considering how to evaluate more than current ESG practices, with an eye toward

which firms will be good ESG companies in the future. It is one of the more challenging issues, but it can help better position portfolios toward stocks expected to achieve the biggest

improvements in sustainability outcomes. QMA’s ability to evolve thinking around ESG reflects the importance of an active approach.