ESG Investing For Dummies. Brendan Bradley

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Despite the significant contributions that data providers have achieved in evolving ESG investing, asset owners and managers should understand the intrinsic limitations of this data, as much as the challenges of relying on any one data provider.

      Quality data has always been the lifeblood of investment analysis. While “quality” can be expressed in different ways, most investors agree that consistency and comparability in the accessibility of data among companies are critical elements of an operational data set. However, the current environment imposes barriers to realizing such quality when a company’s ESG data practices are rather ad hoc. Associations and regulators don’t always require companies to report on all of their ESG data; therefore, they can decide which ESG factors are material to their business performance and what information to reveal to investors. As such, asset owners and investment managers can be left to find their own solutions to these challenges, which can create an additional stream of inconsistent, non-comparable, and less material information.

      

These opposing methodologies have repercussions for investors. In selecting a given provider, investors are, in effect, associating themselves with that company’s ESG investment ethos in terms of data acquisition, materiality, aggregation, and weighting. This selection is further complicated by the lack of transparency into those practices. Most data providers regard their policies as proprietary information. By depending on an ESG data provider’s score, asset owners are accepting the assessments of that provider without a full appreciation of how the provider determined those ratings.

      ESG momentum importance

      In addition to considering current ESG ratings, investors looking for positive alpha generation within an ESG framework can seek changes in ESG ratings. This is known as ESG momentum, and various studies have shown that using this strategy helps outperform the established benchmarks. Positive ESG rating momentum is defined as when a company’s ESG rating has improved by more than 10 percent on the previous year. On the contrary, negative ESG rating momentum occurs when a company’s ESG rating has fallen by more than 10 percent on the previous year, and neutral momentum occurs when the rating remains unchanged or within the –10 to +10 percent range.

      The principle behind an ESG momentum strategy is that future stock performance is connected to a change in the ESG quality of the company and potentially a reduction in future liabilities. Various studies have shown that buying more stocks with improving ESG ratings can lead to investment outperformance. The premise of this idea is that companies with lower ESG scores have more improvement potential and should therefore be included in a fund’s investment universe, although this does introduce timing issues on when the optimal time is to invest in such stocks. However, this is no different from the challenges facing active fund managers within a traditional investment approach.

      

The contrary opinion is that investors should view companies that embrace new, improved ESG policies with skepticism, and ought to focus on companies with a proven ESG record. Moreover, changes in the methodology of a data provider could create false momentum signals. To counter this, investors need to consider using multiple data providers to blend the momentum score. This helps reduce variations and gives a clearer picture of ESG momentum.

      Applying artificial intelligence and data science to ESG analysis

      Several investors mention the lack of high-quality information as the biggest challenge in adopting ESG principles. Industry bodies are developing international standards and guidance for ESG disclosure, but in the absence of standards, the burden lies with individual companies and investors to ensure quality ESG disclosures and to confirm the sustainability of vendors, suppliers, customers, and counterparties. But how do you verify it?

For most companies, ESG verification implies asking such partners to abide by the vendor code of conduct. However, artificial intelligence could play a central role in collecting, verifying, and analyzing ESG performance by using techniques from Natural Language Processing (NLP; programmatically mining information from text), graph analytics (understanding how different entities influence each other’s ESG), and Machine Learning (ML; predicting how ESG factors will influence investment performance in given conditions). Moreover, ML could be used to generate missing values for companies that have incomplete reporting by using the known rating of an established ESG company and defining the similarities between the companies and their industry sector.

      

It’s clear that without solving their fundamental data problem, companies won’t have an accurate understanding of their own ESG metrics (garbage in, garbage out). However, as the industry evolves toward a standardized set of metrics and reporting formats, investors will deploy AI to verify evidence of materiality, evaluate investment risk, and forecast investment return. Eventually, ML will produce automatic investment decisions integrating ESG factors, just as it does in traditional investing. Therefore, the investment professionals who understand how to leverage AI resources to contextualize and produce ESG data will be best positioned as ESG data standardizes.

Defining an ESG Policy

      Developing a responsible investment policy doesn’t need to be a burdensome task. The methodology applied to “policy writing” needs to be inclusive to confirm representation of all relevant and material viewpoints. You could use existing channels of communication with stakeholders and integrate their input on the contents of your policy. Such approaches to an ESG policy should be informed by the internal review process, appointment of external service providers, stakeholder soundings, and so on. Within the planning stage, it’s crucial to ensure that ownership of the policy and outcomes is driven by the highest possible management within the organization. In addition, cultural fit and organizational governance buy-in are essential elements in effective policy-making. Planning can also be supported by following wider industry guidance on ESG integration — there is no need to reinvent the wheel when there is so much best practice and peer analysis that can be followed.

      This section provides a “whistle-stop” overview of some of the key factors to incorporate. Flip to Chapter 13 for details on building an ESG strategy.

      Familiarize yourself with ESG and asset owner–specific legislation

Local jurisdictional law may require pension funds and other investors to have a statement of investment principles, or their fiduciary duty may oblige trustees to consider any ethical or ESG issues that are financially material. Similarly, other jurisdictions explicitly require diversity and inclusion to be considered as material ESG factors in their investment analysis and decision-making. In short, given the growing acceptance of responsible investment practices, most pension funds already subscribe to various methods of ESG investing. In many countries, the corporate governance and stewardship codes can also provide valuable insight when developing an ESG policy, which should consider the performance of investment portfolios to varying degrees across companies, sectors, regions, and asset classes.

      

Furthermore, many of the points suggested here for money managers will be similar for individual companies implementing their own ESG policies.

      Undertake a peer review

      It

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