ESG Benchmarking: Part #3 - Race to the Top

As explained in previous posts in this series (again, disclaimer made in the first post still stands!), investors may be better versed with the key issues that Contemporaries focus on, and are given more up-to-date, standardized and reliable ESG ratings for companies. However, data scores given are more volatile when comparing Contemporaries with Traditionalists. When comparing TVL with MSCI, TVL has higher standard deviation and variance in ratings, with standard deviations of 1.41 and 0.97 respectively. 

However, this is not to say that this volatility is bad. Humans have a habit of lowering entropy or seeking out patterns, some of which may not entirely exist. In the AI world, it is purely algorithmic, determined by a machine. There is no conscious decision making that is forcing the AI algorithm to lower deviation in data. It simply uses the information at hand, does some calculations, and comes up with a rating. 

This is a good thing that reinforces the objectivity of Contemporary approaches. Nevertheless, it is not to say that the Contemporary approach is entirely perfect. AI algorithms are highly dependent on large volumes of data to enhance learning and provide outputs closer to the “true value” we are looking for. Therefore, Contemporaries would definitely need lots more data to help fine tune their system. For example, data is not evenly distributed geographically: there is more data to be scrapped in the developed world than there is in developing countries. There is no equality in data access, and things such as data censorship, government suppression of information, or simply a lack of infrastructure to relay data online can all affect the reliability of the AI algorithm used by Contemporaries. 

Moreover, Contemporaries are completely dependent on external sources of data. It is important that there are data vendors available to them, or rather more data vendors may be needed. But more global data sources are becoming increasingly available, and may be used in the near future to help enhance the Contemporary method of ESG rating. Two examples include the Global Forest Watch (GFW) and MethaneSAT. GFW provides forest data globally, using satellite data as well as a network of partners to collect this information. This data is publicly available and free to use. MethaneSAT is a Jeff Bezos-funded initiative utilizing satellites to collect methane data all around the world and at a faster rate than scientists can measure from Earth. Data is publicly available to see how companies and governments are progressing with their carbon initiatives. 

MethaneSAT boasts several impressive feats. Its satellites can identify emissions across the globe and measure the amount of emissions released from pre-determined locations. From there, its algorithms are able to calculate the rate at which methane is escaping into the atmosphere as well as the total emissions from both individual points or regions.

The key points about these data vendors is that they provide global data and are publicly available. Having more data sources like this would thus be advantageous to Contemporaries, as they help tackle the issue of geographic informational inequality and enforce the reliability of data due given the freedom of public scrutiny. However, there needs to be more of these types of data sources, and data vendors of this sort for the other two aspects of ESG, Social and Corporate Governance.

Additionally, corporate disclosures are also an important source of information that has not been given much attention by Contemporaries, although to avoid greenwashing or providing a false image of a company, mandates should be put in place to enforce responsible auditing on ESG issues and more transparency in company disclosure.

Perhaps a better approach for Contemporaries would be to look at the discrepancies between the corporate disclosures and the internet data they collect. Discrepancies could be used as a confidence weighting on ratings for each component of ESG to ultimately determine a company’s rating. Whatever approach is taken, it is clear more data and richer sources are therefore required to help tackle the issues of volatility and increasing equality in access to data. 

What are the issues we should be worried about?

A major concern regarding Contemporaries is that they are run by profit-driven businesses. Their work is not publicly available or free to use, whereas Traditionalists like Sustainalytics have their ESG ratings available online. There is a lot of talk on how powerful their AI tools are, but not much of it can be publicly scrutinised unless you request for a demo, raising justifiable concerns about how reliable the technology actually is. Moreover, Contemporaries tend to be closely associated with international financial centres (IFCs)—TVL, for example, is owned by FactSet, an American financial services company in the second largest hedge fund state of Connecticut. 

Since their methodology is not easily examined,  due to limited disclosure, it is unclear as to the sort of parameters used or the assumptions made in developing its ESG rating model. Moreover, algorithms are not entirely subjective in their creation. They are subject to the scrutiny of their makers, and may have certain biases or other hints of subjectiveness built into the foundation of their AI system. Without a true value to compare ESG ratings with, we would not be entirely sure how well the algorithm really works.

With the uncertainty in the actual formulation of the Contemporary AI analyst, and the profit-driven nature of Contemporaries themselves, Hughes, Urban, and Wójcik (2021) note that Contemporaries may “complement rather than substitute” the Traditionalists in the near future (Hughes, Urban, and Wójcik, 2021). Although Contemporaries seem more democratic and provide more transparent ratings, Contemporaries also have areas of opaqueness that can affect the way ESG ratings are done. Perhaps if Contemporaries were not profit driven and rather more altruistic could AI truly replace traditional approaches to ESG.

Check out my first two posts on AIs and ESG benchmarking: ESG Benchmarking: Part #1 - ESG, AI, and the Investment Industry and ESG Benchmarking: Part #2 - AI, the New Hope of ESG

By MUHAMMED Hazim, Segi Enam intern, 30 Aug 2021 | LinkedIn

Edited by Nadirah SHARIF

ESG Benchmarking: Part #1 - ESG, AI, and the Investment Industry

The Environment, Social, and Governance (ESG) criteria is the way forward. This standard is used to provide social credit ratings for companies, and see how they tackle ESG-related issues to become a more sustainable company. ESG is in high demand amongst investors, because they want to invest in places that align with their own values. International organisations have endorsed the use of ESG-related standards, including the United Nations (UN) via its Principles for Responsible Investment (PRI), an initiative to make sure investment companies incorporate ESG issues into their investment decisions. From 2006 to 2018, Assets under Management (AUM) has grown from 6.5 to 81.7 trillion dollars under the PRI initiative (Eccles & Klimenko, 2019), showcasing a growing interest in ESG principles amongst investors.

However, ESG is also important to investors for financial reasons. Companies that ignore ESG issues tend to lose money (meaning investors do too). The Bank of America estimates that up to $600 billion has been lost to ‘ESG controversies’ in the S&P 500 since 2013 (Reuters, 2020). On the other hand, the Bank of America reports that the highest performing ESG firms are outperforming the lowest performing firms by more than 40%, become high quality stocks, are less volatile, and have higher three year returns (Eccles & Klimenko, 2019). 

ESG is thus very important: it makes sure companies are held accountable for their business, and for investors, they can put their money in a company that is profitable and that they are confident in. To that end, sustainability ratings are widely used, and there are various agencies, such as MSCI, Bloomberg, and Sustainalytics that do ESG ratings for companies. 

The Edge Markets (2021): RHB has downgraded various plantation companies amidst growing ESG concerns.

A question you might be thinking of is that if ESG ratings exist for companies, why do people still make investments in companies that get caught up in ESG controversies, like Dieselgate 2015? There is an issue with the current rating system, and a tech alternative powered by Artificial Intelligence (AI) has entered the playing field. In this series of posts, we will look at how ESG is currently being benchmarked, its limitations, and how effective the new AI-powered benchmarking is.

TRADITIONAL RATING AGENCIES

Quick Disclaimer: 

Before I continue with this post, I would like to note that various ideas noted down in this paper were primarily derived from the work of Arthur Hughes, Michael Urban, and Dariusz Wójcik from the School of Geography and the Environment at the University of Oxford (Hughes, Urban & Wójcik, 2021). My area of exploration on the topic was to compare the different approaches to ESG rating, and their paper is the first comparative study on the human-driven and AI approach to ESG benchmarking. There was one other informative paper related to ESG benchmarking (In, Rook, Monk & Rajagopal, 2019), but it was a general paper on possible alternative ESG data sources rather than a commentary analyzing how the human and AI based approaches work. In these series of posts, I have supplemented their work with some of my own research and commentary to enhance the discussion on how ESG affects investors.

What about them? How do they work?

Traditional rating agencies refer to the big players in ESG rating such as MSCI, Bloomberg, and Sustainalytics. In this post, I will be referring to them as Traditionalists. The analysis and final output benchmarking by Traditionalists is subject to the discretion of a human analyst. In terms of their modus operandi, Traditionalists identify key issues and apply weightings to them to use as criteria for a company’s ESG rating. Key issues refer to ESG-related issues that affect a company financially. Data used alongside the key issues and weightings come from corporate data, online data, and face-to-face contact (F2F) to come up with the final rating for a company. 

What's wrong with what they’re currently doing?

For starters, Traditionalists take a rather questionable approach in coming up with ratings by relying heavily on company disclosure—it is estimated that 45% of benchmarking is based on disclosures. This becomes a problem if a company chooses to be less transparent about their ESG problems. Therefore, there is a justifiable concern regarding transparency of these data sources, particularly the integrity and reliability of this data. 

There is actually a term for an extreme version of this problem known as greenwashing, where a company influences and misleads their ESG ratings, and is a major problem in the Traditionalist ESG rating system. Moreover, there is a lack of standardization in the selection of key issues and weightings: in the case of MSCI, weightings are decided for the entire year, and key issues are subject to change at the discretion of MSCI. Therefore, the methodology in determining the rating for one year may be different from another. 

So how confident can an investor be in a Traditionalist rating? To add to the organizational subjectivity, Traditionalists rely heavily on the brainpower of a select few, often leaving analysts with the tall task of examining a long list of companies—MSCI, for instance, rates about 14,000 companies with a manpower of only 185, meaning that a single research analyst handles an average of almost 80 companies (Hughes, Urban & Wójcik, 2021). 

This complication  has led to major flaws in ESG ratings. Take Boohoo, for example, a major fashion retailer in the UK. They had been rated highly and included in various ESG funds, until they got caught up in a wage scandal for underpaying workers (Chatelin, 2020). On the other hand, an environmentally conscious but small company could be given a lower rating than they deserve due to limited company disclosure (Reuters, 2020).

Source: Sustainalytics (accessed Jul 2021)

From my own data sleuthing, Traditionalists continue to have shortcomings in their reporting. Looking at Top Glove, which has been removed from three ESG indexes amidst forced labor allegations, poor labor conditions, and a poor safety protocols that led to Covid-19 infecting 25% of its workforce (The Business Times, 2021; Nikkei Asia, 2021), Top Glove still has a medium ESG risk rating of 26.1 (scoring is on a scale of 0–40, with 0 being the worst and 40 being the best) and a Level 2 (moderate) controversy level (on a scale of 1–5, 1 being low and 5 being severe) (Sustainalytics, accessed Jul 2021). From a chronological perspective, the forced labor allegations happened in March 2021, and the poor labor conditions and Covid situation in June 2021. Sustainalytics last updated their rating in April 2021.

All in all, the Traditionalist approach, although viable for the most part for investment decisions, is riddled with significant amounts of subjectivity, is not necessarily reliable or up-to-date, and possibly suffers from a lack of transparency. A quote obtained during an interview with a research analyst conducted by Hughes, Urban & Wójcik (2021) best summarizes the limitations of the Traditionalist approach:

“With MSCI scores, there’s a lot more research that goes into them than I think people give them credit for. But it is somewhat arbitrary because they cover so many companies.”

By MUHAMMED Hazim, Segi Enam intern, 16 Aug 2021 | LinkedIn

Edited by Nadirah SHARIF