With the stellar performance of ESG funds during the Covid-19 season, Environmental, Social and Governance (ESG) investing has passed a critical test of time globally. It is no longer a niche investment approach. In fact, by March 2019, the United Nations backed Principles for Responsible Investment (PRI) had already registered over 2,300 signatories and represented a whopping US$ 86.3 trillion in assets under management as of December 2019.
Exhibit 1: PRI signatory growth since inception
ESG is also perhaps the only segment in the active money management space which is still attracting record subscriptions from global investors in 2020. According to Morningstar, ESG funds have attracted net inflows of US$71.1 billion globally between April and June this year.
However, despite entering the mainstream, asset managers and asset owners still must overcome significant issues when integrating ESG data into their investment processes.
ESG Factor alignment with investment strategies
Many governments, asset owners, and high-net worth investors are demanding ESG factor integration in their portfolios. Both Portfolio Managers and investors should have clarity on their ESG investing goals and they should align their investment process accordingly. Currently, there are several investment philosophies operating under the ESG bracket. However, we like to categorise these philosophies broadly under the following three investment strategies.
Exhibit 2: ESG Investing approach
Data is the lifeblood of ESG investing. However, its usage is governed distinctively by the investment approach undertaken. The Responsible and Impact Investing approaches are primarily based on screening techniques and use various ESG related qualitative and quantitative filters to either exclude or include countries, asset classes, sectors and securities in the investment model. The underlying data points to support these screening processes may include CO2 emissions, forest cover impact, agricultural land impact, terrestrial and marine wildlife impact, fossil fuel energy consumption, child labour participation, mortality rate impact, corruption impact, gender balance, violence impact, political stability etc. In the Responsible and Impact Investing approaches, the thrust is more on portfolio construction than model maintenance. However, the underlying datasets are monitored by asset managers on a regular basis since any significant deviation in the data attribute may warrant a constituent replacement in the portfolio.
The third approach i.e. ESG Integrated approach is radically different from the other two and has more complex data requirements from vendors. It does not believe in a simple exclusion and inclusion criteria and overlays ESG scoring on an existing investment process. To embed ESG in a fundamental style investment process, Portfolio Managers and their teams of Research Analysts must build a continuous distillation process of ESG data. The lack of consensus in sustainability reporting standards and low correlation in ESG ratings offered by various market data vendors are two common deficiencies in the ESG investing landscape. To mitigate these issues, the Portfolio Managers and their team have to look under the hood beyond headline ESG scores and ratings which may require studying rating methodologies of different ESG data vendors, reviewing data normalisation techniques used for relative ESG rankings, studying various sustainability frameworks like GRI, SASB, CDP etc., avoiding greenwashing and PR traps and navigating size biases which favours larger firms.
Implementing an ongoing ESG data distillation process requires avoiding several forms of data bias and it often leads to sourcing ESG data from multiple data vendors. Vendors share various ESG datasets via FTPs and APIs. However, the onus of formatting, normalising and standardising all the different datasets to make them consistent and comparable against each other lies with the asset managers and asset owners.
ESG Market Data Vendor Landscape
While investee companies still publish limited disclosures on ESG issues, ironically there is an overabundance of ESG ratings and data service providers in the market. At the end of 2016, there were more than 125 ESG data providers offering ESG research, ratings, rankings and indices that covered more than 50,000 companies globally, as per a report published by The Global Initiative for Sustainability Ratings (GISR). The vendors can be categorised under well-established global market data providers like MSCI, Bloomberg, Refinitiv and FTSE and ESG-exclusives like Sustainalytics, Arabesque, Vigeo Eiris, TruValue Labs, Trucost, etc. (see Exhibit 3)
Exhibit 3: Popular ESG Data providers
These vendors collect ESG datasets primarily from six sources including company’s annual reports and websites, newswires, CSR reports, stock exchange filings and NGO websites. They perform data quality checks and maintain audit logs and lineage in their systems.
Important factors to consider when institutionalising ESG data operations
Buy-side firms should consider the following four pillar model in their pursuit of ESG data:
- Methodology: Asset managers who intend to leverage ESG factor inputs into their investment process should strive to understand the ESG scoring or ranking model of the data vendors. The existence of low co-relations between ESG rating providers is a known issue. According to research conducted by StateStreet, the rating correlation between two leading ESG vendors, MSCI and Sustainalytics is 0.53 which is quite low. In the credit rating world, the corresponding coefficient between Moody’s and S&P would be 0.90 for issuer credit rating. The divergence in ESG ratings originates from the constituent level. For instance, BHP Group Limited has been currently assigned an ESG overall score of 66.73 and 31.2 by Arabesque s-ray and Sustainalytics respectively. Both vendors use a rating scale of 0 to 100, yet they calculate different scores. This disparity in ESG scores can be caused by different assignment of weights on materiality of issues while sometimes it can arise due to different evaluation techniques used within categories. Unless the research analyst or the Portfolio Manager really understands the ESG scoring framework of different vendors, these scores can sound counterintuitive. Some prominent vendors also use a different rating scale altogether, e.g. MSCI rates companies on ‘AAA to CCC’ scale with AAA being the leader and CCC being the laggard with respect to their exposure to ESG risks. To make two different scales comparable, the results have to be normalised by in-house subject matter experts.
To navigate the above inconsistencies between various ESG vendor scores and ratings and to gain rich insights into company’s ESG risk exposure and mitigation framework, it is recommended for asset managers to overlay their proprietary ESG scoring model on top of vendor data.
- Vendor Selection: Despite the presence of more than a hundred vendors in the ESG data space, the data feeds remain expensive. Asset managers and asset owners should engage specialist consulting firms in their ESG data vendor selection process to ensure that the cost & benefits of the feed service is aligned well, and the datasets are immune to any forms of bias. The data feeds should be fit-for-purpose, provide adequate security universe coverage for portfolio construction and maintenance. The role of matching the right ESG metrics with the investment framework can be played by an external consultant. The consultant should bring appropriate expertise and experience of dealing with ESG data vendors.
- Intangibles: Unlike classic financial data where quantitative analysis revolves primarily around a company’s published balance sheet, income and cashflow statements, in ESG many relevant alternative data points have to be extracted via unconventional sources like social media, satellite images, etc. and then processed and normalised to gain insights on a firm’s performance on ESG factors.
- Data Quality: For the successful outcome of an ESG strategy, the investment process relies upon high-quality ESG data. ESG datasets are first sourced from publicly available information like a company’s quarterly and annual statements, media coverage and various other reports from NGOs. Then, it is aggregated, transformed, normalised and re-published via APIs and data feeds for downstream investment research consumption. Just like any other datasets, ESG datasets have to conform to a reasonable data quality standard.
To build a single source of truth internally for ESG datasets, asset managers should try to maintain an in-house central repository to integrate and validate all forms of ESG datasets from multiple vendors at a constituent security, sector, asset class and sovereign levels. A repository in the form of an ESG data mart can offer coherence and scale advantages to both front and middle office teams.
Data quality dimensions for ESG datasets
The trillions of dollars riding on ESG’s shoulders has elevated the stakes on data quality for ESG datasets. We have outlined our best practice model on responding to data dimension challenges in the below exhibit.
Exhibit 4: Challenges and solutions to data quality dimensions for ESG datasets
ESG data vendors should always attach a data validation certificate to client data feeds. This helps in adding transparency and building credibility around overall data quality standards of the data feeds. This certificate will capture high level KPIs of the underlying data elements and highlight outlier records and data anomalies.
How Shoreline can help?
Our expertise in developing ESG data operating models combined with our deep understanding of leading market vendor’s offerings in the ESG world allows us to set you up for success in your ESG investment programs. With our unparalleled knowledge of investment firm’s operating models, system architecture, technology landscape and data service providers, we are able to assist both asset managers and asset owners with the selection of an ESG data supplier that best fits their investment process.
For more information, please contact us.