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We Are Entering The Third Era of ESG Integration

Data Science and AI   |   ESG

By Bahar Gidwani  |  July 16, 2020

We appear to be entering the third era of Environmental, Social, and Governance (ESG) investment integration. A growing number of asset owners and managers are interested in ESG.

For example, more than 2,800 investors representing 90% of world financial assets have now committed to the UN Principles for Responsible Investment (UNPRI). In response, companies are clarifying and harmonizing their reporting methodologies. More companies are reporting sustainability information. According to CSRHub, around 50,000 entities have shared information about their sustainability performance either directly (about 12,000 have incorporated sustainability data in their public filings) or through participating in sustainability-related organizations or reporting systems.

ESG data providers are participating in this new wave of investor interest by offering:

  • Broader coverage of entity types (i.e., public, private, not-for-profit) and coverage of more types of investments (e.g., equities, debt, REITs)
  • Comparable, stable scores with enough history that an ESG factor can be used in a quantitative model or automated screening process
  • Methods for integrating ESG with other financial and market return datasets

Third-era investors are driven to integrate ESG data through three themes:

  • Marketing. To attract and retain assets, investors highlight their ESG methodology and follow it, even if it may result in underperformance.
  • Risk avoidance. Asset managers who prioritize safety and downside reduction may see ESG data as an additional tool for identifying and avoiding risk.
  • Materiality. Quantitative analysts have been using “alternative” datasets for years. ESG data may provide a new opportunity for algorithm-driven alpha generation.

ESG data is being integrated into investment processes through direct purchase of individual data sets. They are also being integrated into various types of data curation and distribution systems. ESG data used to be a subcategory of “alternative data.” It is now an independent category and the number of providers and the variety of datasets available has grown. 

Third-era investment strategies will rely on datasets that have these characteristics:

  • Broad, deep coverage. To be useful in an investment process, a dataset should cover the equities an investor holds, but also most (if not all equities) in similar entities. In some industries, there are large privately-held competitors. ESG-oriented analysts should like datasets that also cover private companies.
  • Streamlined factors, complete coverage of the factors, and a long stable history. Some ESG datasets offer two hundred, one thousand, or even several thousand indicators. This amount of detail can overwhelm investors who are relatively new to ESG issues. It is also difficult to fill in all of the “slots” in a dataset. Many major ESG sources cannot complete 70% or more of their bottom level of indicators. Finally, most financial analysts are used to studying market patterns over long-time horizons. No ESG data set goes back further than the 1990s—so analysts still cannot see how ESG factors relate to stock performance through a wide range of market conditions. Still, it is helpful if an ESG source has at least ten years of history and if it has made few or no changes to its methodology during this time.
  • Works well when combined with other datasets. Many ESG data users purchase more than one type of data. They extract value from combining these datasets to examine different aspects of a company’s sustainability performance. It is important to pick datasets that have enough identifying information (e.g., ticker codes, ISINs, name variations) that can be combined. It also helps if it is easy to pull data from a data provider’s site or application programming interface (API).

Third-era investors will demand ESG datasets that give them marketing differentiation, ways to reduce risk, and opportunities to generate alpha. They are likely to use several datasets and combine them in a proprietary way, as they seek to make their understanding of ESG data part of their competitive advantage in the investment marketplace.

This blog post has been written by a third-party contributor and does not necessarily reflect the opinion of FactSet Research Systems Inc. 

For information on accessing ESG ratings via the CSRHub ESG Business Intelligence data feed, please visit the Open:FactSet Marketplace.

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Bahar Gidwani

Co-founder, CTO, CSRHub

Mr. Bahar Gidwani is the co-founder and Chief Technology Officer (CTO) of CSRHub, the world's broadest source of corporate social responsibility information. Prior, he worked on Wall Street with Kidder, Peabody, and McKinsey & Co. He founded, built, and ran several large technology-based businesses for years. Mr. Gidwani earned an MBA from Harvard Business School, an undergraduate degree in Physics and Astronomy, holds a CFA, and was one of the first people to receive the FSA (Fundamentals of Sustainability Accounting) designation from SASB.


The information contained in this article is not investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.