Regardless of one’s views about ESG, its centrality to many investors is borne out by the rapid integration and growth of ESG-oriented retail and institutional investing. It’s also important to recognize that the lack of uniformity of ESG ratings means that providers will always need to make choices. And where there is choice, there is subjectivity.
Quantitative Consensus-Based ESG Holdings Analysis
The application of artificial intelligence and machine learning to the consumption of ESG data has provided a solution to the problem of subjectivity of various ESG ratings agencies. No matter one’s position on ESG, there is a best practice for all sides based on quantitative oversight that pairs portfolio holdings data with ESG consensus ratings.
ESG Portfolio Monitor
Abel Noser Solutions’ ESG Portfolio Monitor provides clients with an independent assessment of their managers’ ESG portfolio holdings, traded securities & investment activity.
ESG Portfolio Monitor leverages sophisticated artificial intelligence and machine learning to consume ESG data from multiple rating vendors and other sources. Robust statistical analysis normalizes and weights those data inputs to generate ‘consensus ESG ratings.’ This minimizes the inherent subjectivity associated with the use of any single ESG rating firm. Also, the consumption of such large amounts of data enables significantly greater coverage, and facilities more frequent rating updates.