Global AI

There is no trade-off between financial returns and positive societal footprint

Our research, Sustainable Investment-Exploring the Linkage Between Alpha, ESG, and SDG’s, a TOP 10-listed Social Science Research Network paper, shows there is no trade-off between financial returns vs. positive societal footprint. We worked with the $77 billion UN Pension, and our research shows that an ESG momentum portfolio creates a statistically significant Alpha relative to the MSCI US index and a relatively better societal impact. 

Our forthcoming research shows constructing a portfolio based on our 6-years of daily SDG scores for 20,000 firms also creates alpha and outperforms the MSCI benchmark. Our daily SDG scores are leading indicators of MSCI ratings.

The controversy around ESG investing is because it frequently excludes companies based on various criteria which can create conflicts with fiduciary duty. It also suffers from a lack of useful ESG data for investment purposes due to competing ‘materiality’ frameworks affected by selection bias and based on human judgement, not robust scientifically-driven data.  ESG metrics are typically only updated annually, & are unaudited. This causes major noise  across vendors who provide company ESG scores. 

A Big Data AI-driven investing approach is an objective investment tool, based on daily, robust analytical data & does not exclude any company but instead measures their impact to society across a variety of angles. Using AI we eliminate self-reporting biases, accessing massive amounts of data from more than 100,000 sources, over 150 countries and 60 languages. AI uncovers hidden risks and shows positive AND negative ESG and SDG scores.  ESG metrics ONLY produce positive scores. 

ESG investing is not the problem; the lack of useful scientifically-driven ESG data for investment purposes is the culprit. The solution is to use statistically, scientifically-driven robust analytics and data for investment purposes, not self-reporting ‘greenwashing.’

Close Bitnami banner