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ESG Systematic Scoring

ESG data integrated with financial and momentum analytics

About the Dataset

ESG Systematic Scoring uses machine learning and big data analysis to evaluate the performance and sustainability of listed companies. Its rules-based approach to stock selection integrates ESG data with financial and momentum analysis, systematically combining over 250+ ESG metrics with news signals from over 50,000 sources across 15 languages. The following scores are produced daily by this data set: 

1) UN Global Compact (GC Score), derived from normative principles set by the United Nations. 

2) Environmental, Social, and Governance (ESG Score), a sector-specific assessment of companies’ performance using financially material sustainability criteria. 

3) Preferences filter for both scores, allowing anyone to better understand each company’s business relationships and how those activities may align with investment values. 

The data set follows a quantitative and algorithmic approach to data collection, allowing for increased transparency across over 7,000 global corporations, and easy integration into a diverse range of use cases.

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