Eagle Alpha Spotlight – NLP & AI for ESG Analysis

In the ESG landscape, some of the pain points for asset managers, are a lack of comparable, high-quality, high-frequency raw data, a lack of standardized definitions of sustainable activities, conflicting ESG taxonomies, and divergence in scoring methodologies across traditional ESG data vendors. This conundrum remains despite a proliferation of ESG data vendors since current ESG scoring methodologies are limited by voluntary and sparse non-financial input data and must be supplemented by alternative datasets such as events data, real-time data, satellite imagery data, traditional and social media data, all of which capture material issues and events not reflected in a company’s financial and regulatory filings. Extracting, evaluating, and standardizing these alternative datasets require significant manual effort, creating strong incentives for the use of machine learning (ML), artificial intelligence (AI), and natural language processing (NLP) techniques.

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