Development of Machine Learning Methods to Improve ESG Scores and Responsible Investment Decisions - Part 1

Explore a 43-minute conference talk from the Toronto Machine Learning Series (TMLS) on developing machine learning methods to enhance ESG scores and responsible investment decisions. Delve into Elham Kheradmand's research as a postdoctoral fellow at the University of Montreal, focusing on sustainable finance and AI applications. Learn about the Principles for Responsible Investment (PRI) framework and the challenges in measuring ESG performance. Discover a proposed rating framework that uses Natural Language Processing (NLP) techniques to automatically extract ESG information from company disclosures. Examine the five-component framework: downloader, reader, cleaner, extractor, and analyzer. Gain insights into NLP approaches such as cosine similarity, sentiment analysis, and named entity recognition. Understand how this research aims to transform qualitative, unstructured text data into quantitative measurements and structured data for improved ESG analysis and responsible investment decision-making.