Environmental, Social and Governance (ESG) Investing has received a tremendous amount of attention in recent years, with investors and companies increasingly focusing on ESG metrics. With vast amounts of data available to compute such metrics, the use of data science and machine learning (ML) techniques in this context is of ever greater importance.
This workshop will be the first of its kind by specifically focusing on ML for ESG. The workshop will appeal to a wide range of audience including investment practitioners, commercial ESG score providers as well as machine learning researchers.
We invite papers on Machine Learning for Environmental, Social and Governance (ESG) Investing. Topics of interest include, but are not limited to, the following:
Climate modeling, global warming and their effect on the economy and stock markets
Reputational and environmental risks (wildfires, tornados, biodiversity loss etc.) and their effects on the economy and stock markets
Identifying and/or analysing diversity in firm management and governance using machine learning
Identifying and/or analysing social awareness of companies, fund managers, etc.
Machine learning ESG factors
Machine learning for portfolio construction to include ESG
ESG investing and investors' (behavior) modeling
ML for ESG ratings and scoring
NLP/text analysis on ESG reports, news, social media, etc.
The use of non-standard data, such as satellite imagery, for ESG analytics.
ESG Regulations and Machine Learning
etc.
Overview of Industry Challenges
Short papers from financial industry practitioners that introduce domain specific problems and challenges to academic researchers. These papers should describe problems that can inspire new research directions in academia, and should serve to bridge the information gap between academia and the financial industry.
Algorithmic Tutorials
Short tutorials from academic researchers that explain current solutions to challenges related to the technical areas mentioned above, not necessarily limited to the financial domain. These tutorials will serve as an introduction and enable financial industry practitioners to employ/adapt latest academic research to their use-cases.
All submissions must be PDFs formatted in the Standard ACM Conference Proceedings Template. Submissions are limited to 4-8 content pages, including all figures and tables but excluding references. All accepted papers will be presented as posters and some would be selected for oral presentations, depending on schedule constraints. Accepted papers will be posted on the workshop website.
Following the conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed.
Papers should be submitted on CMT3 by 16th September, 2022
Submission URL: https://cmt3.research.microsoft.com/ML4ESG2022
Submission deadline: 16th September, 2022
Author notification: 18th September, 2022
Workshop: 2nd November, 2022
Organization Committee
Dr. Stefan Zohren (Associate Professor, Oxford-Man Institute, University of Oxford, Turing Institute and Man Group),
Mr Brian Bruce (CEO and CIO, Hillcrest Asset Management; Editor-in-Chief, Journal of ESG and Impact Investing, and Journal of Behavioral Finance),
Dr Dhagash Mehta (Head of Applied ML Research, BlackRock, Inc.)
Dr Steven Reece (Head of Machine Learning Research and Data Science, Oxford University School of Geography and the Environment).