Our AI writing assistant, WriteUp, can assist you in easily writing any text. Click here to experience its capabilities.

Discovering alternative ESG insights using NLP

View Original View Raw

Summary

This article examines how text analytics can be used to develop unique insights into Environmental, Social and Governance (ESG) investing, which is an area of growing interest among professional and individual investors. The article gives a case study example of how SumUp's text analytics platform can be used to create ESG scores for ten large stocks in the Information Technology sector. The example illustrates how portfolio managers can use the platform to create their own parameters for analysis and implementation and to gain insights into non-traditional ESG exposures. The article also discusses the potential of the platform to be used in areas such as investment grade or high yield credit where there is less practical ESG implementation and data.

Q&As

What is ESG investing and how is it growing?
ESG investing is an investment strategy that focuses on Environmental, Social and Governance factors. It is growing rapidly in terms of the number of data providers and users, adoption by institutional investors, as well as fund flows and investment vehicles.

How can text analytics be used to gain insights into ESG investing?
Text analytics can be used to develop unique insights into ESG investing by extracting key topics related to each of the ESG pillars from corporate filings and measuring sentiment for each of the topics.

How can SumUp's text analytics platform be used to assess ESG exposures?
SumUp's text analytics platform can be used to score securities along multiple ESG dimensions by determining the extent to which various companies are exposed to positive or negative ESG factors.

What is the case study used to illustrate SumUp's text analytics platform?
The case study used to illustrate SumUp's text analytics platform is a portfolio manager looking to emphasize ESG factors and over/underweight individual securities. The case study uses corporate filings from ten large stocks in the Information Technology sector to create ESG scores for each company.

What are some potential areas to expand into for asset allocators looking for additional ESG exposure?
Potential areas to expand into for asset allocators looking for additional ESG exposure include investment grade or high yield credit.

AI Comments

👍 This article provides a great example of how text analytics can be used to create unique insights into ESG investing. It is an excellent example of how SumUp's platform can provide a powerful alternative to ESG data vendors.

👎 The article does not provide any concrete examples of how these insights can be applied in practice and how they can be used to make informed investment decisions. Furthermore, the article does not mention any potential drawbacks of using SumUp's platform.

AI Discussion

Me: It's about using Natural Language Processing (NLP), Computational Linguistics, and Machine Learning to gain insights into Environmental, Social and Governance (ESG) investing. The article provides a case study to illustrate how this can be done with SumUp's text analytics platform.

Friend: Interesting. What are the implications of this article?

Me: This article shows how text analytics can be used to gain insight into ESG investing and develop unique insights. It also demonstrates how investors can use SumUp's platform to score securities along multiple ESG dimensions and create their own parameters for analysis and implementation. This could be a powerful alternative to ESG data vendors, as it allows investors greater flexibility and control over the development of proprietary insights. Additionally, the platform can be applied to non-standard areas such as investment grade or high yield credit, where there is less practical ESG implementation and data.

Action items

Research and evaluate the potential of using text analytics to develop unique ESG insights.
Explore the use of SumUp’s text analytics platform to score securities along multiple ESG dimensions.
Experiment with open-source Jupyter notebooks to reproduce and adapt code for ESG analysis.

Technical terms

Natural Language Processing (NLP): A form of artificial intelligence that enables computers to understand and process human language.
Computational Linguistics: The study of how computers process and analyze natural language.
Machine Learning: A type of artificial intelligence that enables computers to learn from data and make predictions.
Environmental, Social and Governance (ESG): A set of standards for a company's operations that focus on environmental, social, and corporate governance factors.
Text Analytics: The process of extracting meaningful information from text data.
Jupyter Notebook: An open-source web application that allows users to create and share documents that contain live code, equations, visualizations, and narrative text.
API: An application programming interface that enables two applications to communicate with each other.