We recently wrote an article ‘How data is accelerating ESG’ which highlighted the significant gaps not being addressed by traditional ESG data and analysis. We went on to show how AI-derived ESG data is filling those gaps through comprehensive, up-to-date and objective data, and how it can be used to add value in alongside analyst-led research.
In this article, we discuss a particularly interesting ESG case study which showcases the growing value-adding role AI-derived ESG data is playing in the professional investment world.
The Company: Texwinca Holdings (321.HK,TXWHF)
Hong Kong based Texwinca Holdings is engaged in the provision of yarn dyeing, fabric knitting & finishing services, and the production and sales of dyed yarns, raw fabrics and finished knitted fabrics.The company is vertically integrated and is focused on delivering “cost effective services” in this labour-intensive industry.
Despite having a market cap of over US$2 billion, there was no ESG data available on Texwinca until the launch of the ESG Analytics platform. All major ESG data providers such as Bloomberg, MSCI, Refinitiv had decided not to cover the stock, likely because of its market cap. Considering Texwinca is a mid-cap with a long term track record as a listed stock, it's the classic case of analyst and size bias.
ESG Analytics’ findings on Texwinca
Our initial research into Texwinca reveals that the company is not active on social media, and does not provide any useful information on its website, an unfavourable start to any ESG analysis.
Their corporate website also leaves a lot to be desired.
All major ESG data providers such as Bloomberg, MSCI, Refinitiv had decided not to cover the stock, likely because of its market cap.
Using AI allows us to have broader, deeper coverage - filling the gaps left by existing analysis. By sifting through thousands of data sources including press releases, news, earnings transcripts and corporate disclosures, ESG Analytics is able to hone in on the potential problems while categorizing and tagging the material topics in a way that can be contextually understood. As we cover most companies based in Hong Kong, our algorithm notified us on a few flags for Texwinca to watch out for:
In Texwinca’s case, the topics picked up which were highlighted included potential human rights violations and environmental sustainability issues as shown below.
Context is also hugely valuable for investors. ESG Analytics’ sentiment analysis feature is able to make sense of how a company’s ESG positioning evolves over time by integrating all the discovered ESG information into a polarity chart which tracks any changes which are occurring on a scale of positive (+1) or negative (-1).
As shown below, there was a significant decrease in Texwinca’s ESG sentiment in early 2019 with a swing from strongly positive to strongly negative.
Digging deeper into the material topics uncovered, we are presented with a data source showing that Norway’s largest pension fund (infamous ESG investor) KLP, excluded Texwinca from its portfolio along with two other companies after discovering systematic violations of human rights in its factories in Vietnam.
That’s serious stuff for ESG aware and unaware investors alike. A company which is violating human rights will not be investible for any ESG investors. And non-ESG aware investors will want to know this information since it raises serious questions about Texwinca’s management team. This is need to know information for all investors, and thanks to the power of AI, ESG Analytics’ team were able to uncover it.
This is just one company. Imagine scaling this up across your entire portfolio, keeping track of many clients or simply keeping track of material industry topics - that's the power of big data processing.
Texwinca is one of many companies which remain uncovered or under-covered by traditional analyst-led ESG data providers. And yet, as shown above, there are significant ESG issues at play at Texwinca which warrant urgent investor attention.
AI-derived ESG data analysis like ESG Analytics’ is the only way to address the ESG information and timing gaps prevalent across the investment world. As unstructured data continues to balloon, this strategy ensures professional investors are utilizing the best available ESG investment information.