Let’s explore how NLP techniques can be applied to each source.
Self-Reported Sustainability Assessments
A company’s sustainability assessment is often published on its website. While the report can provide detailed ESG-related information, it can also be biased because it is voluntary and unregulated. Companies may avoid reporting negative information.
Nonetheless NLP can be used to automate tasks such as summarization, keyword extraction, topic modeling, document classification, or sentiment analysis. These types of techniques are especially useful when one has sustainability assessments from multiple companies and wants to process them together to reveal trends and outliers. For example, topic modeling could reveal that most companies report on topics related to COVID-19 and employee health, yet a few companies avoid reporting on this topic. Text summarization could then be applied to quickly assess these outliers’ overall messaging.
SEC-related documents, in theory, are more transparent and regulated. Required Form 10-Ks may contain ESG information that a company discloses to the SEC. A company’s annual meeting proxy statements, also required by the SEC, are also a good source of potential ESG disclosures. In addition, shareholder resolutions can be a valuable source of the types of ESG issues raised by shareholders.
As with self-reported sustainability assessments, NLP techniques such as topic modeling and text summarization can be used to extract insight from these documents. In fact, recent NLP work on shareholder resolutions has reported revealing topics related to emissions and energy, boards, regulations, politics, governance, product management, and accountability.
While these SEC-related text sources are typically more transparent and reliable than self-reported sustainability reports, there is increased scrutiny around them, leading the SEC to recently announce the formation of an ESG task force to consider a more well-defined regulatory framework.
The most transparent text source is news articles. Not only is news more transparent, but the frequency at which timely information is provided allows NLP to be used to monitor ESG-related events as they are reported. In addition to applying NLP to news articles in the manner described in the previous two sections, two reported examples exist where state-of-the-art techniques for language understanding were applied to automatically classify news articles into at least 20 ESG-related categories such as physical impacts of climate change, employee diversity and inclusion, business ethics, etc. (Mukherjee, 2020; Nugent et al., 2020).
Elder Research has developed a proof-of-concept called the News Analyzer that scrapes news articles from the Internet and uses these NLP technologies to filter them by relevance and sentiment before displaying them to users. This technology could be extended into the ESG domain to offer clients informative monitoring of breaking ESG-related news about companies.
ESG Regulation Development
In addition to deriving insights about companies to monitor their ESG status, NLP could aid in the development of disclosure standards. These NLP techniques can reveal ways in which companies may evade disclosure of adverse behavior.
Consider a scenario where text classifiers are leveraged in a process to collect news articles about companies’ environmental violations. NLP can be used to compare these companies’ SEC-related documents and self-reported sustainability assessments against a group of non-offenders’ documents to reveal distinguishing characteristics that could inform new regulations to prevent future circumvention of the system.
The current lack of ESG standardization, comparability, and transparency makes it difficult for individual investors, portfolio managers, and government agencies to fully understand a company’s sustainability. However, NLP, a core competency of Elder Research, offers techniques for data-driven monitoring and regulation development that could dramatically change the present reality for the common good.
Related Blog Posts
Elder Research and our partner HData have published blog posts focused on NLP trends and NLP in Regulatory Technology (RegTech) which will provide more detailed information about NLP.
Trends in Natural Language Processing
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Natural Language Processing for RegTech: Uncovering Hidden Patterns in Regulatory Documents
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