Asset managers, such as hedge funds, private equity, venture capital, and corporate finance, are looking for companies that align with their values–environmental, social, and governance (ESG) analysis is one way to identify them.
Making fast, accurate, and informed decisions on portfolio investmetn is more important than ever. Furthermore, knowing how to make the most out of the immense amount of data available is critical.
But, what does that mean? And, how can you make sure you invest your money in a way that considers these issues?
The answer is simple: ESG sentiment analysis using Natural Language Processing (NLP).
In this article, we gathered all the information you need to know regarding the use of NLP in ESG sentiment analysis:
- A short definition of ESG sentiment analysis
- Why is ESG sentiment analysis important
- What challenges does NLP ESG analysis face
- How does NLP help with ESG sentiment analysis
- How to use NLP with Accern for better sentiment analysis
Let’s dig in!
Environment, Social, and Governance (ESG) is a holistic approach to measuring and managing environmental, social, and governance risks and opportunities.
The Environment Factor
The environmental criteria consider a company’s impact on the environment. For example, how the company addresses the issue of global warming. Overall, the environmental factor focuses on how much attention a company pays to conserving the natural world.
The Social Factor
The social criteria considers the company’s treatment of employees, customers, and other stakeholders.
Here are some examples of the social factors:
- Customer Satisfaction
- Data Protection
- Labor Standards
The Governance Factor
The governance criteria considers the company’s compliance with laws and regulations.
Here are some examples of the governance factors:
- Board composition
- Political contributions
- Bribery and corruption
- Anti-Money Laundering and Know Your Customer (KYC)
What are ESG Metrics
There is no one-size-fits-all answer to this question, as different companies have different goals. However, we can separate the metrics into quantitative and qualitative categories.
Understanding Quantitative ESG Metrics
Quantitative ESG metrics are numerical values that can be compared over time. Companies use these metrics to track the progress of their strategies easily.
Quantitative metrics show information about percentages, distances, quantities, etc. To give concrete examples, we can mention:
- Greenhouse gas emissions in carbon dioxide equivalents (CO2e)
- Employee satisfaction on a scale from 1 to 10
- Money spent on community improvement
Understanding Qualitative ESG Metrics
Qualitative ESG metrics are in a textual form. They are hard to collect, compile and compare. They usually come in the form of reports, meeting notes, etc.
Qualitative data is unstructured data that is more difficult to analyze than quantitative data.
An example of a qualitative metric can be the ESG strategy of a company. The strategy is an ESG metric, yet it is not quantifiable since it is rather an abstract construct.
However, it is not impossible to get valuable insights from unstructured data. With Accern, you can extract insights from unstructured data and also structure or quantify this data – for example with a sentiment score – that is fed into predictive models and other algorithms.
Just upload your data or configure your data source in the Accern NoCodeNLP Platform, and you can extract and compile useful information that will help your ESG efforts, in days not months.
Why is ESG Analysis Critical?
ESG analysis is vital because it can help investors and companies identify and manage environmental, social, and governance risks and opportunities.
In addition, investors can:
- Align their values with their investments and help companies understand and improve their reputations.
- Assess a company’s financial risks and opportunities related to ESG issues. For example, if a company is exposed to environmental risks, such as climate change, water scarcity, or pollution, ESG analysis can help investors understand the financial implications of those risks.
- Identify well-positioned companies to benefit from ESG trends. For example, investors may be interested in companies developing sustainable products or services or reducing carbon emissions.
Finally, ESG analysis can help companies understand and improve their reputations. For example, if a company’s ESG ratings are low, it may be indicative of poor environmental, social, and governance practices, which allows them to look over their activities and see where there is space for improvement.
Thus, the company can take concrete steps to improve its activities and, consequently, its reputation.
The automotive industry is a relevant example of how challenging ESG analytics can be. For instance, BMW scores 85% in the overall ESG rating, which is an above-average score. The score considers the three ESG factors and gives an average result. This high ESG score puts BMW in the category of a sustainable company. Therefore, investors will trust it more and consider it a safer investment, increasing BMW’s reputation and profitability. Thus the importance of NLP ESG analytics.
NLP technologies allow users to understand the public perception of target companies.
In conclusion, ESG analysis allows investors to be confident that the investment will:
- have a high ROI
- not hurt their reputation
- align with their values
ESG Analytics Challenges
ESG is now a big focus for both companies and investors.
This attention has created a new position that did not exist a few years ago: Chief Sustainability Officer, an individual who is in charge of maintaining all ESG-related work in the company.
From gathering data to sustainability reports, the CSO is the focal person for portraying and keeping the company on a sound ESG track. However, the task does not come without challenges.
Below are some challenges that a CSO might face in ESG analytics.
Defining ESG Metrics
It is essential to define ESG metrics because they are a vital part of the ESG analysis process.
Without a clear definition of what constitutes an ESG metric, it can be challenging to compare and contrast different companies’ ESG performance.
Furthermore, defining the ESG metrics can help companies set concrete goals and objectives for their ESG initiatives.
The definition of ESG metrics will vary depending on the investor’s or company’s specific goals and objectives. Some challenges that can make it difficult to define ESG metrics include:
- Lack of a standard definition or framework for ESG
- Variety of ESG data sources available
- Difficulty of measuring some ESG factors
CSOs consider all these points when defining the ESG metrics they want to set and track.
Furthermore, they try to portray only the metrics they feel will benefit. This leads to an issue of materiality – whether a specific measure has financial significance and is effective.
Materiality surfaces when the companies need to decide on which metrics to report on and track.
ESG Data Collection
Internal Data Collection
Collecting ESG data can be complicated because it requires significant resources and expertise.
Furthermore, internal data may be biased or inaccurate if it is not collected and managed correctly.
Additionally, most companies store the data in spreadsheets. You can imagine how many departments there are in big corporations. So, there could be hundreds of spreadsheets hosting all the data.
Moving all the internal data to a centralized system represents a challenge.
External Data Collection
External ESG data collection typically involves working with third-party data providers.
These data providers typically have more resources and expertise in collecting and managing ESG data than regular companies.
Data providers may charge companies a fee for access to their data or may only be willing to share their data with companies with a signed agreement.
Data validation is important because it helps ensure that the data used for ESG analysis is accurate and reliable. Challenges include:
- Errors can occur while collecting data if the data is not collected properly or if it is not managed correctly.
- Data collectors may also make errors when coding or categorizing data.
- Data validation can be challenging because there is no one-size-fits-all approach to verification, and different companies will have different data needs.
- Data validation typically requires a significant amount of resources and expertise.
How Does ESG NLP Sentiment Analysis Work
One of the main challenges investors face when utilizing an ESG strategy is the lack of standardized data and the barriers to accessing relevant data sets.
Plus, given the qualitative and subjective nature of many ESG factors, it can be difficult for finance professionals to effectively and efficiently analyze all the data available and gather meaningful insights.
AI and sentiment analysis tools can help process large amounts of data related to ESG factors and analyze news and announcements from specific companies that meet the ESG screening criteria.
Investors can benefit from sentiment analysis by reducing the hands-on time required to read through each news story while still obtaining transparent and comparable data on ESG factors to stay competitive and relevant in today’s industry.
When using sentiment analysis to assess a company and its ESG factors, investors can easily see the news sentiment or tone among stakeholders from millions of available data pieces worldwide, which include:
- social media posts
- news stories
- press releases
Sentiment analysis can analyze each piece and determine the consensus emotion or tone.
The algorithm can determine whether the piece is positive, negative, or neutral, to what degree, and the pattern of such sentiments by scoring the text data.
This capability benefits ESG investing and can give quantitative value to unstructured data like news stories and analyst opinions.
Firms can compare companies, perform due diligence, and forecast future stock movements.
Overall, sentiment analysis helps investors find lucrative investment opportunities based on ESG criteria.
Here is an explanation of how the Accern Platform uses NLP to capture human emotion in text data. Accern’s No-Code AI tool analyzes sentiment. The Accern Sentiment and Relevance Model operates with high accuracy, allowing users to determine when positive, negative, neutral, or meaningful information exists in their data. (Download a whitepaper, A Benchmark of Popular Sentiment Analysis Models on Financial News, to understand the accuracy of the Accern analysis models.)
The Accern sentiment analysis models allow you to generate four types of insights:
- Document Sentiment
- Entity Sentiment
- Event Sentiment
- Signal Sentiment
To make informed and meaningful ESG investments, finance professionals need a way to identify markers within companies to measure against their ESG criteria and invest accordingly.
With most of the data and information in the field being unstructured and subjective, creating standardized or universal signals to qualify companies is often challenging.
With AI and sentiment analysis, firms can more easily turn news stories, reports, and filings into quantitative data and signals that can be analyzed and compared across companies and industries.
With a lack of available data in the field, investors can have an upper edge over competitors when using AI to:
- Capture the news sentiment on a company
- Determine how they measure up to ESG criteria
- Make better informed investment decisions.
How to Leverage ESG Sentiment Analysis with the Accern NoCodeNLP Platform
Get Insight on ESG Factors
ESG factors will become even more of a priority among investors over the coming decade.
Now seen as a requirement for most asset managers to stay competitive, firms need a quick and easy way to implement sentiment analysis into their operations and support their ESG strategy.
Through AI, they can extract insights, identify investment opportunities, and stay competitive with regard to ESG.
With Accern, you can quickly obtain insights into a company’s ESG practices.
Get Real-Time Alerts
Social media platforms are dynamic real-time indicators where fast changing opinions can impact ESG.
Various factors can influence opinions on social media:
- Timing of the post
- Number of likes or shares
- Overall tone of the conversation
These factors can affect how stakeholders perceive a company’s environmental, social, and governance practices.
With Accern, you do not have to worry about this anymore. You can include an intelligent alert in your workflows to issue real-time alerts on any ESG factor.
Get Insightful Summaries
Text summaries and sentiment scores can quickly assess long articles or large documents by determining the overall contents of the text. This can be useful for understanding the general opinion of a customer review or a news article.
Additionally, sentiment analysis can help identify key phrases and topics associated with positive or negative sentiment. This can help researchers understand what aspects of a product or service are most important to customers or identify potential areas of improvement.
Watch this 3-min video on how the Accern NoCodeNLP Platform provides detailed summaries that can be added in your workflows.
With AI solutions like the Accern NoCodeNLP Platform, investors don’t need to hire a data specialist or software engineer to reap the benefits of sentiment analysis in their firm.
Analysts without a technical background as well as data teams can use this AI / ML / NLP solution to determine the news sentiment around recent developments or announcements and let investors quickly sift through the piles of data available to make informed investment decisions.
Do you want to see how your ESG strategy could benefit from sentiment analysis?
Schedule a demo to learn more about the Accern NoCodeNLP Platform and how it can drive ESG-focused insights for your investment portfolio.Share this Post!