The reason why fast and accurate sentiment analysis of news data is critical because most information in the financial services industry is qualitative rather than quantitative; hence, sentiment analysis of news can help asset managers quickly gain insights on the latest developments and make ESG-driven investment decisions accordingly.
Importance of News Data for ESG Sentiment Analysis
Information contained in news, social, blogs, reviews etc. plays a significant role in ESG investing as it can forecast future price movements, company valuation, and profitability. The ESG sentiment contained in this unstructured data is very helpful for investors who need to stay up-to-date on all emerging news stories and require actionable data to make decisions.
The challenge of processing vast amounts of information is two-fold:
- Many companies aren’t fully transparent about their social or environmental initiatives or the measurable impact of such strategies.
- There is an ever-increasing flow of news stories, blogs, reviews, including quarterly filings and reports by companies
The result is that it becomes impossible for individual analysts, or even small teams, to manually process the information and understand the sentiment of the information.
AI/ML/NLP Driven ESG Sentiment Analysis
Using Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP), analysts and researchers at financial services firms can quickly and accurately sift through all the noise in the data to identify essential points, uncover sentiment which then helps them make informed investment decisions using ESG criteria.
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.
NLP 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 related to 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, including social media posts, news stories, press releases, interviews, etc. Sentiment analysis can analyze each of these pieces and determine the consensus emotion or tone.
The algorithm can determine whether the piece is positive, negative or neutral, and to what degree, and the pattern of such sentiments by scoring the text data. This capability is extremely helpful in sustainable investing and can give a quantitative value to unstructured data like news stories and analyst opinions.
Firms can compare companies, perform due diligence and forecast future stock movements. They can more efficiently and reliably find lucrative investment opportunities based on ESG criteria.
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 being unstructured text and subjective, it is often challenging to create standardized or universal signals to qualify companies.
With NLP 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 readily available data in the field, investors can gain an edge over competitors by using NLP to capture news sentiment on a company, determine how it measures up to ESG criteria and make informed investment decisions.
Accern NoCodeNLP Platform
With the Accern NoCodeNLP Platform, citizen data scientists such as analysts and researchers can and investors don’t need to hire a data scientist or software engineer to reap the benefits of ESG sentiment analysis. With no technical knowledge, any analyst or business expert can determine the news sentiment around recent developments or announcements and quickly make informed investment decisions.
Using the pre-assembled data sources – global news and public data and content from leading data providers including FactSet, Morningstar, Dow Jones, Naviga, and more – users can build ESG-focused NLP models with historical and real-time information.
With data on thousands of public and private companies and over 26 ESG events that come out-of-box including but not limited to fuel management, supply chain, employee health, safety, and wellbeing, labor relations, social impact, and business ethics, investors and financial firms can identify a company’s ESG practices by calculating accurate ESG sentiment scores.
Schedule a demo to learn more about the Accern NoCodeNLP Platform and how it can drive ESG-focused insights for your investment portfolio.