At a Glance: Alexandria Real-Time ESG

This dataset uses proprietary algorithms that are trained by financial research analysts to provide accurate and consistent classifications across a deep history of news.

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Content Category: Sentiment

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Alexandria's Real-Time ESG dataset is the product of applying proprietary algorithms on ESG-related news. Each news article is summarized into a record of data showing the company and the specific ESG topic as well as the positive, negative, or neutral sentiment in that article.

ESG information is complex, subjective, and requires industry knowledge to drive sound conclusions from the data. Alexandria's classifiers are built using machine-learning algorithms that are trained by financial research analysts to replicate their own analysis and yield accurate and consistent classifications across a deep history of news.

This product encompasses roughly 40,000 global companies and over 60 hierarchical ESG event topics that are captured consistently over time to better forecast risk, direction, and volatility.

Data Overview

Asset Class: Public Companies 

Data Frequency: Event-driven

Delivery Frequency: Hourly

History: Data available back to 2000

aag alexandria real-time esg

Data Methodology

Alexandria Real-Time ESG allows users to leverage valuable sentiment analysis from ESG-related news articles tagged to companies and topics. Alexandria receives streaming news feeds from Dow Jones and a number of global press release services, which they analyze in real time. Each article is processed in 10-30 minutes, capturing entities, topics, and the sentiment related to that topic. A record of data is created for every topic found in the article, showing both the company and the sentiment related to that topic, as well as serving as a summary of every article in structured form.

Alexandria's blend of machine-learning algorithms is trained to observe hundreds of thousands of news articles that have been read and labeled by professional financial and ESG analysts for both topic and sentiment. Each article and its associated topics, sentiment, and metadata are transformed into tables for efficient analysis.

Use Cases

ESG Event Analysis 

  • Quantify and analyze the impact of ESG events as they unfold using hourly article-level sentiment, topics, and metadata. This information enables users to determine whether ESG events offer risks or opportunities using quantifiable metrics that reflect the opinion of professional ESG analysts.
    • This product is complimentary to other ESG datasets that either reflect company-reported ESG information, or supply indicators that rely on human-driven research and are updated less frequently. 
    • Users can leverage the hierarchical structure of article-level topic tags to customize how specific ESG topics are incorporated into their analysis. For instance, Alexandria uses labels such as "ESG/ENVIRONMENT/ENERGY/SOLAR" and "ESG/ENVIRONMENT/WATER" so that users can decide whether they want to look at all ESG issues (i.e., ESG), focus on the environment (i.e., ESG/ENVIRONMENT), or drill into underlying topics (i.e., ESG/ENVIRONMENT/WATER or ESG/ENVIRONMENT/ENERGY). 
    • This data fits well into short-, medium- and long-term trading strategies. For example, Alexandria ran a case study on the S&P 500 universe that divided companies by net sentiment of ESG-related news. Rebalancing monthly, the study found that those companies with positive ESG sentiment had substantially better annual returns than those with negative sentiment, offering suggestions for how users can further customize this analysis. 

Sector and Macro Analysis 

  • Identify trends in ESG topics and sentiment across sectors and markets. With over 60 hierarchical topics covered, users can track sentiment across specific topics and capture emerging themes as new topics are added by Alexandria.

The details provided above are as of May 2020.

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Please visit the product page on the Open:FactSet Marketplace for more information: Alexandria Real-Time ESG.  

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