DynamicESG: A Dataset for Dynamically Unearthing ESG Ratings from News Articles

This paper introduces the DynamicESG dataset, a unique resource for dynamically extracting ESG ratings from news articles. The ESG rating, a novel metric employed annually to gauge a company's sustainability, relies heavily on corporate disclosure and other external information, especially news narratives. Our dataset, comprising a wide spectrum of news over a twelve-year span, annotates articles in accordance with MSCI ESG ratings methodology and SASB standards, with relevance to ESG issues. DynamicESG provides a comprehensive means of investigating the relationship between public discourse, ESG-related events, and subsequent ESG rating adjustments. We detail our data collection, curation, annotation procedure, and inter-rater agreement, ensuring high data quality and usability. Importantly, our dataset includes a temporal dimension, enabling the analysis of longitudinal trends in ESG ratings and their correlation with news coverage. Moreover, the dataset incorporates an opportunity/risk tendency, thus permitting analysis from diverse perspectives to discern if the news is beneficial or detrimental to the company. We believe this dataset will serve as a valuable resource for researchers in fields such as corporate social responsibility, sustainable investing, machine learning, and natural language processing. Initial analysis using the dataset underscores its potential to facilitate new insights into the dynamics of ESG ratings and the influence of news media on these ratings.