Identifying Aspect-Regimes for Enhanced ESG-Investing Through News Data

ESG (Environmental, Social, Governance)-thematics play an important role for investing as they can represent additional risk for a company and subsequently its stock price. These ESG-thematics can be extracted from alternative data sources such as news data. However, the extend to which an ESG-event influences a company can vary greatly depending on the ESG-aspect of the event and the current expectations of the affected company. In this paper, we present an approach where we leverage Dynamic Time Warping (DTW) in order to identify periods in which certain ESG-aspects are closely aligned with the stockprice movement of a company. We demonstrate this approach using more than 50,000 news articles mined for 42 german companies between January 2023 and September 2024, (i) answering the question of which aspects are important for a company in what time-frames and (ii) showing that this approach increases the performance of an ESG-sensitive portfolio by a large amount.