Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/298798 
Year of Publication: 
2024
Citation: 
[Journal:] Schmalenbach Journal of Business Research (SBUR) [ISSN:] 2366-6153 [Volume:] 76 [Issue:] 2 [Year:] 2024 [Pages:] 197-244
Publisher: 
Springer, Heidelberg
Abstract: 
In this paper, we examine in a systematic manner how investors react to the sentiment of instant ESG news. Instead of acquiring proprietary ESG news or events datasets directly from specific ESG data providers, we extract fresh ESG news directly from a plethora of raw news articles. We showcase how the latest development in NLP (i.e. the BERT model) can be applied to build a comprehensive and fresh ESG news dataset, and how company ESG news sentiment can be efficiently recognized by a machine. Overall, we find that the market reacts to ESG news based on news sentiment. On the event day, positive ESG news has an average abnormal return of 0.31% while negative ESG news leads to a mean value of -0.75%. More interestingly, we find that the impact of ESG news may depend on the company's historical ESG record. The negative impact of negative ESG news has less severe consequences for companies with an overall better ESG record, while the positive impact of positive ESG news may be more pronounced for companies with a worse ESG record.
Subjects: 
BERT
ESG
Instant ESG News
NLP
Sentiment Analysis
JEL: 
G12
Q51
Q56
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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