An Event Study of the Impact of Negative ESG News on Stock Returns
- DOI
- 10.2991/978-94-6463-198-2_145How to use a DOI?
- Keywords
- Simple Linear Regression Analysis; Event study; ESG news; stock return
- Abstract
Environmental, social and governance (ESG) has undergone heated discussion in recent years as the world is becoming more and more conscious about the impact of climate change and the urgent need to address this problem. ESG has profound effects on many sectors; the financial industry is particularly affected by it. The development of data analysis software enables this research to examine the relationship between negative ESG news and stock return. Applying the event study methodology, we analysed how stock returns of H&M, Tagen Group, Amazon, and Volkswagen reacted to different ESG news by using Stata to do simple linear regression analysis and establish the model. The preliminary results show that some stocks react significantly to negative ESG news, but some do not. One possible implication of this is that the extent to which negative ESG news influences stock return depends on many factors, such as company capitalisation, industry, and geographical location.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Xuanlin Mu AU - Yanchao Shi PY - 2023 DA - 2023/08/10 TI - An Event Study of the Impact of Negative ESG News on Stock Returns BT - Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023) PB - Atlantis Press SP - 1384 EP - 1398 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-198-2_145 DO - 10.2991/978-94-6463-198-2_145 ID - Mu2023 ER -