Differences in stock pricing efficiency between ESG-rated stocks and non-ESG-rated stocks
- DOI
- 10.2991/978-94-6463-326-9_35How to use a DOI?
- Keywords
- ESG Rating; Stock Pricing Efficiency; Risk premium
- Abstract
Environmental, social, and corporate governance, also known as ESG, has become mainstream in international enterprises by assessing the sustainability of business operations and their impact on social values from three dimensions of environmental, social, and corporate governance. Recently, it was gradually accepted by Chinese investors. Based on Chinese ESG rating and A-share market data from CSMAR, this paper constructs a novel Fama-French four-factor model to analyze the difference in their performances. The results show that: (1) the ESG-rated companies have significantly higher risk premiums than the unrated ones, indicating that the ESG rate is one of the price determinants; (2) compared with unrated companies, the rated ones have higher price efficiency; (3) in the rated group, the higher the rate is, the better the stock is priced. This study contributes to a better understanding of ESG stock performances in the Chinese market and the characteristics of ESG investment strategy, helping to further improve the exuberance of the Chinese A-share market.
- 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 - Yunhua Huang AU - Sheng Li AU - Keyi He AU - Tianyi Mao PY - 2023 DA - 2023/12/30 TI - Differences in stock pricing efficiency between ESG-rated stocks and non-ESG-rated stocks BT - Proceedings of the 2023 3rd International Conference on Business Administration and Data Science (BADS 2023) PB - Atlantis Press SP - 337 EP - 350 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-326-9_35 DO - 10.2991/978-94-6463-326-9_35 ID - Huang2023 ER -