JOURNAL ARTICLE

Characteristics of ESG Fixed-Income ETFs.

  • Published In: Journal of Wealth Management, 2025, v. 27, n. 4. P. 20 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Kanuri, Srinidhi 3 of 3

Abstract

ESG fixed-income ETFs invest in bonds that raise money for a positive Environmental, Social, and Governance (ESG) impact. These funds invest in a variety of green bonds, sustainable development bonds, and social bonds. Investors in ESG fixed-income ETFs include major institutional investors such as pension funds, hedge funds, and insurance companies. There has been a massive growth in the number of fixed-income ETFs that invest in ESG bonds. As of April 2023, ESG fixed-income ETFs managed over $50 billion in assets. This article looks at the risk and return characteristics of ESG fixed-income ETFs and compares them to passively managed bond ETFs. Our results indicate that ESG fixed-income ETFs have underperformed investment grade and high-yield bond ETFs while outperforming treasury and international-sovereign bond ETFs. In addition, ESG fixed-income ETFs had the lowest risk, maximum drawdown, and downside deviation compared to all other categories except Treasury bond ETFs. Finally, ESG fixed-income ETFs underperformed investment grade, treasury, and international bond ETFs but outperformed high-yield bond ETF during the COVID-19 pandemic. Overall, we find that ESG fixed-income ETFs are a suitable alternative for investors who seek exposure to bonds with a positive social and environmental impact. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Wealth Management. 2025/03, Vol. 27, Issue 4, p20
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2025
  • ISSN:1534-7524
  • DOI:10.3905/jwm.2024.1.256
  • Accession Number:183116686
  • Copyright Statement:Copyright of Journal of Wealth Management is the property of With Intelligence Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.