US Funds’ returns-based ESG extraction and implementation: a multifaceted quantile regression approach

Farah Nasri and Salim Ben Sassi

Journal of Sustainable Finance & Investment, 2025, vol. 15, issue 1, 205-233

Abstract: This study introduces a novel ESG intrinsic-based return factor and its application in asset pricing. This factor is extracted using a parallelized rolling window estimation and extreme value-weighted quantile portfolios. It carries a positive risk premium, indicating that investors are willing to assess its risk exposure. We further show that higher returns can be obtained in the top 30% quantiles using a long-only trading strategy. We apply a Monotone Composite Quantile Regression Neural Network (MCQRNN) model to explain US fund returns and address the needs of investors seeking to optimize their investment strategies. This model surpasses traditional benchmark models by performing deep quantile estimation and considering the nonlinear relationships between fund returns and six firm-based characteristics. This approach empowers investors by explaining the core principles of impact investing and highlighting how our constructed ESG risk factor can generate competitive returns even in volatile markets when its risk is well assessed.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/20430795.2024.2420916 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jsustf:v:15:y:2025:i:1:p:205-233

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TSFI20

DOI: 10.1080/20430795.2024.2420916

Access Statistics for this article

Journal of Sustainable Finance & Investment is currently edited by Dr Matthew Haigh

More articles in Journal of Sustainable Finance & Investment from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().