Asset Returns: Reimagining Generative ESG Indexes and Market Interconnectedness

Gordon Dash (), Nina Kajiji and Bruno G. Kamdem
Additional contact information
Gordon Dash: Finance Area, College of Business and Interdisciplinary Neuroscience Program, University of Rhode Island, 7 Lippitt Road, Kingston, RI 02881, USA
Nina Kajiji: Department of Computer Science and Statistics, College of Arts and Sciences, University of Rhode Island, 9 Greenhouse Road, Kingston, RI 02881, USA
Bruno G. Kamdem: Department of Business Management, School of Business, SUNY Farmingdale, 2350 Broadhollow Road, Farmingdale, NY11735, USA

JRFM, 2024, vol. 17, issue 10, 1-21

Abstract: Financial economists have long studied factors related to risk premiums, pricing biases, and diversification impediments. This study examines the relationship between a firm’s commitment to environmental, social, and governance principles (ESGs) and asset market returns. We incorporate an algorithmic protocol to identify three nonobservable but pervasive E, S, and G time-series factors to meet the study’s objectives. The novel factors were tested for information content by constructing a six-factor Fama and French model following the imposition of the isolation and disentanglement algorithm. Realizing that nonlinear relationships characterize models incorporating both observable and nonobservable factors, the Fama and French model statement was estimated using an enhanced shallow-learning neural network. Finally, as a post hoc measure, we integrated explainable AI (XAI) to simplify the machine learning outputs. Our study extends the literature on the disentanglement of investment factors across two dimensions. We first identify new time-series-based E, S, and G factors. Second, we demonstrate how machine learning can be used to model asset returns, considering the complex interconnectedness of sustainability factors. Our approach is further supported by comparing neural-network-estimated E, S, and G weights with London Stock Exchange ESG ratings.

Keywords: ESG factor construction; modeling asset returns; Explainable AI; factor ranking (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1911-8074/17/10/463/pdf (application/pdf)
https://www.mdpi.com/1911-8074/17/10/463/ (text/html)

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:gam:jjrfmx:v:17:y:2024:i:10:p:463-:d:1497914

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().