Social Sustainability in European Banks: A Machine Learning Approach using Interval- Based Composite Indicators

Carlo Drago, Loris Di Nallo and Maria Lucetta Russotto

No 336986, FEEM Working Papers from Fondazione Eni Enrico Mattei (FEEM)

Abstract: Promoting social information reporting and disclosure can promote sustainable banking. The paper aims to measure banking social sustainability by constructing a new interval-based composite indicator using the Thomson Reuters database. In this work, we propose an approach to constructing interval-based composite indicators that enhance the composite indicator’s construction sensibly, allowing us to measure the uncertainty due to the choices in the composite indicator design. The methodological approach employed is based on a Monte-Carlo simulation and allows for improving the information the composite indicators can obtain. So, we measure the value of the social indicator and its subcomponents and the value’s uncertainty due to the different possible weights. The results show that the best international ESG practices in European banks relate to French and United Kingdom Banks, primarily than Italian banks. Finally, we analyze innovative perspectives and propose policy recommendations, considering the growing attention to the issue of ESG disclosure and its adherence to reality, to support sustainable banking ecosystems.

Keywords: Financial Economics; Research Methods/ Statistical Methods (search for similar items in EconPapers)
Pages: 36
Date: 2023-06-28
New Economics Papers: this item is included in nep-ban, nep-big and nep-env
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/336986/files/NDL2023-013.pdf (application/pdf)

Related works:
Working Paper: Social Sustainability in European Banks: A Machine Learning Approach using Interval- Based Composite Indicators (2023)
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:ags:feemwp:336986

DOI: 10.22004/ag.econ.336986

Access Statistics for this paper

More papers in FEEM Working Papers from Fondazione Eni Enrico Mattei (FEEM) Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().