Abstract
This study aims to
analyze the relationships between the economic performance of Italian listed
banks and their GRI disclosure (GRID), understood as the level of disclosure of
their non-financial reports according to the GRI standards. The study selected 6 among the Italian listed
banks with the highest capitalization as of 31/12/2020 and analyzed the
relationships between their economic performance and their GRID by applying
three models: Linear Regression, Support Vector Machines, and Decision
Trees. The research highlighted the existence of positive
relationships between the economic performance of banks – measured in terms of
capitalization, size and leverage – and their GRID, while the relationship with
profitability is negative. Unlike the analyzes that
see disclosure as a factor capable of improving economic performance, this
research starts from the assumption that the best economic performance favors a
wider disclosure. Furthermore, the study applies machine learning
which represents a non-traditional methodology, not yet fully exploited in the
field of sustainability reporting.
JEL classification numbers: M21.
Keywords: Non-Financial
reporting, GRI standards, Banking sector, Economic performance, Machine
learning.