The Environmental Score and the Financial Statement: A Machine Learning Analysis for Four European Stock Indexes

Rita D’Ecclesia, Susanna Levantesi (), Gabriella Piscopo and Kevyn Stefanelli
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Rita D’Ecclesia: Sapienza University of Rome
Susanna Levantesi: Sapienza University of Rome
Gabriella Piscopo: Federico II University of Naples
Kevyn Stefanelli: Sapienza University of Rome

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2024, pp 112-118 from Springer

Abstract: Abstract Following the principles of a sustainable economy, companies are increasingly adopting business strategies that seek to harmonize profit objectives with their environmental, social, and governance (ESG) policies. The financial sector’s growing awareness of climate and environmental risks underscores the necessity for developing sustainable investments that endorse activities with minimal environmental impact. Sustainability, incorporating environmental, social, and governance considerations, is a strategic priority in this paradigm. This study focuses on the environmental risk aspect, encompassing a company’s overall environmental impact and potential risks arising from environmental issues. The primary objective is to discern the structural features of listed firms that influence their sustainability levels, as measured by their “E” score. Leveraging balance sheet information from a selection of European listed firms, our investigation aims to reveal potential relationships between corporate financial variables and the E score. To unravel complex, non-linear relationships within one of the most environmentally conscious markets, namely the European market, we employ advanced techniques such as the random forest and gradient-boosting machine algorithms. This approach allows us to deeply understand how financial variables interplay with a firm’s environmental sustainability, offering insights into the intricate dynamics shaping sustainable practices in a corporate context.

Keywords: Environmental score; corporate finance; machine learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-64273-9_19

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DOI: 10.1007/978-3-031-64273-9_19

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