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2023 | OriginalPaper | Chapter

Machine Learning, ESG Indicators, and Sustainable Investment

Authors : Ariel A. G. Lanza, Enrico Bernardini, Ivan Faiella

Published in: Financial Risk Management and Climate Change Risk

Publisher: Springer Nature Switzerland

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Abstract

This study proposes a novel approach to partially overcome the current inconsistencies in the ESG scores by using Machine Learning (ML) techniques to identify the most material E, S, or G indicators that better contribute to the construction of efficient portfolios. The novelty of the chapter is threefold: (a) the large array of ESG indicators (more than 220) analyzed for a long-time span (from 2007 to 2019), (b) the ML model-free methodology, and (c) the disentangle of the contribution of ESG specific indicators to the portfolio performance from the traditional style and macroeconomic factors. According to our results, more information content may be extracted from the available raw ESG data for portfolio construction. Half of the ESG indicators identified with our approach are environmental. Among environmental indicators, some refer to companies’ exposure and ability to manage climate change risk, namely, the transition risk. This chapter shows that a European equity market investor who had applied our technique would have achieved a substantial extra annualized return.

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Unfortunately applying those results to our work is not straightforward for two reasons, the first is that this study was conducted on data from Sustainalytics, but its reporting methodology changed recently, hence we have a limited time series to use with the new methodology and the coverage for European equities is rather limited. The second reason is that materiality was assessed through SASB tables, which have been originally designed for the US firms and it might be arguable to squarely apply them to European firms.

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Title: Machine Learning, ESG Indicators, and Sustainable Investment
Authors: Ariel A. G. Lanza
Enrico Bernardini
Ivan Faiella
Publisher: Springer Nature Switzerland
Book: Financial Risk Management and Climate Change Risk
Print ISBN: 978-3-031-33881-6

Electronic ISBN: 978-3-031-33882-3

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-3-031-33882-3_10