Mind the gap! Machine Learning, ESG Metrics and Sustainable Investment

58 Pages Posted: 24 Jul 2020

Date Written: June 26, 2020

Abstract

This work proposes a novel approach for overcoming the current inconsistencies in ESG scores by using Machine Learning (ML) techniques to identify those indicators that better contribute to the construction of efficient portfolios. ML can achieve this result without needing a model-based methodology, typical of the modern portfolio theory approaches. The ESG indicators identified by our approach show a discriminatory power that also holds after accounting for the contribution of the style factors identified by the Fama-French five-factor model and the macroeconomic factors of the BIRR model. The novelty of the paper is threefold: a) the large array of ESG metrics analysed, b) the model-free methodology ensured by ML and c) the disentangling of the contribution of ESG-specific metrics to the portfolio performance from both 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 purposes and half of the ESG indicators identified using our approach are environmental. Among the environmental indicators, some refer to companies' exposure and ability to manage climate change risk, namely the transition risk.

Keywords: portfolio construction, factor models, sustainable investment, ESG, machine learning

JEL Classification: C63, G11, Q56

Suggested Citation

Lanza, Ariel and Bernardini, Enrico and Faiella, Ivan, Mind the gap! Machine Learning, ESG Metrics and Sustainable Investment (June 26, 2020). Bank of Italy Occasional Paper No. 561, Available at SSRN: https://ssrn.com/abstract=3659584 or http://dx.doi.org/10.2139/ssrn.3659584

Ariel Lanza

Kellogg School of Management - Department of Finance ( email )

Evanston, IL 60208
United States

Enrico Bernardini

Bank of Italy ( email )

Via Nazionale 91
Rome, 00184
Italy

Ivan Faiella (Contact Author)

Bank of Italy ( email )

Via Nazionale 91
00184 Roma
Italy

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