Mind the gap! Machine learning, ESG metrics and sustainable investment
Ariel Lanza (),
Enrico Bernardini () and
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Ariel Lanza: Kellogg School of Management, Northwestern University (PhD student)
Enrico Bernardini: Banca d'Italia
No 561, Questioni di Economia e Finanza (Occasional Papers) from Bank of Italy, Economic Research and International Relations Area
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 (search for similar items in EconPapers)
JEL-codes: C63 G11 Q56 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cmp and nep-env
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Persistent link: https://EconPapers.repec.org/RePEc:bdi:opques:qef_561_20
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