ESG investments: Filtering versus machine learning approaches

TL;DR

This paper presents a machine learning algorithm that links ESG profiles to financial performance, outperforming traditional ESG screening methods by capturing non-linear relationships for stock selection.

Contribution

It introduces a novel machine learning approach that models non-linear links between ESG features and financial returns, improving stock screening strategies.

Findings

Machine learning captures non-linear ESG-performance relationships.

The proposed strategy outperforms traditional ESG screening methods.

There is detectable alpha in ESG profiles accessible through advanced techniques.

Abstract

We designed a machine learning algorithm that identifies patterns between ESG profiles and financial performances for companies in a large investment universe. The algorithm consists of regularly updated sets of rules that map regions into the high-dimensional space of ESG features to excess return predictions. The final aggregated predictions are transformed into scores which allow us to design simple strategies that screen the investment universe for stocks with positive scores. By linking the ESG features with financial performances in a non-linear way, our strategy based upon our machine learning algorithm turns out to be an efficient stock picking tool, which outperforms classic strategies that screen stocks according to their ESG ratings, as the popular best-in-class approach. Our paper brings new ideas in the growing field of financial literature that investigates the links between ESG behavior and the economy. We show indeed that there is clearly some form of alpha in the ESG profile of a company, but that this alpha can be accessed only with powerful, non-linear techniques such as machine learning.