Identifying Risk Variables From Raw ESG Data Using Its Hierarchical Structure

TL;DR

This paper presents a framework for identifying relevant raw ESG variables with hierarchical structures that better predict financial risk than aggregated scores, validated across US sectors.

Contribution

The study introduces a novel framework tailored for hierarchical ESG data to select risk-relevant variables, enhancing risk assessment accuracy.

Findings

Raw ESG variables selected are more relevant to financial risk than aggregated scores.

Selected variables provide additional insights beyond traditional financial factors.

Framework robustness is validated using out-of-sample data.

Abstract

Environmental, Social, and Governance (ESG) data provides non-financial insights into corporations. In this study, we aim to identify relevant ESG raw variables to assess financial risk, measured by logarithmic volatility of return. We propose a framework specifically designed for ESG datasets characterized by a hierarchical data structure and a significantly larger number of variables than observations. We show that raw variables selected by the proposed framework are significantly more relevant to financial risk than aggregated ESG scores. Furthermore, these selected risk variables provide additional insights beyond the traditional financial factors. We validate the robustness of this framework using out-of-sample data. We illustrate our framework using company data from various sectors of the US economy. We further identify the specific ESG risk variables relevant to large and small companies within each sector.