Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Chen, Zhi, Feinstein, Zachary, Florescu, Ionut
Institution: Stevens Institute of Technology
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7077875693
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