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Forecasting the Environmental, Social, and Governance Rating of Firms by Using Corporate Financial Performance Variables: A Rough Set Approach

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  • Fernando García

    (Business School, Universidad Politécnica de Valencia, 46022 Valencia, Spain)

  • Jairo González-Bueno

    (Faculty of Business Administration, Universidad Pontificia Bolivariana, Bucaramanga 681017, Colombia)

  • Francisco Guijarro

    (Research Institure for Pure and Applied Mathematics, Universidad Politécnica de Valencia, 46022 Valencia, Spain)

  • Javier Oliver

    (Business School, Universidad de Valencia, 46022 Valencia, Spain)

Abstract

The environmental, social, and governance (ESG) rating of firms is a useful tool for stakeholders and investment decision-makers. This paper develops a rough set model to relate ESG scores to popular corporate financial performance measures. This methodology permits handling with information in an uncertain, ambiguous, and imperfect context. A large database was gathered, including ESG scores, as well as industry sector and financial variables for publicly traded European companies during the period 2013–2018. We carried out 500 simulations of the rough set model for different values in the discretization parameter and different grouping scenarios of firms regarding ESG scores. The results suggest that the variables considered are useful in the prediction of ESG rank when firms are clustered in three or four equally balanced groups. However, the prediction power vanishes when a larger number of groups is computed. This would suggest that industry sector and financial variables serve to find big differences across firms regarding ESG, but the significance of the model drops when small differences in ESG performance are scrutinized.

Suggested Citation

  • Fernando García & Jairo González-Bueno & Francisco Guijarro & Javier Oliver, 2020. "Forecasting the Environmental, Social, and Governance Rating of Firms by Using Corporate Financial Performance Variables: A Rough Set Approach," Sustainability, MDPI, vol. 12(8), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:8:p:3324-:d:347755
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    5. Valeria D’Amato & Rita D’Ecclesia & Susanna Levantesi, 2021. "Fundamental ratios as predictors of ESG scores: a machine learning approach," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 1087-1110, December.
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    7. Kezhi Yang & Tingting Zhang & Chenyun Ye, 2024. "The Sustainability of Corporate ESG Performance: An Empirical Study," Sustainability, MDPI, vol. 16(6), pages 1-19, March.
    8. Nikunj Sachin & R. Rajesh, 2022. "An empirical study of supply chain sustainability with financial performances of Indian firms," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(5), pages 6577-6601, May.
    9. Hsio-Yi Lin & Bin-Wei Hsu, 2023. "Empirical Study of ESG Score Prediction through Machine Learning—A Case of Non-Financial Companies in Taiwan," Sustainability, MDPI, vol. 15(19), pages 1-19, September.
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