Evaluating the Financial Performance of CSR Strategies and Sustainable Operations in Mexican Companies: An Explainable Machine Learning Approach
Journal
Mathematics
ISSN
2227-7390
Publisher
MDPI AG
Date Issued
2026
Author(s)
Type
Article
Abstract
Research on how corporate social responsibility (CSR) practices linked to sustainable operations (SO) affect corporate financial performance (FP) is still limited. This study presents a novel methodological proposal to measure the individual impact of such practices on the profitability of companies listed on the Mexican Stock Exchange. The method employed consists of a Random Forest (RF) model complemented by Explainable Machine Learning (XML) techniques, namely Individual Conditional Expectation (ICE), Partial Dependence Plots (PDPs) and SHapley Additive exPlanations (SHAP), to calculate the individualized marginal effect in the return on assets (RoA), return on equity (RoE) and return on investment capital (ROIC) for each company, explained by the environmental, social, and governance scores provided by Bloomberg (Bloomberg Finance, L.P., New York, NY, USA), such as the market capitalization, debt-to-equity ratio, sales growth, and years since listing. The novelty of this model lies in the application of RF and XML, which offers a comprehensive and interpretable perspective on the CSR–FP relationship and the use of lagged explanatory variables to avoid endogeneity problems, overcoming the limitations of traditional analyses. The results indicate that environmental scores exhibit the most consistent contribution to FP, whereas social and governance effects are highly metric-dependent. The SHAP analysis reveals substantial heterogeneity in the drivers of firm FP, highlighting the relevance of XML methods. © The authors © MDPI
License
Acceso Abierto
How to cite
Jiménez-Casillas, L. E., Rodríguez-Aguilar, R., Velázquez-Salazar, M., & García-Álvarez, S. (2026). Evaluating the Financial Performance of CSR Strategies and Sustainable Operations in Mexican Companies: An Explainable Machine Learning Approach. Mathematics, 14(3), 557. https://doi.org/10.3390/math14030557