Financial Forecasting Using ESG Indicators: A Random Forest-Based Predictive Approach - Santos, Marco Antonio Sousa; da Silva, Adriano Rivolli; Ortoncelli, André Roberto (Anais do Brazilian e-Science Workshop (BreSci); 2025: Anais do XIX Brazilian e-Science Workshop, 2025)
Growing demand for sustainability transparency calls for quantitative tools to assess its financial impact. Despite limited models linking ESG and resource consumption to financial performance, this study integrates synthetic and real data from 1,086 companies (2015–2025), including Investics DARTS and Yahoo Finance sources, to improve data quality and representativeness. Using Random Forest and linear regression within a data mining framework, and after thorough preprocessing, we predicted corporate revenue. Results show environmental ESG scores and resource usage as strong predictors, with Random Forest reaching R2 ≈ 0.99. Complementary analysis reveals that higher market valuations correlate with better environmental performance, underscoring the financial importance of robust ESG metrics.