Uncovering the Nexus between ESG Reports and ESG Scores across Various Liquidity Levels: Evidence from Publicly Traded Turkish Companies by Machine Learning Algorithms

Considering the recent restructuration of environmental, social, and governance (ESG) reports in Türkiye, this study uncovers effectiveness of ESG reports in ESG score estimation across diverse liquidity levels. Accordingly, the study examines four different samples as the full sample, Borsa Istanbul 100 (XU100) index, Borsa Istanbul 50 (XU050) index, and Borsa Istanbul 30 (XU030) index, where 102, 60, 43, and 26 companies exist, respectively. The study considers restructured ESG reports for 2022 and 2023 and performs five different machine learning (ML) algorithms. The findings demonstrate that (i) among all segments, environment segment includes principles that have the highest importance, while social, common, and governance segments follow, respectively; (iii) absolute and relative variable importance of ESG principles differentiate; (iii) super learner (SL) is the best ML algorithm, where its estimative power (R2) is around 95\% for the best estimation. Thus, the results demonstrate that the estimative power of restructured ESG reports in the estimation of ESG scores is quite high. Hence, the study highlights a varying contribution of ESG segments and principles to the ESG scores of the companies and reveals a nonlinear need by companies to focus on highly important ESG principles so that companies can stimulate their ESG scores.