Environmental, social and governance (ESG) rating prediction using machine learning approaches

Chowdhury, Mohammad Ashraful Ferdous; Abdullah, Mohammad; Azad, Md. Abul Kalam; Sulong, Zunaidah; Islam, M. Nazmul

doi:10.1007/s10479-023-05633-7

Cited by 4 publications

(1 citation statement)

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“…For example, in the study of (Teplova et al, 2023) a neural network and Shapley values were used to reveal the impact of different ESG indicators on stock liquidity in the Russian market. Six machine learning approaches were used to forecast ESG rating of companies using firm-specific and macroeconomic predictors in the research (Chowdhury et al, 2023). ESG rating was also analysed by (Gospodarowicz et al, 2024) using financial, spatial and systemic importance variables observed for banks by employing a multinomial ordered logit model.…”

Section: Other Methodsmentioning

confidence: 99%

Determinants of Sustainability Performance of Manufacturing Companies Using Two-stage Data Envelopment Analysis

Sutiene,

Vaz,

Vaitiekuniene

2024

Preprint

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The investigation of business performance through the lens of sustainability has become an increasingly attractive topic among scholars. This study contributes to the field by proposing a two-stage methodology. First, to assess companies' efficiency in terms of sustainability, the scores of the Environmental, Social and Governance (ESG) pillars are combined into the single weighted sustainability performance indicator using the 'Benefit of Doubt' (BoD) model, which is maximized for each company by comparing it against the best performers in terms of ESG scores based on Data Envelopment Analysis (DEA). Then, in the second stage, the significant determinants are identified after the efficiency estimates are regressed on the company performance indicators using Tobit panel regression. To demonstrate this approach, we selected 559 companies from the manufacturing sector, as this industry continues to face challenges in reducing environmental impact, improving resource efficiency, and fostering social responsibility. The main findings include the examination of top performers and underperforming companies in terms of sustainability, alongside key financial indicators identified in the study.

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Section: Other Methodsmentioning

confidence: 99%