{"title":"Towards the estimation of ESG ratings: A machine learning approach using balance sheet ratios","authors":"Federico Cini , Annalisa Ferrari","doi":"10.1016/j.ribaf.2024.102653","DOIUrl":null,"url":null,"abstract":"<div><div>Despite the persistence of methodological inconsistency and uncertainty, ESG ratings are useful for assessing Environmental (E), Social (S), and Governance (G) risk, individually and as a system (ESG). The ESG rating class is the only investment selection parameter that measures asset class sustainability. This paper tests whether a selected set of balance sheet variables and a dynamic measure of systemic risk, observed at time t, have information content useful to identify a firm’s ESG rating class of at time t+1. Using EuroStoxx 600 firms for the period 2016–2021, we apply a Machine Learning (ML) model. Specifically, a Random Forest (RF) classification model estimates the ESG rating at time t+1 with unprecedented accuracy in the international literature. This agile and parsimonious model offers important information to the sustainable investor for making strategic investment decisions and paves the way for ESG rating estimation for unlisted companies and SMEs.</div></div>","PeriodicalId":51430,"journal":{"name":"Research in International Business and Finance","volume":"73 ","pages":"Article 102653"},"PeriodicalIF":6.3000,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research in International Business and Finance","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S027553192400446X","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 0
Abstract
Despite the persistence of methodological inconsistency and uncertainty, ESG ratings are useful for assessing Environmental (E), Social (S), and Governance (G) risk, individually and as a system (ESG). The ESG rating class is the only investment selection parameter that measures asset class sustainability. This paper tests whether a selected set of balance sheet variables and a dynamic measure of systemic risk, observed at time t, have information content useful to identify a firm’s ESG rating class of at time t+1. Using EuroStoxx 600 firms for the period 2016–2021, we apply a Machine Learning (ML) model. Specifically, a Random Forest (RF) classification model estimates the ESG rating at time t+1 with unprecedented accuracy in the international literature. This agile and parsimonious model offers important information to the sustainable investor for making strategic investment decisions and paves the way for ESG rating estimation for unlisted companies and SMEs.
期刊介绍：
Research in International Business and Finance (RIBAF) seeks to consolidate its position as a premier scholarly vehicle of academic finance. The Journal publishes high quality, insightful, well-written papers that explore current and new issues in international finance. Papers that foster dialogue, innovation, and intellectual risk-taking in financial studies; as well as shed light on the interaction between finance and broader societal concerns are particularly appreciated. The Journal welcomes submissions that seek to expand the boundaries of academic finance and otherwise challenge the discipline. Papers studying finance using a variety of methodologies; as well as interdisciplinary studies will be considered for publication. Papers that examine topical issues using extensive international data sets are welcome. Single-country studies can also be considered for publication provided that they develop novel methodological and theoretical approaches or fall within the Journal''s priority themes. It is especially important that single-country studies communicate to the reader why the particular chosen country is especially relevant to the issue being investigated. [...] The scope of topics that are most interesting to RIBAF readers include the following: -Financial markets and institutions -Financial practices and sustainability -The impact of national culture on finance -The impact of formal and informal institutions on finance -Privatizations, public financing, and nonprofit issues in finance -Interdisciplinary financial studies -Finance and international development -International financial crises and regulation -Financialization studies -International financial integration and architecture -Behavioral aspects in finance -Consumer finance -Methodologies and conceptualization issues related to finance
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