[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-290655-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-290655-en":131},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","a-machine-learning-based-approach-to-identify-key-drivers-for-improving-corporates-esg-ratings-abstract","A MACHINE LEARNING BASED APPROACH TO IDENTIFY KEY DRIVERS FOR IMPROVING CORPORATE’S ESG RATINGS - Abstract","","Investors increasingly incorporate non-financial factors into risk analysis and growth assessments, and machine learning models for predicting ESG scores support more informed portfolio decisions. The approach uses wide-ranging alternative datasets to improve accuracy and reliability by continuously updating ESG factor mappings. A dedicated ML algorithm predicts ESG ratings from a company’s financial and non-financial attributes extracted from Moody’s Orbis and S&P Ratings, targeting firms with limited ESG availability.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/a-machine-learning-based-approach-to-identify-key-drivers-for-improving-corporates-esg-ratings-abstract/290655/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/a-machine-learning-based-approach-to-identify-key-drivers-for-improving-corporates-esg-ratings-abstract/290655.png","ImageObject",300,407,{"name":42,"@type":43},"Levi","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",18,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"How does the proposed approach improve ESG rating prediction?","Question",{"text":63,"@type":64},"It uses machine learning algorithms to predict ESG ratings from a mix of financial and non-financial attributes, helping improve accuracy and reliability through evolving datasets and mappings.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which data sources are used for the financial and non-financial attributes?",{"text":68,"@type":64},"Financial and non-financial attributes are extracted from Moody’s Orbis and Ratings from S&P.",{"name":70,"@type":61,"acceptedAnswer":71},"What is the objective of predicting ESG ratings for companies with limited ESG availability?",{"text":72,"@type":64},"To provide a suitable investment framework and to identify a company’s riskiness by forecasting an ESG score and its potential increase or decrease.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290655,1789642276,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":25,"category_name":129,"show_sort_weight":97,"slug":130},19,"General","general",{"code":4,"msg":82,"data":132},{"doc_id":79,"user_id":133,"nickname":42,"user_avatar":134,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":140,"language":141,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":12,"update_tm":80,"read_time":145,"preview_page_count":4},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","A MACHINE LEARNING BASED APPROACH TO IDENTIFY KEY DRIVERS FOR IMPROVING CORPORATE’S ESG RATINGS  \nDwijendra Dwivedi 1, Saurabh Batra 2 & Yogesh Kumar Pathak3  \nABSTRACT  \nInvestors increasingly non-financial factors as part of their risk analysis process and growth assessments of corporates. Machine learning (ML) models for predicting ESG scores are an extremely useful tool to help investors make more informed decisions on their portfolios. Such a tool with wide-encompassing alternative data can be useful to the investors. The use of such datasets and machine learning models for ESG ratings can continuously improve the accuracy and reliability of those models. Using machine learning algorithms to identify key drivers of ESG ratings is an effective way of improving portfolio performance. Although the current state of ESG ratings is relatively static, data collection and mapping methodologies are evolving. As more data becomes available, the noise in ESG factors will become less important.  \nThis unique document provides a machine learning algorithm for predicting an ESG rating based on a company's financial and non-financial attributes. The financial and non-financial attributes of corporations are extracted from Moody's Orbis and Ratings from S&P. The objective here is to predict the ESG rating of companies where the ESG rating is not easily accessible. At the same time, this approach would allow investors to have a suitable framework for investments based on ESG ratings. With the latest financial and non-financial disclosure by a corporate an ESG score can be predicted which can be used to identify its riskiness with a corresponding increase/decrease of ESG score.  \nKeywords: ESG Investing, ML model, Socially responsible investing, Sustainable investing, Green finance.  \nReceived: 24/11/2022  \nAccepted: 27/03/2023  \nDOI: [https://doi.org/10.37497/sdgs.v11i1.242](https://doi.org/10.37497/sdgs.v11i1.242)  \n1Kracow University of Economics,(Poland) . E-mail: dwivedy@gmail.com Orcid id: [https://orcid.org/0000-0001-](https://orcid.org/0000-0001-)[ ](https://orcid.org/0000-0001-)7662-415X  \n2Delhi University, (India), E-mail: saurabhbatra18@gmail.com Orcid id: [https://orcid.org/0000-0002-0939-702X](https://orcid.org/0000-0002-0939-702X)  \n3Indian Institute of Management Lucknow (India) . [E-mail:](E-mail: ykpathak@gmail.com)[ ](E-mail: ykpathak@gmail.com)[ykpathak@gmail.com](E-mail: ykpathak@gmail.com)  \n1. INTRODUCTION  \nThe corporate social responsibility (CSR) initiatives and their influence on corporate financial performance have been actively researched (Francesco et al., 2018) . The term encompasses how the company contributes to the preservation of the environment, for instance, by reducing air pollutant emission, issuing green bonds, and participating in other activities, which can help solve the problem of climate change. Moreover, environmental, social, and governance (ESG) initiatives cover social issues, such as caring for employees by providing good work conditions, effective human capital management, and health and safety programs. Additionally, corporate governance is implied in ESG activities. ESG refers to the three central factors in measuring the sustainability and societal impact of a company or a business. These criteria have proven to be influential on performance due to increased scrutiny on companies to comply with sustainable business models. The central factors are as below:  \nEnvironmental:  \nEnvironmental considerations include the contribution of a business or government to climate change through greenhouse gas emissions, as well as waste management and energy efficiency. As a result of renewed efforts to address global warming, it is becoming increasingly important to reduce emissions and decarbonize.  \nSocial  \nSocial aspects include human rights, labor standards in the supply chain, any exposure to illegal child labor, and more common issues such as respect for workplace health and safety. Asocial s","cbCaip7BK63rXKQ5","https://ap.wps.com/l/cbCaip7BK63rXKQ5","pdf",409064,15,"English","# Abstract\n# Introduction\n## Environmental, Social, and Governance (ESG) Factors\n## Role of Machine Learning in ESG Prediction\n## Prior Approaches and Data Challenges","[{\"question\":\"How does the proposed approach improve ESG rating prediction?\",\"answer\":\"It uses machine learning algorithms to predict ESG ratings from a mix of financial and non-financial attributes, helping improve accuracy and reliability through evolving datasets and mappings.\"},{\"question\":\"Which data sources are used for the financial and non-financial attributes?\",\"answer\":\"Financial and non-financial attributes are extracted from Moody’s Orbis and Ratings from S\\u0026P.\"},{\"question\":\"What is the objective of predicting ESG ratings for companies with limited ESG availability?\",\"answer\":\"To provide a suitable investment framework and to identify a company’s riskiness by forecasting an ESG score and its potential increase or decrease.\"}]","A MACHINE LEARNING BASED APPROACH TO IDENTIFY KEY DRIVERS FOR IMPROVING CORPORATE’S ESG RATINGS - Abstract | PDF",38]