17906 8 97295 2 299762 document 97295 Intelligent Systems in Accounting Finance and Management - 2022 - Svanberg - Corporate governance performance ratings with.pdf application/pdf 498e6f33d05e542e6cf0be45642773b2 MD5 3817308 2022-03-24 09:55:52 https://pure.iiasa.ac.at/id/eprint/17906/1/Intelligent%20Systems%20in%20Accounting%20%20Finance%20and%20Management%20-%202022%20-%20Svanberg%20-%20Corporate%20governance%20performance%20ratings%20with.pdf 17906 1 1 application/pdf text en public cc_by_nc_nd_4

Intelligent Systems in Accounting Finance and Management - 2022 - Svanberg - Corporate governance performance ratings with.pdf

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/97295 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/97295 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/97295 archive 353 disk0/00/01/79/06 2022-03-24 09:57:30 2024-04-29 12:40:16 2022-03-24 09:57:30 article show Svanberg J. Ardeshiri T. Samsten I. Öhman P. Neidermeyer P.E. Rana T. Semenova N. Danielson M. 9381 pub ASA CAT artificial intelligence, ESG, governance controversies, machine learning, performance of ESG ratings, prediction, socially responsible investment We use machine learning with a cross-sectional research design to predict governance controversies and to develop a measure of the governance component of the environmental, social, governance (ESG) metrics. Based on comprehensive governance data from 2,517 companies over a period of 10 years and investigating nine machine-learning algorithms, we find that governance controversies can be predicted with high predictive performance. Our proposed governance rating methodology has two unique advantages compared with traditional ESG ratings: it rates companies' compliance with governance responsibilities and it has predictive validity. Our study demonstrates a solution to what is likely the greatest challenge for the finance industry today: how to assess a company's sustainability with validity and accuracy. Prior to this study, the ESG rating industry and the literature have not provided evidence that widely adopted governance ratings are valid. This study describes the only methodology for developing governance performance ratings based on companies' compliance with governance responsibilities and for which there is evidence of predictive validity. 2022-03-18 published 10.1002/isaf.1505 3245 public Intelligent Systems in Accounting, Finance and Management 29 1 50-68 TRUE Wiley no info:eu-repo/semantics/article