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Based on comprehensive governance data from 2,517 companies over a period of 10\u2009years and investigating nine machine\u2010learning 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.<\/jats:p>","DOI":"10.1002\/isaf.1505","type":"journal-article","created":{"date-parts":[[2022,3,18]],"date-time":"2022-03-18T13:45:16Z","timestamp":1647611116000},"page":"50-68","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Corporate governance performance ratings with machine learning"],"prefix":"10.1002","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4436-5920","authenticated-orcid":false,"given":"Jan","family":"Svanberg","sequence":"first","affiliation":[{"name":"Centre for Research on Economic Relations, and The Royal Melbourne Institute of Technology University of G\u00e4vle  G\u00e4vle Sweden"}]},{"given":"Tohid","family":"Ardeshiri","sequence":"additional","affiliation":[{"name":"University of G\u00e4vle  G\u00e4vle Sweden"}]},{"given":"Isak","family":"Samsten","sequence":"additional","affiliation":[{"name":"Department of Computer and Systems Sciences Stockholm University  Stockholm Sweden"}]},{"given":"Peter","family":"\u00d6hman","sequence":"additional","affiliation":[{"name":"Department of Economics, Geography, Law and Tourism, Centre for Research on Economic Relations Mid Sweden University  Sundsvall Sweden"}]},{"given":"Presha E.","family":"Neidermeyer","sequence":"additional","affiliation":[{"name":"West Virginia University  Morgantown West Virginia USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2050-7004","authenticated-orcid":false,"given":"Tarek","family":"Rana","sequence":"additional","affiliation":[{"name":"The Royal Melbourne Institute of Technology, School of Accounting, Information Systems &amp; Supply Chain RMIT University  Melbourne VIC Australia"}]},{"given":"Natalia","family":"Semenova","sequence":"additional","affiliation":[{"name":"Department of Accounting and Logistics, School of Business and Economics Linnaeus University  V\u00e4xj\u00f6 Sweden"}]},{"given":"Mats","family":"Danielson","sequence":"additional","affiliation":[{"name":"Department of Computer and Systems Sciences Stockholm University  Stockholm Sweden"},{"name":"International Institute for Applied Systems Analysis (IIASA)  Laxenburg Austria"}]}],"member":"311","published-online":{"date-parts":[[2022,3,18]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.3905\/jwm.2021.1.130"},{"key":"e_1_2_9_3_1","volume-title":"Introduction to machine learning","author":"Alpaydin E.","year":"2010"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.2469\/faj.v74.n3.2"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10880-011-9278-8"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpa.2003.08.005"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1099-1123.2010.00417.x"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2010.07.033"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1468-0335.2009.00843.x"},{"key":"e_1_2_9_10_1","doi-asserted-by":"crossref","unstructured":"Berg F. 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