Machine Learning Algorithms to Study the Impact of Sustainability on Financial Success: Evidence from US Stock Market

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dc.contributor.authorDoğruel, Merve
dc.contributor.authorKüçükgöde, Haydar Anıl
dc.date.accessioned2025-07-23T20:42:39Z
dc.date.available2025-07-23T20:42:39Z
dc.date.issued2025
dc.description.abstractIn recent years, as in almost every field, the concept of sustainability has become a concept that has been given considerable importance and researched in both the academic and business worlds in the field of finance. This study aims to find the best model for estimating financial success within the framework of sustainability by adding environmental risk score, social risk score, and governance risk score in addition to classical indicators in stock returns. For this purpose, data obtained from 300 US companies across 11 distinct industries is utilized. The features evaluated are the environmental risk score, social risk score, governance risk score, as well as profitability, liquidity, leverage, RoA, and beta, which are used in classical studies. To obtain the best modeling predictions, various machine learning algorithms were used instead of classical statistical methods. Based on the F1 performance metrics of the seven machine learning algorithms tested, the model with the highest performance is Random Forest, an ensemble learning model. Based on the Random Forest model, environmental and social risk scores are particularly important features for financial success. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
dc.identifier.doi10.1007/978-3-031-83266-6_8
dc.identifier.endpage134
dc.identifier.issn27306038
dc.identifier.scopus2-s2.0-105002457933
dc.identifier.scopusqualityN/A
dc.identifier.startpage119
dc.identifier.urihttps://doi.org/10.1007/978-3-031-83266-6_8
dc.identifier.urihttps://hdl.handle.net/20.500.14704/1088
dc.identifier.volumePart F249
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Nature
dc.relation.ispartofContributions to Finance and Accounting
dc.relation.publicationcategoryKitap Bölümü - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250711
dc.subjectEnvironmental score
dc.subjectGovernance score
dc.subjectMachine learning
dc.subjectRandom forest
dc.subjectSocial score
dc.subjectSustainable finance
dc.titleMachine Learning Algorithms to Study the Impact of Sustainability on Financial Success: Evidence from US Stock Market
dc.typeBook Chapter

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