{"title":"探索全球保险公司的股权成本:来自机器学习方法的证据","authors":"Indranarain Ramlall, Dineshwar Ramdhony","doi":"10.1016/j.mlwa.2025.100726","DOIUrl":null,"url":null,"abstract":"<div><div>This study investigates the determinants of the WACC for insurance firms, integrating both financial and non-financial factors through advanced machine learning techniques. Analyzing data from 2012 to 2022 for a large sample of 190 insurance companies in the world, we compare nine ML models, revealing that XGBoost and LightGBM outperform traditional methods. Key drivers of WACC include beta, dividend yield, and earnings per share, with Emission score also showing significant influence. This study fills gaps in insurance finance literature by introducing ML-based WACC modeling, enhancing predictive accuracy, and providing policy recommendations for regulatory reporting and Emission score disclosures. From a policy perspective, the global insurance sector is at a crucial turning point, where ESG integration in granular form is found to be vital for financial stability. By mandating standardized ESG disclosures in alignment with the ISSB and TCFD frameworks, regulators can reduce insurers’ cost of equity, enabling a balance between financial sustainability and environmental responsibility, while promoting long-term value creation for both investors and society.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100726"},"PeriodicalIF":4.9000,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring the cost of equity for insurance companies in the world: evidence from machine learning approaches\",\"authors\":\"Indranarain Ramlall, Dineshwar Ramdhony\",\"doi\":\"10.1016/j.mlwa.2025.100726\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study investigates the determinants of the WACC for insurance firms, integrating both financial and non-financial factors through advanced machine learning techniques. Analyzing data from 2012 to 2022 for a large sample of 190 insurance companies in the world, we compare nine ML models, revealing that XGBoost and LightGBM outperform traditional methods. Key drivers of WACC include beta, dividend yield, and earnings per share, with Emission score also showing significant influence. This study fills gaps in insurance finance literature by introducing ML-based WACC modeling, enhancing predictive accuracy, and providing policy recommendations for regulatory reporting and Emission score disclosures. From a policy perspective, the global insurance sector is at a crucial turning point, where ESG integration in granular form is found to be vital for financial stability. By mandating standardized ESG disclosures in alignment with the ISSB and TCFD frameworks, regulators can reduce insurers’ cost of equity, enabling a balance between financial sustainability and environmental responsibility, while promoting long-term value creation for both investors and society.</div></div>\",\"PeriodicalId\":74093,\"journal\":{\"name\":\"Machine learning with applications\",\"volume\":\"22 \",\"pages\":\"Article 100726\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2025-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Machine learning with applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666827025001094\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Machine learning with applications","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666827025001094","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Exploring the cost of equity for insurance companies in the world: evidence from machine learning approaches
This study investigates the determinants of the WACC for insurance firms, integrating both financial and non-financial factors through advanced machine learning techniques. Analyzing data from 2012 to 2022 for a large sample of 190 insurance companies in the world, we compare nine ML models, revealing that XGBoost and LightGBM outperform traditional methods. Key drivers of WACC include beta, dividend yield, and earnings per share, with Emission score also showing significant influence. This study fills gaps in insurance finance literature by introducing ML-based WACC modeling, enhancing predictive accuracy, and providing policy recommendations for regulatory reporting and Emission score disclosures. From a policy perspective, the global insurance sector is at a crucial turning point, where ESG integration in granular form is found to be vital for financial stability. By mandating standardized ESG disclosures in alignment with the ISSB and TCFD frameworks, regulators can reduce insurers’ cost of equity, enabling a balance between financial sustainability and environmental responsibility, while promoting long-term value creation for both investors and society.