探索全球保险公司的股权成本:来自机器学习方法的证据

IF 4.9
Indranarain Ramlall, Dineshwar Ramdhony
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引用次数: 0

摘要

本研究通过先进的机器学习技术整合金融和非金融因素,调查了保险公司WACC的决定因素。我们分析了全球190家保险公司2012年至2022年的大样本数据,比较了9种机器学习模型,发现XGBoost和LightGBM优于传统方法。WACC的主要驱动因素包括beta、股息收益率和每股收益,排放得分也有显著影响。本研究通过引入基于ml的WACC模型,提高预测准确性,并为监管报告和排放评分披露提供政策建议,填补了保险金融文献的空白。从政策角度来看,全球保险业正处于一个关键的转折点,人们发现,以颗粒形式整合ESG对金融稳定至关重要。通过强制要求与ISSB和TCFD框架保持一致的标准化ESG披露,监管机构可以降低保险公司的股权成本,实现财务可持续性和环境责任之间的平衡,同时促进投资者和社会的长期价值创造。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Machine learning with applications
Machine learning with applications Management Science and Operations Research, Artificial Intelligence, Computer Science Applications
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98 days
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