体积热力学
质量(理念)
差速器(机械装置)
业务
经济
会计
精算学
热力学
物理
量子力学
摘要
ABSTRACT This paper investigates how ESG disclosure affects corporate tail risk by distinguishing between disclosure quantity and quality. Using a dataset of 4044 Chinese A‐share listed companies from 2011 to 2022, I employ a machine‐learning approach to quantify ESG disclosure from corporate textual data, while tail risk is estimated using extreme value theory. The empirical results show that both ESG disclosure quantity and quality significantly reduce firms' tail risk. The impact is more pronounced for firms in polluting industries, those with high financial constraints, and smaller firms. I identify three potential channels: decreased ESG‐related incidents, reduced information asymmetry between managers and outside investors, and enhanced stakeholder relationships. These findings highlight the importance of comprehensive and credible ESG disclosure in promoting market stability, with implications for sustainability reporting regulations and corporate risk management strategies.
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