识别企业社会责任报告中的财务重要内容

IF 0.9 Q3 BUSINESS, FINANCE
Tina Lang
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引用次数: 0

摘要

本研究考察了企业社会责任(CSR)报告中的财务实质性内容与这些报告的决策有用性之间的关系。利用可持续性披露标准和机器学习主题建模算法,开发了企业特定的CSR报告中财务实质性内容的定量度量。假设提供更多财务上重要的企业社会责任内容的公司改善了他们的信息环境,这使得分析师能够做出更准确的盈利预测。研究结果证实了披露财务材料CSR内容的程度与分析师预测准确性之间的正相关关系。本研究证明了使用机器学习识别非结构化自愿披露中的财务实质性内容的有效性,并为有关企业社会责任活动及其相关披露的财务重要性的文献做出了贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying Financially Material Content in Corporate Social Responsibility Reports

This study examines the relationship between financially material content in corporate social responsibility (CSR) reports and the decision usefulness of these reports. Utilizing sustainability disclosure standards and a machine learning topic modeling algorithm, a firm-specific quantitative measure of financially material content in CSR reports is developed. It is hypothesized that firms providing greater amounts of financially material CSR content enhance their information environment, which enables analysts to make more accurate earnings predictions. The findings confirm a positive relationship between the extent of financially material CSR content disclosed and analyst forecast accuracy. This research demonstrates the effectiveness of using machine learning to identify financially material content within unstructured voluntary disclosures and contributes to the literature on the financial materiality of CSR activities and their related disclosures.

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来源期刊
CiteScore
2.30
自引率
7.10%
发文量
69
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