机器学习检测公司报告中的操纵性环境披露
Yuanzhe Li1,2,3, Junyuan Li4,5, Yutong Zheng5
1Carbon Neutrality Institute, China University of Mining and Technology, Xuzhou, 221116, China. yuanzhe001@e.ntu.edu.sg.
Scientific reports
|November 22, 2025
概括
检测操纵性的环境披露是具有挑战性的. 这项研究引入了一种机器学习模型,使用财务数据,情绪和公众注意力来识别中国公司的操纵行为,显示出强大的预测能力.
科学领域:
- 环境会计环境会计
- 机器学习应用程序 机器学习应用程序
- 公司融资公司融资
背景情况:
- 检测操纵性环境披露对监管机构和投资者构成重大挑战.
- 现有的方法经常与此类披露的复杂性和多面性质作斗争.
研究的目的:
- 开发和评估一种机器学习框架,用于识别中国上市公司的操纵性环境披露.
- 整合财务指标,文本情绪和公众关注数据,以提高检测准确度.
主要方法:
- 采用了一个随机森林机器学习模型.
- 该模型使用来自企业报告和百度指数趋势的多源功能进行训练.
- 用SHAP分析来确定模型的可解释性.
主要成果:
- 优化的模型表现出强的表现,特别是在类不平衡下 (ROC-AUC = 0.94,PR-AUC = 0.78).
- 确定操纵风险的关键决定因素包括金融压力,异常的公众关注和情绪偏差.
- 均衡的指标证实了模型的真正预测能力,避免了过度拟合.
结论:
- 可解释机器学习提供了一个强大的方法来加强数据驱动的环境监督.
- 拟议的框架有效地识别了在中国市场背景下对环境披露的潜在操纵.
- 由于所使用的指标具有特定的上下文性质,建议在不同的监管环境中进一步验证.
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