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ESG2PreEM:使用预训练组合模型的自动化ESG评级评估框架.

Haein Lee1, Seon Hong Lee1, Heungju Park2

  • 1Department of Applied Artificial Intelligence/ Department of Human Artificial Intelligence Interaction, Sungkyunkwan University, 03063, Seoul, South Korea.

Heliyon
|February 26, 2024
PubMed
概括

本研究介绍了使用自然语言处理 (NLP) 和机器学习模型 (如BERT和ALBERT) 的自动化ESG评级策略. 开发的框架实现了80.79%的准确性,为ESG评估提供了一种新的方法.

关键词:
贝尔特 (BERT) 公司这是ESG ESG.合唱团组合在一起.自然语言处理 (NLP)预先训练的语言模型

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科学领域:

  • 商业与金融 商业与金融
  • 计算机科学 计算机科学
  • 环境科学 环境科学

背景情况:

  • 环境,社会和治理 (ESG) 标准对于企业的可持续性和公司价值至关重要.
  • 现有的ESG评级方法可以通过自动化文本分析来增强.
  • 该 Refinitiv-可持续领导力监测提供了一个全面的数据集,用于ESG分类.

研究的目的:

  • 建议并验证使用基于文本的数据对ESG标准进行评级的自动化策略.
  • 为了利用先进的NLP模型进行自主ESG分类.
  • 与已建立的ESG评级机构对比拟框架的表现.

主要方法:

  • 从LexisNexis新闻档案中收集的数据用于ESG分类.
  • 使用了来自变压器的双向编码器表示 (BERT),强大优化的BERT方法 (RoBERTa) 和A Lite BERT (ALBERT) 模型.
  • 实现了自主ESG文档分类的投票组合模型.
  • 使用道斯工业平均线 (DJIA) 的公司验证了框架.

主要成果:

  • 结合BERT和ALBERT组合模型的准确度达到80.79%,批量大小为20.
  • 该框架在与DJIA公司进行验证时显示出可靠的性能.
  • 自动化的ESG评级显示与MSCI提供的评级相似.

结论:

  • 复杂的自然语言处理 (NLP) 技术可以有效地从大型文本数据集中提取有价值的见解.
  • 拟议的自动化ESG评级策略为传统方法提供了可行且准确的替代方案.
  • 这项研究有助于提高ESG评估标准的稳定性和效率.