可解释的ESG情绪混合深度学习用于资产回报预测,具有量化交互和延迟意识的部署
Sasmita Mishra1, Zefree Lazarus Mayaluri2, Chee Yoong Liew3
1Department of Business Management, C. V. Raman Global University, Bhubaneswar, India.
Scientific reports
|March 4, 2026
概括
本研究介绍了一种混合人工智能模型,将ESG评分和情绪分析结合起来,以改善财务预测. 该模型有效地整合了这些替代数据源,在准确性和风险调整回报方面超过了传统方法.
科学领域:
- 量化金融 量化金融
- 机器学习 机器学习
- 金融计量经济学 金融计量经济学
背景情况:
- 金融时间序列预测越来越多地使用ESG评分和新闻情绪等替代数据.
- 这些信号之间的相互作用及其对预测准确性的影响仍然不太清楚.
- 现有的模型很难有效地将多种不同的替代数据源融合在一起,以进行可靠的预测.
研究的目的:
- 为资产回报预测开发一个可解释的混合框架.
- 研究环境,社会和治理 (ESG) 评分与金融情绪之间的相互作用.
- 量化这些替代数据源对不同市场制度预测准确性的贡献.
主要方法:
- 一个混合框架,结合了时间融合变压器 (TFT) 和支向量回归 (SVR) 剩余校正器.
- 关闭ESG特征与基于方面的金融情绪 (基于FinBERT的ABSA) 的晚期融合.
- 财务级,防泄漏的前进验证协议和制度特定分析.
主要成果:
- 与基线相比,混合模型实现了较高的总平均绝对误差,RMSE和方向精度.
- 发现ESG情绪的相互作用具有统计学意义,并且依赖于制度.
- 在动荡时期,情绪信号更为重要,而在平静的市场中,ESG评分更具影响力.
结论:
- 拟议的混合框架有效地整合了ESG和情绪数据,以改善财务预测.
- 该模型显示了风险调整后的性能改善和更低的提款,特别是在压力下.
- 一个延迟优化的变种支持近实时部署,突出显示了实际应用性.
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