可解释的机器学习来预测资本成本
Niklas Bussmann1, Paolo Giudici1,2, Alessandra Tanda1,2
1Department of Economics and Management, University of Pavia, Pavia, Italy.
Frontiers in artificial intelligence
|April 25, 2025
概括
这项研究表明,公司规模,财务业绩和非财务因素,包括国家机构质量,在很大程度上预测了一个公司.
科学领域:
- 金融 金融 金融 金融 金融
- 经济学 经济学 经济学
- 人工智能的人工智能
背景情况:
- 前期资本成本反映了投资者对公司风险的看法.
- 以前的文献主要集中在金融因素上,忽视了非金融方面.
- 了解资本成本的预测因素对于投资决策至关重要.
研究的目的:
- 调查金融和非金融因素对公司前期资本成本的影响.
- 通过使用先进的分析方法,识别投资者风险感知的关键预测因素.
- 探索国家层面的机构质量在确定资本成本方面的作用.
主要方法:
- 应用XGBoost算法用于预测建模.
- 使用可解释的人工智能 (AI) 方法:Shapley值和洛伦兹模型选择.
- 全球数据集的分析,包括1400多家上市公司.
主要成果:
- 证实了财务指标的重要性,如公司规模和股权回报率 (ROE).
- 确定了非金融公司的特征和国家机构质量作为关键预测因素.
- 证明了公司投资组合风险在评估资本成本时的相关性.
结论:
- 非金融指标和国家机构质量对于预测股权事先成本至关重要.
- 投资者对风险的看法受到传统金融指标之外的更广泛因素的影响.
- 这些发现支持未来关于环境,社会和治理 (ESG) 标准和国家因素的研究.
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