为金融网络开发预警系统:一个可解释的机器学习方法
Daren Purnell1, Amir Etemadi1, John Kamp1
1School of Engineering and Applied Science, George Washington University, Washington, DC 20052, USA.
Entropy (Basel, Switzerland)
|September 27, 2024
概括
本研究引入了一种使用Shapley值和Borda计数的新方法,以确定金融稳定性的关键指标. 该方法成功地用最小的变量预测了不稳定趋势,提高了复杂金融网络的透明度.
科学领域:
- 金融网络分析 金融网络分析
- 计量经济学 计量经济学
- 机器学习 机器学习
背景情况:
- 金融网络表现出复杂,非线性和时间变化的关系,使得不稳定的早期检测具有挑战性.
- 识别有影响力的变量来预测金融网络的不稳定性需要强大的数据驱动方法.
研究的目的:
- 开发一种新的方法来选择表明金融网络不稳定的变量.
- 创建一个可解释的线性模型来预测网络参与者之间的关系值权重.
主要方法:
- 杆化的沙普利值和修改的博尔达计数结合统计和机器学习技术.
- 开发了用于金融网络分析的数据驱动变量选择过程.
- 使用了2023年3月谷银行失败的数据进行验证.
主要成果:
- 这种新的方法成功地发现了不稳定性趋势,在3160个输入变量中只使用了14个输入变量.
- 该方法证明了能够精确确定金融网络不稳定的关键指标的能力.
- 为了提高透明度,生成了节的线性模型.
结论:
- 拟议的方法提供了一个强大的工具,用于早期警告金融网络的不稳定性.
- 这种方法提高了复杂的金融系统的透明度和可解释性.
- 变量选择技术对金融稳定性监测有重大影响.
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