Purnell Daren, Etemadi Amir, Kamp John
School of Engineering and Applied Science, George Washington University, Washington, DC 20052, USA.
Entropy (Basel). 2024 Sep 17;26(9):796. doi: 10.3390/e26090796.
Identifying the influential variables that provide early warning of financial network instability is challenging, in part due to the complexity of the system, uncertainty of a failure, and nonlinear, time-varying relationships between network participants. In this study, we introduce a novel methodology to select variables that, from a data-driven and statistical modeling perspective, represent these relationships and may indicate that the financial network is trending toward instability. We introduce a novel variable selection methodology that leverages Shapley values and modified Borda counts, in combination with statistical and machine learning methods, to create an explainable linear model to predict relationship value weights between network participants. We validate this new approach with data collected from the March 2023 Silicon Valley Bank Failure. The models produced using this novel method successfully identified the instability trend using only 14 input variables out of a possible 3160. The use of parsimonious linear models developed by this method has the potential to identify key financial stability indicators while also increasing the transparency of this complex system.
识别那些能为金融网络不稳定提供早期预警的影响变量具有挑战性，部分原因在于系统的复杂性、故障的不确定性以及网络参与者之间非线性、时变的关系。在本研究中，我们引入了一种新颖的方法来选择变量，从数据驱动和统计建模的角度来看，这些变量代表了这些关系，并可能表明金融网络正趋向于不稳定。我们引入了一种新颖的变量选择方法，该方法利用夏普利值和改进的博尔达计数法，并结合统计和机器学习方法，来创建一个可解释的线性模型，以预测网络参与者之间的关系价值权重。我们用从2023年3月硅谷银行倒闭事件中收集的数据验证了这种新方法。使用这种新颖方法生成的模型仅从3160个可能的输入变量中选取了14个，就成功识别出了不稳定趋势。通过这种方法开发的简约线性模型有可能识别关键的金融稳定指标，同时还能提高这个复杂系统的透明度。
