运筹与管理2023，Vol.32Issue(11)：212-219,8.DOI:10.12005/orms.2023.0374
基于机器学习技术的系统性金融风险监测预警
Systemic Financial Risk Monitoring and Early Warning Based on Machine Learning Model
摘要
Abstract
With the rapid development of financial technology,the financial industry has experienced or is under-going major changes at many levels.In the field of financial risk management,due to the increasing complexity of the modern financial system,the limitations of traditional risk modeling methods have become increasingly prominent,while machine learning methods are good at capturing the complex nonlinear relationship between variables,have many inherent advantages over traditional economic analysis and prediction technologies,and therefore can better meet people's modeling requirements and analysis and prediction demands for economy and finance,a typical complex and open giant system.So,we aim to give a more effective risk analysis system using machine learning methods. This paper proposes a new systemic financial risk monitoring and early warning system based on machine learning techniques,selecting early warning indicators from eight levels:economic fundamentals,money supply,fiscal conditions,securities and interest rate markets,price indices,foreign exchange and exchange rate mar-kets,leverage and banking system,and using five classical machine learning models and its integrated models to forecast systemic financial risk.In order to open the black box of machine learning,we deconstruct the machine learning early warning model using feature importance,and partial dependency plots(PDP).Feature importance is commonly used in tree models to analyze the importance of variables,while PDP is applied to different models,and its core idea is to examine the effect of different values of a feature on the output value of the model.The PDP method can not only identify the relative importance of variables,but also examine the non-linear effects of variables.Our sample interval is from January 2005 to December 2020,and all raw data are obtained from the Wind database. The research results show that:(1)Compared with traditional linear models,machine learning models are good at capturing nonlinear relationships,and perform well both in and out of the sample.(2)Compared with Lasso model,SVM and other single model,integrated models have better prediction capabilities by improving the robustness of prediction results.(3)PDP model can effectively identify the nonlinearity and importance of features,thereby helping to open the black box of machine learning;among all the early warning variables,exchange rate,money supply,market interest rates and industrial product prices are key factors affecting systemic financial risks.Monitoring these key variables will help prevent systemic financial risks in an early stage.Our research work is helpful for promoting the application of artificial intelligence in the field of finance by providing a new technical framework for the systemic risk monitoring and early warning.关键词
系统性金融风险/风险预警/机器学习/非线性建模Key words
systemic financial risk/risk warning/machine learning/nonlinear modeling分类
管理科学引用本文复制引用
李红权,周亮..基于机器学习技术的系统性金融风险监测预警[J].运筹与管理,2023,32(11):212-219,8.基金项目
国家自然科学基金面上项目(71871092) （71871092）
宏观经济大数据挖掘与应用湖南省重点实验室资助项目(2019TP1009) （2019TP1009）
