MACHINE LEARNING METHODS FOR SYSTEMIC RISK ANALYSIS IN FINANCIAL SECTORS

被引：194

作者：

Kou, Gang ^{[2
]}

Chao, Xiangrui ^{[1
]}

Peng, Yi ^{[1
]}

Alsaadi, Fawaz E. ^{[3
]}

Herrera-Viedma, Enrique ^{[4
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Management & Econ, 2006 Xiyuan Ave, Chengdu 611731, Sichuan, Peoples R China

[2] Southwestern Univ Finance & Econ, Sch Business Adm, 555 Liutai Ave, Chengdu 611130, Sichuan, Peoples R China

[3] King Abdulaziz Univ, Fac Comp & IT, Dept Informat Technol, Jeddah, Saudi Arabia

[4] Univ Granada, Dept Comp Sci & Artificial Intelligence, Calle Periodista Daniel Saucedo Aranda S-N, E-18014 Granada, Spain

来源：

TECHNOLOGICAL AND ECONOMIC DEVELOPMENT OF ECONOMY | 2019年 / 25卷 / 05期

基金：

中国国家自然科学基金;

关键词：

financial systemic risk; machine learning; big data; network analysis; EUROPEAN BANKING; BIG DATA; CAPITAL REQUIREMENTS; DERIVATIVES MARKET; COMPLEX-SYSTEMS; STABILITY; CREDIT; SENTIMENT; DEFAULT; GOVERNANCE;

D O I：

10.3846/tede.2019.8740

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Financial systemic risk is an important issue in economics and financial systems. Trying to detect and respond to systemic risk with growing amounts of data produced in financial markets and systems, a lot of researchers have increasingly employed machine learning methods. Machine learning methods study the mechanisms of outbreak and contagion of systemic risk in the financial network and improve the current regulation of the financial market and industry. In this paper, we survey existing researches and methodologies on assessment and measurement of financial systemic risk combined with machine learning technologies, including big data analysis, network analysis and sentiment analysis, etc. In addition, we identify future challenges, and suggest further research topics. The main purpose of this paper is to introduce current researches on financial systemic risk with machine learning methods and to propose directions for future work.

引用

页码：716 / 742

页数：27

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