Machine learning methods for systemic risk analysis in financial sectors | Technological and Economic Development of Economy
Gang Kou Affiliation
Gang Kou School of Business Administration, Southwestern University of Finance and Economics, No.555, Liutai Ave, Wenjiang Zone, Chengdu, 611130, China
; Xiangrui Chao Affiliation
Xiangrui Chao School of Management and Economics, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, China
Yi Peng School of Management and Economics, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, China
Enrique Herrera-Viedma Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Calle Periodista Daniel Saucedo Aranda, s/n, Granada, 18014, Spain
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.
Keyword : financial systemic risk, machine learning, big data, network analysis
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