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      MACHINE LEARNING METHODS FOR SYSTEMIC RISK ANALYSIS IN FINANCIAL SECTORS

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          Abstract

          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.

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              Econometric measures of connectedness and systemic risk in the finance and insurance sectors

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                Author and article information

                Journal
                Technological and Economic Development of Economy
                Vilnius Gediminas Technical University
                2029-4913
                2029-4921
                September 13 2019
                May 29 2019
                : 25
                : 5
                : 716-742
                Affiliations
                [1 ]School of Business Administration, Southwestern University of Finance and Economics, No.555, Liutai Ave, Wenjiang Zone, Chengdu, 611130, China
                [2 ]School of Management and Economics, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, China
                [3 ] Department of Information Technology, Faculty of Computing and IT, King Abdulaziz University, Jeddah, Saudi Arabia
                [4 ]Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Calle Periodista Daniel Saucedo Aranda, s/n, Granada, 18014, Spain
                Article
                10.3846/tede.2019.8740
                1dbd475a-854b-4073-888f-a414498eff19
                © 2019
                History

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