Machine learning methods for systemic risk analysis in financial sectors

    Gang Kou Affiliation
    ; Xiangrui Chao Affiliation
    ; Yi Peng Affiliation
    ; Fawaz E. Alsaadi Affiliation
    ; Enrique Herrera-Viedma Affiliation


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.

First published online 29 May 2019

Keyword : financial systemic risk, machine learning, big data, network analysis

How to Cite
Kou, G., Chao, X., Peng, Y., Alsaadi, F. E., & Herrera-Viedma, E. (2019). Machine learning methods for systemic risk analysis in financial sectors. Technological and Economic Development of Economy, 25(5), 716-742.
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May 29, 2019
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