@misc{10481/59467,
year = {2019},
url = {http://hdl.handle.net/10481/59467},
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.},
organization = {This research has been partially supported by grants from the National Natural Science Foundation
of China (#U1811462, #71874023, #71771037, #71725001, and #71433001).},
publisher = {Vilnius Gediminas Technical University Press},
keywords = {Financial systemic risk},
keywords = {Machine learning},
keywords = {Big data},
keywords = {Network analysis},
title = {Machine learning methods for systemic risk analysis in financial sectors.},
author = {Kou, Gang and Chao, Xiangrui and Peng, Yi and Alsaadi, Fawaz E, and Herrera Viedma, Enrique},
}