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.
Highlights
In finance, systemic risk is a crisis that leads to the collapse of an entire financial system or entire market of an area or country, even global markets
The aim of this paper is to introduce recent advances made in systemic financial risk using machine learning methods, focusing specially on assessment methods based on data mining and the traditional statistical and econometrics model, as well as the primary results in financial regulation for systemic risk
Before the financial crisis in 2008, we found that the study of regulatory issues in the context of systemic financial risk appeared to have been neglected over many years, and that early studies had tried to respond to the risk of a single bank run
Summary
Systemic risk is a crisis that leads to the collapse of an entire financial system or entire market of an area or country, even global markets. The greatest impact of the global financial crisis in 2008, with strong economic destructive power and causing a huge chain reaction to destroy the financial industry, enabled systemic risk to be regarded as a crucial factor in relation to financial safety. The global economy has not fully recovered. Technological and Economic Development of Economy, 2019, 25(5): 716–742 from the aftermath of these events derived from the destructive effects of systemic financial risk. Over the past decade, a large amount of ground-breaking academic research has focused on systemic financial risks, including the study of the financial ecosystem, financial supervision, monitoring cross-border capital flows, etc. Systematic risk is always potentially hidden in modern large-scale financial systems, so that intelligent and automatic machine learning methods become a concerned tool to assess and detect the systemic risk from increasingly complex financial network, big data of financial transactions, and market sentiments together with risk proclivity, etc
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