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Responding financial systemic risk should develop more accurate regulation policies using intelligent methods based on financial big data in the future

MACHINE LEARNING METHODS FOR SYSTEMIC RISK ANALYSIS IN FINANCIAL SECTORS

TECHNOLOGICAL AND ECONOMIC DEVELOPMENT OF ECONOMY, no. 5.0 (2019): 716-742

被引用67|浏览220
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摘要

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 cont...更多

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简介
  • 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.
  • 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
重点内容
  • 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
  • Responding financial systemic risk has become critical to modern financial market and financial safety
  • For introducing current researches on assessment and measurement of financial systemic risk combined with machine learning technologies, we survey existing literatures and methodologies including big data analysis, complex network analysis, sentiment analysis and classic econometric models
  • We proposed many further research directions, such as big data analysis, data-driven research, and policy analysis associated to data science
  • Responding financial systemic risk should develop more accurate regulation policies using intelligent methods based on financial big data in the future
方法
  • Data and areas

    Allen and Gale, 2000; Souza, Silva, Tabak, and Guerra, 2016; Multilayer networks; BIS data; US banking

    Battiston, Farmer, and Flache, 2016; Haldane, 2015; Acemoglu, probability; economic system, the Bank

    Ozdaglar, and Tahbaz-Salehi, 2015; Haldane and May, 2011; theory, complexity theory, of England, the

    Prasanna, Haldane, and Kapadia, 2011; Hu, Zhao, Hua, and ecology, epidemiology and

    European Union, Wong, 2012; Hu, Schwabe, and Li, 2015; Ferrara, Langfield, finance, etc.
  • Multivariate network; multivariate graphical models and other emerging economies
  • Classic methods such as statistical methods and economics are most commonly used in financial evidence research.
  • There are different insights for studying systemic financial risk, including loss evolution, identification of “too big to fail” institutions and impacts on the financial system and even the economy.
  • The authors' concerns in this paper are detection methodologies for the recognition process using machine learning methods, this section will introduce related developments of systemic risk research with network-based insights, big data analysis, and sentiment mining and econometrics methods
结果
  • Poledna et al (2015) recognized that a financial network should be treated as a multilayer network connected by credit, derivatives, foreign exchange, and securities, and that the expected loss will be underestimated by up to 90% of the total risk if networks are only focused on a single layer.
结论
  • Responding financial systemic risk has become critical to modern financial market and financial safety.
  • Many machine learning methods have been developed to detect and identify the systemic risk in financial markets and sectors.
  • For introducing current researches on assessment and measurement of financial systemic risk combined with machine learning technologies, the authors survey existing literatures and methodologies including big data analysis, complex network analysis, sentiment analysis and classic econometric models.
  • Responding financial systemic risk should develop more accurate regulation policies using intelligent methods based on financial big data in the future
表格
  • Table1: Classification of objectives and representative literatures
Download tables as Excel
基金
  • This research has been partially supported by grants from the National Natural Science Foundation of China (#U1811462, #71874023, #71771037, #71725001, and #71433001)
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作者
Xiangrui Chao
Xiangrui Chao
Fawaz E. Alsaadi
Fawaz E. Alsaadi
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