The DOI matches its Crossref record; 98 of the 98 references it cites were found in a registry or an index.
MACHINE LEARNING METHODS FOR SYSTEMIC RISK ANALYSIS IN FINANCIAL SECTORS
Open accessAbstract
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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Self-citationAshta, A., & Herrmann, H. (2021) · Strategic Change · doi:10.1002/jsc.2404 · Cited by 373
Liu, C., Kou, G., Zhou, X., Peng, Y., Sheng, H., & Alsaadi, F. E. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.06.021 · Cited by 150
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Metadata
- Authors
- Kou, G., Chao, X., Peng, Y., Alsaadi, F. E., & Herrera-Viedma, E.
- Venue
- Technological and Economic Development of Economy
- Published
- 2019-05-29
- Volume
- 25
- Issue
- 5
- Pages
- 716-742
- ISSN
- 1392-8619 (ISSN-L)
- 2029-4913
- 2029-4921
- OpenAlex
- W2947412763
- Cited by
- 320
- References
- 98
- FWCI
- 50.63
- Type
- journal article
- Language
- en
- Open access
- gold
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- 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. https://doi.org/10.3846/tede.2019.8740
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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