Application of Neural Network with Autocorrelation in Long-Term Forecasting of Systemic Financial Risk
Carrying out early warning of systemic financial risk is a prerequisite for timely adjustment of monetary policy and macroprudential policy to effectively prevent and resolve systemic financial risks. This paper constructs a systemic financial risk monitoring and early warning system for China’s ban...
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| Veröffentlicht in: | Computational intelligence and neuroscience 2022-09, Vol.2022, p.1-7 |
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| container_title | Computational intelligence and neuroscience |
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| creator | Zhang, Junzhi Chen, Lei |
| description | Carrying out early warning of systemic financial risk is a prerequisite for timely adjustment of monetary policy and macroprudential policy to effectively prevent and resolve systemic financial risks. This paper constructs a systemic financial risk monitoring and early warning system for China’s banking industry based on isolated forest anomaly detection and neural network with autocorrelation mechanism and uses low-frequency data with high credibility to effectively identify the ten factors that have the greatest impact on systemic financial risk in China’s banking industry, improving the prospective and accuracy of risk early warning. The conclusions can help regulators to adjust their policies prospectively to curb the rise of systemic financial risks. |
| doi_str_mv | 10.1155/2022/7131143 |
| format | Article |
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This paper constructs a systemic financial risk monitoring and early warning system for China’s banking industry based on isolated forest anomaly detection and neural network with autocorrelation mechanism and uses low-frequency data with high credibility to effectively identify the ten factors that have the greatest impact on systemic financial risk in China’s banking industry, improving the prospective and accuracy of risk early warning. The conclusions can help regulators to adjust their policies prospectively to curb the rise of systemic financial risks.</description><identifier>ISSN: 1687-5265</identifier><identifier>EISSN: 1687-5273</identifier><identifier>DOI: 10.1155/2022/7131143</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Anomalies ; Autocorrelation ; Banking ; Banking industry ; Banks ; Banks (Finance) ; Commercial banks ; Consumer Price Index ; Credit risk ; Early warning systems ; Equity financing ; Financial risk ; Forecasting techniques ; Forecasts and trends ; GDP ; Gross Domestic Product ; Growth rate ; Interest rates ; Macroeconomics ; Monetary policy ; Neural networks ; Regulation of financial institutions ; Risk ; Stochastic models</subject><ispartof>Computational intelligence and neuroscience, 2022-09, Vol.2022, p.1-7</ispartof><rights>Copyright © 2022 Junzhi Zhang and Lei Chen.</rights><rights>COPYRIGHT 2022 John Wiley & Sons, Inc.</rights><rights>Copyright © 2022 Junzhi Zhang and Lei Chen. 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The conclusions can help regulators to adjust their policies prospectively to curb the rise of systemic financial risks.</description><subject>Anomalies</subject><subject>Autocorrelation</subject><subject>Banking</subject><subject>Banking industry</subject><subject>Banks</subject><subject>Banks (Finance)</subject><subject>Commercial banks</subject><subject>Consumer Price Index</subject><subject>Credit risk</subject><subject>Early warning systems</subject><subject>Equity financing</subject><subject>Financial risk</subject><subject>Forecasting techniques</subject><subject>Forecasts and trends</subject><subject>GDP</subject><subject>Gross Domestic Product</subject><subject>Growth rate</subject><subject>Interest rates</subject><subject>Macroeconomics</subject><subject>Monetary policy</subject><subject>Neural networks</subject><subject>Regulation of financial institutions</subject><subject>Risk</subject><subject>Stochastic 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This paper constructs a systemic financial risk monitoring and early warning system for China’s banking industry based on isolated forest anomaly detection and neural network with autocorrelation mechanism and uses low-frequency data with high credibility to effectively identify the ten factors that have the greatest impact on systemic financial risk in China’s banking industry, improving the prospective and accuracy of risk early warning. The conclusions can help regulators to adjust their policies prospectively to curb the rise of systemic financial risks.</abstract><cop>New York</cop><pub>Hindawi</pub><doi>10.1155/2022/7131143</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0003-1468-715X</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Computational intelligence and neuroscience, 2022-09, Vol.2022, p.1-7 |
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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central Open Access; Wiley Online Library Open Access; PubMed Central; Alma/SFX Local Collection |
| subjects | Anomalies Autocorrelation Banking Banking industry Banks Banks (Finance) Commercial banks Consumer Price Index Credit risk Early warning systems Equity financing Financial risk Forecasting techniques Forecasts and trends GDP Gross Domestic Product Growth rate Interest rates Macroeconomics Monetary policy Neural networks Regulation of financial institutions Risk Stochastic models |
| title | Application of Neural Network with Autocorrelation in Long-Term Forecasting of Systemic Financial Risk |
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