China Emergency Management Research Center, Wuhan University of Technology, Wuhan, Hubei 430070, China.
School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, Hubei 430070, China.
Comput Intell Neurosci. 2022 May 18;2022:7325798. doi: 10.1155/2022/7325798. eCollection 2022.
With the downward pressure of China's economy and the impact of the epidemic, the accumulated market risk has increased the liquidity pressure of the banking industry, and the mismatch between deposit maturity and loan maturity is the main cause for the increase of liquidity risk. The twenty-first century is the era of rapid and in-depth development of data management technology. The explosive growth of massive financial data makes the information data related to the liquidity risk of commercial banks present the characteristics of complexity, diversity, and heterogeneity. The traditional risk early warning model cannot deal with the influence between a large number of influencing factors and the nonlinear factors of commercial bank liquidity risk. Based on this transformation, the circular neural network model is introduced into the field of liquidity risk early warning of commercial banks from the perspective of the mismatch risk of financing maturity of commercial banks, and the driving factors and risk warning signs of liquidity risk of commercial banks are further analyzed from the institutional level, policy level, industry level, and micro commercial bank level. This paper uses network crawler technology, text analysis, and grounded analysis technology to intelligently identify the liquidity risk of commercial banks and establishes an early warning index system based on the influencing factors of commercial banks and internal liquidity risk. Also, it constructs an intelligent early warning model of commercial bank liquidity risk based on deep learning and uses the data of commercial banks from 2000 to 2020 for early warning. The results show that the constructed model has high accuracy, which can provide support for banks and relevant government departments to formulate and resolve bank liquidity risk.
随着中国经济下行压力加大和疫情影响，银行业市场风险累积抬升，流动性风险增加的主要原因是存贷期限错配。21 世纪是数据管理技术快速深入发展的时代，海量金融数据的爆发式增长使得与商业银行流动性风险相关的信息数据呈现出复杂性、多样性和异质性的特点。传统的风险预警模型无法处理大量影响因素之间以及商业银行流动性风险的非线性因素之间的影响。基于这种转变，从商业银行融资期限错配的角度，将循环神经网络模型引入商业银行流动性风险预警领域，从制度层面、政策层面、行业层面和微观商业银行层面进一步分析商业银行流动性风险的驱动因素和风险预警信号。本文运用网络爬虫技术、文本分析和扎根分析技术，对商业银行的流动性风险进行智能识别，建立基于商业银行影响因素和内部流动性风险的预警指标体系，并基于深度学习构建商业银行流动性风险智能预警模型，利用 2000 年至 2020 年商业银行的数据进行预警。结果表明，所构建的模型具有较高的准确性，可为银行和相关政府部门制定和化解银行流动性风险提供支持。
