Zhejiang University of Finance & Economics Dongfang College, Haining, Zhejiang 310012, China.
Comput Intell Neurosci. 2022 Jan 30;2022:7536492. doi: 10.1155/2022/7536492. eCollection 2022.
The financial crisis of listed companies will bring huge losses to investors, so it is very important to establish a financial early warning model for investors and other stakeholders. The forward neural network model of particle swarm optimization is used to model and analyze the financial early warning of listed companies. In terms of data selection, earnings management indicators are substituted into the model for the common phenomenon of earnings management in listed companies. The results show that the accuracy of the model considering earnings management factors is improved from 65% to 70%. In the process of modeling, this paper uses the logistic regression model to further modify the model. The empirical results show that the accuracy of the model can be improved from 70% to 75%. When using the forward neural network model based on particle swarm optimization to make an empirical analysis of financial early warning of listed companies, adding quantitative indicators of earnings management can improve the accuracy of the model. In the demonstration, the correction of logistic regression model can also improve the accuracy of the particle swarm neural network financial early warning model. This greatly reduces the risk that companies with poor financial conditions will face bankruptcy and liquidation.
上市公司的财务危机将给投资者带来巨大损失，因此为投资者和其他利益相关者建立财务预警模型非常重要。使用粒子群优化的前馈神经网络模型对上市公司的财务预警进行建模和分析。在数据选择方面，将盈余管理指标替代到模型中，以应对上市公司普遍存在的盈余管理现象。结果表明，考虑盈余管理因素的模型的准确性从 65%提高到 70%。在建模过程中，本文使用逻辑回归模型进一步修改模型。实证结果表明，模型的准确性可以从 70%提高到 75%。当使用基于粒子群优化的前馈神经网络模型对上市公司的财务预警进行实证分析时，添加盈余管理的定量指标可以提高模型的准确性。在演示中，逻辑回归模型的校正也可以提高粒子群神经网络财务预警模型的准确性。这大大降低了财务状况不佳的公司面临破产和清算的风险。
