基于BP神经网络的不同学习方案下上市公司信用风险评价

Assessment based on different learning schemes of BP neural network of listed company's credit risk

企业的信用风险评级是金融领域的一个重要问题,采用BP神经网络来研究上市公司的信用风险评价问题。首先构建了上市公司信用评价的财务指标体系,然后根据3个不同的隐层结点,生成3种不同的神经网络模型。设计7种不同的学习-验证比例,选取了不同行业上市公司的财务数据,利用MATLAB中的神经网络工具箱编程进行实证分析在哪种模型和学习-验证比例下能够更好的对企业进行信用风险评价。

Credit risk analysis of enterprises is an important topic in financial field. This paper employs BP neural network to solve this problem. Indexes system of company and three different BP neural networks have been bulit in this paper.The neural networks are trained using financial data from different industries. We use Matlab program and neural network to get the results in seven learning schemes with different training-to-validation data ratios. Experimental results suggest which neural network model and under which learning scheme can deliver optimum performance.