基于小波神经网络的上市公司财务危机预测①

提出了基于小波神经网络的上市公司财务危机预测模型,分析了公司财务指标的选取方法。小波神经网络的训练采用自适应调整学习率及动量系数的方法,以避免陷入局部极小值。与多元统计方法、Logit及Probit模型进行比较,结果表明,该方法预测精度高,第一类错误及第二类错误显著减小。

Prediction of Financial Distress for Listed Companies Based on Wavelet Neural Network

A prediction model of financial distress based on wavelet neural network for listed companies is proposed. The method of selecting financial ratios of companies is analyzed. The self-adaptive learning rate and momentum coefficient are used to avoid the local minimum point in the training process of wavelet neural network. The prediction results show that compared with multiple discriminate analysis, and Logit and Probit models, the prediction with this method is more accurate and type I and type II errors are reduced significantly.