基于动态BP神经网络的财务危机预警算法研究

Efficient financial forecast based on dynamic back-propagation neural network

为进一步提升模型合理性和预测结果准确度,充分考虑公司财务情况历史值的影响,通过对不同时期的财务面板数据赋以不同权重,设计提出了一种基于Logit-动态BP神经网络的财务危机预警机制。实证结果显示,基于面板数据的新模型能更好地体现财务危机的发生机理,因而具备良好预警精度;在对财务危机公司及财务正常公司预警实验中,其预测性能均优于现有Logit回归分析模型和传统神经网络模型。

Most of the classical methods in the investigations of financial forecast are generally based on a static pre-waming modeling by only exploring the single-period financial data, such as the signalvariable analysis, multiple-variables analysis, Logit regression analysis, which unfortunately ignores the potential influences from the historical data. In order to enhance the accuracy and stability of the financial forecasting, a promising dynamic back propagation （BP） neural network relying on the Logit nonlinear mapping is proposed to perform financial forecasting. The historical panel data of financial companies is also fully taken into consideration in this new method, and different weights associated with different period data is used. The experimental results have demonstrated the effectiveness and the fair accuracy of the new forecasting model.