基于Stacking-Bagging-Vote多源信息融合模型的财务预警应用

Application of Stacking-Bagging-Vote multi-source information fusion model for financial early warning

集成重采样技术可以在一定程度上解决财务预警研究中样本的不平衡性难题,而不同的集成模型与不同的重采样集成技术有不同的适配性。研究发现,Up-Down集成采样与Tomek-Smote集成采样分别适配于Bagging-Vote集成模型和Stacking融合模型。基于此,构建了Stacking-Bagging-Vote(SBV)多源信息融合模型。首先,将基于Up-Down集成采样的Bagging-Vote模型与基于Tomek-Smote采样的Stacking模型进行融合;然后,加入股票的交易数据,并对该数据用卡尔曼滤波进行处理,从而形成数据层次和模型层次的交互式融合优化;最终,得到SBV多源信息融合模型。该融合模型不仅在预测性能上有了较大的提升,能较好地兼顾模型的预测准确度和预测精确率,并且可以根据利益相关者的实际需要,通过调整模型参数,来选择对应的SBV多源信息融合模型进行财务预警预测。

Ensemble resampling technology can solve the problem of imbalanced samples in financial early warning research to some extent.Different ensemble models and different ensemble resampling technologies have different suitabilities.It is found in the study that Up-Down ensemble sampling and Tomek-Smote ensemble sampling were respectively suitable for Bagging-Vote ensemble model and Stacking fusion model.Based on the above,a Stacking-Bagging-Vote(SBV)multi-source information fusion model was built.Firstly,the Bagging-Vote model based on Up-Down ensemble sampling and the Stacking model based on Tomek-Smote sampling were fused.Then,the stock trading data were added and processed by Kalman filtering,so that the interactive fusion optimization of data level and model level was realized,and the SBV multi-source information fusion model was finally obtained.This fusion model not only has a great improvement in the prediction performance by taking into account prediction accuracy and prediction precision simultaneously,but also can select the corresponding SBV multi-source information fusion model to perform the financial early warning to meet the actual needs of different stakeholders by adjusting the parameters of the model.