Empirical Study of Financial Affairs Early Warning Model on Companies Based on Artificial Neural Network

  • Tian Bo
  • Qin Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


This paper attempts to develop an intelligent financial distress forecasting pattern using Artificial Neural Networks (ANNs) by taking advantage of the ANNs for recognizing complex patterns in data and universal functional approximation capability. Using STzhujiang and Non-STshenzhenye stocks as the study samples, the objective is to make ANN model as financial affairs early warning research tool through building an intelligent and individual financial distress forecasting patterns. The model built for individual industries would be even more predictive than general models built with multi-industry samples. Results show that ANNs are valuable tools for modeling and forecasting ST and Non-ST companies whether they are being in financial distress. The simulation result shown that ANNs models can be applied in financial affairs early warning system. The companies can build their own financial distress forecasting patterns based on their own running surroundings using proposed ANNs models.


Hide Node Financial Distress Financial Ratio ANNs Model Bankruptcy Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tian Bo
    • 1
  • Qin Zheng
    • 1
  1. 1.School of ManagementXi’an Jiaotong UniversityXi’anChina

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