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

  • Tian Bo
  • Qin Zheng
Conference paper
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Altman, E.L.: Financial Ratios Discriminate Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance 23(3), 589–609 (1968)CrossRefGoogle Scholar
  2. 2.
    Altman, E.L.: Accounting Implications of Failure Prediction Models. Journal of Accounting Auditing and Finance, 4–19 (1982)Google Scholar
  3. 3.
    Pendharkar, P.C., Rodger, J.A.: An Empirical Study of Impact of Crossover Operators on the Performance of Non-binary Genetic Algorithm Based Neural Approaches for Classification. Computers & Operations Research 31, 481–498 (2004)MATHCrossRefGoogle Scholar
  4. 4.
    Edmister, R.: An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction. Journal of Finance and Quantitative Analysis 7, 1477–1493 (1972)CrossRefGoogle Scholar
  5. 5.
    Johnson, C.: Ratio Analysis and the Prediction of Firm Failure. Journal of Finance 25, 1166–1168 (1970)CrossRefGoogle Scholar
  6. 6.
    Jones, F.L.: Current Techniques in Bankruptcy Prediction. Journal of Accounting Literature 6, 131–164 (1987)Google Scholar
  7. 7.
    Bhattacharyya, S., Pendharkar, P.C.: Inductive Evolutionary and Neural Techniques for Discrimination. Decision Sciences 29(4), 871–899 (1998)CrossRefGoogle Scholar
  8. 8.
    Nanda, S., Pendharkar, P.C.: Development and Comparison of Analytical Techniques for Predicting Insolvency Risk. International Journal of Intelligent Systems in Accounting, Finance and Management 10, 155–168 (2001)CrossRefGoogle Scholar
  9. 9.
    Pendharkar, P.C.: A Computational Study on the Performance of ANNs under Changing Structural Design and Data Distributions. European Journal of Operational Research 138, 155–177 (2002)MATHCrossRefGoogle Scholar
  10. 10.
    Hornik, K.: Approximation Capabilities of Multilayer Feedforward Networks. Neural Networks 4, 251–257 (1991)CrossRefGoogle Scholar
  11. 11.
    Hornik, K.: Some New Results on Neural Network Approximation. Neural Networks 6, 1069–1072 (1993)CrossRefGoogle Scholar
  12. 12.
    Nanda, S., Pendharkar, P.C.: Development and Comparison of Analytical Techniques for Predicting Insolvency Risk. International Journal of Intelligent Systems in Accounting, Finance and Management 10, 155–168 (2001)CrossRefGoogle Scholar
  13. 13.
    Reed, R.: Pruning Algorithm -A survey. IEEE Transactions on Neural Networks 4(5), 740–747 (1993)CrossRefGoogle Scholar
  14. 14.
    Roy, K.L.S., Mukhopadhyay, S.: A Polynomial Time Algorithm for the Construction and Training of a Class of Multilayer Perceptrons. Neural Networks 6, 535–545 (1993)CrossRefGoogle Scholar
  15. 15.
    Wang, Z., Massimo, C.D., Tham, M.T., Morris, A.J.: A Procedure for Determining the Topology of Multilayer Feed-forward Neural Networks. Neural Networks 7, 291–300 (1994)CrossRefGoogle Scholar
  16. 16.
    CSMAR database: The Corporation Financial Statement Database of China. Shenzhen GuoTaiAn Information & Technology Corporation, Xiang Gang University Financial Institute (2004) Google Scholar
  17. 17.
    ShangHai Stock Exchange Financial Statement Database (2004) Google Scholar
  18. 18.
    ShenZhen stock exchange financial statement database (2004)Google Scholar

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

Personalised recommendations