Abstract
The Self-Organizing Map is used in the analysis of the financial statements, aiming at the extraction of models for corporate bankruptcy. Using data from one year only often seems to be insufficient, but straightforward methods that utilize data from several consecutive years typically suffer from problems with rule extraction and interpretation. We propose a combination of two Self-Organizing Maps in a hierarchy to solve the problem. The results obtained with our method are easy to interpret, and offer much more information of the state of the company than would be available if data from one year only were used. Using our method, three different types of corporate behaviour associated with high risk of bankruptcy can be recognized, together with some characteristic features of enterprises that have a very low bankruptcy risk.
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References
Altman E. I. (1968, September). Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy. The Journal of Finance (4), 589–609.
Argenti J. (1976). Corporate collapse-the causes and symptoms. McGraw-Hill.
Back B., G. Oosterom, K. Sere, and M. van Wezel (1994). A comparative study of neural networks in bankruptcy prediction. In Multiple Paradigms for Artificial Intelligence (SteP94). Finnish Artificial Intelligence Society.
Heikkonen J., P. Koikkalainen, and E. Oja (1993, July). Self-organizing maps for collision-free navigation. In World Congress on Neural Networks, Volume III of International Neural Network Society Annual Meeting, pp. 141–144. Neural Network Society.
Kiviluoto K. and P. Bergius (1997, June). Analyzing financial statements with the self-organizing map. In Proceedings of the workshop on selforganizing maps (WSOM’97), Espoo, Finland, pp. 362–367. Neural Networks Research Centre, Helsinki University of Technology.
Kohonen T. (1995). Self-Organizing Maps. Springer Series in Information Sciences 30. Berlin Heidelberg, New York: Springer.
MartÃn-del-BrÃo B. and C. Serrano-Cinca (1993). Self-organizing neural networks for the analysis and representation of data: Some financial cases. Neural Computing & Applications 1, 193–206.
Shumsky S. A. and A. Yarovoy (1997, June). Neural network analysis of Russian banks. In Proceedings of the workshop on self-organizing maps (WSOM’97), Espoo, Finland. Neural Networks Research Centre, Helsinki University of Technology.
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© 1998 Springer Science+Business Media Dordrecht
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Kiviluoto, K., Bergius, P. (1998). Exploring Corporate Bankruptcy with Two-Level Self-Organizing Map. In: Refenes, AP.N., Burgess, A.N., Moody, J.E. (eds) Decision Technologies for Computational Finance. Advances in Computational Management Science, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5625-1_29
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DOI: https://doi.org/10.1007/978-1-4615-5625-1_29
Publisher Name: Springer, Boston, MA
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