Abstract
This article presents the way how creditor can predict the trends of debtors financial standing. We propose the model for forecasting changes of financial standings. Model is based on the Self-organizing maps as a tool for prediction, grouping and visualization of large amount of data. Inputs for training of SOM are financial ratios calculated according any discriminate bankruptcy model. Supervised neural network lets automatically increase accuracy of performance via changing of weights of ratios.
Chapter PDF
Similar content being viewed by others
Keywords
References
Atiya, A.F.: Bankruptcy prediction for credit risk using neural networks: a survey and new results. IEEE Transactions on Neural Networks 12(4), 929–935 (2001)
Altman, E.: Financial Ratios, Discrimination Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance (1968)
Altman. E.: Predicting Financial Distress of Companies: Revisiting the Z-Score and ZETA® Models (2000), working paper at http://pages.stern.nyu.edu/~ealtman/Zscores.pdf
Deboeck, G.: Financial Applications of Self-Organizing Maps. American Heuristics Electronic Newsletter (Jan. 1998)
Deboeck, G.: Self-Organizing Maps Facilitate Knowledge Discover. In Finance. Financial Engineering News (1998)
EDGAR Online, Inc. (1995-2006), http://pro.edgar-online.com
Galindo, J., Tamayo, P.: Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications. Computational Economics 15 (2000)
Kiviluoto, K.: Predicting bankruptcies with the self-organizing map. Neurocomputing 21, 191–201 (1998)
Kohonen, T.: The Self-Organizing Map. Proceedings of the IEEE 78, 1464–1480 (1990)
Ohlson, J.A.: Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research (Spring), 109–131 (1980)
Martin-del-Prio, K., Serrano-Cinca, C.: Self-Organizing Neural Network: The Financial State of Spanish Companies. In: Trippi, R., Turban, E. (eds.) Neural Networks in Finance and Investing. Using Artificial Intelligence to Improve Real-World Performance, pp. 341–357. Probus Publishing, Chicago (1993)
Merkevičius, E., Garšva, G., Girdzijauskas, S.: A Hybrid SOM-Altman Model for Bankruptcy Prediction. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3994, pp. 302–9743. Springer, Heidelberg (2006)
Nørgaard, M.: Neural Network Based System Identification Toolbox Version 2. Technical Report 00-E-891, Department of Automation Technical University of Denmark (2000), http://kalman.iau.dtu.dk/research/control/nnsysid.html
Shumway, T.: Forecasting Bankruptcy More Accurately: A Simple Hazard Model. Journal of Business 74(1), 101–124 (2001)
Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: SOM toolbox for Matlab 5. Technical report A57, Helsinki University of Technology, Finland (2000)
Zmijewski, M.E.: Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research 24(Suppl.), 59–82 (1984)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Merkevičius, E., Garšva, G., Simutis, R. (2007). Neuro-discriminate Model for the Forecasting of Changes of Companies Financial Standings on the Basis of Self-organizing Maps. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2007. ICCS 2007. Lecture Notes in Computer Science, vol 4488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72586-2_63
Download citation
DOI: https://doi.org/10.1007/978-3-540-72586-2_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72585-5
Online ISBN: 978-3-540-72586-2
eBook Packages: Computer ScienceComputer Science (R0)