BANKRUPTCY PREDICTION BASED ON INDEPENDENT COMPONENT ANALYSIS

Ning Chen, Armando Vieira

Abstract

Bankruptcy prediction is of great importance in financial statement analysis to minimize the risk of decision strategies. It attempts to separate distress companies from healthy ones according to some financial indicators. Since the real data usually contains irrelevant, redundant and correlated variables, it is necessary to reduce the dimensionality before performing the prediction. In this paper, a hybrid bankruptcy prediction algorithm is proposed based on independent component analysis and learning vector quantization. Experiments show the algorithm is effective for high dimensional bankruptcy data and therefore improve the capability of prediction.

References

  1. A. Hyvarinen, J. Karhunen, E. O. (2001). Independent component analysis. John Wiley & Sons.
  2. Armando Vieira, N. B. (2003). A training algorithm for classification of high dimensional data. Neurocomputing, 50(1):461-472.
  3. Bingham, E. (2001). Topic identification in dynamical text by extracting minimum complexity time components. In Proc. 3rd International Conference on Independent Component Analysis and Blind Signal Separation, pages 546-551.
  4. E. Merkevicius, G. Garsva, R. S. (2004). Forecasting of credit classes with the self-organizing maps. Information Technology And Control, Kaunas, Technologija, 4(33):61-66.
  5. E. Oja, K. Kiviluoto, S. M. (2000). Independent component analysis for financial time series. In Proc. IEEE 2000 Symp. on Adapt. Systems for Signal Proc. Comm. and Control AS-SPCC, pages 111-116, Lake Louise, Canada.
  6. Erkki Oja, M. H. e. (2005). The fastica package for matlab. http://www.cis.hut.fi/projects/ica/fastica/.
  7. J.C. Neves, A. V. (2006). Improving bankruptcy prediction with hidden layer learning vector quantization. European Accounting Review, 15(2):253-271.
  8. K. Kiviluoto, P. B. (1997). Analyzing financial statements with the self-organizing map. In Proc. Workshop SelfOrganizing Maps, pages 362-367.
  9. Kohonen, T. (1997). Self-organizing maps. Springer Verlag, Berlin, 2nd edition.
  10. M. Dash, H. L. (1997). Feature selection for classification. Intelligent Data Analysis, 1:131-156.
  11. P. Ravi Kumara, V. R. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques-a review. European Journal of Operational Research, 180(1):1-28.
  12. Tsai, C.-F. (2008). Feature selection in bankruptcy prediction. Knowledge-Based Systems.
Download


Paper Citation


in Harvard Style

Chen N. and Vieira A. (2009). BANKRUPTCY PREDICTION BASED ON INDEPENDENT COMPONENT ANALYSIS . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8111-66-1, pages 150-155. DOI: 10.5220/0001536301500155


in Bibtex Style

@conference{icaart09,
author={Ning Chen and Armando Vieira},
title={BANKRUPTCY PREDICTION BASED ON INDEPENDENT COMPONENT ANALYSIS},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2009},
pages={150-155},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001536301500155},
isbn={978-989-8111-66-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - BANKRUPTCY PREDICTION BASED ON INDEPENDENT COMPONENT ANALYSIS
SN - 978-989-8111-66-1
AU - Chen N.
AU - Vieira A.
PY - 2009
SP - 150
EP - 155
DO - 10.5220/0001536301500155