Financial distress model prediction using SVM+

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2010, Pages

  Ribeiro, Bernardete ^a   Silva, Catarina ^b   Vieira, Armando ^c   Gaspar Cunha, A ^d   Das Neves, João C ^e  

^a UNIVERSITY OF COIMBRA   (Portugal)

^b POLYTECHNIC INSTITUTE OF LEIRIA   (Portugal)

^c INSTITUTO SUPERIOR DE ENGENHARIA DO PORTO   (Portugal)

^d UNIVERSITY OF MINHO   (Portugal)

^e UNIVERSITY OF LISBON   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING;

ASYMMETRIC INFORMATION; COMPREHENSIVE METHOD; DECISIONS MAKINGS; FINANCIAL DISTRESS; FINANCIAL DISTRESS PREDICTION; GENERALISATION; HOLISTIC PERSPECTIVES; LEARNING MODELS; MODEL IMPROVING; MODEL PREDICTION;

DECISION MAKING;

EID: 79959476836     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2010.5596729     Document Type: Conference Paper

References (19)

1. E. I. Altman. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4):589-609, September 1968.
2. E. I. Altman. Corporate Financial Distress and Bankruptcy: A Complete Guide to Predicting and Avoiding Distress and Profiting from Bankruptcy. John Wiley & Sons, 2nd edition, 1993.
3. A.F. Atiya. Bankruptcy prediction for credit risk using neural networks: A survey and new results. Neural Networks, IEEE Transactions on, 12(4):929-935, Jul 2001.
4. Feng Cai and Vladimir Cherkassky. SVM+ regression and multi-task learning. In Proceedings of International Joint Conference on Neural Networks, Atlanta, Georgia, 2009.
5. Corinna Cortes and Vladimir Vapnik. Support-vector networks. In Machine Learning, pages 273-297, 1995.
6. T. E. Fawcett. An introduction to ROC analysis. Pattern Recognition Letters, (27):861-874, 2006.
7. Huang Fu-yuan. A genetic fuzzy neural network for bankruptcy prediction in chinese corporations. In International Conference on Risk Management & Engineering Management (ICRMEM '08), pages 542-546, Nov. 2008.
8. L. Liang and V. Cherkassky. Connection between SVM+ and multitask learning. In Proc of the IJCNN, Hong-Kong, 2008.
9. G.A. Rekba Pai, R. Annapoorani, and G.A. Vijayalakshmi Pai. Performance analysis of a statistical and an evolutionary neural network based classifier for the prediction of industrial bankruptcy. In IEEE Conference on Cybernetics and Intelligent Systems, volume 2, pages 1033-1038, 2004.
10. Tonatiuh Pena, Serafin Martinez, and Bolanle Abudu. Bankruptcy prediction: A comparison of some statistical and machine learning techniques. Technical Report TR-No 2009-18, 2009.
11. P. Ravi Kumar and V. Ravi. Bankruptcy prediction in banks and firms via statistical and intelligent techniques - a review. European Journal of Operational Research, 180(1):1-28, July 2007.
12. B Ribeiro, C Silva, A Vieira, and J Carvalho das Neves. Extracting discriminative features using non-negative matrix factorization in financial distress data. In M. Kolehmainen et al. (Eds.), editor, Int Conf on Adaptive and Natural Computing Algorithms, volume 4432, pages 537-547, Berlin Heidelberg, April 2009. Lecture Notes in Computer Science (LNCS), Springer-Verlag.
13. B. Ribeiro, A. Vieira, and J.C. das Neves. Sparse bayesian models: Bankruptcy-predictors of choice? In Neural Networks, 2006. IJCNN '06. International Joint Conference on, pages 3377-3381, July 2006.
14. B Ribeiro, A Vieira, J Duarte, C Silva, J C das Neves, Q Liu, and A H. Sung. Learning manifolds for bankruptcy analysis. In M. Köppen et al. (Eds.), editor, Int. Conf. on Neural Information Processing, volume 5506, pages 722-729, Berlin Heidelberg, 2009. Lecture Notes in Computer Science (LNCS), Springer-Verlag.
15. V. Vapnik. Estimation of Dependences Based on Empirical Data: Empirical Inference Science. Springer-Verlag New York, Inc., 1982-2006.
16. V. Vapnik, A. Vashist, and N. Pavlovitch. Learning using hidden information (learning with teacher). In Proceedings of International Joint Conference on Neural Networks, pages 3188-3195, 2009.
17. Vladimir Vapnik and Akshay Vashist. A new learning paradigm: Learning using privileged information. Neural Networks, 22(5-6):544-557, July 2009.
18. A. Verikas, Z. Kalsyte, M. Bacauskiene, and A. Gelzinis. Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: A survey. Soft Computing-A Fusion of Foundations, Methodologies and Applications, 2009.
19. Wei Wen Wu. Beyond business failure prediction. Expert Systems with Applications, (37):2371-2376, 2010.