A stable credit rating model based on learning vector quantization

Intelligent Data Analysis

Volumn 15, Issue 2, 2011, Pages 237-250

  Chen, Ning ^a   Vieira, Armando ^a   Ribeiro, Bernardete ^b   Duarte, João ^a   Neves, João ^c  

^a INSTITUTO SUPERIOR DE ENGENHARIA DO PORTO   (Portugal)

^b UNIVERSITY OF COIMBRA   (Portugal)

^c UNIVERSITY OF LISBON   (Portugal)

Author keywords

bankruptcy; Credit rating; learning vector quantization; stability; transition matrix

Indexed keywords

BANKRUPTCY; CREDIT RATINGS; ECONOMIC PERFORMANCE; LEARNING VECTOR QUANTIZATION; PRIVATE COMPANIES; REMAINING YEARS; TRANSITION MATRICES; TRANSITION MATRIX;

PRIVATIZATION; VECTOR QUANTIZATION; VECTORS;

RATING;

EID: 79953297716     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/IDA-2010-0465     Document Type: Article

References (18)

1. J.L. Bellovary, D.E. Giacomino and M.D. Akers, A review of bankruptcy prediction studies: 1930 to present, Journal of Financial Education 33 (2007), 1-43.
2. M.A. Boyacioglu, Y. Kara and O.K. Baykan, Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey, Expert Systems with Applications 36 (2009), 3355-3366.
3. N. Chen and A. Vieira, Bankruptcy prediction based on independent component analysis, In: Proc. of 1st International Conference on Agents and Artificial Intelligence (ICAART), Porto, Portugal, 2009, pp. 150-155.
4. N. Chen, A. Vieira, J. Duarte, B. Ribeiro and J.C. Neve, Costsensitive learning vector quantization for financial distress prediction, in:Lecture Notes in Artificial Intelligence (LNAI 5816), Springer, L.S. Lopes et.al., (eds.) 14th Portuguese Conference on Artificial Intelligence (EPIA), Portugal, 2009, pp. 374-385.
5. Z. Huang, H. Chen, C.J. Hsu, W.H. Chen and S. Wu, Credit rating analysis with support vector machines and neural networks: A market comparative study, Decision Support Systems 37 (2004), 543-558.
6. T. Kohonen, The selforganizing map, Neurcomputing 21 (1998), 1-6.
7. T. Kohonen, Selforganizing aps, Springer Verlag, Third edition, 2001.
8. Y.C. Lee, Application of machines to corporate credit rating prediction, Expert Systems with Applications 33 (2007), 67-74.
9. X.Y. Liu and Z.H. Zhou, The influence of class imbalance on costsensitive learning: An empirical study, In: Proc. of IEEE International Conference on Data Mining (ICDM), Hong Kong, 2006, pp. 970-974.
10. J. Neves and A. Vieira, Improving bankruptcy prediction with hidden layer learning vector quantization, European Accounting Review 15(2) (2006), 253-271.
11. F. Pasiouras, C. Gaganis and M. Doumpos, A multicriteria discrimination approach for the credit rating of Asian banks, Annals of Finance 3 (2007), 351-367.
12. P. Ravi Kumar and V. Ravi, Bankruptcy prediction in banks and firms via statistical and intelligent techniquesa review, European Journal of Operational Research 180 (2007), 1-28. (Pubitemid 46123864)
13. B. Ribeiro, A. Vieira, J. Duarte, C. Silva, J.C. Neves, Q. Liu and A.H. Sung, Learning manifolds for bankruptcy analysis, in: Advances in NeuroInformation Processing, M. Koppen et al., ed., Lecture Notes in Computer Science, 2009, pp. 722-729.
14. K.S. Shin and I. Han, A casebased approach using inductive indexing for corporate bond rating, Decision Support Systems 32 (2001), 41-52. (Pubitemid 32866916)
15. Laboratory of Computer and Information Sciences & Neural Networks Research Center, Helsinki University of Technology, SOM toolbox, http://www.cis.hut.fi/projects/somtoolbox.
16. A. Verikas, Z. Kalsyte, M. Bacauskiene and A. Gelzinis, Hybrid and ensemblebased soft computing techniques in bankruptcy prediction: A survey, Soft ComputingA Fusion of Foundations, Methodologies and Applications (Online) (2009).
17. A. Vieira, J. Duarte, B. Ribeiro and J. Neves, Accurate prediction of financial distress of companies with machine learning algorithms, In: Proc. of International Conference on Adaptive and Natural Computing algorithms, Lecture Notes in Computer Science (LNCS), Springer Verlag, Heidelberg, 2009, pp. 569-579.
18. J. Zhang, V. Avasarala and R. Subbu, Evolutionary optimization of transition probability matrices for credit decisionmaking, European Journal of Operational Research 200(2) (2010), 557-567.