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Credit rating using fuzzy algorithms

dc.book.titleActas de la XVI Conferencia de la Asociación Española para la Inteligencia Artificial
dc.contributor.authorHernández Morales, Moisés
dc.contributor.authorRodríguez González, Juan Tinguaro
dc.contributor.authorMontero De Juan, Francisco Javier
dc.date.accessioned2023-06-19T15:55:07Z
dc.date.available2023-06-19T15:55:07Z
dc.date.issued2015
dc.descriptionV Simposio de Lógica Difusa y Soft Computing.
dc.description.abstractThis article is devoted to the replication of the nternal methodologies of credit rating agencies for rating lassification using fuzzy algorithms. To achieve this goal, the usage of different types of fuzzy algorithms (evolutionary and non-evolutionary fuzzy rule learning for classification) is explored, departing from historical data on credit ratings (ratings) and fourteen financial ratios used as explanatory variables. This study is a preliminary work focused on presenting the problem and the methodology used in order to lay the foundation for further improvement work.en
dc.description.departmentDepto. de Estadística e Investigación Operativa
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedTRUE
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/34890
dc.identifier.isbn978-84-608-4099-2
dc.identifier.officialurlhttp://simd.albacete.org/actascaepia15/papers/00539.pdf
dc.identifier.urihttps://hdl.handle.net/20.500.14352/35796
dc.language.isoeng
dc.page.final548
dc.page.initial539
dc.page.total1265
dc.publication.placeAlbacete
dc.publisherCAEPIA'15
dc.rights.accessRightsrestricted access
dc.subject.cdu004.8
dc.subject.keywordCredit rating
dc.subject.keywordCorporate rating
dc.subject.keywordFuzzy algorithms
dc.subject.keywordFuzzy classification
dc.subject.ucmInteligencia artificial (Informática)
dc.subject.unesco1203.04 Inteligencia Artificial
dc.titleCredit rating using fuzzy algorithmsen
dc.typebook part
dcterms.references1. Altman EI (1968) Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy. J Finance 23(4):589–609 2. Ammar S, Duncombe W, Hou Y (2001) Using fuzzy rule- based systems to evaluate overall financial performance of governments: an enhancement to the bond rating process, Public Budgeting and Finance 21 (4) 91–110. 3. Atiya AF (2001) Bankruptcy prediction for credit risk using neural networks: a survey and new results. IEEE Trans Neural Netw 12(4):929–35 4. Beaver WH (1966) Financial ratios as predictors of failure. J Acc Res 4:71–102. 5. Brennan D, Brabazon A (2004) Corporate Bond Rating using Neural Networks, Proc. Of the Conf. on Artificial Intelligence, CSREA Press, Las Vegas, USA. 6. Brabazon A, O'Neill M, Matthews R, Ryan C (2002) Grammatical Evolution and Corporate Failure Prediction, Proc. Genetic and Evolutionary Computation Conf, Morgan Kaufmann,New York. 7. Chi Z, Yan H, Pham T (1996) Fuzzy Algorithms: With Applications To Image Processing and Pattern Recognition. World Scientific. 8. Delahunty A, O'Callaghan D (2004) Artificial Immune Systems for the Prediction of Corporate Failure and Classification of Corporate Bond Ratings, University College Dublin,Dublin,http://mis.ucd.ie/research/theses/Delahunty-OCallaghan.pdf. 9. Garcia D, González S, Pérez R (2014) Overview of the SLAVE learning algorithm: A review of its evolution and prospects. International Journal of Computational Intelligence Systems 7:6 0-1221. 10. González A, Perez R (2009) Improving the genetic algorithm of SLAVE. Mathware and Soft Computing 16 59-70. 11. Hajek P (2011) Municipal credit rating modelling by neural networks. Decis Support Syst 51(1): 108–118. 12. Hajek P (2012) Credit rating analysis using adaptive fuzzy rule-based systems: an industry-specific approach. Central European Journal of Operations Research,Volume 20, Issue 3, pp 421-434. 13. Hajek P (2015), Adaptive Fuzzy Rule-Based Systems for Credit Rating Analysis, in: Proc. of the 28th Int. Conf. Mathematical Methods in Economics (Ceske Budejovice, Czech Republic,in press). 14. Hajek P (2015), Probabilistic Neural Networks for Credit Rating Modelling, in: Proc. of the Int. Conf. on Neural Computation (Valencia, Spain, in press). 15. Huang Z, Chen H, Hsu CJ, Chen WH, Wu S (2004) Credit rating analysis with support vector machines and neural networks: a market comparative study, Decision Support Systems 37 (4) 543–558. 16. Hühn J, Hüllermeier E (2009) FURIA: an algorithm for unordered fuzzy rule induction. Data Mining and Knowledge Discovery 19:3 293-319. 17. Ishibuchi H, Nakashima T, Murata T (1999) Performance evaluation of fuzzy classifier systems for ultidimensional pattern classification problems, IEEE Trans. on Systems,Man, and Cybernetics - Part B: Cybernetics, vol. 29. no. 5, pp 601-608. 18. Ishibuchi H, Yamamoto T, Nakashima T (2005) Hybridization of Fuzzy GBML Approaches for Pattern Classification Problems. IEEE Transactions on Systems, Man and Cybernetics - Part B: Cybernetics 35:2 359-365. 19. Kim JW, Weisstroffer HR, Redmond RT (1993) Expert systems for bond rating: a comparative analysis of statistical, Rule-based and Neural Network Systems, Expert Systems 10 (3) 167–172. 20. Kim KS, I. Han, (2001) The cluster-indexing method for case-based reasoning using selforganizing maps and learning vector quantization for bond rating cases, Expert Systems with Applications 21 (3) 147–156. 21. Kim KS (2005) Predicting bond ratings using publicly available information, Expert Systems with Applications 29 (1) 75–81. 22. Kopacek L, Olej V (2010) Municipal creditworthiness modelling by artificial immune systems, Acta Electrotechnica et Informatica 10 (1) 3–11. 23. Lee YC (2007) Application of support vector machines to corporate credit rating prediction, Expert Systems with Applications 33 (1) 67–74. 24. Ohlson JA (1980) Financial ratios and the probabilistic prediction of bankruptcy. J Acc Res 18(1):109–131. 25. Trustorff J, Konrad PM, Leker J (2011) Credit risk prediction using support vector machines. Rev Quant Finance Acc 36:565–81. 26. Van Leekwijck W, Kerre EE (1999) Defuzzification: criteria and classification, Fuzzy Sets and Systems, 108 (2), 159–178. 27. Wang LX, Mendel JM (1992) Generating fuzzy rule by learning from examples, IEEE Trans. Syst., Man, and Cybern., 22(6):1414-1427.
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