Have a personal or library account? Click to login
Sovereign Rating Analysis through the Dominance-Based Rough Set Approach Cover

Sovereign Rating Analysis through the Dominance-Based Rough Set Approach

Foundations of Computing and Decision Sciences
Volume 45 (2020): Issue 1 (March 2020)
By: Ayrton Benedito Gaia Do Couto and  Luiz Flavio Autran Monteiro Gomes  
Open Access
|Mar 2020

References

  1. [1] Błaszczyński, J., Greco, S., Matarazzo, B., Słowiński, R., Szelag, M., jMAF - Dominance-based Rough Set Data Analysis Framework. Chapter 5 [In]: A. Skowron, Z. Suraj (Eds.), Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam, Volume 1, Intelligent Systems Reference Library, vol. 42. Springer, 2013, pp. 185-209.10.1007/978-3-642-30344-9_5
    Search in Google ScholarBack to article
  2. [2] Cantor, R., Packer, F., 1996, Determinants and impact of sovereign credit ratings, Federal Reserve Bank of New York Economic Policy Review, New York, 2, 2, pp. 37-54.10.2139/ssrn.1028774
    Search in Google ScholarBack to article
  3. [3] Diniz, T., Amaral, H., Ferreira, B., 2012. Evaluation of sovereign risk: the impact of economic, political and social variables, Global Economy and Management, 17, 3, pp. 9-31.
    Search in Google ScholarBack to article
  4. [4] Frascaroli, B. F., Oliveira, J. C. T., 2013. Sovereign risk ratings and the macroeconomical foundations of countries: a study based on panel data, IX Meeting of Bahia’s Economy, Financing and Development, pp. 769-785.
    Search in Google ScholarBack to article
  5. [5] Frascaroli, B. F., Silva, L. C., Filho, O. C. S., 2009. Sovereign risk ratings and the macroeconomical foundations of countries: a study using neural artificial networks, Brazilian Journal of Finance, 7.1.10.12660/rbfin.v7n1.2009.1428
    Search in Google ScholarBack to article
  6. [6] Gomes, L.F.A.M, Gomes, C.F.S., 2019. Principles and methods for decision making: Multicriteria approach. 6th ed., GEN/Atlas, São Paulo.
    Search in Google ScholarBack to article
  7. [7] Gouvêa, M. A., Gonçalves, E. B., Mantovani, D. M. N., 2013. Credit risk analysis with the application of logistic regression and neural networks, Journal of Reviewed and Commented Accounting, 24, 4, pp. 96-123.
    Search in Google ScholarBack to article
  8. [8] Greco, S., Matarazzo, B., Slowiñski, R., 2001. Rough Sets Theory for Multicriteria Decision Analysis, European Journal of Operational Research, 129 (1), pp. 1-47.10.1016/S0377-2217(00)00167-3
    Search in Google ScholarBack to article
  9. [9] Minussi, J. A., Damacena, C., Ness Jr., W. L., 2002. A bankruptcy forecasting model using logistic regression, RAC, 6, 3, pp. 109-128.10.1590/S1415-65552002000300007
    Search in Google ScholarBack to article
  10. [10] Pawlak, Z., 1982. Rough sets, International Journal of Information & Computer Sciences, 11, pp. 341-356.10.1007/BF01001956
    Search in Google ScholarBack to article
  11. [11] Pawlak, Z., 1991. Rough sets, Theoretical aspects of reasoning about data, Kluwer Academic Publishers, Dordrecht.10.1007/978-94-011-3534-4_7
    Search in Google ScholarBack to article
  12. [12] Pawlak, Z., 2000. Rough sets and decision analysis, INFOR: Information Systems and Operational Research, 38 (2), 132-144.10.1080/03155986.2000.11732405
    Search in Google ScholarBack to article
  13. [13] Pawlak, Z., 2002a. Rough set theory and its applications, Journal of Telecommunicatios and Information Technology, 3, 7-10.10.26636/jtit.2002.140
    Search in Google ScholarBack to article
  14. [14] Pawlak, Z., 2002b. Rough sets, decision algorithms and Bayes’ theorem, European Journal of Operational Research, 136, pp. 181-189.10.1016/S0377-2217(01)00029-7
    Search in Google ScholarBack to article
  15. [15] Pawlak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, W., 1995. Rough sets, Communications of the ACM, 38, 11, pp. 89-95.10.1145/219717.219791
    Search in Google ScholarBack to article
  16. [16] Pawlak, Z., Slowinski, R., 1994. Rough set approach to multi-attribute decision analysis, European Journal of Operational Research, Invited Review, 72, pp. 443-459.10.1016/0377-2217(94)90415-4
    Search in Google ScholarBack to article
  17. [17] Rossi Jr., J. L., Vasconcelos, L., 2007. Determinants of countries’ risk classification, Journal of Economics and Management, 6, 2, pp. 236-256.
    Search in Google ScholarBack to article
  18. [18] Selau, L. P. R., Ribeiro, J. L. D., 2009. A systematics for building and choosing credit risk forecasting models, Management & Production, 16, 3, pp. 398-413.10.1590/S0104-530X2009000300007
    Search in Google ScholarBack to article
  19. [19] Silva, D. F. L., Silva, L. G. O., Ferreira, L., Filho, A. T. A., 2017, Risk management in sovereign titles: implementation of a multi-criteria classification model, XLIX Brazilian Symposium on Operational Research, pp. 296-306.
    Search in Google ScholarBack to article
  20. [20] Słowiński, R., Greco, S., Matarazzo, B., 2012. Rough set and rule-based multicriteria decision aiding, Operations Research, 32, 2, pp. 213-269.10.1590/S0101-74382012000200001
    Search in Google ScholarBack to article
  21. [21] Soares, G.O.G., Coutinho, E.S., Camargos, M.A., 2012. Determinants of Credit Risk of Brazilian Companies, Journal of Reviewed and Commented Accounting, 23, 3, pp. 109-143.
    Search in Google ScholarBack to article
  22. [22] Standard & Poor’s. http://www.spratings.com/sri/, 2019 (accessed 26 April 2019).
    Search in Google ScholarBack to article
  23. [23] Standard & Poor’s. Corporate ratings criteria. http://regulationbodyofknowledge.org/wp-content/uploads/2013/03/StandardAndPoors_Corporate_Ratings_Criteria.pdf, 2013 (accessed 30 April 2019).
    Search in Google ScholarBack to article
  24. [24] Standard & Poor’s. Sovereign Government Rating Methodology and Assumptions. http://www.concertedaction.com/wp-content/uploads/2012/05/Standard-Poors-Sovereign-Government-Rating-And-Methodology.pdf, 2011 (accessed 14 February 2016).
    Search in Google ScholarBack to article
  25. [25] Widz, S., Ślęzak, D., 2012. Rough Set Based Decision Support ─ Models Easy to Interpret. In: Peters G., Lingras P., Ślęzak D., Yao Y. (eds) Rough Sets: Selected Methods and Applications in Management and Engineering. Advanced Information and Knowledge Processing. Springer, London, pp. 95-112.10.1007/978-1-4471-2760-4_6
    Search in Google ScholarBack to article
  26. [26] Zadeh, L.A., 1965. Fuzzy sets, Information and Control, 8, pp. 338–353.10.1016/S0019-9958(65)90241-X
    Search in Google ScholarBack to article
DOI: https://doi.org/10.2478/fcds-2020-0001 | Journal eISSN: 2300-3405 | Journal ISSN: 0867-6356
Journal RSS Feed
Language: English
Page range: 3 - 16
Submitted on: Nov 21, 2018
Accepted on: Dec 5, 2019
Published on: Mar 27, 2020
Published by: Poznan University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Multi-Criteria Decision Analysis,
Sovereign rating,
Dominance principle,
Dominance-based Rough Set Approach
Related subjects:
Computer sciences,
Artificial intelligence,
Software development

© 2020 Ayrton Benedito Gaia Do Couto, Luiz Flavio Autran Monteiro Gomes, published by Poznan University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 45 (2020): Issue 1 (March 2020)Next article