The main purpose of this article is to determine the factors affecting credit rating and to develop the credit rating system based on statistical methods, fuzzy logic and artificial neural network. Variables used in this study were determined by the literature review and then the number of them was reduced by using stepwise regression analysis. Resulting variables were used as independent variables in the logistic model and as input variables for ANN and ANFIS model. After evaluating the models and comparing with each other, the ANFIS model was chosen as the best model to forecast credit rating. Rating determination was made for the countries that haven’t had a credit rating. Consequently, the ANFIS model made consistent, reliable and successful rating forecasts for the countries.
References
[1]
Cantor, R. and Packer, F. (1996) Determinants and Impact of Sovereign Credit Ratings. Journal of Fixed Income, 6, 76-91. https://doi.org/10.3905/jfi.1996.408185
[2]
Ratha, D., De, P.K. and Mohapatra, S. (2011) Shadow Sovereign Ratings for Unrated Developing Countries. World Development, 39, 295-307.
https://doi.org/10.1016/j.worlddev.2010.08.006
[3]
Bennell, J.A., Crabbe, D., Thomas, S. and Gwilym, O.A. (2005) Modelling Sovereign Credit Ratings: Neural Networks versus Ordered Probit. Expert System with Applications, 30, 415-425. https://doi.org/10.1016/j.eswa.2005.10.002
[4]
Subasi, A. and Gursoy, I.M. (2010) EEG Signal Classification Using PCA, ICA, LDA and Support Vector Machines. Expert System with Applications, 37, 8659-8666.
https://doi.org/10.1016/j.eswa.2010.06.065
[5]
Harris, T. (2015) Credit Scoring Using the Clustered Support Vector Machine. Expert System with Applications, 42, 741-750.
https://doi.org/10.1016/j.eswa.2014.08.029
[6]
Jones, S., Johnstone, D. and Wilson, R. (2015) An Empirical Evaluation of the Performance of Binary Classifiers in the Prediction of Credit Ratings Changes. Journal of Banking & Finance, 56, 72-85. https://doi.org/10.1016/j.jbankfin.2015.02.006
[7]
Beers, T.D. and Cavanaugh, M. (2004) Sovereign Credit Ratings: A Primer. Standard & Poor’s, New York.
[8]
Bissoondoyal-Bheenick, E. (2005) An Analysis of the Determinants of Sovereign Ratings. Global Finance Journal, 15, 251-280.
https://doi.org/10.1016/j.gfj.2004.03.004
[9]
Butler, A.W. and Fauver, L. (2006) Institutional Environment and Sovereign Credit Ratings. Financial Management, 35, 53-79.
https://doi.org/10.1111/j.1755-053X.2006.tb00147.x
[10]
Depken, C., LaFountain, C. and Butters, R. (2007) Corruption and Creditworthiness: Evidence from Sovereign Credit Ratings. Working Papers 0601, Department of Economics, University of Texas at Arlington.
[11]
Busse, M. and Hefeker, C. (2007) Political Risk, Institutions and Foreign Direct Investment. European Journal of Political Economy, 23, 397-415.
https://doi.org/10.1016/j.ejpoleco.2006.02.003
[12]
Elkhoury, M. (2009) Credit Rating Agencies and Their Potential Impact on Developing Countries. Compendium on Debt Sustainability, 165-180.
[13]
Roychoudhury, S. and Lawson, R.A. (2010) Economic Freedom, the Credit Ratings and the Sovereign default spread. Journal of Financial Economic Policy, 2, 149-162.
https://doi.org/10.1108/17576381011070201
[14]
Bundala, N.N. (2013) Do Economic Growth, Human Development and Political Stability Favour Sovereign Creditworthiness of a Country? A Cross Country Survey on Developed and Developing Countries. International Journal of Advances in Management and Economics, 1, 32-46.
[15]
Williams, G., Alsakka, R. and Gwilym, O. (2013) The Impact of Sovereign Rating Actions on Bank Ratings in Emerging Markets. Journal of Banking & Finance, 37, 563-577. https://doi.org/10.1016/j.jbankfin.2012.09.021
[16]
Leonard, K.J. (1995) The Development of Credit Scoring Quality Measures for Consumer Credit Applications. International Journal of Quality & Reliability Management, 12, 79-85. https://doi.org/10.1108/02656719510087346
[17]
Hajek, P. and Michalak, K. (2013) Feature Selection in Corporate Credit Rating Prediction. Knowledge-Based Systems, 51, 72-84.
https://doi.org/10.1016/j.knosys.2013.07.008
[18]
Li, W., He, Q., Fan, X. and Fei, Z. (2012) Evaluation of Driver Fatigue on Two Channels of EEG Data. Neuroscience Letters, 506, 235-239.
https://doi.org/10.1016/j.neulet.2011.11.014
[19]
Zhao, C., Zheng, C., Zhao, M., Tu, Y. and Liu, J. (2011) Multivariate Autoregressive Models and Kernel Learning Algorithms for Classifying Driving Mental Fatigue Based on Electroencephalographic. Expert System with Applications, 38, 1859-1865.
https://doi.org/10.1016/j.eswa.2010.07.115
[20]
Boostani, R., Sadatnezhad, K. and Sabeti, M. (2009) An Efficient Classifier to Diagnose of Schizophrenia Based on the EEG Signals. Expert System with Applications, 36, 6492-6499. https://doi.org/10.1016/j.eswa.2008.07.037
[21]
Huang, Z., Chen, H., Hsu, C.J., Chen, W.H. and Wu, S. (2004) Credit Rating Analysis with Support Vector Machines and Neural Networks: A Market Comparative Study. Decision Support Systems, 37, 543-558.
https://doi.org/10.1016/S0167-9236(03)00086-1
[22]
Kim, K.S. and Han, I. (2001) The Cluster-Indexing Method for Case-Based Reasoning Using Self-Organizing Maps and Learning Vector Quantization for Bond Rating Cases. Expert System with Applications, 21, 147-156.
https://doi.org/10.1016/S0957-4174(01)00036-7
[23]
Gujarati, D.N. (2003) Student Solutions Manual for Use with Basic Econometrics. 4th Edition, McGraw Hill, New York.
[24]
Hosmer, D.W. and Lemeshow, S. (2004) Applied Logistic Regression. 2th Edition, Wiley, New York.
[25]
Hand, D.J. and Henley, W.E. (1997) Statistical Classification Methods in Consumer Credit Scoring: A Review. Journal of Royal Statistical Society, 160, 523-541.
https://doi.org/10.1111/j.1467-985X.1997.00078.x
[26]
Yu, L., Wang, S. and Lai, K.K. (2008) Forecasting Crude Oil Price with an EMD-Based Neural Network Ensemble Learning Paradigm. Energy Economics, 30, 2623-2635. https://doi.org/10.1016/j.eneco.2008.05.003
[27]
Smith, K.A. and Gupta, J.N.D. (2002) Neural Networks in Business: Techniques and Applications. IGI Global, New York.
[28]
Jardin, P. and Séverin, E. (2012) Forecasting Financial Failure Using a Kohonen Map: A Comparative Study to Improve Model Stability over Time. European Journal of Operational Research, 221, 378-396.
https://doi.org/10.1016/j.ejor.2012.04.006
[29]
Takagi, T. and Sugeno, M. (1985) Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Transactions on Systems Man & Cybernetics, 15, 116-132. https://doi.org/10.1109/TSMC.1985.6313399
[30]
Jang, J.S.R. (1993) ANFIS: Adaptive-Network-Based Fuzzy Inference system. IEEE Transactions on Systems Man & Cybernetics, 23, 665-685.
https://doi.org/10.1109/21.256541
[31]
Jang, J. (1991) Fuzzy Modeling Using Generalized Neural Networks and Kalman Filter Algorithm. Proceeding of 9th National Conference Artificial Intelligence, 91, 762-767.
[32]
Jang, J.S.R. (1993) ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems Man & Cybernetics, 23, 665-685.
https://doi.org/10.1109/21.256541
[33]
Jang, J.S.R. (1996) Input Selection for ANFIS learning. Proceeding IEEE 5th International Fuzzy Systems, 2, 1493-1499. https://doi.org/10.1109/FUZZY.1996.552396
[34]
Fullér, R. (1995) Neural Fuzzy System.
[35]
Lin, C.T. and Lee, C.S.G. (1991) Neural-Network-Based Fuzzy Logic Control and Decision System. IEEE Transaction on Computers, 40, 1320-1336.
https://doi.org/10.1109/12.106218
[36]
Leshno, M. and Spector, Y. (1996) Neural Network Prediction Analysis: The Bankruptcy Case. Neurocomputing, 10, 125-147.
https://doi.org/10.1016/0925-2312(94)00060-3
[37]
Blanco, A., Pino-Mejías, R., Lara, J. and Rayo, S. (2013) Credit Scoring Models for the Microfinance Industry Using Neural Networks: Evidence from Peru. Expert System with Applications, 40, 356-364. https://doi.org/10.1016/j.eswa.2012.07.051
