.svg){kind=logo}
A COMPARATIVE STUDY OF FORECASTING CORPORATE CREDIT RATINGS USING ARTIFICIAL NEURAL NETWORKS, SUPPORT VECTOR MACHINE, RANDOM FOREST, THE NAIVE BAYES, DECISION TREE AND $K$-NEAREST NEIGHBOR
Keywords:
credit rating, artificial intelligence techniques, machine learning, accounting variables.DOI:
https://doi.org/10.17654/0972361724010Abstract
The paper proposes a credit rating model that relies on accounting variables in order to measure the level of credit rating of Egyptian companies registered in the Egyptian Stock Exchange, in the period from (2016-2021). It relied on 34 accounting variables for analyzing the basic relationships between accounting variables and credit ratings for Non-Financial Egyptian Listed companies through the application of modern artificial intelligence techniques. Results show that the artificial neural networks model outperformed the rest of the models, as it achieved an accuracy rate of (89.2%), while the SVM model achieved an accuracy rate of (87.8%), the random forest model achieved an accuracy rate of (87.6%), the decision tree model achieved an accuracy rate of (82.4%), the K-nearest neighbor model achieved an accuracy rate of (68.1%), and the Naive Bayes model achieved an accuracy rate of (51.8%).
Received: October 7, 2023
Revised: November 16, 2023
Accepted: November 29, 2023
References
Najjar and B. Al-Najjar, Developing a multi stage predicting system for corporate credit rating in emerging markets: Jordanian case, Journal of Enterprise Information Management 27 (2014), 475-487.
I. Goldstein and C. Huang, Credit rating inflation and firms’ investments, The Journal of Finance 75(6) (2020), 2929-2972.
G. Batta, The direct relevance of accounting information for credit default swap pricing, Journal of Business Finance and Accounting 38(9-10) (2011), 1096-1122.
I. J. Catanach, Rural Credit in Western India, 1875-1930: Rural Credit and the Co-operative Movement in the Bombay Presidency, University of California Press, 1970.
L. H. Ederington, Classification models and bond ratings, Financial Review 20(4) (1985), 237-262.
A. Belkaoui, The impact of socio-economic accounting statements on the investment decision: an empirical study, Accounting, Organizations and Society 5(3) (1980), 263-283.
W. Chen and L. Shi, Credit scoring with F-score based on support vector machine, Proceedings 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC), IEEE, 2013, pp. 1512-1516.
K. Fu, D. Cheng, Y. Tu and L. Zhang, Credit card fraud detection using convolutional neural networks, International Conference on Neural Information Processing, Springer, Cham, 2016, pp. 483-490.
P. Golbayani, D. Wang and I. Florescu, Application of deep neural networks to assess corporate credit rating, 2020. arXiv preprint arXiv:2003.02334.
S. Lee and W. S. Choi, A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis, Expert Systems with Applications 40(8) (2013), 2941-2946.
A. Blanco, R. Pino-Mejias, J. Lara and S. Rayo, Credit scoring models for the microfinance industry using neural networks: evidence from Peru, Expert Systems with Applications 40(1) (2013), 356-364.
P. Hajek, V. Olej and O. Prochazka, Predicting corporate credit ratings using content analysis of annual reports - a Naive Bayesian network approach, Finance.Com, Springer, Cham, 2016, pp. 47-61.
X. Zhang, Y. Yang and Z. Zhou, A novel credit scoring model based on optimized random forest, 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), IEEE, 2018, pp. 60-65.
S. Khemakhem and Y. Boujelbene, Credit risk prediction: a comparative study between discriminant analysis and the neural network approach, Accounting and Management Information Systems 14(1) (2015), 60.
P. Du Jardin, A two-stage classification technique for bankruptcy prediction, European J. Oper. Res. 254(1) (2016), 236-252.
X. Feng, Z. Xiao, B. Zhong, J. Qiu and Y. Dong, Dynamic ensemble classification for credit scoring using soft probability, Applied Soft Computing 65 (2018), 139-151.
E. I. Altman and E. Hotchkiss, Corporate Financial Distress and Bankruptcy: Predict and Avoid Bankruptcy, Analyze and Invest in Distressed Debt, John Wiley & Sons, Vol. 289, 2006.
E. I. Altman, E. Hotchkiss and W. Wang, Corporate Financial Distress, Restructuring, and Bankruptcy: Analyze Leveraged Finance, Distressed Debt, and Bankruptcy, John Wiley and Sons, 2019.
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(4) (2004), 543-558.
P. Hajek, Credit rating analysis using adaptive fuzzy rule-based systems: an industry-specific approach, Central European Journal of Operations Research 20 (2012), 421-434.
P. F. Pai, Y. S. Tan and M. F. Hsu, Credit rating analysis by the decision-tree support vector machine with ensemble strategies, Int. J. Fuzzy Syst. 17 (2015), 521-530.
S. Garcia, J. Luengo and F. Herrera, Data Preprocessing in Data Mining, Springer International Publishing, Cham, Switzerland, Vol. 72, 2015, pp. 59-139.
N. Kambhatla and T. K. Leen, Dimension reduction by local principal component analysis, Neural Comput. 9(7) (1997), 1493-1516.
D. T. Larose and C. D. Larose, k-nearest neighbor algorithm, 2014.
DOI: 10.1002/9781118874059.ch7.
C. Sammut and G. I. Webb, (Eds.), Encyclopedia of Machine Learning, Springer Science and Business Media, 2011.
S. B. Kotsiantis, Decision trees: a recent overview, Artificial Intelligence Review 39 (2013), 261-283.
Y. Liu, Y. Wang and J. Zhang, New machine learning algorithm: random forest, Information Computing and Applications: Third International Conference, ICICA 2012, Chengde, China, Proceedings, Vol. 3, 2012, pp. 246-252.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Pushpa Publishing House, Prayagraj, India
This work is licensed under a Creative Commons Attribution 4.0 International License.
____________________________
Attribution: Credit Pushpa Publishing House as the original publisher, including title and author(s) if applicable.
No Derivatives: Modifying or creating derivative works not allowed without written permission.
Contact Pushpa Publishing House for more info or permissions.
Journal Impact Factor: 
