Prediction of financial strength ratings using machine learning and conventional techniques
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Received November 1, 2017;Accepted December 19, 2017;Published December 26, 2017
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Author(s)Link to ORCID Index: https://orcid.org/0000-0001-5580-1276Link to ORCID Index: http://orcid.org/0000-0002-1042-4056
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DOIhttp://dx.doi.org/10.21511/imfi.14(4).2017.16
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Article InfoVolume 14 2017, Issue #4, pp. 194-211
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Financial strength ratings (FSRs) have become more significant particularly since the recent financial crisis of 2007–2009 where rating agencies failed to forecast defaults and the downgrade of some banks. The aim of this paper is to predict Capital Intelligence banks’ financial strength ratings (FSRs) group membership using machine learning and conventional techniques. Here the authors use five different statistical techniques, namely CHAID, CART, multilayer-perceptron neural networks, discriminant analysis and logistic regression. They also use three different evaluation criteria namely average correct classification rate, misclassification cost and gains charts. The data are collected from Bankscope database for the Middle Eastern commercial banks by reference to the first decade of the 21st century. The findings show that when predicting bank FSRs during the period 2007–2009, discriminant analysis is surprisingly superior to all other techniques used in this paper. When only machine learning techniques are used, CHAID outperform other techniques. In addition, the findings highlight that when a random sample is used to predict bank FSRs, CART outperform all other techniques. The evaluation criteria have confirmed the findings and both CART and discriminant analysis are superior to other techniques in predicting bank FSRs. This has implications for Middle Eastern banks, as the authors would suggest that improving their bank FSR can improve their presence in the market.
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JEL Classification (Paper profile tab)G21, G24, C14, C38
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References38
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Tables9
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Figures4
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- Figure 1. An evaluation chart for the five predictive statistical modelling techniques
- Figure 2. MLP feed-forward NN architecture (one hidden layer)
- Figure 3. Gain charts for machine learning techniques using 2007–2009 testing sub-sample1 and 33% testing sub-sample2
- Figure 4. Gain charts for conventional techniques using 2007–2009 testing sub-sample1 and 33% testing sub-sample2
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- Table 1. Descriptive statistics for banks, by country and whether rated by CI based on size (ln total assets)
- Table 2. A synopsis of CI bank FSRs numerical ratings and rating categories
- Table 3. Correlation matrix for predictor variables
- Table 4. Descriptive statistics for the 14 financial indicators
- Table 5. Group statistics for the 14 financial indicators
- Table 6. Descriptive statistics for non-financial indicators
- Table 7. Classification results for the three machine learning modelling techniques
- Table 8. Classification results for the two conventional modelling techniques
- Table 9. Error rates, estimated misclassification costs and gain chart ranking for all the five modelling techniques
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- Abdallah, W. M. (2013). The impact of Financial and Non-Financial mesures on Banks’ Financial Strength Ratings: The Case of the Middle East (Ph.D. Thesis, Salford University, UK).
- Abdou, H. A. (2009a). An Evaluation of Alternative Scoring Models in Private Banking. Journal of Risk Finance, 10(1), 38-53.
- Abdou, H. A. (2009b). Genetic Programming for Credit Scoring: The Case of Egyptian Public Sector Banks. Expert Systems with Applications, 36(9), 11402- 11417.
- Abdou, H. A., Pointon, J., & El-Masry, A. (2008). Neural Nets versus Conventional Techniques in Credit Scoring in Egyptian Banks. Expert Systems with Applications, 35(3), 1275-1292.
- Abdou, H. A., Tsafack, M., Ntim, C., & Baker, R. (2016). Predicting creditworthness in retail banking with limited scoring data. Knowledge-Based Systems, 103, 89-103.
- Abdou, H., Pointon, J. (2009). Credit scoring and decision-making in Egyptian public sector banks. International Journal of Managerial Finance, 5(4), 391-406.
- Akkoc, S. (2012). An Empirical Comparison of Conventional Techniques, Neural Networks and the Three Stage Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) Model for Credit Scoring Analysis: The Case of Turkish Credit Card Data. European Journal of Operational Research, 222(1), 168-178.
- Altman, E. I. (1971). Corporate Bankruptcy in America. Heath Lexington Books.
- Altman, E. I., & Sametz, A. W. (1977). Financial Crises: Institutions and Markets in a Fragile Environment. John Wiley & Sons Inc.
- Bijak, K., & Thomas, L. C. (2012). Does Segmentation always Improve Model Performance in Credit Scoring? Expert Systems with Applications, 39(3), 2433- 2442.
- Brown, I., & Mues, C. (2012). An Experimental Comparison of Classification Algorithms For Imbalanced Credit Scoring Data Sets. Experts Systems with Applications, 39(3), 3446- 3453.
- Canbas, S., Cabuk, A., & Kilic, S. B. (2005). Prediction of Commercial Bank Failure via Multivariate Statistical Analysis of Financial Structures: The Turkish Case. European Journal of Operational Research, 166(2), 528-546.
- Chandra, D. K., Ravi, V., & Bose, I. (2009). Failure Prediction of Dotcom Companies using Hybrid Intelligent Techniques. Expert Systems with Applications, 36(3), 4830-4837.
- Chen, Y-S. (2012). Classifying credit ratings for Asian banks using integrating feature selection and the CPDA-based rough sets approach. Knowledge-Based Systems, 26, 259-270.
- Chen, Y-S., & Cheng, C-H. (2013). Hybrid models based on rough set classifiers for setting credit rating decision rules in the global banking industry. Knowledge- Based Systems, 39, 224-239.
- Diomande, M. A., Heintz, J., & Pollin, R. (2009). Why U.S. Financial Markets Need a Public Credit Rating Agency. The Economist, 6(6), 1-4.
- Falavigna, G. (2012). Financial Ratings with Scarce Information: A Neural Network Approach. Expert Systems with Applications, 39(2), 1784-1792.
- Hammer, P. L., Kogan, A., & Lejeune, M. A. (2012). A Logical analysis of Banks’ Financial Strength Ratings. Expert Systems with Applications, 39(9), 7808-7821.
- Huang, Z., Chen, H., Hsu, C.-J., Chen, W.-H., & Wu, S. (2004). Credit Rating Analysis with Support vector machines and Neural Networks: A Market Comparative Study. Decision Support System, 37(4), 543-558.
- Kao, L.-J., Chiu, C.-C., & Chiu, F.-Y. (2012). A Bayesian Latent Variable Model with Classification and Regression Tree Approach for Behavior and Credit Scoring. Knowledge-Based Systems, 36, 245-252.
- Kim, K.-J., & Ahn, H. (2012). A Corporate Credit Rating Model Using Multi-Class Support vector machines with An Ordinal Pairwise Partitioning Approach. Computers & Operations Research, 39(8), 1800-1811.
- Kolari, J., Glennon, D., Shin, H., & Caputo, M. (2002). Predicting Large US Commercial Bank Failures. Journal of Economics & Business, 54(4), 361-387.
- Koyuncugil, A. S., & Ozgulbas, N. (2012). Financial Early Warning System Model and Data Mining Application for Risk Detection. Expert Systems with Applications, 39(6), 6238-6253.
- Kumar, P. R., & Ravi, V. (2007). Bankruptcy Prediction in Banks and Firms via Statistical and Intelligent Techniques- A Review. European Journal of Operational Research, 180(1), 1-28.
- Laere, E. V., Vantieghem, J., & Baesens, B. (2012). The Difference Between Moody’s and S&P Bank Ratings: Is Discretion in the Rating Process Causing a Split? (RMI Working Paper No. 12/05).
- Lee, T.-S., Chiu, C.-C., Chou, Y.-C., & Lu, C.-J. (2006). Mining the Customer Credit using Classification and Regression Tree and Multivariate Adaptive Regression Splines. Computational Statistics & Data Analysis, 50(4), 1113-1130.
- Li, H., Sun, J., & Wu, J. (2010). Predicting Business Failure using Classification and Regression Tree: An Empirical Comparison with Popular Classical Statistical Methods and Top Classification Mining Methods. Expert Systems with Applications, 37(8), 5895- 5904.
- Loannidis, C., Pasiouras, F., & Zopounidis, C. (2010). Assessing Bank Soundness with Classification Techniques. OMEGA, 38(5), 345-357.
- Oelerich, A., & Poddig, T. (2006). Evaluation of Rating Systems. Expert Systems with Applications, 30(3), 437-447.
- Pasiouras, F., Gaganis, C., & Doumpos, M. (2007). A Multicriteria Discrimination Approach for the Credit Rating of Asian Banks. Annals of Finance, 3(3), 351-367.
- Poon, W. P. H., & Firth, M. (2005). Are Unsolicited Credit Ratings Lower? International Evidence from Bank Ratings. Journal of Business Finance & Accounting, 32(9-10), 1741-1771.
- Poon, W. P. H., Firth, M., & Fung, H.-G. (1999). A Multivariate Analysis of the Determinants of Moody’s Bank Financial Strength Ratings. Journal of International Financial Markets, Institutions and Money, 9(3), 267-283.
- Poon, W. P. H., Lee, J., & Gup, B. E. (2009). Do Solicitations Matter in Bank Credit Ratings? Results from a Study of 72 Countries. Journal of Money,Credit and Banking, 41(2-3), 285-314.
- Ravi, V., & Pramodh, C. (2008). Threshold Accepting Trained Principal Component Neural Network and Feature Subset Selection: Application to Bankruptcy Prediction in Banks. Applied Soft Computing, 8(4), 1539-1548.
- Ravi, V., Kurniawan, H., Thai, P. N. K., & Kumar, P. R. (2008). Soft Computing System for Bank Performance Prediction. Applied Soft Computing, 8(1), 305-315.
- SPSSInc (2012). PASW Modeler 14 Algorithms Guide. Chicago: IBM SPSS Inc.
- Zhao, H., Sinha, A. P., & Ge, W. (2009). Effects of Feature Construction on Classification Performance: An Empirical Study in Bank Failure Prediction. Expert Systems with Applications, 36(2), 2633-2644.
- 4. Abdou, H. A., Alam, S. T. & Mulkeen, J. (2014). Would credit scoring work for Islamic finance? A neural network approach. International Journal of Islamic and Middle Eastern Finance and Management, 7(1), 112-125.
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Acknowledgments
I would like to thank the Business School at Al Ahliyya Amman University, Jordan. Specifically, many thanks go to the departments of marketing. -
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