Prediction of financial strength ratings using machine learning and conventional techniques

Abdou, HAH; Abdallah, WM; Mulkeen, J; Ntim, CG; Wang, Y

Abstract

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.

Citation

Abdou, H., Abdallah, W., Mulkeen, J., Ntim, C., & Wang, Y. (2017). Prediction of financial strength ratings using machine learning and conventional techniques. Investment Management and Financial Innovations, 14(4), 194-211. https://doi.org/10.21511/imfi.14%284%29.2017.16

• Journal Article Type: Article
• Acceptance Date: Dec 19, 2017
• Online Publication Date: Dec 26, 2017
• Publication Date: Dec 26, 2017
• Deposit Date: Jan 11, 2018
• Publicly Available Date: Jan 11, 2018
• Journal: Investment Management and Financial Innovation
• Print ISSN: 1810-4967
• Electronic ISSN: 1812-9358
• Volume: 14
• Issue: 4
• Pages: 194-211
• DOI: https://doi.org/10.21511/imfi.14%284%29.2017.16
• Publisher URL: http://dx.doi.org/10.21511/imfi.14(4).2017.16
• Related Public URLs: https://businessperspectives.org/journals/investment-management-and-financial-innovations