Prediction of financial strength ratings using machine learning and conventional techniques

Abdou, H.A., Abdallah, W.M., Mulkeen, J., Ntim, C.G. and Wang, Y. ORCID: 0000-0001-5438-4255, 2017. Prediction of financial strength ratings using machine learning and conventional techniques. Investment Management and Financial Innovations, 14 (4), pp. 194-211. ISSN 1810-4967

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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.

Item Type: Journal article
Publication Title: Investment Management and Financial Innovations
Creators: Abdou, H.A., Abdallah, W.M., Mulkeen, J., Ntim, C.G. and Wang, Y.
Publisher: Business Perspectives
Date: 26 December 2017
Volume: 14
Number: 4
ISSN: 1810-4967
Identifiers:
NumberType
10.21511/imfi.14(4).2017.16DOI
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Divisions: Schools > Nottingham Business School
Depositing User: Jonathan Gallacher
Date Added: 25 Sep 2019 09:45
Last Modified: 25 Sep 2019 09:45
URI: http://irep.ntu.ac.uk/id/eprint/37761

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