Hussein A. Abdou

This paper examines the impact of mergers and acquisitions (M&A) on the financial performance of the Nigerian market after consolidation. The authors use data from all Nigerian banks that survived the consolidation between 2001 and 2009. Logistic regression models are structured to determine the influence of M&A activities on the financial performance of the Nigerian market. Also, the authors critically evaluate the findings by shedding the light on the lessons other developing nations can learn from the Nigerian market. The results show that M&A have a positive influence on the financial performance of the Nigerian market. Still, M&A are not enough to achieve the wider objectives of banking sector reform. Towards this end, corporate governance reform must take place vis-à-vis consolidation exercises especially when these M&A are regulatory based rather than market based. The investigation uses a novel approach by comparing pre- and post- M&A results performance of merged banks as well as comparing these results with non-merged banks. Finally, the paper puts the results in context of wider reforms and considers the effectiveness of the M&A as a tool for banking sector reform in developing countries. The investigation offers insights into the policy of banking consolidation which can be useful for policy makers in Nigeria and other similar economies

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.