This thesis develops a range of prediction models for the purpose of predicting the acquisition of commercial banks in the European Union using publicly available data. Over the last thirty years, there have been approximately 30 studies that have attempted to identify potential acquisition targets, all of them focusing on non-bank sectors. We consider that prediction models developed specifically for the banking industry are essential due to the unusual structure of banks' financial statements, differences in the environment in which banks operate and other specific characteristics of banks that in general distinguish them from non-financial firms. We focus specifically on the EU banking sector, where M&As activity has been considerable in recent years, yet academic research relating to the EU has been rather limited compared to the case of the US. The methodology for developing prediction models involved identifying past cases of acquired banks and combining these with non-acquired banks in order to evaluate the prediction accuracy of various quantitative classification techniques. In this study, we construct a base sample of commercial banks covering 15 EU countries, and financial variables measuring capital strength, profit and cost efficiency, liquidity, growth, size and market power, with data in both raw and country-adjusted (i.e. raw variables divided by the average of the banking sector for the corresponding country) form. In order to allow for a proper comparative evaluation of classification methods, we select common subsets of the base sample and variables with high discriminatory power, dividing the sample period (1998-2002) into training sub-sample for model development (1998-2000), and holdout sub-sample for model evaluation (2001-2002). Although the results tend to support the findings of studies on non-financial firms, highlighting the difficulties in predicting acquisition targets, the prediction models we develop show classification accuracies generally higher than chance assignment based on prior probabilities. We also consider the use of equal and unequal matched holdout samples for evaluation, and find that overall classification accuracy tends to increase in the unequal matched samples, implying that equal matched samples do not necessarily overstate the prediction ability of models. The main goal of this study has been to compare and evaluate a variety of classification methods including statistical, econometric, machine learning and operational research techniques, as well as integrated techniques combining the predictions of individual classification methods. We found that some methods achieved very high accuracies in classifying non-acquired banks, but at the cost of relatively poor accuracy performance in classifying acquired banks. This suggests a trade-off in achieving high classification accuracy, although some methods (e.g. Discriminant) performed reasonably well in terms of achieving balanced overall classification accuracies of above chance predictions. Integrated prediction models offer the advantage of counterbalancing relatively poor performance of some classification methods with good performance of others, but in doing so could not out-perform all individual classification methods considered. In general, we found that the outcome of which method performed best depended largely on the group classification accuracy considered, as well as to some extent on the choice of the discriminatory variables. Concerning the use of raw or country-adjusted data, we found no clear effect on the prediction ability of the classification methods.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:440789 |
| Date | January 2005 |
| Creators | Pasiouras, Fotios |
| Publisher | Coventry University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://curve.coventry.ac.uk/open/items/ecf1b00d-da92-9bd2-5b02-fa4fab8afb0c/1 |
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