Application of Quantitative Techniques for the Prediction of Bank Acquisition Targets (Series on Computers and Operations Research) (Series on Computers and Operations Research)

In recent years, the banking industry has faced significant challenges due to deregulation, globalization, financial innovation, and intensified global competition. In response to these challenges, banks have adopted strategies to grow and expand their activities, with mergers and acquisitions (M & As) being one of the most popular over the last decade. This unique book thus discusses the use of quantitative classification methods for the prediction of bank acquisitions. With an overview of the M & A trends in the EU banking industry and a survey of the motives for M & As, the authors compare various statistical and computational methodologies used to analyze and predict bank acquisitions. The material constitutes a useful basis for researchers and practitioners in banking management to develop and analyze investment decisions related to M & As 1. Banks M&As : motives and evidence. 1.1. Overview. 1.2. M&As trends in the European Union. 1.3. Reasons and motives for banks M&As. 1.4. Studies on Banks' M&As -- 2. Studies on the prediction of acquisition targets. 2.1. Introduction. 2.2. Studies employing statistical techniques. 2.3. Studies using econometric techniques. 2.4. Other studies. 2.5. Conclusions -- 3. Methodological framework for the development of acquisition targets prediction model. 3.1. Introduction. 3.2. Sampling considerations. 3.3. Variables selection process. 3.4. Method selection. 3.5. Aspects of model evaluation. 3.6. Conclusion -- 4. Data and preliminary analysis. 4.1. Introduction. 4.2. Data sources. 4.3. Samples construction. 4.4. Identification of candidate variables. 4.5. Financial variables and country (Industry) adjustment. 4.6. Variables reduction process. 4.7. Conclusion -- 5. Development of acquisitions prediction models. 5.1. Introduction. 5.2. Summary of results of model comparisons. 5.3. Development and evaluation of prediction models. 5.4. Conclusions -- 6. Integration of prediction models. 6.1. Introduction. 6.2. Integrated (multi-classifiers) models. 6.3. Development and evaluation of integration models. 6.4. Conclusions -- 7. Conclusions. 7.1. Introduction. 7.2. Why prediction models for EU banks. 7.3. Summary ofthe findings. 7.4. Why classification results differ across methods? 7.5. Suggestions for further research In recent years, the banking industry has faced significant challenges due to deregulation, globalization, financial innovation, and intensified global competition. In response to these challenges, banks have adopted strategies to grow and expand their activities, with mergers and acquisitions (MetAs) being one of the most popular over the last decade. This unique book thus discusses the use of quantitative classification methods for the prediction of bank acquisitions. With an overview of the MetA trends in the EU banking industry and a survey of the motives for MetAs, the authors compare various statistical and computational methodologies used to analyze and predict bank acquisitions. The material constitutes a useful basis for researchers and practitioners in banking management to develop and analyze investment decisions related to MetAs Over the last two decades, a number of significant changes occurred in the banking industry, such as deregulation, globalisation, financial innovations, improvements in communication and computing technology, increased competition from within the sector and from non-bank financial intermediaries, to name a few.