In this paper, we investigate the relative performance of both linear and non-linear support vector machines (SVMs) models with a polynomial and an RBF kernel in the development of classification models for the prediction of EU bank acquisition targets. The training sample consists of 274 banks,...

This paper uses stochastic frontier analysis to provide international evidence on the impact of the regulatory and supervision framework on bank efficiency. Our dataset consists of 2,853 observations from 615 publicly quoted commercial banks operating in 74 countries during the period 2000-2004....

In this paper we develop classification models for the identification of acquisition targets in the EU banking industry, incorporating financial variables that are mostly unique to the banking industry and originate from the CAMEL approach. A sample of 168 non-acquired banks matching 168...

This paper uses stochastic frontier analysis and Tobit regressions to provide international evidence on the impact of regulatory, supervision and environmental factors on bank efficiency. Our contribution is twofold. First, we use a newly constructed database of 3,086 observations from 677...

The purpose of the present study is the development of classification models that could be used in the identification of acquirers and targets in the Asian banking sector. We use a sample of 52 targets and 47 acquirers that were involved in acquisitions in 9 Asian banking markets during...

We investigate the association between the regulatory and supervision framework and the likelihood of being involved in acquisitions either as acquirer or target in the Asian banking industry over the period 1998-2004. The sample consists of 473 commercial banks operating in 9 markets, a total...

In this paper we use a sample of 894 banks from 79 countries to develop a multicriteria decision aid model, for the classification of banks in three groups on the basis of their soundness. The model is developed with the UTADIS method, through a ten-fold cross-validation procedure using six...

This study investigates the efficiency of k-Nearest Neighbours (k-NN) in developing models for estimating auditors' opinion, as opposed to models developed with discriminant and logit analyses. The sample consists of 5,276 financial statements, out of which 980 received a qualified audit...

In the present study we investigate the development of a classification model for discriminating between bankrupt, financially distressed and non-failed firms. The sample consists of 44 Greek firms that filed for bankruptcy over the period 2000-2003, 32 distressed firms that had serious...

We use country-level data and bank level data from 71 countries and 857 banks to investigate the impact of bank regulations, supervision, market structure, and bank characteristics on individual bank ratings. The results indicate that less cost efficient banks with higher than average levels of...