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 2853 observations from 615 publicly quoted commercial banks operating in 74 countries during the period 2000-2004....

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 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,...

The present study investigates the efficiency of four classification techniques, namely discriminant analysis, logit analysis, UTADIS multicriteria decision aid, and nearest neighbours, in the development of classification models that could assist auditors during the examination of Asian...

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...