Developing a model to restructure the overpopulated banking industry in Lebanon

Abstract

The purpose of this research is to develop a multivariate statistical model to classify the commercial banks in Lebanon in cohesive categories on the basis of their financial characteristics revealed by the financial ratios. This article uses factor analysis as a data reduction technique to reduce 52 financial ratios into seven financial ratios that adequately explain differences in performance among 50 banks in Lebanon. Implication for mergers between commercial banks is based on cluster analysis that is used to divide Lebanese banks into four performance clusters. Multidiscriminant analysis is used to examine the relative importance of the financial ratios discriminating between these clusters.

Renewable Energy and Economic Growth Nexus: A Case of United Arab Emirates

“…The results reflect that the variables have normal distribution. The natural logarithm of the variables have been taken in order to minimize the outliers and skewness issues (Charbaji, 2001;Gujarati, 2008).…”

A path analytic study of the attitude toward e‐government in Lebanon

“…E-commerce, in Lebanon is no longer a thing of the future. Internet waves have landed on Lebanese shores and the depersonalized dealings are moving up from ATM to Pay-by-the-Phone to e-banking (Charbaji, 2001). Until now, the tactics of most banks in Lebanon rested on upgrading commercial banking by the development of information technology and human capital (Banque Audi, 2001).…”

Investment Decision Making Using a Combined Factor Analysis and Entropy-Based Topsis Model

“…This method has been widely used in financial analysis. For example, Charbaji (2001) used factor analysis as a data reduction technique to reduce the financial ratios of Lebanon banks from 52 into 7 financial ratios. Cheng and Arif (2007) used factor analysis in Malaysia commercial banks to reduce 21 accounting and financial ratios into four factors.…”