Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 263171
Loughborough University

Loughborough University Institutional Repository

Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/6815

Title: The classification patterns of bank financial ratios
Authors: Kordogly, Rima
Keywords: Bank financial ratios
Classification patterns
Commercial banks
Congruency coefficient
De Novo banks
Factor analysis
Parallel analysis
Principal component analysis
Savings banks
Transformation analysis
Issue Date: 2010
Publisher: © Rima Kordogly
Abstract: Financial ratios are key units of analysis in most quantitative financial research including bankruptcy prediction, performance and efficiency analysis, mergers and acquisitions, and credit ratings, amongst others. Since hundreds of ratios can be computed using available financial data and given the substantial overlap in information provided by many of these ratios, choosing amongst ratios has been a significant issue facing practitioners and researchers. An important contribution of the present thesis is to show that ratios can be arranged into groups where each group describes a separate financial aspect or dimension of a given firm or industry. Then by choosing representative ratios from each group, a small, yet comprehensive, set of ratios can be identified and used for further analysis. Whilst a substantial part of the financial ratio literature has focused on classifying financial ratios empirically and on assessing the stability of the ratio groups over different periods and industries, relatively little attention has been paid to the classifying of financial ratios of the banking sector. This study aims to explore the classification patterns of 56 financial ratios for banks of different type, size and age. Using data from the Uniform Bank Performance Report (UBPR), large samples of commercial, savings, and De Novo (newlychartered) commercial banks were obtained for the period between 2001 and 2005, inclusive. Principal Component Analysis (PCA) was performed on a yearly basis to classify the banks’ ratios after applying the inverse sinh transformation to enhance the distributional properties of the data. The number of patterns were decided using Parallel Analysis. The study also uses various methods including visual comparison, correlation, congruency, and transformation analysis to assess the time series stability and cross-sectional similarity of the identified ratio patterns. The study identifies 13 or 14 ratio patterns for commercial banks and 10 or 11 ratio patterns for savings banks over the period on which the study is based. These patterns are generally stable over time; yet, some dissimilarity was found between the ratio patterns for the two types of banks – that is, the commercial and savings banks. A certain degree of dissimilarity was also found between the financial patterns for commercial banks belonging to different asset-size classes. Furthermore, four ratio patterns were consistently identified for the De Novo commercial banks in the first year of their operations. However, no evidence of convergence was found between the ratio patterns of the De Novo commercial banks and the ratio patterns of the incumbent (that is, long established) commercial banks. The findings of this study bring useful insights particularly to researchers who employ bank financial ratios in empirical analysis. Methodologically, this research pioneers the application of the inverse sinh transformation and parallel analysis in the area of the ratio classification literature. Also, it contributes to the use of transformation analysis as a factor comparison technique by deriving a significance test for the outputs of this analysis. Moreover, this is the only large scale study to be conducted on the classification patterns of bank financial ratios.
Description: A Doctoral Thesis. Submitted in partial fulfillment of the requirements for the award of Doctor of Philosophy of Loughborough University.
URI: https://dspace.lboro.ac.uk/2134/6815
Appears in Collections:PhD Theses (Business)

Files associated with this item:

File Description SizeFormat
Kordogly R Vol 1.pdfThesis2.31 MBAdobe PDFView/Open
Kordogly R Vol 2.pdfAppendices4.77 MBAdobe PDFView/Open


SFX Query

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.