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Classification of financial ratios of public sector banks in india using multivariate statistical analysis

Author: 
Chandrasekaran, R. (Retd.) and Madhanagopal, R.
Subject Area: 
Physical Sciences and Engineering
Abstract: 

In the face of growing competition, the policy changes and the operational environment in respect of the Indian banking system, there has been an increased focus on profitability despite other social objectives being important. Moreover, the setting up of a new competitive environment has resulted in new challenges for the public sector banks to retain their shares. Keeping this in mind, the present study was undertaken to examine the classification pattern of financial ratios of all public sector banks in India, and a comparative analysis of the banks with their corporate offices in three different regions of the country. The present study is diagnostic and exploratory in nature and makes use of secondary data. The relevant secondary data have been collected from Capita online database for a period of 10 years from 2000-2001 to 2009-2010. Out of many possible financial ratios, the fifteen consistent profitability ratios are considered for the study. Based on the head office, all the 27 public sector banks in India are categorized into location of three different regions viz., Southern region (SR), Western region (WR) and North-eastern region (NER). The northern and eastern region banks are jointly presented for the analysis, since it is observed that the bank with their head offices in northern and eastern regions are few in number and only when they are clubbed, a balanced study among the regions can be conveniently attempted. The underlying objective is to examine the classification pattern of financial ratios that contribute to the overall performance of the public sector banks in India. For this purpose, Factor analysis (FA), Discriminant analysis (DA) and perceptual maps have been used. Factor Analysis on 19 variables (Financial ratios) resulted in four underlying categories (factors). Each factor is named in an appropriate manner considering the factor loadings and constituent variables (ratios). Representative ratios are identified for each such factor. To validate the result of factor analysis and to reach final conclusion regarding the representative ratios, Cluster analysis is performed. Multivariate Discriminant Analysis is performed for the original variables and using Standardized Canonical Discriminant Function Coefficients Perceptual mapping was drawn.

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