scholarly journals A Comparison of Principal Component Analysis and Maximum Likelihood Factor Analysis in Bank Health Ratio

2021 ◽  
Vol 22 (2) ◽  
pp. 147
Author(s):  
Firdaus Firdaus ◽  
Sigit Nugroho ◽  
Haryo Widodo
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Maximum Likelihood ◽  
Principal Component ◽  
Component Analysis ◽  
Extraction Methods ◽  
Financial Ratios ◽  
Financial Ratio ◽  
Matrix Correlation ◽  
Health Ratio

The use of factor analysis methods to reduce variable dimensions is generally known and has been used in various disciplines. The two famous extraction methods of factor analysis are principal component analysis and maximum likelihood. This study aimed to compare both, principal component analysis and maximum likelihood. By their constructed matrix correlation, applied to bank financial ratios. The study is developed from an initial set of 22 ratios of healthy indexed banks. The use of bank financial data aims to identify the structure of the financial ratio of healthy indexed banks. There are 10 variables satisfying the criteria of factor analysis techniques to be considered in the analysis. Both principal component analysis and maximum likelihood suggest three factors that can be used to represent 10 variables.Keywords: factor analysis; principal component analysis; maximum likelihood; financial ratios; bank health.

scholarly journals Principal component analysis of financial statements. A compositional approach

2020 ◽  
Vol 29 ◽  
pp. 18-37
Author(s):  
Miquel Carreras Simó ◽  
Germà Coenders
Keyword(s):  
Principal Component Analysis ◽  
Compositional Data ◽  
Principal Component ◽  
Financial Statements ◽  
Component Analysis ◽  
Financial Ratios ◽  
Compositional Data Analysis ◽  
Retail Sector ◽  
Financial Ratio ◽  
Compositional Approach

Financial ratios are often used in principal component analysis and related techniques for the purposes of data reduction and visualization. Besides the dependence of results on ratio choice, ratios themselves pose a number of problems when subjected to a principal component analysis, such as skewed distributions. In this work, we put forward an alternative method drawn from compositional data analysis (CoDa), a standard statistical toolbox for use when data convey information about relative magnitudes, as financial ratios do. The method, referred to as the CoDa biplot, does not rely on any particular choice of financial ratio but allows researchers to visually order firms along the pairwise financial ratios for any two accounts. Non-financial magnitudes and time evolution can be added to the visualization as desired. We show an example of its application to the top chains in the Spanish grocery retail sector and show how the technique can be used to depict strategic management differences in financial structure or performance, and their evolution over time.

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