A PCA Approach for Comparing Performances of Major Indian Banks during 2004-05 to 2013-14

 

Dr. Subhabaha Pal¹*, Prof. (Dr.) Satyabrata Pal², Dr. Kaushik Bhattacharjee³

¹Assistant Professor-IT & Systems, T. A. Pai Management Institute, Manipal

²Honorary Visiting Professor, Indian Statistical Institute, Kolkata,

Ex-Dean PG Studies, Bidhan Chandra Krishi Viswavidyalaya, Nadia, West Bengal

³Associate Professor-Finance, T. A. Pai Management Institute, Manipal

 

ABSTRACT:

Principal Component Analysis is a well-known procedure, used extensively, applied in respect of the matter of comparison of financial performances. This paper first discusses the major methodologies for bank performance comparison and then compares performances of 37 leading Indian banks during the period 2004-05 to 2013-14 on yearly basis using the principal component analysis (PCA) approach. This study throws light on how the major banks have performed during the above-said period (on yearly basis) and also on a comparative evaluation of their performances (year-wise) over the mentioned period. The Principal Component Analysis (PCA)has revealed the nice result (information)that SBI has performed better than other banks in general over the above-said period excepting that in case of some years it has enlisted itself in the same category as with the ICICI bank in respect of performance, falling back the remaining Banks in the trail.

 

 

 

 

INTRODUCTION:

The paper primarily enlightens on a way to study and to make a comparison among performances in respect of different banks. The banks are the backbones of a country’s economy (Bharat, 2012). The commercial banks play a pivotal role in the country’s economy for two reasons – they provide a major source of financial intermediation and their checkable deposit liabilities represent the bulk of the nation’s money stock (Piyu, 1992).

 

As banks are seen as special, given their pivotal role in providing credit to enterprises, a great deal of attention is paid to the performance of banks, expressed explicitly in terms of competition, concentration, efficiency, productivity and profitability (Jacob and Jaap, 2008). Since, many of these indicators cannot be observed directly, various indirect measures in the form of simple indicators or complex models have been devised and used in theory and in practice (Jacob, 2010). Some of the methods for measuring and comparing Bank performance include CAMELS, Data Envelopment Analysis (DEA), Financial Ratio Analysis, Canonical Correlation Analysis (CCA) and Multi-dimensional Scaling (MDS). In the following a brief presentation on the above methods and the related papers utilizing those methods has been given. The rating system, CAMELS, developed in the US, has been one of the extensively used methods to measure the efficiency and performance of the bank (Swati, 2011). The acronym CAMELS stand for the following factors that examiners use to rate bank institutions, namely, Capital Adequacy, Asset Quality, Management, Earnings, Liquidity and Sensitivity to the Market Risk (Jose, 1999).According to (Swati, 2011), CAMELS method provides a measurement of a bank’s current overall financial, managerial, operational and compliance-related performances. Though originally developed in USA, the CAMELS method has been applied widely in other countries as well for measurement of bank’s performance. The following table gives the details of the studies on bank performance in different countries based on CAMELS methodology.

 

Table 1 – Studies on Bank Performance in different countries based on CAMELS methodology

Country Name	List of Papers
USA	(Jose, 2011), (Uyen, 2011)
Romania	(Baltes Nicolae, Rodean and Maria-Daciana, 2014)
Bangladesh	(Abdul Awwal Sarkar, 2012)
Malaysia	(Rubayah, Zulkornain, Alias Radam and Noriszura, 2012)
Morocco	(El Mehdi, 2014)
Pakistan	(Haseeb and GulZeb, 2011)
Iran	(Malihe Rostami, 2015)
Japan	(M. J. Said and P. Saucier, 2013)
Nepal	(K. J. Baral, 2005)
Malta	(Mikail, Habib and Aykut, 2014)
India	(S. Bhayani, 2006), (R.K. Gupta and S. Kaur, 2008), (B.S. Bodla, R. Verma, 2006), (Sneha S. Shukla, 2015), ((S.M. Tariq, Adeel Maqbool and Syed Imran, 2012), (Apoorva Trivedi, Anisur, Yasir and Arafat Elahi, 2015), (Sushendra Kumar, Parvesh Kumar, 2013), (Prasad K.V.N. and Ravinder, 2012), (Ruchi, 2014), (Anita Makkar and Shveta Singh, 2013)

 

The Data Envelopment Analysis (DEA) is also a widely used methodology for assessing bank performance.DEA is a nonparametric method falling in the disciplines, namely, mathematics (operations research), economics, statistics used for the estimation of production frontiers. It is used to empirically measure the productive efficiency of decision making units (or DMUs). The following table presents the details of the studies undertaken on bank performances in different countries based on DEA methodology.

 

Table 2 – Studies on Bank Performance in different countries based on DEA methodology

Country Name	List of Papers
USA	(Piyu Ye, 1995)
Taiwan	(Chiang Kao, Shiang-Tai Liu, 2004)
Japan	(M. J. Said, P. Saucier, 2003)
India	(Sathya Swaroop Debasish, 2006), (Sunil and Rachita, 2009)

 

Financial Ratio Analysis is another widely used technique to measure bank performance. The following table presents the details of the studies carried out on bank performance in different countries based on Financial Ratio Analysis methodology.

 

Table 3 – Studies on Bank Performance in different countries based on Financial Ratio Analysis

Country Name	List of Papers
South Africa	(M. Kumbirai and R. Webb, 2010)
Greece	(Kyriaki and Constantin, 2008)
India	(Rohit Bansal, 2014), (Ashish Gupta and V S Sundram, 2015), (Nikhil Kumar and Narendra Kumar, 2016), (Khushboo, Naveena and Neha, 2015)

 

Canonical Correlation Analysis is another widely used methodology for measurement and comparison of bank performance. Canonical Correlation Analysis (CCA) is a statistical method used for exploring the relationships between two multivariate sets of variables (vectors), all measured on the same set of individuals. Canonical correlation takes into account of the fact that the attribute, bank performance, is a multi-dimensional concept, including both quantitative and qualitative aspects, and cannot be measured by one variable in isolation but only by examining several measures of performance jointly interacting. A search of literature reveals that (Donald, Wallace and Peter, 1974) and (Nasser Arshadi and Edward C. Lawrence, 1987) are two papers which use canonical correlation analysis for comparing bank performance. The papers (A. Karthigeyan, V. Marippan and B. Rangaiah, 2013), (Prathap B N, 2013), (Abdul Naser V, 2013), (P. K. Jain and V. Gupta, 2004) and (Mihir Dash and Ravi Pathak, 2016) are some of the papers composed on Indian perspective, which use canonical correlation analysis for comparison of bank performances. Principal Component Analysis is also a well-known method which can be used for comparison of financial performance. This paper employs the principal component analysis methodology to compare performances of Indian banks. The Indian banking sector has been liberalized since the late nineties with the financial reforms imposed on Indian economy and as a sequel the foreign banks have been able to start operations in India since sometime around 1998-99. The first decade of the twenty-first century has put forth major challenges in the Indian banking sector as the Indian liberalized economy has become susceptible to the effects of turmoil resulted from the occurrence of major world economic events as the consequence of such events has arrived to the doorstep of Indian banking sectors (because of the reforms undertaken) at the time of the said chaos (international), though, it may be added that the banking sector had been immune to such effects prior to liberalization. Many new private banks have come up during the above decade and the major public banks have been trying to consolidate their positions in the sector endeavoring to adapt to the new economic scenario since then. A few studies have been directed towards assessment of performance of the Indian banking sector during this period. As has been spelt out at the very outset that it is needed to compare the performances of different banks over the period of 10 years from 2004-2005 to 2013-2014 in order to understand how these banks have progress differently in this crucial decade aftermath of the globalization and liberalization of the Indian economy. This paper is devoted to address the gap by comparing the performances of the 37 major Indian banks every year from 2004-2005 to 2013-2014 using the Principal Component Analysis methodology. The study compares the performances of the 37 banks over the years and gives useful insight in the Indian banking sector during the period 2004 to 2014.

 

DATA:

The data used pertains to the financial results on the following parameters (financial ratios) of the 37 banks during the study period 2004-2005 to 2013-2014. The financial parameters are mentioned in the following table.

 

METHODS AND METHODOLOGIES:

Multi-dimensional Scaling and Principal Component Analysis techniques have been used in this study of performance of Indian banks.

 

Principal Component Analysis (PCA):

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. Principal component analysis (PCA) is a technique used to emphasize variation and bring out strong patterns in a dataset. It's often used to make data easy to explore and visualize. The method of PCA is applied on the data, which provides very useful insights on the performances of Banks.

 

Table 4 – Financial Parameters

Parameter Name
SDROE	Standard Deviation of Return on Equity
Size	Bank Size
Loan Asset	LogE(Loan Asset)
Equity Asset	LogE(Equity Asset)
Loan Loss	LogE(Loan Loss Provision
Liquidity	Available Liquid (cash) assets
Dividend Payout	Dividend Payout
NIM	Net Interest Margin

Table 5 – Bank Details

Bank Name	Short
Allahabad Bank	ALB
Andhra Bank	ANB
Axis Bank Ltd.	AXB
Bank Of Baroda	BOB
Bank Of India	BOI
Bank Of Maharashtra	BOM
Canara Bank	CNB
Central Bank Of India	CBI
City Union Bank Ltd.	CUB
Corporation Bank	CRP
Dena Bank	DNB
Dhanlaxmi Bank Ltd.	DHB
Federal Bank Ltd.	FDB
H D F C Bank Ltd.	HDFC
I C I C I Bank Ltd.	ICICI
I N G Vysya Bank Ltd. [Merged]	ING
Indian Bank	IND
Indian Overseas Bank	OVRS
Indusind Bank Ltd.	INDUS
Jammu and Kashmir Bank Ltd.	JKB
Karnataka Bank Ltd.	KAR
KarurVysya Bank Ltd.	KAV
Kotak Mahindra Bank Ltd.	KOTAK
Lakshmi Vilas Bank Ltd.	LKMV
Oriental Bank Of Commerce	OBC
P N B	PNB
Punjab and Sind Bank	PSB
South Indian Bank Ltd.	SIB
State Bank Of Bikaner and Jaipur	SBBJ
State Bank Of India	SBI
State Bank Of Mysore	SBM
State Bank Of Travancore	SBT
Syndicate Bank	SYND
Uco Bank	UCO
United Bank Of India	UBI
Vijaya Bank	VIJ
Yes Bank Ltd.	YES

 

 

RESULTS AND DISCUSSIONS:

PCA Results:

Principal Component Analysis is performed with the above-mentioned data-set for each year and the first 2 components are found to be absorbing the variation imbibed under the Dispersion matrix formed by eight parameters. It is revealed that the first principal component only is accounting for the 99.05% of the variation where the second component is accounting or the 0.04% of the variation. Another important finding is that the parameter, NIM has the maximum effect in the construction of the principal components. The Graphs/Figures (1 to 10) are constructed based on the results obtained in regard to the first and second principal components corresponding to the ten years respectively.

 

 

Figure 1: 2004-2005 Bank Performance PCA Analysis

 

Figure 2: 2005-2006 Bank Performance PCA Analysis

 

Figure 3: 2006-2007 Bank Performance PCA Analysis

 

Figure 4: 2007-2008 Bank Performance PCA Analysis

 

Figure 5: 2008-2009 Bank Performance PCA Analysis

 

Figure 6: 2009-2010 Bank Performance PCA Analysis

 

Figure 7: 2010-2011 Bank Performance PCA Analysis

 

Figure 8: 2011-2012 Bank Performance PCA Analysis

 

Figure 9: 2012-2013 Bank Performance PCA Analysis

 

Figure 10: 2012-2013 Bank Performance PCA Analysis

 

A look at the PCA plots of the first 2 components reveal instantaneously that SBI is leading over all other banks in each year in the 10 year period (2004-2005 to 2014-2015). Though in a few years, ICICI is also showing better performance than rest banks (excluding SBI) but, revealingly, in each of those 5years (2005-2006, 2006-2007, 2007-2008, 2008-2009, 2009-2010) also ICICI is far behind the SBI. In the 4 years (2010-2011, 2011-2012, 2012-2013, 2013-2014), the performances of ICICI and HDFC are neck to neck, slightly better than the rest (excluding SBI). The study is presenting an overall comparison of the performances of 37 Indian banks over years from 2004-05 to 2013-14 and it also provides insights (required for policy framing)on the performances of the banks. The paper concludes with the message that Performance and SBI are the twins in the banking system scenaio in India.

 

SUMMARY:

This paper first reviews the major researches that have taken place on the topic of Indian bank performances during the last decade. It then sets out the task of comparison of bank performances of 37 leading Indian banks during the period 2004-05 to 2013-14 using the principal component analysis approach. Principal component analysis reveals that SBI has no competitor in respect of performance during the above-said period (the other banks can be considered to fall in a cluster) and it has thus ascribes almost all banks (excepting ICICI in case of a few years) in the same category. This paper brings out the major features in the Indian Bank performances prevailed over the last decade (commencing from the year, 2004 – 2005).

 

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