Performance Analysis of Indian Public Sector Banks based on Their ...

IJMSS Vol.04 Issue-03 (March, 2016) ISSN: 2321-1784 International Journal in Management and Social Science (Impact Factor- 5.276)

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International Journal in Management and Social Science http://www.ijmr.net.in email id- [email protected] Page 83

Performance Analysis of Indian Public Sector Banks based on Their Investment Level

Dr. P. MARIAPPAN, Associate Professor in Mathematics

Bishop Heber College Trichy - 620 017, Tamilnadu, India

Abstract

Purpose The objective of the research study is to investigate and analyze the operation efficiency of the Public Sector Banks of India based on their investment individually and identify the best performing Bank.

Design/ Methodology For this survey, researchers collected data on the Public Sector Banks for the financial years 2004-2015 from the official websites of individually them considering six input and four output variables. The Data envelopment Analysis [DEA] technique has been employed; BCG Matrix [Efficiency with ROI] and GE McKinsey matrix [Efficiency with ROI] for same.

Findings Our Study reveals that as per the analysis of DEA:

Bank of Baroda secures the top position in high Investment

Andhra Bank and Vijaya Bank secure first position in low investment.

BCG Matrix:

Bank of Baroda, Canara Bank, Central Bank of India, IOB, Oriental Bank of Commerce, PNB and Bank of India in the high investment group lies in high profitability as well as high efficiency quadrants.

According to low investment, Andhra Bank, Bank of Mahasatra, Syndicate Bank and UCO Bank lies in high profitability as well as high efficiency quadrants.

McKinsey Matrix:

In High investment group, Bank of Baroda lies on MH [Medium Quadrant].

Andhra Bank and Syndicate Bank are those Banks lies on MH [Medium Quadrant]. By combining all the analysis, the researcher identifies that in the Public Sector Bank, namely Bank of Baroda is functioning effectively based on high investment. In case of Low investment, Andhra Bank is performing good.

Keywords

Data Envelopment Analysis, BCG Matrix, McKinsey Matrix, Efficiency




Introduction

The development in the Indian Banking Industry has been more qualitative than quantitative and it is expected to stay the same in the forthcoming age. Established on the projections prepared in the "India Vision 2020" organized by the Planning Commission and the Draft, 10th Plan, the report predicts that the rate of enlargement in the balance-sheets of Banks is likely to slow down. The entire assets of all scheduled commercial Banks by end-March 2010 is estimated at Rs 40,90,000 crores. That will make up around 65 per cent of GDP at current market prices as compared to 67 per cent in 2002-03. Bank assets are anticipated to rise at an annual composite rate of 13.4 per cent during the remainder of the decade as against the growth rate of 16.7 percent that existed between 1994-95 and 2002-03. It is anticipated that there will be great additions to the capital base and reserves along the liability side.

The importance of private sector in the Indian economic system over the last 15 years has been terrific. The opening up of Indian economy has headed to the free inflow of foreign direct investment (FDI) along with modern cutting edge engineering, which increased the importance of private sector in the Indian economy considerably.

Previously, the Indian market was dominated by the government enterprises, but the scene in Indian market changed as soon as the markets were opened for investments. This examined the advance of the Indian private sector companies, which prioritized customer's needs and rapid help. This further fueled competition amongst same industry players and even in government systems.

In the early 1990s, the then Narasimha Rao government embarked on a policy of liberalization, licensing a small act of private Banks. These got to be known as New Generation tech-savvy Banks, and included Global Trust Bank (the foremost of such new generation Banks to be set up), which later amalgamated with Oriental Bank of Commerce, Axis Bank (earlier as UTI Bank), ICICI Bank and HDFC Bank. This movement, along with the rapid development in the economy of India, revitalized the Banking sector in India, which has experienced rapid increase with substantial contribution from all the three sectors of Banks, namely, government, Banks, private Banks and foreign Banks. The new policy shook the Banking sector in India altogether. Bankers, till this time, were used to the 4-6-4 method (Borrow at 4%; Lend at 6%; Go home at4) of operation. The new wave ushered in a modern outlook and tech-savvy methods of going for traditional Banks. All this contributed to the retail boom in India. People haven't just demanded more from their Banks, but also took in more. Currently, Banking in India is fairly mature in terms of supply, product range and reach-even though reach in rural India still remains a challenge for the 83 private sector and foreign Banks. In March 2006, the Reserve Bank of India allowed Warburg Pincus to increase its stake in Kotak Mahindra Bank (a private sector Bank) to 10%. This is the first time an investor had been permitted to have more than 5% in a private sector Bank since the RBI announced norms in 2005that any stake exceeding 5% in the private sector Banks would need to be vetted by them.

DEA was first preceded by (Charnes et al., 1978) as a Mathematical Programming Model with the avail of the theoretical framework presented by (Farrell, 1957), for computing the relative efficiencies of multiple Decision Making Units (DMUs), and it comes under the special category of




Fractional Programming. DEA is a particular technique which provides a comparative ratio for each unit in terms of yield and input. The ratio is stated as efficiency scores for each unit. The measure of performance lies in the range 0 to 1. If the performance measure is 1 then the system is believed to be highly efficient and if the measure is tending towards 0, the efficiency is otherwise. Ace of the important roles of DEA is that the efficiency scores indicate the gap for potential improvements and developments for inefficient DMUs. DEA firstly applied for evaluating the efficiency of Bank branches, is a tool for evaluating relative efficiency since it first identifies the Bank’s efficiency frontier and then compares it with other Banks. It allows ranks to be awarded to the Banks according to their technical efficiency scores and also to single out the driving forces for inefficiencies. In the Banking industry, the DEA model is preferable to an econometric approach of efficiency measurement because it has a number of advantages. They are:

It can simultaneously analyze several inputs and outputs, which is an alternative characteristic, because production in the Banking industry often takes multiple inputs and end products.

It does not require any assumptions about the operative phase of technology, and

It counts on a maximal performance measure for each production unit relative to all other production units in the observed population with the only stipulation that each production unit lies on or below the external.

McKinsey matrix is a nine-cell (three by three) multi-factor matrix used to perform business portfolio analysis as one of the steps in the strategic planning process. It has two dimensions; across the horizontal axis is Industry Attractiveness (IA) and along the vertical axis is, Business Strengths (BS). Industry attractiveness measures market potential in terms of growth in sales and profits. Business strengths, instead, measures the current forces of an organization in the market, it is based on the objective rating of the ability that the organization has to satisfy market demands, as compared to its competitors.

The growth-share matrix (the product portfolio, BCG-matrix, Boston matrix, Boston Consulting Group analysis, portfolio diagram) is a chart that had been created by Bruce D. Henderson for the Boston Consulting Group in 1970 to help corporations with analyzing their business units or product lines. This helps the company allocate resources and is used as an analytical tool in brand marketing, product management, strategic management, and portfolio analysis.

Review of Literature

The Financial Management Tool known as Ratio Analysis Technique [RAT] has been practiced for many years to evaluate the performance of the Banks. The financial statements are tested to find different ratios and then compare them with the defined Benchmark. In this research paper, the traditional parametric technique is combined with the non – parametric method DEA to investigate the performance of the Banks.

http://en.wikipedia.org/wiki/Boston_Consulting_Group



http://en.wikipedia.org/wiki/Bruce_D._Henderson


http://en.wikipedia.org/wiki/Corporation

http://en.wikipedia.org/wiki/Business

http://en.wikipedia.org/wiki/Product_line

http://en.wikipedia.org/wiki/Brand



http://en.wikipedia.org/wiki/Product_management

http://en.wikipedia.org/wiki/Strategic_management

http://en.wikipedia.org/wiki/Modern_portfolio_theory




The role of ownership and competition in enhancing efficiency of the Banking industry cannot be over stressed. Developing countries have had Banking systems that are characterized by high levels of state ownership. Recent cross-country studies find that entry by foreign Bank increases competition, efficiency, and Banking sector stability, factors that should benefit all borrowers. On the other hand, opponents of foreign Bank entry argue that this procedure might still harm access to credit in particular by underserved sectors such as small and medium-sized enterprises.

(Seiford and Zhu, 1999) studied the profitability and marketability of the top 55 U.S. commercial Banks by applying the DEA model and concluded that large Banks performed better with regard to profitability than small size Banks, while small size Banks have the better characteristic of marketability as compared to large size Banks.

(Meadows et al, 2002) examined the cost and profit efficiency of 832 European Banks based in ten European Union Countries (period 1993 – 1996). The yield on assets (ROE) and return on equity (ROA) were acquired as performance measures to check profit efficiency of the Banks using DEA. This survey was prepared based on the four dimensions, that is to say the market characteristics, differences in size, other Bank characteristics and specialization. Variations in profit terms were found to be larger than the variations in price terms.

(Park and Weber, 2006) examined the profitability of all Korean Banks by testing with (traditional hypothesis approach) market structure hypothesis against efficient structure hypothesis applied after examination of the panel data (for the period of 1992-2002); with the aid of (DEA) model. The result of this work indicates that the performance measures significantly affects the profitability of Banks.

(Kablan, 2007) examined the efficiency of West African Economic Monetary Union (WAEMU) Banks after the period of Banking crises (1993-1996). Data Envelopment Analysis method (DEA) was employed for measuring technical efficiency and Stochastic Frontier Analysis (SFA) with cost functions. The result of the survey reveals that WAEMU Banks' efficiency is responsive to variables like financial soundness, the ratio of bad loans per country, the Banking concentration and the GDP per capita. Detailed analysis reveals that local private Banks are most efficient, followed by foreign Banks.

(Mostafa, 2008) has evaluated the comparative efficiency of the top 100 African Banks using a cross-sectional data for the year 2005. He reasoned out that the performance of several Banks is sub-optimal, suggesting the potential for substantial advances.

(Sufian, 2009) studied the efficiency of the Malaysian Banking sector during the Asian Crisis of 1997 for the period of 1995-1999. The efficiency of individual Banks was computed by DEA technique. They considered the Profitability as the major ingredient which was used to evaluate the efficiency with other explanatory variables, like Bank size and ownership. The result of this survey indicated that as there is a positive association between the Efficiency of Banks and loans intensity and the relationship is otherwise for the economic conditions and expense preference behavior.




(Khalid Al Khathlan and Syed Abdul Malik, 2010) examined the comparative efficiency of Saudi Banks uses annual data from 2003 through 2008 using DEA. The consequences indicate that, on a relative scale, Saudi Banks were efficient in the management of their financial resources. In addition, the results would provide crucial information about the Saudi Banks’ financial conditions and management performance for the benefit of Bank regulators, managers and Bank stock investors.

(Mehmet Hasan Eken and Suleyman Kale, 2011) examined the performance model for assessing the comparative efficiency and potential improvement capabilities of Bank branches by identifying their strengths and weaknesses and the yield and profitability aspects of the limbs. Under both productivity and profitability approaches, efficiency characteristics of branches, which are grouped according to different sizes and regions, have similar tendencies. In both analyses, it is apparent that branch size and scale efficiency are linked up to each other. As branch size increases scale efficiency increases as well, and later on the most productive scale size, however, as size increases efficiency decreases. Also small and too large branches need special care. Putin production and profit efficiency scores on two scales reveals the performing characteristics of branches. Each region needs different treatment. Branches with low output-low profit efficiency should be evolved towards high yield-high profit efficiency region.

(Mohammad Romel Bhuia, Azizul Baten, Anaton Abdulbasah Kamil et al., 2012) analyzed the comparative efficiency of Bangladesh online Banks during 2001 – 2007 by utilizing Data Envelopment Analysis. Established along the several online sampled Banks, the findings reveal that the most efficient Banks were AL-Arafah Islami Bank Limited, Shahajalal Islami Bank Limited, Eastern Bank Limited, and the less efficient Banks over the survey period were Janata Bank Limited, Uttara Bank Limited, United Commercial Bank Limited, Pubali Bank Limited, and AB Bank Limited. Among the three groups Group-1 (n=20), Group-2 (n=18), Group-3 (n=15) it was mentioned that the individual efficiency level of Banks are increasing group by group. The efficiency level of Group-2 was slightly increased from the efficiency level of Group-1. The source of efficiency of the sampled Banks was found to be lower for technical efficiency and scale efficiency rather than pure technical efficiency.

(Aswini Kumar Mishra, Jigar N, Gadhia, Bibhu Prasad Kar et al., 2013) tested the soundness and the second is to measure the efficiency of 12public and private sector Banks based on market cap. As far as the first aim is concerned, CAMEL approach has been applied over a period of twelve years (2000-2011), and it is demonstrated that private sector Banks are at the height of the list, with their performances in terms of soundness being the best. Public Sector Banks like Union Bank and SBI have taken a back seat and display low economic soundness in comparison. On the other hand, the present study takes in an effort to assess the efficiency change of these selected Banks operating in India during 2010-2012. By using frontier based non-parametric technique, Data Envelopment Analysis, provides significant insights on the efficiency of different Banks and places the private sector ones at an advantageous location and thereby hints at the possibility of further improvisation of most of the Public Sector Banks. DEA results exhibit that among the Public Sector Banks, the performance of Bank of India, Canara Bank and Punjab National Bank got dampened in the last two years under study, whereas among the private sector Banks, except the case of Axis




Bank, which was not found to be satisfactory at all, the remaining private sector Banks show marked consistency in their efficiency level during the period under survey.

(Zanella A, Camanho AS and Dias TG, 2015) discussed different models that can be utilized to construct composite indicators with both desirable and undesirable output indicators. Two approaches are considered. The first is an indirect approach, based on a traditional Data Envelopment Analysis model, requiring a prior transformation in the measurement scale of the undesirable outputs. The moment is a direct attack, established on a directional distance function model. The function of a directional distance function allows for the accommodation of undesirable indicators in their original shape. The primary limitations of these advances are discussed related to the data transformation in the shell of the indirect approach and the possibility to obtain negative margin rates of exchange between the desirable and undesirable end products in the sheath of the direct attack. These events contribute to the proposition of a new composite indicator model based on a directional distance function that overcomes the restrictions associated with the existing accesses. The incorporation of information on the relative importance of individual indicators using weight restrictions is discussed. Offered here is an enhanced formulation of weight restrictions, in the sort of assurance regions type I, that reflects the proportional importance of the indicators in percentage terms. The models are illustrated in the assessment of Brazilian hydropower plants and are suitable for any assessment involving the aggregation of key performance indicators whenever undesirable outputs are present.

(Kyung Taek Kim, Deok Joo Lee, Sung Joon Park et al., 2015) investigated the new and renewable energy (NRE) has been paid much attention as a core alternative energy that can respond to the depletion of fossil fuel, the global movement to address climate change, and recent high oil prices because it is more environment-friendly and sustainable than fossil fuel. As the scale of investment in NRE has increased, an intriguing issue of the efficiency of the investment has been raised since strategic selection and focused investment allows policy goals to be achieved with limited resources and budget. Especially, since there are several forms of renewable energy sources, the efficiency of each NRE technology must be tested to find suitable technologies for the environments of each target country and eventually realize efficient investments in NRE. The aim of this report is to measure the investment efficiency of three NRE technologies – wind power, photovoltaic, and fuel cells – with the DEA (data envelopment analysis) method considering the two policy objectives of public investment, technical evolution and broader dissemination of NRE in Korea. The results indicate that wind power is the most efficient renewable energy in Korea from the perspective of government investment.

This paper differs entirely from all other previous works by comparing and analyzing the current operation of the Public Sector Banks of India individually, in terms of their efficiency and profitability versus efficiency for the period [2004 – 2015] using the DEA, BCG Matrix and McKinsey Matrix. As a unique nature the researcher used Return on Investment to construct the BCG and McKinsey Matrices. This study classifies the Public Sector Banks into two classes as efficient and inefficient based on their investment. The remedial steps are discussed in order to better the efficiency and profitability of the Banks.

http://www.sciencedirect.com/science/article/pii/S1364032115001872






Research Methodology

Data Collection

For this subject field, the required data of all the Public Sector Banks have been withdrawn from their respective official sites for the financial years 2004 – 2015.

Pearson Correlation between Variables

Altogether there are seventeen variables identified both input and output variables put together. The affiliation of the variables is studied using correlation analysis before finalizing the number of variables for the survey. It discovers that only ten variables are closely related and they obey the isotonic hypothesis. The same is depicted in the following Table 1.

Table 1 [a] Relationship between the variables under study

O1 O2 O3 O4 I1 I2 I3 I4 I5 I6

O1 1

O2 1 1

O3 .996** .996** 1

O4 .994** .994** .995** 1

I1 .963** .963** .952** .946** 1

I2 .996** .996** .994** .992** .939** 1

I3 .970** .968** .974** .983** .913** .966** 1

I4 .997** .997** .996** .998** .954** .995** .973** 1

I5 .991** .991** .982** .977** .969** .988** .940** .983** 1

I6 1** 1** .996** .994** .963** .997** .967** .997** .992** 1

**. Correlation is significant at the 0.01 level (2-tailed).

Selection of Inputs and Outputs

Surveying the literature on the application of Data Envelopment Analysis (DEA), different studies have used different combination of inputs and yields. Established along the correlation analysis, the researcher considered six input variables and four output variables in order to hold an elaborate study. The variables used for this subject are listed below:




Table 1 [b] Variables which relate to Banking Sector considered

Input Variables Output Variables

Operating Expenses Operating Income

Total Expenditure Net Interest Income

Capital Reserves Investments

Interest Expenses Assets

Overhead Expenses

Operating Non-Interest expenses

CCR and BCC Model

The original CCR model was pertinent but to that expertise which is categorized by constant returns to scale. The major advancement was extended by Charnes, and Cooper (BCC) model to facilitate expertise that reveals variable returns to scale. This work has used input-oriented DEA model, which emphasizes on the minimization of inputs and the maximization of outputs held at their current degrees. Also the BCC model with variable return to scale is considered.

General Form of CCR Model:

The general form Output Maximization DEA [CCR] model can be exemplified in the form of Fractional Programming Model as follows:

Here the general model is constructed to maximize the efficiency of the qth output variable:

- jth output value of the qth DMU

- jthoutput variable of the qth DMU

- ith input value of the qth DMU

- ith input variable of the qth DMU

Eq - Efficiency of the qth DMU

Subject to the constraints

1; q = 1,2,…,n

Solving this fractional programming problem directly is so tedious; hence the fractional programming model is converted into regular linear programming model as described below:




Subject to the constraints = 1

;

The general form of input minimization DEA [CCR] linear programming model can be represented as follows:

Subject to the constraints

= 1; ≤ 0;

General form of BCC Model

The DEA envelopment program for considering variables return to scale is as follows:

Subject to the Constraints

;

;

;

Empirical Results

Before starting to estimate efficiency of Public Sector Banks in India to categorize those Banks based on investment (i.e.) Median = 41730.31. When compared Banks with the median value above means it occurs under high investment otherwise comes under a low investment group.

Constant Return to Scale

Table 2[a] and [b] communicates that the DEA efficiency score Constant return to scale under the CCR Model. The Analysis report strongly communicates six Public Sector Banks attained maximum efficiency score based on high investment and In case of Low investment, two Banks attain maximum efficiency based on the input oriented technical efficiency [CRS] for the year 2004 – 2015. It is noted that there is a changing trend in their mean of technical efficiency of Public Sector Banks in India from 2004to 2015, the score lies in the interval [0.99, 1] for high investment and [0.95, 1] for a low investment. The average efficiency of all the Public Sector Banks for the entire period is less than 1.




Variable Return to Scale

Table 3[a] and [b] communicates that the DEA efficiency score Variable Return to Scale under the BCC Model. In BCC Model there is an increment in the number of Public Sector Banks, which establishes the consistency in their execution.

It is noted that there is no variableness trend in their mean of pure technical efficiency of Public Sector Banks in India from 2004 to 2015 based on high investment and [0.99, 1] for a low investment. The average efficiency of all the Public Sector Banks for the entire period is less than 1.

Scale Efficiency

Table 4[a] and [b] shows the mean efficiency each year by decomposing technical efficiency into pure technical efficiency and scale efficiency. Decomposing technical efficiency into pure technical efficiency and scale efficiency allows us to gather insight into the master sources of inefficiencies. Founded on high investment, the average index of technical efficiency during the study period varies in between 99% to 100% and scale efficiency varying at 91% to 100%.

Based on low investment, the average index of technical efficiency during the study period varies in between 95% to 100%, pure technical efficiency varying at 99% to 100%and of scale efficiency varying at 85% to 100%.

Overall mean efficiency

Among nineteen Public Sector Banks considered for the study, all Banks comes under the high investment group are highly consistent with the efficiency score of 1 and stands first. The ranks of Public Sector Banks are given in the Table 5(a).

Based on low investment, except Dena Bank other Banks which are extremely uniform with the efficiency score of 1 and stands first. The ranks of remaining Public Sector Banks are given in the Table 5(b).

McKinsey matrix

In 1971 McKinsey and Co produced the line screen for General Electric to differentiate the potential for future profit in each of the 43 strategic business units. This matrix is also known as the industry attractiveness – business strength matrix and the nine-box matrix. GE / McKinsey Matrix is basically segregated into nine cells or nine alternative positioning of any product, service, or SBU. And each SBU has a distinguished situation which depends upon the competitive intensity and market attractiveness. The SBUs are placed or place in the Matrix after a careful thought process based on an analytic thinking of several factors and variables affecting the market attractiveness and competitive strength. Any wrong position of the any SBU will result in a wrong decision. Hence, it is pertinent to engage the key individuals capable of understanding the importance of each gene and its assessment of effects along the market attractiveness and competitive strength. Once the SBUs are carefully and thoughtfully positioned the GE Matrix enables the club to bring up alternative strategies with respect to each SBU.




Figure 1 Mc Kinsey Matrix

Crow - If the line unit is hard against a strong attractiveness, you get the commercial enterprise. This signifies, that you are ready to put a higher portion of your imaginations in these occupations. These business units have high market attractiveness and high business unit effectiveness. They are most likely to be successful if backed up with more resources. The quadrants marked in green are the spots where you can develop your clientele.

Hold - If the business unit strength or attractiveness is average, then you hold the business as it is. It might be that the market is dropping in value, or that there is a much higher competition, which the business unit will be hard set to catch up. In both the cases, the business unit might not give optimum returns even if resources are placed. So, in this example, you wait and apply the business unit to determine if the market environment changes or if the business unit gains importance in the marketplace as compared to other actors.

Harvest - If the business unit or market has become unattractive, and so you either sell or liquidate the business or you can carry it for any residual value that it holds. This strategy is practiced in the GE McKinsey matrix when the business unit effectiveness is weak and the grocery store has lost its attractiveness. The best step in this instance is to harvest the weak businesses and reinvest the money earned into business units which are in development.

Therefore, based on the GE McKinsey matrix, you can handle your product portfolio efficiently and can choose the right decision on producing, hold or harvest for your wares.

Table 1 [c] McKinsey Matrix for High Investment HIGH MED UIM LOW

HIGH GROW GROW HOLD

MEDIUM

GROW Bank of Baroda

HOLD Canara Bank

Central Bank of India

IOB

Oriental Bank of Commerce SBI

PNB

HARVEST

Corporation Bank

IDBI Bank

Bank of India

LOW HOLD NIL

HARVEST NIL

HARVEST NIL




McKinsey Matrix for Low Investment

HIGH MED UIM LOW

HIGH GROW GROW HOLD

MEDIUM GROW

Andhra Bank

Syndicate Bank

HOLD

UCO Bank

Union Bank of India

Vijaya Bank

Indian Bank

HARVEST

Allahabad Bank

Bank of Maharashtra

LOW HOLD NIL

HARVEST NIL

HARVEST NIL

BCG Matrix

Management consultants at the Boston Consulting Group developed their matrix in the early 1970s. They projected it to help managers at big corporations decide which business units they should invest in firms.

Figure 2 BCG Matrix

Table 1 [d] BCG Matrix for High Investment

Profit

SLEEPER QUADRANT

NIL

STAR QUADRANT Bank of Baroda

Canara Bank


IOB Oriental Bank of Commerce

PNB

Bank of India

QUESTION MARK QUADRANT

NIL

DOG QUADRANT

Corporation Bank IDBI Bank

SBI

Efficiency




Table 1 [e] BCG Matrix for Low Investment Profit

SLEEPER QUADRANT

NIL

STAR QUADRANT

Andhra Bank

Bank of Maharashtra

Syndicate Bank

UCO Bank

QUESTION MARK QUADRANT

NIL

DOG QUADRANT

Allahabad Bank

Dena Bank

Union Bank of India

Vijaya Bank

Indian Bank Efficiency

Conclusion

As per the three different analyses the researcher concludes that only the Public Sector Bank, namely, Bank of Baroda functions efficiently in high investment groups. According to Low investment, Andhra Bank performs well. The other Banks should take necessary step to improvise and the attain the efficiency layer.

References

AlKhathlan K. and Malik SA (2010) Are Saudi Banks Efficient? Evidence Using Data Envelopment Analysis (DEA). International Journal of Economics and Finance 2(2), 53-58.

Aswini Kumar Mishra, Jigar N Gadhia Bibhu PrasadKar, et al. (2013) Are Private Sector Banks More Sound and Efficient than Public Sector Banks? Assessments Based on Camel and Data Envelopment Analysis Approaches. Research Journal of Recent Sciences 2(4), 28-35.

Charnes A, Cooper WW and Rhodes E (1978) Measuring the efficiency of decision making units. European Journal of Operation Research 2:429-444.

Farrel MJ (1957) The measurement of Productivity efficiency. Journal of Royal Statistical Society (A), 120: 253 – 281.

Kablan S (2007) Measuring Bank Efficiency in Developing Countries: The Case Of WAEMU (West African Economic Monetary Union). Paper for African Economic Research Consortium.

Kyung Taek Kim, Deok Joo Lee, Sung Joon Park et al.,(2015) Measuring the efficiency of the investment for renewable energy in Korea using data envelopment analysis. Renewable and Sustainable Energy Reviews 47:694– 702.

Mohammad Romel Bhuia, Azizul Baten, Anaton Abdulbasah Kamil, and Nandini Deb (2012) Evaluation of Online Bank Efficiency in Bangladesh: A Data Envelopment Analysis (DEA) Approach. Journal of Internet Banking and Commerce 17(2): 1-17.

Park KH and Weber WL (2006) Profitability of Korean Banks: Test of market structure versus efficient structure. Journal of Economics and Business, 58: 222–239.




http://www.sciencedirect.com/science/journal/13640321







Maudos J, Pastor JM, Perez F et al., (2002) Cost and profit efficiency in European Banks. Journal of International Financial Markets, Institutions and Money 12: 33-58.

Mehmet Hasan Eken and Suleyman Kale (2011) Measuring Bank branch performance using Data Envelopment Analysis (DEA): The case of Turkish Bank branches. African Journal of Business Management 5(3): 889-901.

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http://www.sciencedirect.com/science/article/pii/S037722171500243X









ANNEXURE Table 2[a] Constant Return to Scale based on high investment

Name of the Bank 2004-

05 2005-

06 2006-

07 2007-

08 2008-

09 2009-

10 2010-

11 2011-

12 2012-

13 2013-

14 2014-

15 Mean Efficiency

Bank of Baroda 1 1 1 1 1 1 1 1 1 1 1 1

Canara Bank 1 1 1 1 1 1 1 1 1 1 1 1 Central Bank of India 1 1 1 1 1 0.97 1 1 1 1 1 1

Corporation Bank 1 1 1 1 1 1 0.97 0.95 1 1 1 1

IDBI Bank 1 1 1 1 1 1 1 0.99 1 1 1 1

IOB 1 1 1 1 1 1 1 1 0.95 0.96 1 0.99

Oriental Bank of Commerce

1 1 1 1 1 .91 1 1 0.97 0.99 1 0.99

PNB 1 1 1 1 1 1 1 0.95 0.98 1 1 0.99

SBI 1 1 0.96 1 1 1 1 1 0.98 0.98 1 0.99

Bank of India 1 1 1 1 1 1 1 0.98 1 1 1 1

Mean 1 1 1 1 1 0.99 1 0.99 0.99 0.99 1

Table 2[b] Constant Return to Scale based on low investment


05 2005-

06 2006-

07 2007-

08 2008-

09 2009-

10 2010-

11 2011-

12 2012-

13 2013-

14 2014-

15 Mean

Efficiency

Allahabad Bank 1 1 1 1 1 1 1 1 0.97 0.949 1 0.99

Andhra Bank 1 1 1 1 1 1 1 1 1 1 1 1 Bank of

Maharashtra 0.98 0.98 1 1 1 1 1 1 1 0.98 1 1

Dena Bank 0.89 1 1 1 1 0.80 1 1 1 0.93 1 0.97

Syndicate Bank 0.97 1 1 1 1 1 1 1 1 0.99 1 1

UCO Bank 1 1 1 1 1 0.99 1 0.97 1 1 1 1

Union Bank of India 1 1 1 0.99 1 1 0.99 1 1 0.93 1 0.99

Vijaya Bank 1 1 1 1 1 1 1 1 1 1 1 1

Indian Bank 1 1 1 1 1 0.80 1 1 1 1 1 0.99

Mean 0.98 1 1 1 1 0.95 1 1 1 0.98 1




Table 3[a] Variable Return to Scale based on High Investment


05 2005-

06 2006-

07 2007-

08 2008-

09 2009-

10 2010-

11 2011-

12 2012-

13 2013-

14 2014-

15 Mean

Efficiency

Bank of Baroda 1 1 1 1 1 1 1 1 1 1 1 1

Canara Bank 1 1 1 1 1 1 1 1 1 1 1 1 Central Bank of India 1 1 1 1 1 1 1 1 1 1 1 1

Corporation Bank 1 1 1 1 1 1 1 1 1 1 1 1

IDBI Bank 1 1 1 1 1 1 1 1 1 1 1 1

IOB 1 1 1 1 1 1 1 1 0.97 0.96 1 1


1 1 1 1 1 1 1 1 0.98 1 1 1

PNB 1 1 1 1 1 1 0.96 0.96 1 1 1 1

SBI 1 1 1 1 1 1 1 1 1 1 1 1

Bank of India 1 1 1 1 1 1 0.98 0.98 1 1 1 1

Mean 1 1 1 1 1 1 1 1 1 1 1

Table 3[b] Variable Return to Scale based on Low Investment


05 2005-

06 2006-

07 2007-

08 2008-

09 2009-

10 2010-

11 2011-

12 2012-

13 2013-

14 2014-

15 Mean

Efficiency

Allahabad Bank 1 1 1 1 1 1 1 1 1 0.99 1 1

Andhra Bank 1 1 1 1 1 1 1 1 1 1 1 1

Bank of Maharashtra

1 1 1 1 1 1 1 1 1 1 1 1

Dena Bank 1 1 1 1 1 1 1 1 1 1 1 1

Syndicate Bank 1 1 1 1 1 1 1 1 1 1 1 1

UCO Bank 1 1 1 1 1 1 1 1 1 1 1 1

Union Bank of India 1 1 1 1 1 1 1 1 1 1 1 1

Vijaya Bank 1 1 1 1 1 1 1 1 1 1 1 1

Indian Bank 1 1 1 1 1 0.94 1 1 1 1 1 1

Mean 1 1 1 1 1 .99 1 1 1 1

1




Table 4[a] Scale Efficiency based on high investment

Name of the Bank 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15

Bank of Baroda 1 1 1 1 1 1 1 1 1 1 1

Canara Bank 1 1 1 1 1 1 0.99 1 1 1 1

Central Bank of India 1 1 1 1 1 0.98 1 1 1 1 1

Corporation Bank 1 1 1 1 1 1 0.97 0.95 1 1 1 IDBI Bank 1 1 1 1 1 1 1 1 1 1 1

IOB 1 1 1 1 1 1 1 1 0.98 1 1

Oriental Bank of Commerce 1 1 1 1 1 0.91 1 1 0.99 0.99 1

PNB 1 1 1 1 1 1 1 0.99 0.98 1 1

SBI 1 1 0.96 1 1 1 1 1 0.98 0.98 1

Bank of India 1 1 1 1 1 1 1 1 1 1 1

Table 4[b] Scale Efficiency based on low investment

Name of the Bank 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15

Allahabad Bank 1 1 1 1 1 1 1 1 0.97 0.96 1

Andhra Bank 1 1 1 1 1 1 1 1 1 1 1

Bank of Maharashtra 0.98 0.98 1 1 1 1 1 1 1 0.98 1

Dena Bank 0.89 1 1 1 1 0.80 1 1 1 0.93 1

Syndicate Bank 0.97 1 1 1 1 1 1 1 1 0.99 1

UCO Bank 1 1 1 1 1 0.98 1 0.97 1 1 1

Union Bank of India 1 1 1 0.99 1 1 0.99 1 1 0.93 1

Vijaya Bank 1 1 1 1 1 1 1 1 1 1 1

Indian Bank 1 1 1 1 1 0.85 1 1 1 1 1




Table 5[a] Overall mean efficiency of all the measures put together for high investment

Table 5[b] Overall mean efficiency of all the measures put together for low investment

Name of the Banks Mean efficiency [CRS] Mean efficiency [VRS] Mean of mean efficiency Rank based mean on efficiency

Bank of Baroda 1 1 1 1

Canara Bank 1 1 1 1

Central Bank of India 1 1 1 1

Corporation Bank 1 1 1 1

IDBI Bank 1 1 1 1

IOB 0.99 1 1 1

Oriental Bank of Commerce 0.99 1 1 1

PNB 0.99 1 1 1

SBI 0.99 1 1 1

Bank of India 1 1 1 1

Name of the Banks Mean efficiency [CRS] Mean efficiency [VRS] Mean of mean efficiency Rank based mean on efficiency

Allahabad Bank 0.99 1 1 1

Andhra Bank 1 1 1 1

Bank of Maharashtra 1 1 1 1

Dena Bank 0.97 1 .99 2

Syndicate Bank 1 1 1 1

UCO Bank 1 1 1 1

Union Bank of India 0.99 1 1 1 Vijaya Bank 1 1 1 1

Indian Bank 0.99 1 1 1




Table 6[a] Tabulated Form of McKinsey Matrix based on high investment

Name of the Bank 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15

Bank of Baroda LL ML MH MH MH MH HH HL ML MH MH

Canara Bank LH MH ML MH ML ML LH HL ML MH ML

Central Bank of india ML ML ML MH ML LH HH HH ML ML ML

Corporation Bank MH MH ML ML ML ML LL LL ML ML ML

IDBI Bank ML ML ML ML ML ML HL LL ML ML ML

IOB MH MH ML ML ML MH HL LH LH LH ML Oriental Bank of Commerce MH MH MH MH MH LH HH HH LH LL ML

PNB ML MH MH ML MH MH LH LL LH ML MH

SBI ML ML LH ML MH ML HH HH LH LH MH

Bank of India MH ML LH MH LH ML LL LH MH MH MH

Table 6[b] Tabulated Form of McKinsey Matrix based on low investment

Name of the Bank 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15

Allahabad Bank ML MM MH MM ML ML ML ML LL LH MH

Andhra Bank MH MH ML MH MH MH ML MH MH HL MH

Bank of Maharashtra LL LL MH MH MH MM ML MH ML LH MH Dena Bank LM ML ML ML ML LL MM ML ML LL ML

Syndicate Bank LH MH MM MH MH MH MH MH MH MH ML

UCO Bank MH MH MH MM MM LL ML LL MH HL ML

Union Bank of India MH MH MH LH ML ML LH MH MM LM MM

Vijaya Bank ML ML ML ML MH MH HL ML ML HL ML

Indian Bank ML ML ML ML ML LL MH MM MH HH MH




Table 7[a] Final Table for McKinsey Matrix based on high investment NAME OF THE

BANK 2004-05

2005-06

2006-07

2007-08

2008-09

2009-10

2010-11

2011-12

2012-13

2013-14

2014-15

MEAN ROUNDED OFF

RATINGS

COMMENTS

Bank of Baroda 1 4 6 6 6 6 9 7 4 6 6 6 MH GROW

Canara Bank 3 6 4 6 4 4 3 7 4 6 4 5 MM HOLD


4 4 4 6 4 3 9 9 4 4 4 5 MM HOLD

Corporation Bank 6 6 4 4 4 4 1 1 4 4 4 4 ML HARVEST

IDBI Bank 4 4 4 4 4 4 7 1 4 4 4 4 ML HARVEST

IOB 6 6 4 4 4 6 7 3 3 3 4 5 MM HOLD


6 6 6 6 6 3 9 9 3 1 4 5 MM HOLD

PNB 4 6 6 4 6 6 3 1 3 4 6 5 MM HOLD

SBI 4 4 3 4 6 4 9 9 3 3 6 5 MM HOLD

Bank of India 6 4 3 6 3 4 1 3 6 6 6 4 ML HARVEST

Table 7[b] Final Table for McKinsey Matrix based on low investment NAME OF THE

BANK 2004-

05 2005-

06 2006-

07 2007-

08 2008-

09 2009-

10 2010-

11 2011-

12 2012-

13 2013-

14 2014-

15 MEAN

ROUNDED OFF RATIN

GS COMME

NTS

Allahabad Bank

4 5 6 5 4 4 4 4 1 3 6 4 ML HARVES

T

Andhra Bank 6 6 4 6 6 6 4 6 6 7 6 6 MH GROW

Bank of Maharashtra

1 1 6 6 6 5 4 6 4 3 6 4 ML HARVES

T

Dena Bank 2 4 4 4 4 1 5 4 4 1 4 3 LH HOLD

Syndicate Bank 3 6 5 6 6 6 6 6 6 6 4 6 MH GROW

UCO Bank 6 6 6 5 5 1 4 1 6 7 4 5 MM HOLD

Union Bank of India

6 6 6 3 4 4 3 6 5 2 5 5 MM HOLD

Vijaya Bank 4 4 4 4 6 6 7 4 4 7 4 5 MM HOLD

Indian Bank 4 4 4 4 4 1 6 5 6 9 6 5 MM HOLD




Table 8 [a] Mean efficiency [based on CRS and VRS] and Return on Investment based on high Investment

NAME OF THE BANK 2004-05 ROI 2005-06 ROI 2006-07 ROI 2007-08 ROI 2008-09 ROI

Bank of Baroda 1 0.17 1 0.20 1 0.26 1 0.28 1 0.06 Canara Bank 1 0.20 1 0.24 1 0.25 1 0.29 1 0.04

Central Bank of India 1 0.17 1 0.19 1 0.23 1 1 1 0.03

Corporation Bank 1 0.22 1 0.25 1 0.24 1 0.27 1 0.04

IDBI Bank 1 0.11 1 0.21 1 0.25 1 0.25 1 0.04

IOB 1 0.21 1 0.23 1 0.24 1 0.28 1 0.03

Oriental Bank of Commerce 1 0.20 1 0.25 1 0.26 1 0.29 1 0.31

PNB 1 0.17 1 0.23 1 0.26 1 0.26 1 0.31

SBI 1 0.17 1 0.22 0.98 0.27 1 0.12 1 0.23

Bank of India 1 0.21 1 0.22 1 0.26 1 0.90 1 0.31

MEAN 1 0.1814 1 0.22 1 0.25 1 0.40 1 0.14

Table 8.1 [a] Mean efficiency [based on CRS and VRS] and Return on Investment based on high Investment

NAME OF THE BANK 2009-10 ROI 2010-11 ROI 2011-12 ROI 2012-13 ROI 2013-14 ROI 2014-15 ROI Bank of Baroda 1 0.30 1 0.31 1 0.031 1 0.29 1 0.34 1 0.35

Canara Bank 1 0.22 1 0.28 1 0.30 1 0.28 1 0.31 1 0.30

Central Bank of India 0.98 0.27 1 0.28 1 0.32 1 0.13 1 0.28 1 0.28

Corporation Bank 1 0.24 0.99 0.21 0.98 0.13 1 0.26 1 0.27 1 0.31

IDBI Bank 1 0.26 1 0.073 0.99 0.28 1 0.25 1 0.26 1 0.23

IOB 1 0.28 1 0.25 1 0.32 0.96 0.34 0.96 0.32 1 0.30

Oriental Bank of Commerce 0.95 0.29 1 0.29 1 0.30 0.97 0.30 0.99 0.31 1 0.30

PNB 1 0.28 0.98 0.28 0.95 0.30 0.99 0.32 1 0.31 1 0.31

SBI 1 0.25 1 0.28 1 0.34 0.99 0.34 0.99 0.34 1 0.31

Bank of India 1 0.25 0.99 0.25 0.98 0.32 1 0.34 1 0.33 1 0.36

MEAN 0.99 0.26 0.99 0.25 0.99 0.27 0.99 0.29 0.99 0.31 1 0.30




Table 8 [b] Mean efficiency [based on CRS and VRS] and Return on Investment based on Low investment

NAME OF THE BANK 2004-05 ROI 2005-06 ROI 2006-07 ROI 2007-08 ROI 2008-09 ROI

Allahabad Bank 1 0.17 1 0.21 1 0.26 1 0.27 1 0.04 Andhra Bank 1 0.21 1 0.23 1 0.23 1 0.29 1 0.32

Bank of Maharashtra 0.9885 0.16 0.99 0.21 1 0.24 1 0.29 1 0.23

Dena Bank 0.944 0.18 1 0.21 1 0.23 1 0.26 1 0.02

Syndicate Bank 0.986 0.19 1 0.54 1 0.24 1 0.28 1 0.31

UCO Bank 1 0.19 1 0.22 1 0.27 1 0.27 1 0.21

Union Bank of India 1 0.22 1 0.23 1 0.26 0.99 0.28 1 0.04

Vijaya Bank 1 0.17 1 0.21 1 0.23 1 0.24 1 0.30

Indian Bank 1 0.16 1 0.18 1 0.21 1 0.24 1 0.04

MEAN 0.99 0.18 1 0.58 1 0.24 1 0.27 1 0.17

Table 8.1 [b] Mean efficiency [based on CRS and VRS] and Return on Investment based on low Investment

NAME OF THE BANK 2009-10 ROI 2010-11 ROI 2011-12 ROI 2012-13 ROI 2013-14 ROI 2014-15 ROI

Allahabad Bank 1 0.22 1 0.26 1 0.29 0.98 0.30 0.97 0.32 1 0.35

Andhra Bank 1 0.31 1 0.34 1 0.38 1 0.34 1 0.13 1 0.35

Bank of Maharashtra 1 0.22 1 0.25 1 3.25 1 0.31 0.99 0.32 1 0.35

Dena Bank 0.90 0.21 1 0.27 1 0.30 1 0.26 0.97 0.27 1 0.3

Syndicate Bank 1 0.30 1 0.33 1 0.37 1 0.38 1 0.34 1 0.31

UCO Bank 0.99 0.22 1 0.27 0.98 0.32 1 0.32 1 0.27 1 0.28

Union Bank of India 1 0.22 1 0.28 1 0.34 1 0.31 0.97 0.31 1 0.34

Vijaya Bank 1 0.25 1 0.23 1 0.28 1 0.29 1 0.25 1 0.28

Indian Bank 0.87 0.21 1 0.27 1 0.32 1 0.33 1 0.32 1 0.35

MEAN 0.97 0.24 1 0.28 1 0.65 1 0.32 0.99 0.28 1 0.32




Table 8.2[a] BCG Ratings for high investment NAME OF THE

BANK 2004-

05 2005-

06 2006-

07 2007-

08 2008-

09 2009-

10 2010-

11 2011-

12 2012-

13 2013-

14 2014-

15 MEAN

ROUNDED OFF RATIN

GS COMME

NTS

Bank of Baroda 1 3 5 3 3 2 5 3 5 5 5 4 ST GOOD

Canara Bank 4 5 5 3 3 1 5 5 3 5 3 4 ST GOOD


3 3 3 5 3 2 5 5 3 5 3 4 ST GOOD

Corporation Bank 5 5 3 3 3 1 1 1 3 5 3 3 D

IDBI Bank 3 3 3 3 3 1 3 5 3 5 3 3 D

IOB 5 5 3 3 3 2 5 5 2 2 3 4 ST GOOD


5 5 5 3 5 2 5 5 2 2 3 4 ST GOOD

PNB 3 5 5 3 5 2 2 2 2 5 5 4 ST GOOD

SBI 3 3 2 3 5 1 5 5 2 2 5 3 D

Bank of India 5 3 5 5 5 1 2 2 5 5 5 4 ST GOOD

Table 8.2[b] BCG Ratings for low investment NAME OF THE

BANK 2004-

05 2005-

06 2006-

07 2007-

08 2008-

09 2009-

10 2010-

11 2011-

12 2012-

13 2013-

14 2014-

15 MEAN

ROUNDED OFF RATIN

GS COMME

NTS

Allahabad Bank 3 3 5 5 3 3 3 3 1 2 5 3 D

Andhra Bank 5 3 3 5 5 5 5 3 5 3 5 4 ST GOOD

Bank of Maharashtra

1 1 3 5 5 3 3 5 3 4 5 4 ST GOOD

Dena Bank 1 3 3 3 3 1 3 3 3 1 3 3 D

Syndicate Bank 2 5 3 5 5 5 5 3 5 4 3 4 ST GOOD

UCO Bank 5 3 5 5 5 3 3 1 5 3 3 4 ST GOOD

Union Bank of India

5 3 5 2 3 3 2 3 3 2 5 3 D

Vijaya Bank 3 3 3 3 5 5 3 3 3 3 3 3 D

Indian Bank 3 3 3 3 3 1 3 3 5 5 5 3 D

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