40Review Article Economics of Agriculture 4/2013
UDC: 336.717
APPLICATION OF DEA METHODOLOGY IN MEASURING EFFICIENCY IN THE BANKING SECTOR1
Radojka Maletic2, Marija Kreca3, Predrag Maletic4
Summary
The most important aspect of any business is efficiency. The goal is to achieve greater outputs with lower inputs, or to use the available inputs to the maximum. In this paper, the use of DEA technique will be illustrated in case of measuring operation efficiency of the banking sector in Serbia, which currently has 33 banks. The efficiency of banks will be measured using two models with different input-output indicators, followed by a comparative analysis of the results using the BCG matrix. Banks are ranked according to their efficiency, and the similarities and differences that were observed in the study were commented.
Key words: Banks, DEA, business efficiency, super efficiency, BCG matrix. JEL: C67, D61, G21
Introduction
The global economic crisis has hit hard the Serbian economy and its effects are visible in the banking sector. Banks in Serbia are in very restrictive conditions set by the National Bank of Serbia, in the conditions of strong competition, along with the processes of reform and privatization of their clients. Important changes in the banking sector were enabled by the adoption of the new Law on banks in November 2005. Its full implementation began on 1 October 2007. The new law outlines procedures for strict control of banking, based on the same or similar parameters for consolidation (Mihajlovic et al., 2009).
Numerous authors have dealt with the examination of efficiency of the banking sector by applying different methods in different locations. Given the different approaches and goals,
1 This paper is the result of the research financed by the Ministry of Science and Technological Development, Project number OI 179028 "Rural Labour Market and Rural Economy of Serbia -Income Diversification as a Toll to Overcome Rural Poverty".
2 Radojka Maletic, Ph.D., Full Professor, University of Belgrade, Faculty of Agriculture, Nemanjina Street no. 6, Belgrade, Serbia, Phone: +381 11 261 53 15, E-mail: maletic@agrif.bg.ac.rs
3 Marija Kreca, M.Sc., University of Belgrade, Faculty of Organizational Sciences, Belgrade, Serbia, E-mail: marija.kreca@yahoo.com
4 Predrag Maletic, M.Sc., Raiffeisen Banka, Belgrade, Serbia, E-mail: predrag.maletic@yahoo.com EP 2013 (60) 4 (843-855) 843
authors have used a variety of indicators in their research that have monitored the efficiency of banking sector. However, successful analysis of business requires multi criteria comparative analysis of business performance, so the usage of some of the appropriate parametric or nonparametric methods is recommended. For evaluation of relative efficiency, frequently the nonparametric method DEA (Data Envelopment Analysis) is used.
Mohamed (2006) has systematized the numerous approaches in researches of business efficiency of banks in the world using the DEA technique. He has stated that Wu et al. (2006) observed in Canada 142 banks, and monitored the number of employees and costs for input indicators, while for output the following was observed: deposit, income and bank loans. He has further stated that Sakar (2006) in Turkey analysed 11 banks and monitored: input - branch numbers, employees per branch, assets, loans, deposits, and outputs: ROA (return on assets), ROE (return on equity), and interest income (assets), interest income (operating income), and non-interest income (assets). Then in 2002, Mukherjee et al., in India, monitored and analysed 68 banking operations, where the input was: net worth, borrowings, operating expenses, employees, number of branches, and outputs: deposits, net profit, deposit, non-interest income, interest income. Authors Howland and Rowse (2006) observed 162 banks in Canada, where the observed input indicators included: non-sales FTE, sales FTE, the size, the city, the employment rate, and three output indicators: deposits, loans, the average number of products /customers, client loyalty. He also presented in detail many other researches around the world in the aforementioned study (Mohamed, 2006).
On the other hand Milind (2003) carried out the banks efficiency analysis in India on the basis of two models (model A inputs are: interest expenses, non-interest expenses, and outputs: net interest income, net non-interest income, and model B inputs are: deposits, employees, and outputs: net loans, non-interest income).
Authors Fries and Taci (2004) compare the performance of289 banks in 15 post-communist countries, using an intermediate approach. The results show that foreign banks are more competitive and have better results in cost efficiency than domestic banks. Also, research carried out in Poland by the author Guzowska et al. (2004) shows that better results are obtained if the banks are divided into two groups, domestic and foreign.
In surrounding countries, one of the first studies to appear was the comparison of bank efficiency in Croatia during the transition period from 1995 to 2000 (Jermic, Vujcic, 2002). Trend efficiency ratio was positive, thanks to changes in the ownership structure. At the beginning of the period there was one foreign and 53 domestic banks and in the end there were 20 foreign and 20 national banks.
In Serbia, the first research in this field, using the DEA method, was conducted by Bulajic et al. (2011) and (2011). In this research 30 banks were monitored and their observed indicators were: input - measurement for operational costs engaging capital, measurement for the cost of available capital, measurement of overall risk exposures, and asset value for January 1st in observed year, employees, number of affiliates and bank branches. Considered outputs were: the value of total assets on December 31st in observed year (total assets at beginning of year
plus profit/loss) as a measurement of business performance, total revenues in the period of a year, interest income.
Following the example of Milind Sathye's research (2003), in the present analysis the same input and output indicators will also be used for the purpose of bank ranking in Serbia. Currently, in Serbia 33 banks operate. The goal is to determine which one is the most efficient. Nineteen out of 33 observed banks belong to the private sector. Data were taken from the balance sheets and income statements of banks from the website of the National Bank of Serbia for 20 1 05. According to these data, the highest share of total assets from the balance sheet has Banca Intesa in the amount of 357.163.979.000 dinars, while the lowest share has the Moskovska Bank (3.127.165.000 dinars), because this bank started operation in 2008.
The restructuring process and implementation of reforms led to significant differences in the number of banks that operate since 2003 until today. In fact, at the end of 2003 there were 47 banks, at the end of 2004 - 43 banks were operating, and 40 banks at the end of 2005. Today, there are 33 operating banks. These changes occurred as the result of privatization and bank mergers.
Before taxation in 2010, bank with the largest profit was Banca Intesa (8.5 billion dinars), then AIK Bank Nis (6.2 billion dinars) and Unicredit Bank (3.9 billion dinars). The highest loss had OTP Bank (2.5 billion dinars) and Alpha Bank (1.7 billion dinars).
The largest bank was the Banca Intesa, followed by the Komercijalna Bank, and the largest improvement in ranking list of ten largest banks was recorded for EuroBank EFG6.
The basics of data envelopment analysis
DEA is specially designed technique for measuring of the efficiency of complex entities with diverse inputs and outputs. Increasingly it is being used for evaluation and improvement the operation of numerous business entities7, and its use is expanded on evaluation the efficiency of schools, hospitals, bank branches, production facilities, etc. The analysis provides results based on which we can determine how much are some units inefficient compared to efficient units. In this way, it is possible to determine also how much is necessary to reduce the input and/or to increase the output of unit to become efficient. All of these units are called DMU (Decision Making Unit). For each inefficient DMU, DEA identifies a set of corresponding efficient units that can be used as indicators for improvement.
DEA provides the construction of linear approximation of efficiency limit that is obtained on the bases of available units. Thus, we observe a set of points and construct the line that wraps them (envelope) and that actually represents the limit of efficiency. That limit is the
5 Available at: http://www.nbs.rs/internet/cirilica/50/index.html
6 Available at: http://www.naslovi.net/2011-04-19/beta/dobitak-banka-2010-veci-za-27-odsto/2485562
7 Available at: http://www.decisionsciences.org/decisionline/vol31/31_3/31_3pom.pdf, accessed, June 2011.
maximum of outputs that each decision making unit can achieve with their inputs, and for ineffective units it represents the envelope. There are two approaches:
0 wrapping the input from below (the output is achievable with less input),
0 wrapping the output from top (with the given input it is possible to produce more output).
This powerful tool can handle multiple inputs and outputs, with no requirements that these inputs and outputs are related (they can be heterogeneous), and one additional positive side of this method is the possibility of mutual comparison of DMUs. Like all techniques, it also has its deficiencies. DEA is suitable for comparison of relative efficiency of DMUs, so it performs mutual comparison of DMUs, but not their comparison with the "theoretical maximum". Due to the standard formulation, DEA makes a special linear program for each DMU8. DEA also allows the improvement of inefficient inputs and outputs in order to become efficient. It should be noted that DEA is primarily a diagnostic tool and it doesn't transform the inefficient units to efficient9.
Basic DEA models
Over the last 30 years, the field of usage of DEA method has been extensively updated. The basic idea for development of DEA method is to enable the efficiency measurement in non-profit sector (education) where there are no exact financial measures. Later, DEA method was applied also in the profit sector. Numerous applications have caused the development of new methods and models, but in this paper, for the purpose of understanding the basics of DEA method, CCR (Charnes, Cooper and Rhodes), BCC (Banker, Charns and Cooper) and AP (Andersen Petersen) model are presented. In the CCR model the boundary includes the linear combination of existing DMUs, while in BCC model this border has convex shape10.
Andersen and Petersen (1993) proposed ranking model, i.e. measuring of so called super-efficiency (AP model). Using this model, the unit that is examined is not being considered allowing other DMUs to achieve efficiency greater than 1, which allows the ranking of efficient and inefficient units11. Specifically, this model shows how much the unit can "get worse" but still be efficient. Those so called superefficient units are those with efficiency of over 100%, and the most efficient is the one which is highest ranked. The units with efficiency less than 100% are inefficient and therefore ranked lower. AP model can be defined using the mathematical expression:
8 Available at: http://mat.gsia.cmu.edu/classes/QUANT/NOTES/chap12.pdf, accessed June 2011.
9 Available at: http://www.decisionsciences.oig/decisionline/vol31/31_3/31_3pom.pdf, accessed June 2011.
10 Available at: http://as.nida.ac.th/ornet/conf04/OR-CRN_lecture/sowanee.pdf, accessed August 2011.
11 Available at: http://www.decisionsciences.org/decisionline/vol31/31_3/31_3pom.pdf, accessed June 2011.
With limitations:
The optimal values of efficiency scores hk are obtained by solving the linear model (1)-(5) k-times (once for each DMU in order to compare it with other DMUs). Efficiency score hk is greater or equal to 1 for all efficient units and smaller than 1 for inefficient units. In this way, ranking of units, according to their efficiency, is enabled. The smaller the value of efficiency score hk, the less efficient is the unit.
Given that the results of DEA model are significantly determined by the input-output indicators and even the minor change is reflected in the end result, in this paper two models (sets of indicators) are considered. That is, of all the mentioned indicators two models are designed (A and B) to be independently considered and developed (Table 1).
Evaluation the efficiency of banks in Serbia
Banks are assessed in two aspects, i.e. two models (model A and model B). The purpose of the first model is to define how much interest and non-interest expenses should be reduced so the bank can achieve the highest interest, i.e. non-interest income. On the other hand (model B), the bank employment aspect is observed, but also deposits that affect the output, i.e. increasing operating income and loans and deposit. Determination the total efficiency of bank through these two models refers to reduction of the inputs in order to achieve higher output, i.e. income.
Table 1. Input and output indicators of used models
Model A Model B
Input Output Input Output
- interest expenses - non-interest expenses - interest income - net non-interest income - deposits - employees - loans and deposit - operating income
Besides, the purpose of this analysis, beside the evaluation the efficiency, is bank ranking using both operating criteria. Thus, we are applying the Andersen-Petersen model, assuming the constant return to scale. Given the different size of banks, we assume that hypothesis of variable return to scale would be more appropriate for this kind of research, but it is known from the literature that the solution of the AP model with the hypothesis of variable return to scale is unstable. Since the hypothesis of constant return to scale is stricter, the results are
relevant. If there is a request of comparison of banks by groups, by size of assets, then it's necessary to perform additional analyses using the BCC model.
Data are mathematically processed using software EMS (Efficiency Measurement System)12. During analysis we concluded that there were out layers, i.e. units that had very large scores, thus are excluded from further consideration. For model A the out layers are: Jugobank, Postanska Stedionica and Dunav Bank. For model B these units are: Jugobank, Marfin Bank and AIK Bank. Excluding the out layers the following results were obtained (Table 2).
Table 2. The results of models A and B
DMU Model A DMU Model B
Score (%) Rank Score (%) Rank
Jugoslovenska Bank 155.05 1 Dunav Bank 174.60 1
AIK Bank 154.34 2 Volksbank 140.47 2
Volksbank 122.94 3 ProCredit Bank 121.23 3
Crédit Agricole 119.42 4 Poljoprivredna Bank 115.00 4
Banca Intesa 113.07 5 Piraeus Bank 99.87 5
Société Générale 104.90 6 Raiffeisen Bank 99.47 6
Raiffeisen Bank 103.29 7 Hypo Alpe-Adria 98.57 7
Komercijalna Bank 88.88 8 Société Générale 92.76 8
Eurobank EFG 88.28 9 Unicredit Bank 92.67 9
Erste Bank 87.69 10 Opportunity Bank 91.46 10
Unicredit Bank 87.27 11 Vojvodjanska Bank 90.55 11
Marfin Bank 87.08 12 OTP Bank 89.64 12
Cacanska Bank 86.45 13 Eurobank EFG 85.50 13
Poljoprivredna Bank 86.28 14 Jugoslovenska Bank 83.38 14
OTP Bank 85.86 15 Banca Intesa 81.23 15
Credy Bank 84.64 16 Postanska Stedionica 79.59 16
Srpska Bank 82.98 17 Privredna Bank 75.76 17
Razvojna Bank Voj. 81.13 18 Findomestic Bank 75.05 18
ProCredit Bank 77.87 19 NLB Bank 61.34 19
Univerzal Bank 77.64 20 Erste Bank 56.41 20
Hypo Alpe-Adria 77.41 21 KBC Bank 56.40 21
KBC Bank 74.74 22 Crédit Agricole 53.30 22
Findomestic Bank 74.61 23 Cacanska Bank 51.43 23
NLB Bank 74.57 24 Razvojna Bank Voj. 48.35 24
Vojvodjanska Bank 61.80 25 Srpska Bank 48.26 25
Moskovska Bank 60.47 26 Komercijalna Bank 41.44 26
Piraeus Bank 59.45 27 Moskovska Bank 39.23 27
Opportunity Bank 59.03 28 Alpha Bank 38.42 28
Privredna Bank 55.88 29 Credy Bank 35.52 29
Alpha Bank 51.71 30 Univerzal Bank 31.08 30
Source: Authors calculated this data from defined indicators the balance sheets and income statements of banks in Republic of Serbia;
12 Available at: http://www.wiwi.uni-jena.de/Mikro/pdf/ems.pdf
848 EP 2013 (60) 4 (843-855)
The table above shows that Jugoslovenska Bank is at the first position with super efficiency 155.05%. This bank has small investments compared to other DMUs, while the outputs are slightly higher. This is a benchmark for other 4 banks. The greatest importance is attached to interest expenses, for inputs, and interest income for outputs. In second place is AIK Bank with super efficiency 154.34%. Next ranked is Volksbank, followed by Crédit Agricole and Banca Intesa.
Penultimate is Privredna Bank, a bank of a public sector with low inputs as well as outputs. The last place takes Alpha Bank with efficiency of 51.71%.
According the results of DMUs analysis of model B (Table 2), Dunav Bank takes first place, with super efficiency 174.60%. The reason for this is low inputs. Majority share of the equity of the Dunav Bank has the company "Dunav osiguranje" (Danube Insurance) a.d.o. Belgrade13, so it is clear why this bank has low investments. This bank represents the benchmark to other 9 banks. The greatest importance is assigned to deposits (100%) as input, while for output the operating income (importance 1.75).
The other three banks that are also superefficient are Volksbank (140.47%, the benchmark to other 25 banks), ProCredit Bank (121.23%, the benchmark to other 17 banks) and Poljoprivredna Bank (115.00%, the benchmark to 9 banks).
The last place takes Univerzal Bank, with efficiency of only 31.08%. This bank has large investment, but low output, which causes the inefficiency of this unit. The benchmarks for this unit are Eurobank EFG and Volksbank.
Comparative analysis of models
In order to visually show the efficiency of banks using both models (A and B), comparative analysis was performed using the BCG (Boston Consulting Group) matrix (Figure 1):
Figure 1. BCG matrix
Source: Illustration of BCG matrix taken from website http://mfiles.pl/en/index.php/BCG_ growth-share_matrix
13 Available at: http://www.nbs.rs/internet/cirilica/50/index.html, Accessed June-August, 2011. EP 2013 (60) 4 (843-855) 849
This is a chart created by Bruce Henderson, whose goal was to help the corporation to analyse the operations of its business units or product lines14. The purpose of this matrix is:
0 to provide a simple, clear view of the entire current portfolio of the company to the strategist,
0 it allows making useful decisions about growth strategy, and eventual elimination of unprofitable parts15.
The chart is divided into four parts:
0 Stars - is well placed product with large share on a rapidly growing market, and represents holders of development. Requires large investments in order to maintain that position. If they keep the market share and the market growth slows down, Stars will become Cash Cows. Stars provide the opportunity for quick, loud progress, but not the money.
0 Cash cows - are units with large market share and slow market growth, these are mature business, the sources of cash. They require much less investment than Stars, but they still need the attention in order to provide the company the constant cash flow. Cash Cows finance everything else, including rising Stars, which will also become Cash Cows, when time comes.
0 Question Marks - is a problematical area. These units have small share on a rapidly growing market. Their progress requires a serious investment. Question Marks probably have a potential, but the biggest question is whether it pays off to the company to invest into these units.
0 Dogs - are units that barely manage to remain at zero. They have a small share on the market that barely grows, or that is getting smaller (shrinking). The most common decision made when it comes to the Dogs is to put them to sleep.
The overall objective of this ranking is to help the corporations' analysts to decide which of their units to finance and how much, and which to sell16. In table 3 are represented the efficiency indexes and bank ranks from both analysed business aspects. These results were used to form a modified BCG matrix (Figure 2).
Modified BCG matrix (values on the abscissa are arranged from the smallest to the largest in order to provide more logical review of the banks' efficiency) is shown in Figure 2. On the abscissa, the potential values of the efficiency of model A are illustrated, while the ordinate shows values obtained by the solution of model B.
14 Available at: http://en.wikipedia.org/wiki/Growth-share_matrix, accessed August, 2011 and http://en.wikipedia.org/wiki/Data_envelopment_analysis, accessed June 2011.
15 Available at: http://thinkserbia.wordpress.com/2008/12/12/bcg-matrica-osnove-strategije-proizvodnog-portfolija/, accessed August 2011.
16 Available at: http://en.wikipedia.org/wiki/Growth-share_matrix, accessed August 2011. 850 EP 2013 (60) 4 (843-855)
Table 3. Comparative analysis of models A and B
No. DMU Model A Rank Model B Rank
1 Alpha 0.52 27 0.38 25
2 Cacanska Bank 0.90 9 0.51 20
3 Crédit Agricole 1.19 4 0.55 19
4 Credy Bank 0.85 16 0.37 26
5 Erste Bank 0.88 11 0.57 17
6 Eurobank EFG 0.91 8 1.12 4
7 Findomestic 0.75 21 0.78 14
8 Hypo Alpe-Adria 0.86 13 0.99 7
9 Banca Intesa 1.17 5 0.94 8
10 Jugoslovenska Bank 1.55 1 0.85 13
11 KBC Bank 0.75 20 0.56 18
12 Komercijalna Bank 0.89 10 0.47 23
13 Moskovska Bank 0.60 24 0.39 24
14 NLB Bank 0.76 19 0.62 16
15 Opportunity Bank 0.59 26 1.20 3
16 OTP Bank 0.86 14 0.92 11
17 Piraeus Bank 0.60 25 1.01 5
18 Privredna Bank 0.65 22 0.76 15
19 ProCredit Bank 0.78 18 1.21 2
20 Raiffeisen Bank 1.03 7 1.00 6
21 Razvojna Bank Voj. 0.86 15 0.48 21
22 Société Générale 1.05 6 0.93 9
23 Srpska Bank 0.83 17 0.48 22
24 Unicredit Bank 1.27 2 0.93 10
25 Univerzal Bank 0.86 12 0.31 27
26 Vojvodjanska Bank 0.62 23 0.91 12
27 Volksbank 1.25 3 1.40 1
Source: Authors calculated this data from defined indicators the balance sheets and income statements of banks in Republic of Serbia;
As Figure 2 shows, Volksbank is the only unit in the Stars group, as this is the only bank that is efficient no matter which operational aspect is considered. For this bank we can say that it efficiently uses the capital and labour for the purpose of making a profit. Figure 3 shows both models, and it is obvious that the Jugoslovenska Bank in model A achieved the best results, while for model B the unit with the greatest super-efficiency is Volksbank.
Raiffeisen Bank is located on the border between Stars and Question Marks because this bank achieves almost identical results for both models, which is a higher value compared to other DMUs, as we can see in Figure 3. In the Question Marks field, there are five banks: Jugoslovenska, Unicredit, Crédit Agricole, Banca Intesa and Société Générale. Primarily, Jugoslovenska Bank has proved as superefficient unit with the highest score for model A, while for model B it has efficiency 83.38%, which makes this bank inefficient for that model. Furthermore, UniCredit Bank does not show good results in these models, so we can say that business politics is not adequate because in both cases this was an inefficient unit. Crédit
Agricole and Banca Intesa took bad positions, especially when it comes to model B, so it is necessary to regulate the inefficiency when using input.
Figure 2. Modified BCG matrix
Source: Data from Table 2.
In the Cash Cows field (Figure 2) there are Eurobank EFG, ProCredit Bank, Piraeus Bank and Opportunity Bank. These banks are "mature" branches, and this is confirmed by the graph in Figure 3. This especially refers to ProCredit, Piraeus and Opportunity Bank, particularly when it comes to operational aspect.
Figure 3. Comparative review of models A and B
Source: Data from Table 2.
The Dogs group includes the remaining banks, which showed the worst results: Hypo Alep-Adrian-Bank, OTP Bank, Fin domestic Bank, Privredna Bank, Vojvodjanska Bank, Cacanska Bank, Komercijalna Bank, Erste Bank, Development Bank of Vojvodina (Razvojna Banka Vojvodine), Univerzal Bank, Credy Bank, Srpska Bank, NLB Bank, KBC Bank, Moskovska and Alpha Bank. This conclusion is also made by Figure 3, because these banks were inefficient by all criteria.
Conclusion
The use of DEA method to analyse the efficiency of banks allows identification of the market leaders, the banks that accompany them and those that are very inefficient. Mathematical models that were described and applied in this paper clearly indicate their important role in operational analysis of banking sector of one country at specific period of time. DEA is applicable to a comparative analysis of banks and their grouping according to performance in various aspects of business. Based on the analysis, the bank management could direct the bank development in order to improve the business. Besides, efficient banks can be seen as benchmarks whose results can be observed as target values.
According to given data and obtained results, the following can be concluded: based on model A, the superefficient banks are Jugoslovenska Bank, AIK Bank, Volksbank, Crédit Agricole, Banca Intesa, Société Générale and Raiffeisen Bank. The first two banks (Jugoslovenska Bank and AIK Bank) are the banks of the public sector. It is obvious that they have small investments because they are financed by the state budget. The remaining banks are private banks, so it is clear that the outcome will be like this, bearing in mind the Milind Sathye's study, where the exact same combination of inputs and outputs was used and concluded that the private banks of foreign owners do business better and more efficiently.
Considering the data obtained using the model B, it can be concluded that the superefficient units are Dunav Bank, Volksbank, ProCredit Bank and Poljoprivredna Bank. Once again, among the most efficient banks there are two banks from the public sector - Dunav and Poljoprivredna Bank. Like in model A, in this model the Volksbank appears also as a superefficient unit.
Finally, we have done a comparative analysis and the outlayers were eliminated from both models, so as a result Volksbank got the most important place in the matrix. The disturbing fact is that most of the banks were so inefficient that the management had to consider their "putting to sleep", speaking in BCG matrix manner. However, a certain percentage (15%) of tested banks belongs to "mature" group, which represent the source of cash flow and they were all the banks of the private sector. Only 19% of banks belong to Question Marks group, which means that their progress requires a serious investment.
For this kind of analysis, two aspects have been considered that directly determine business accomplishment of a bank - costs and revenues, but also the employment structure. Some of these banks have shown very different results according to these models, depending on whether it is a bank of the public or private sector, i.e. foreign bank. During this research, the
number of branches has not been considered, and this is a very important indicator when it comes to operation performance of a bank, because if there are branches also in rural areas, for bank that means more profit because of its prevalence throughout the country.
The success for the banks represents a constant influx of new clients, so management has a job to devote itself to the improvement of the bank operation, in order to gain more money and thus become a superefficient unit.
References
1. Andersen, P., Petersen, N., A. (1993): Procedure for Ranking Efficient Units in Data Envelopment Analysis, Management Science, vol. 39(10) pp. 1261-1264.
2. Bulajic, M., Savic, G., Savic, S., Mihailovic, N., Martic, M. (2011): Efficiency assessment of banks in Serbia, TTEM - Technics Technologies Education Management, vol. 6(3), pp. 657-663.
3. Bulajic, M., Savic, G., Savic, S. (2011): DEA approach to ranking of the banks in Serbia, SYM-OP-IS-2011, XXXVIII Simpozijum o operacionim istrazivanjima Zbornik radova, Univerzitet u Beogradu, Ekonomski fakultet, str. 134-137.
4. Fries, S., Taci, A. (2004): Cost Efficiency of Banks in Transition: Evidence from 289 Banks in 15 Post-Communist Countries, Journal of Banking and Finance, vol. 29, pp.55-81.
5. Guzowska, M., Kisielewska, M., Nellis, J., Zarzecki, D. (2004): Efficiency of the Polish Banking Sector - Assessing the Impact of Transformation, Data Envelopment Analysis and Performance Management, editors: Emrouznejad, A., Podinovski, V., Aston Business School, Aston University, pp. 163-170.
6. Howland, M., Rowse, J. (2006): Measuring Bank Branch Efficiency Using Data Envelopment Analysis: Managerial and Implementation Issues, INFOR - Information systems and operational research, vol. 44(1), Ottawa, pp. 49-64.
7. Jermic, I., Vujcic, B. (2002): Efficiency of Banks in Croatia: A DEA Approach, Working Papers W-7, Croatian National Bank, Comparative Economic Studies, no. 44(2002), pp. 169-193.
8. Mukherjee, A., Nath, P., Nath Pal, M. (2002): Performance benchmarking and strategic homogeneity of Indian banks, International Journal of Bank Marketing, vol. 20(3), pp.122-139.
9. Mihajlovic, N., Bulajic, M., Savic, G., (2009): Ranking of banks in Serbia, Yugoslav journal of operational research, vol. 19(2), pp. 323-334.
10.Milind, S. (2003): Efficiency of banks in a developing economy: the case of India, European Journal of Operational Research, Vol. 148(2003), pp. 662-671.
11.Mohamed, M. (2006): Modelling the efficiency of GCC banks: a data envelopment analysis approach, International Journal of Productivity and Performance Management, Vol. 56(7), pp. 623-643.
12. §akar, B. (2006): A Study on Efficiency and Productivity of Turkish Banks In Istanbul Stock Exchange using Malmquist DEA, Journal of American Academy of Business, vol. 8(2), pp. 145-155.
13. Wu, D., Yang, Z., Liang, L. (2006): Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank, Expert Systems with Applications -Elsevier, vol. 31(1), pp. 108-115.
14.http://www.nbs.rs/internet/cirilica/50/index.html
15.http://mat.gsia.cmu.edu/classes/QUANT/NOTES/chap12.pdf
16.http://as.nida.ac.th/ornet/conf04/0R-CRN_lecture/sowanee.pdf
17.http://en.wikipedia.org/wiki/Growth-share_matrix
18.http://en.wikipedia.org/wiki/Data_envelopment_analysis
19. http://www.wiwi.uni-jena.de/Mikro/pdf/ems.pdf
20. http://mfiles.pl/en/index.php/BCG_growth-share_matrix
21.http://thinkserbia.wordpress.com/2008/12/12/bcg-matrica-osnove-strategije-proizvodnog-portfolija/
22. http://www.naslovi.net/2011-04-19/beta/dobitak-banka-2010-veci-za-27-odsto/2485562
23.http://www.decisionsciences.org/decisionline/vol31/31_3/31_3pom.pdf
PRIMENA DEA METODOLOGIJE U MERENJU EFIKASNOSTI U BANKARSKOM SEKTORU
Radojka Maletic17, Marija Kreca18, Predrag Maletic19
Rezime
Najvazniji aspekt svakog poslovanja jeste efikasnost. Cilj je da se uz sto manje ulaze postignu sto veci izlazi, ili raspolozivi ulazi maksimalno iskoriste. U ovom radu ilustrovace se primena DEA tehnike u oceni efikasnosti poslovanja bankarskog sektora Srbije, koji trenutno broji 33 banke. Izmerice se efikasnost banaka pomocu dva modela sa razlicitim ulazno-izlaznim indikatorima, a potom i uporedna analiza dobijenih rezultata pomocu BCG matrice. Banke su rangirane prema svojoj efikasnosti i komentarisane slicnosti i razlike koje su se iskazale.
Kljucne reci: Banke, DEA, efikasnost poslovanja, superefikasnost, BCG matrica.
17 Prof. dr Radojka Maletic, redovni profesor, Univerzitet u Beogradu, Poljoprivredni fakultet, Nemanjina 6, Beograd, Srbija, Telefon: +381 11 261 53 15, E-mail: maletic@agrif.bg.ac.rs
18 Marija Kreca, M.Sc., Univerzitet u Beogradu, Fakultet organizacionih nauka, Beograd, Srbija, E-mail: marija.kreca@yahoo.com
19 Predrag Maletic, M.Sc., Rajfazen banka, Beograd, Srbija, E-mail: predrag.maletic@yahoo.com EP 2013 (60) 4 (843-855) 855
