RATING SYSTEM AS A BANKING PERFORMANCE REGULATOR IN THE CONDITIONS OF TRANSITION ECONOMY Edward Sandoyan, Russian-Armenian State (Slavonic) University Arzik Suvaryan, Russian-Armenian State (Slavonic) University Davit Sahakyan, Russian-Armenian State (Slavonic) University Transition Studies Review: TFBR Transition Finance and Banking Research, Springer Verlag Wien, Volume 12, Number 2, September, 2005, p. 220-230.

ABSTRACT Taking into consideration the importance of having a strong banking system, the issue of bank performance evaluation and analysis becomes essential for the bank regulation process in the countries with emerging economy. Banking performance evaluation and analysis various methods are used in international practice. These methods have many disadvantages and are unusable for the banking systems in the countries with transition economy. In this article a new and complex system of analysis and evaluation of bank performance is described. This is a multidimensional system of bank performance indicators. This system enables to analyze bank performance through integrated indicators, viewing them in corresponding surfaces. Implementation of such system will allow efficiently analyze bank performance by different directions, to reveal existing problems in the bank, to make corresponding regulation decisions for each bank, to group banks according to different criteria, etc. Keywords: multidimensional system, complex analysis, rating system, Authorâ€™s Email Addresses: Davit Sahakyan: davit.sahakyan@gmail.com;

1. INTRODUCTION One of the most acute problems, considerably limiting the abilities for further economical development in the countries with emerging market, is imperfection of financial sector of economy, especially that of banking system. Taking into account the high level of characteristics of similarity of emerging economies in general and banking system in particular, studying the mentioned theme according to the example of Armenia enables to develop common recommendations to be used in other countries of the same group. At present, in Armenia a favorable legislative field, based on international standards and the best achievements of the world banking performance, has been generated for banking performance. From the point of view of currency regulation and control liberal regimes, capital and current accounts flows, as well as the rigidity of prudential licensing and regulation system of banking performance, the existing banking legislation of Armenia can be estimated as a more effective one compared with other legislation systems of other countries with emerging economy. Namely due to basic advantages of existing banking legislation of Armenia until recently system banking crises has been avoided. But starting 2000 certain problems in the banking system of Armenia began to be outlined, needing complex and weighted solution in order to avoid system crisis. During recent years (beginning 2000) 13 banks have bankrupted in Armenia. Thus, it is worth to mention that owing to research the majority of bankruptcies could be avoided due to implementation of more effective forms and methods of banking regulation and supervision by Central Bank. This will allow to reveal and limit unjustified risks, having already been taken by these banks at early stages. Banking supervision department of Central Bank of Armenia uses well known CAMELS system as a rating system, which has the following disadvantages:

the setting of parameters intervals and weights is carried out subjectively, which obviously affects analysis quality and deforms an estimation,

parameters intervals and weights, used for parameters estimation, are fixed and do not reflect the financial market peculiarities for the certain time period, especially for the emerging economy,

there is no estimation system for the banks parameters increase, which does not allow to predict banks ratings in future,

there is no comparative analysis for the banks,

CAMELS is a five-grade system and the number of grades is fixed. For the banking system with a few banks it is more than enough, whereas for banking systems with a huge number of banks it is not enough.

In this paper a new estimation and analysis system is presented. It must include:

comparative analysis of the banks current stage,

banks classification according to the type and volume of performance, comparative estimations, current stage and dynamic development,

general and total estimations of the banks,

exact economical interpretations for grades, 2

the most important weak and strong sides in bank performance,

parameters estimation system in correspondence with external economical environment changes,

estimation of banks performance parameters increase.

For the estimation, intervals changing dynamically within the certain time period and reflecting features of external economical environment and banking system, are used. Statistical and analytical methods, which will have corresponding economical and mathematical interpretation, will be used for parameters estimation. 2. ANALYSIS AND ESTIMATION SYSTEM GENERAL DESCRIPTION The parameters system The three-dimensional system for analysis and estimation of bank performance is hereby described. Let’s develop it step by step. Step 1. First, let’s describe first-dimension system parameters – aik , t , where k is the number of the banks, t is the parameters group index and t C , A, L, E , B , i is the number of parameters in each group i 1, NTt , where NTt is the quantity of parameters in each t group. If t "C" (Capital parameters) the parameters of this group describe the value of capital, for example equity, total capital, etc. If t " A" (Assets quality parameters) the parameters of this group describe the bank active operations, for example the amount of interest bearing assets, loans, etc. If t "L" (Liabilities parameters) the parameters of this group describe the bank passive operations, for example the value of deposits, interest bearing liabilities, etc. If t "E" (Earnings parameters) the parameters of this group describe the results of financial performance, for example the value of profit, managerial expenses, spread, etc. If t "B" (Business activity parameters) the parameters of this group describe bank business activity, for example the number of new contracts, the sum of new deposits, etc. Thus, an absolute values system will occur, which gives quantitive estimation of the results of bank performance. Step 2. To create two-dimensional system let’s describe the following aik , t , p parameters, where aik , t , and b k , 0 1 , and b k , p ( p 1, NP and NP is the number of parameters) are k, p b bank performance parameters, for example equity, total capital, managerial expenses, the number of branches, the number of employees, etc. aik , t , p

From the parameters aik , t , p the ones, for which the deviation of parameters aik , t and b k , p has an economical meaning, are taken.

3

As a result there will be the relative parameters system, which gives qualitive description of the bank performance. Step 3. In order to have three-dimensional system let's describe parameters aik , t , p , q and if

q 0 , aik , t , p , 0 aik , t , p ,

q 1 , aik , t , p ,1

aik , t , p aik , t , p is relative growth in comparison with the previous aik , t , p

period, where a ik , t , p is the same parameter of the previous period,

q 2, a

k , t , p, 2 i

a nk,,it , p aik , t , p

is relative growth in comparison with the same period

aik , t , p

of the previous year, where aik , t , p is the same parameter of the same period of the previous year,

q 3, a

k , t , p, 3 i

aik , t , p aik , t , p is relative growth in comparison with the basic period, aik , t , p

where aik , t , p is the same parameter of a basic period. As a result we will have a system, which gives the description of the bank parameters increase. Parameters estimation methodology For estimation it is worth to implement the system of comparative estimation, for which changing intervals will be used, because banking parameters are changed depending on period of time and changes of external environment and banking system in particular. For example, the parameter of bank profit – a1k , E will be evaluated in the following way:

0, if a1k , E , 0, 0 0 a1kl , E , 0, 0 a1k , E , 0, 0 Average a1kl , E , 0, 0 1, if Min l l , kl , E , 0 , 0 kl , E , 0 , 0 k , E , 0, 0 2 , if Average a a Median a 1 1 1 l l 3, if Median a kl , E , 0, 0 a k , E , 0, 0 Max a kl , E , 0, 0 1 1 1 l l

k , E , 0, 0 1

Ra

(2.1)

where Average a1kl , E , 0, 0 Median a1kl , E , 0, 0 , kl k ; a1k , E , 0, 0 0; k 1, ..., N and N is the l

l

number of banks, l 1, ...., N and N is the number of banks with profit.

In case Median a1kl , E , 0, 0 Average a1kl , E , 0, 0 l

l

0, if a1k , E , 0, 0 0 a1kl , E , 0, 0 a1k , E , 0, 0 Median a1kl , E , 0, 0 1, if Min l l 2, if Median a kl , E , 0, 0 a k , E , 0, 0 Average a kl , E , 0, 0 1 1 1 l l 3, if Average a1kl , E , 0, 0 a1k , E , 0, 0 Max a1kl , E , 0, 0 l l

k , E , 0, 0 1

Ra

4

(2.2)

I.e. the banks with losses receive score “0”. For the banks with positive profit the average and the median are calculated. If the median is less than the average (Fig. 1. positive asymmetry), banks mostly have little profit, but there are few banks which have huge profit and receive the score “3”. The banks with profit less than the median are estimated “1”, and the others – “2”. But when the median is greater than the average, banks have a high level of profit in general, but there are a few banks which have low level of profit and they receive score “1”. The banks, the profit of which is greater than median, receive score “3”, and the others – “2”.

Fig. 1. The description of parameters For the estimation of parameters, for which a low level is a positive phenomenon and which can not have negative value (for example, the average interest of interest bearing liabilities), the following estimation system is used:

Ra3k , E , 0, 0

1, if Median a3k , E , 0, 0 a3k , E , 0, 0 Max a3k , E , 0, 0 2, if Average a3k , E , 0, 0 a3k , E , 0, 0 Median a3k , E , 0, 0 , 3, if Min a k , E , 0, 0 a k , E , 0, 0 Average a k , E , 0, 0 3 3 3

(2.3)

where Averagea3k , E , 0, 0 Mediana3k , E , 0, 0 . In case Mediana3k , E , 0, 0 Averagea3k , E , 0, 0 k , E , 0, 0 3

Ra

1, if Average a3k , E , 0, 0 a3k , E , 0, 0 Max a3k , E , 0, 0 2, if Median a3k , E , 0, 0 a3k , E , 0, 0 Average a3k , E , 0, 0 3, if Min a k , E , 0, 0 a k , E , 0, 0 Median a k , E , 0, 0 3 3 3

(2.4)

For the estimation of the banks parameters increase, in case the increasing is a positive phenomenon (for example the profit increasing), the following formula is used: 0, if a1k , E , 0,1 P1 k , E , 0 ,1 P2 1, if P1 a1 k , E , 0 ,1 Ra1 , k , E , 0 ,1 P3 2, if P2 a1 3, if a1k , E , 0,1 P3

5

(2.5)

where

k , E , 0 ,1 P1 Min0, BSa1k , E , 0,1 , Max , Median a1ki , E , 0,1 Average a1 i , i i k , E , 0 ,1 P3 Max0, BSa1k , E , 0,1 , Max , Median a1ki , E , 0,1 Average a1 i , i i

0, if P1 0 and P3 0 P2 BSa1k , E , 0,1 , if P1 BSa1k , E , 0,1 and P3 BSa1k , E , 0,1 ki , E , 0 ,1 , Median a1ki , E , 0,1 , if P1 Max Average a1ki , E , 0,1 , Median a1ki , E , 0,1 and Max Average a1 i i i i P3 Max Average a1ki , E , 0,1 , Median a1ki , E , 0,1 i i ,

BSa1k , E , 0,1 is the same parameter but for banking system, ki k ; a1k , E , 0,1 0; k 1, ..., N ,

i 1, ..., N i and N i is the number of banks with this parameter increase.

In case the parameter decreasing is a positive phenomenon (for example, the decrease of average interest of interest bearing liabilities), the following formula is used for the estimation: 3, if a3k , E , 0,1 P1 k , E , 0 ,1 P2 2, if P1 a3 k , E , 0 ,1 Ra3 , k , E , 0 ,1 P3 1, if P2 a3 0, if a3k , E , 0,1 P3

where

,

,

k , E , 0 ,1 k , E , 0 ,1 P1 Min0, BSa3k , E , 0,1 , Min Average a3 j , Median a3 j j j

k , E , 0 ,1 k , E , 0 ,1 P3 Max0, BSa3k , E , 0,1 , Min Average a3 j , Median a3 j j j

6

(2.6)

0, if P 0 and P 0 1 3 P2 BSa3k , E , 0,1 , if P1 BSa3k , E , 0,1 and P3 BSa3k , E , 0,1 k j , E , 0 ,1 k , E , 0 ,1 k j , E , 0 ,1 k , E , 0 ,1 , Median a3 j , Median a3 j Min Average a3 , if P1 Min Average a3 and j j j j k , E , 0 ,1 k , E , 0 ,1 P3 Min Average a3 j , Median a3 j j j ,

BSa3k , E , 0,1 is the same parameter but for banking system, k j k ; a3k , E , 0,1 0; k 1, ..., N ,

j 1, ..., N j and N j is the number of banks with this parameter decrease. Cumulative rating calculation After having ratings for all parameters, there is a need to calculate cumulative rating for different groups and fields, for which three calculation cases can be used: An Average – is used in case there are not so many banks in banking system, A simple summing – is used when there are too many banks and they must be analyzed in details, Coefficient – is used in case there are many banks and their grades must be comparative. Coefficient grade is a deviation of the sum of certain group parameters grades and the maximum available sum of the same group parameters grades. This system allows to estimate and analyze not only the performance of banks in general, but also their different fields of performance such as active operations, deposit involving, financial results, the efficiency of usage of the branch network, the efficiency of staff, etc. in particular, and for this only corresponding parameters which are lying on the corresponding surface are considered. So the parameters three-dimensional system for banking performance analysis is developed. This system allows to analyze operatively bank performance for different fields of performance, to discover problems of each bank, to make a corresponding decision for each bank from the regulation point of view, to group banks by different fields and symptoms. 3. RESULTS AND INTERPRETETIONS For analysis and estimation Armenia banks performance a four-grade system, with following grades, is used 0 – “unsatisfactory”, 1 – “satisfactory”, 7

2 – “good” and 3 – “excellent”. Banks quarterly reports, published in the media, were used as information on financial performance of these banks. Let’s adjust the model, described above, to the information we have and we will have the following three-dimensional system for bank performance estimation and analysis. Parameters estimations are made in the following ways. For example, banks profit is estimated in the following way. As shown in table 1, four banks (Armenian Bank of Reconstruction and Development, Bank Mellat, Greece Commerce Bank and Bank Prometey) have losses for the 1st quarter, 2004 and therefore their score is “0”. Unibank has the lowest level of profit – 8,450 thousand drams ( Min a1kl , E 8,450 ) and Bank Converse has the highest level of profit – 582,670 thousand l

drams ( Max a1kl , E 582,670 ). The average profit for the banks with profits is 189,450 l

thousand drams ( Average a1kl , E 189,450 ) and median for these banks is 122,708 thousand

l

drams ( Median a1kl , E 122,762 ). l

Since the median is lower than the average, a positive asymmetry is observed, which means that there are a lot of banks in the banking system, with a low level of profit and there are a few banks with high level of profit. Proceeding from the above mentioned formula (2.2) HSBC, Bank Converse, Ardshininvestbank, ACBA, Armeconombank and Inecobank have score “3”, Armsavingsbank and Bank Anelik have score “2”, Unibank, Bank Artsakh, Armimpexbank, Areximbank, Arminvestbank, Intercombank and Interinvestbank have score “1” and Armenian Bank of Reconstruction and Development, Bank Mellat, Greece Commerce Bank and Bank Prometey have score “0”. All parameters, except the deviation of interest bearing liabilities and total capital, as well as parameter of average interest of interest bearing liabilities, are estimated in the same way. These 1 above mentioned parameters are estimated using formula (2.3) or (2.4): Parameter of profit increase in comparison with the previous period is estimated using formula (2.5). For the estimation we have the parameter of banking system profit increase in comparison with the 4th quarter, 2003 – BSa1k , E ,1 0.27 , the median – Median a1ki , e,1 1.01 , and the

i

average – Average a1ki , e,1 1.56 . And as a result we have P1 0.27 , P2 0 и P3 1.56 . As i

shown in Table 2, for the profit increase the banks will get the following scores: “3” – Armsavingsbank, “2” – Bank Converse, ACBA, Armeconombank, Armimpexbank and Inecobank, “1” – HSBC,

8

“0” –Ardshininvestbank, Unibank, Bank Artsakh, Areximbank, Intercombank, Interinvestbank and Bank Anelik. All increase parameters compared with the 4th quarter, 2003 and the 1st quarter, 2003, except increase parameters of the deviation of interest bearing liabilities and total capital, as well as the average interest of interest bearing liabilities compared with the 4th quarter, 2003 and the 1st quarter, 2003, are estimated in the same way. The mentioned last parameters are estimated using formula (2.6). 4. CONCLUSION Thus, the banks performance evaluation and analysis system was made by using the multidimensional model. This system is more flexible than well known systems, because it allows to analyst to make the indicators system according to the corresponding needs, not to make corresponding intervals for estimation for each indicator. This system allows to estimate and analyze not only the performance of banks in general, but also their different fields of performance such as active operations, deposit involving, financial results, the efficiency of usage of the branch network, the efficiency of staff, the bank development, etc. and, for this, only corresponding parameters, which are lying on the corresponding surface, are considered. So the three-dimensional system for banking performance analysis is developed. This system allows to analyze operatively bank performance for different fields, to discover problems of each bank, to make decisions for each bank from the regulation point of view, to group banks by different fields and symptoms. Thus, because of above mentioned advantages, this system can be easily implemented to the other countries with emerging economy. Because this system includes the increase of all indicators during different time periods, it is easy to develop the dynamics of all parameters, which will allow to develop banks rating and bankruptcy forecasting model. So, the next advantage of this system is that the banks early warning system can be easily developed in the basis of the bank performance estimation and analysis multidimensional system.

9

5. REFERENCES

Arzik Suvaryan, Davit Sahakyan, and Ruzan Sahakyan, Bank Branches Rating System, Economic Policy Current Problems of the Republic of Armenia, Yerevan, 2003, p. 258268. Arzik Suvaryan, Davit Sahakyan, and Ruzan Sahakyan, Commercial Banks Branches Activity Estimation System, Economics Journal, Yerevan, 2000, No 5, p. 3-10. Arzik Suvaryan, Davit Sahakyan, and Ruzan Sahakyan, Complex System For the Analysis and Estimation of Branches Performance of Banks with the Multiple Branches, SocialEconomy Current Problems of the Republic of Armenia, N. 2, Yerevan, 2004, p. 375380. David A. Grigoryan, and Vlad Manole, Determinants of Commercial Bank Performance in Transition: An Application of Data Envelopment Analysis, World Bank Policy Research Working Paper 2850, June 2002. Davit Sahakyan, Classification of Branches of Banks With The Multiple Branches Using Neural Networks, Social-Economy Current Problems of the Republic of Armenia, Yerevan, 2004, p. 415-420. Owen Evans, Alfredo M. Leone, Mahinder Gill, and Paul Hilbers, Macroprudential Indicators of Financial System Soundness, International Monetary Fund, Occasional Paper 192, 2000. Terry J. Watsham, and Keith Parramore, Quantitative Methods in Finance, Published by International Thomson Business Press, 1996.

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Ranjana Sahajwala, and Paul Van den Berg, Supervisory Risk Assessment and Early Warning Systems, Bank For International Settlements, Basel, Switzerland, N. 4, December, 2000

11

6. APPENDIX Table 1. Banks profit and the profit increase.

BANKS

Profit for the 1st quarter, 2004 (in thousands of Armenian drams)

Rating for the profit for the 1st quarter, 2004

Profit increase in comparison with the 4th quarter 2003

Rating for the profit increase in comparison with the 4th quarter 2003

1 HSBC

475,628

3

-0.20

1

2 CONVERSE

582,670

3

1.44

2

3 ARMSAVINGSBANK

137,659

2

5.38

3

4 ARDSHININVESTBANK

354,706

3

-0.68

0

8,450

1

-1.10

0

6 ACBA

222,668

3

0.47

2

7 ARMEKONOMBANK

314,676

3

0.04

2

8 ARCAKH

71,632

1

-0.36

0

9 ANELIK

122,762

2

-0.51

0

105,405

1

1.46

2

-259,602

0

-1.79

0

20,376

1

-1.29

0

13 INECOBANK

367,270

3

0.58

2

14 MELLAT BANK

-33,800

0

-2.68

0

43,783

1

-0.71

0

-22,281

0 -1.69

0

5 UNIBANK

10 ARMIMPEXBANK 11 ARMDEVELOPMENTBANK 12 AREXIMBANK

15 ARMINVESTBANK 16 GREECE COMMERCE BANK 17 PROMETEY BANK

-5,857

0

-0.87

0

18 INTERCOMBANK

9,570

1

-0.76

0

19 INTERINVESTBANK

4,492

1

-2.20

0

0

0

-1.00

0

20 ARMAGROBANK BANKING SYSTEM

2,520,207

-0.27

Median

122,762

1.01

Average

189,450

1.56

Table 2. Banks total ratings.

BANKS

TOTAL RATIN G

1

HSBC

2

2

CONVERSE

2

3

ARMSAVINGSBANK

2

4

ARDSHININVESTBANK

1

5

UNIBANK

2

6

ACBA

2

7

ARMEKONOMBANK

1

8

ARCAKH

1

9

ANELIK

2

10

ARMIMPEXBANK

2

11

ARMDEVELOPMENTBA NK

1

12

AREXIMBANK

1

13

INECOBANK

1

14

MELLAT BANK

2

15

ARMINVESTBANK

1

16

GREECE COMMERCE BANK

1

17

PROMETEY BANK

1

18

INTERCOMBANK

1

19

INTERINVESTBANK

1

20

ARMAGROBANK

0

13

Multidimensional system for banking performance estimation (in English)

Published on Dec 30, 2013

Ed.M.Sandoyan, A.M.Suvaryan, D.A.Sahakyan. Rating System as a Banking Performance Regulator in the Conditions of Transition Economy. Transit...

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