A Multicriteria Decision Framework for Measuring Banks’ Soundness Around the World

8/12/2019 A Multicriteria Decision Framework for Measuring Banks Soundness Around the World

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JOURNAL OF MULTI-CRITERIA DECISION ANALYSIS

J. Multi-Crit. Decis. Anal. 14: 103111 (2006)

Published online in Wiley InterScience

(www.interscience.wiley.com) DOI: 10.1002/mcda.405

AMulticriteria DecisionFramework forMeasuringBanksSoundness Around theWorld

CHRYSOVALANTISGAGANISa, FOTIOSPASIOURAS

b,* and CONSTANTINZOPOUNIDIS

a

aFinancial Engineering Laboratory, Department of ProductionEngineering andManagement,TechnicalUniversityofCrete, University Campus, Chania73100, GreecebSchoolofManagement, Universityof Bath, ClavertonDown,BathBA27AY, UK

ABSTRACT

In this paper, we use a sample of 894 banks from 79 countries to develop a multicriteria decision aid model, for theclassification of banks into three groups on the basis of their soundness. The model is developed with the UTilitesAdditives DIScriminantes (UTADIS) method, through a 10-fold cross-validation procedure using six financial andfour non-financial variables. The ratings of Fitch form the basis for assigning banks into the three groups. Theresults indicate that the asset quality (as measured by loan loss provisions), capitalization, and the market wherebanks operate are the most important criteria (in terms of weights) in classifying the banks. Profitability and

efficiency in expenses management are also important attributes, whereas size and listing in a stock exchange are theleast important ones. UTADIS achieves higher classification accuracies than discriminant analysis and ordinarylogistic regression which are used for benchmarking purposes. Copyright# 2007 John Wiley & Sons, Ltd.

KEY WORDS: multicriteria classification of banks soundness; UTADIS

1. INTRODUCTION

Over the last years, various studies have attemptedto develop models for assessing bank soundnesseither by predicting bank failure or by examiningthe credit ratings of banks. Studies falling within

the first category have used various methodologiessuch as discriminant analysis (DA) (Sinkey, 1975;Canbas et al., 2005), logit analysis (e.g. Rose andKolari, 1985; Pantalone and Platt, 1987; Canbaset al., 2005), probit analysis (Cole and Gunther,1998; Canbas et al ., 2005), multidimensionalscaling approach (Mar-Molinero and Serrano-Cinca, 2001), neural networks (Tam and Kiang,1992), trait recognition (Kolari et al., 2002; Lanineand Vander Vennet, 2006), multicriteria decisionaid (Kosmidou and Zopounidis, 2007) and neuralfuzzy systems (Tung et al., 2004). Many of thesestudies have been successful in predicting bank-

ruptcy. However, one common drawback is thatthey had concentrated on the classification ofbanks into two groups, failed and non-failed.

Obviously, the classification of banks as bad orgood reduces the usefulness of the model.

With respect to the second category, althoughseveral studies have examined the ratings assignedto bonds (Horrigan, 1966; Pinches and Mingo,1973; Kaplan and Urwitz, 1979; Belkaoui, 1983;

Ederington et al., 1987; Kim, 1993; Huang et al.,2004) or countries (e.g. Hammer et al., 2006; Yimand Mitchell, 2005) there have been only a fewstudies that examine the individual ratings as-signed to banks such as the ones of Poon et al.(1999), Poon and Firth (2005), Pasiouras et al.(2006, 2007) and Demirguc-Kunt et al. (2006).However, with the exception of Pasiouras et al.(2007) who developed a classification model forthe rating of Asian banks, these studies are of amore explanatory nature focusing on the determi-nants of ratings rather than on the correctclassification of banks. More precisely, Poon

et al . (1999) examined the determinants ofMoodys ratings, using logistic regression, whilePoon and Firth (2005) focused on the differencesbetween solicited and unsolicited ratings. Pa-siouras et al. (2006) focused on the impact ofbank regulations and supervisory framework onthe ratings, while Demirguc-Kunt et al. (2006)focused on the relation between compliance withBasel core principles and bank soundness.

*Correspondence to: School of Management, Universityof Bath, Claverton Down, Bath BA2 7AY, UK.E-mail: [email protected]

Copyright# 2007 John Wiley & Sons, Ltd.


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While the development of a bankruptcy predictionmodel or a model to replicate all the ratings of acredit agency is beyond the scope of the presentpaper, our work is nevertheless related to these twostrands of the literature. The objective of the present

study is the development of quantitative models forthe classification of banks into different groups onthe basis of their soundness. While our model isbased on the ratings of Fitch, to assess the soundnessof banks,1 we do not attempt to replicate all theratings of Fitch, as the heterogeneous sample used inour study, consisting of 894 banks from 79 countries,could result in poor classifications.2 We, therefore,classify banks into three general groups. The firstgroup contains very strong or strong banks; thesecond one contains adequate banks, while the thirdgroup contains banks with weaknesses or seriousproblems. While we acknowledge that this approach

might reduce the information provided by themodel, we believe that it does not reduce theapplicability of the model and the importance ofour study. For example, several studies have high-lighted the importance of developing early warningsystems to identify troubled banks (e.g. Kolariet al.,2002; Tung et al., 2004; Canbas et al., 2005; Lanineand Vander Vennet, 2006). By focusing on non-failed banks, and distinguishing between healthy,adequate and troubled banks, our model can reducethe expected cost of bank failure, either by minimiz-ing the costs to the public or by taking actions toprevent failure (Thomson, 1991). For example, Ravi

Kumar and Ravi (2007) mention, As a bank or firmbecomes more and more insolvent, it graduallyenters a danger zone. Then, changes to its operationsand capital structure must be made in order to keepit solvent (p. 1). Hence, the model developed in thepresent study could be used in future applications to

provide an assessment of the soundness of banks notrated by Fitch or other agencies (e.g. Moodys) aswell as to monitor changes in the status of banksfrom one year to another.

The model is developed with UTilite s Additives

DIScriminantes (UTADIS) multicriteria techniquefollowing a 10-fold cross-validation procedure.UTADIS is well suited for examining the sound-ness of banks for several reasons. First, the groupsin our study are defined in an ordinal way, inthe sense that banks classified into the first groupare preferred to banks classified into the secondgroup, and so on. Traditional statistical classifica-tion methods as well as popular machine learningtechniques (e.g. neural networks, rule inductionalgorithms and decision trees) cannot cope withthis kind of information. On the other hand,UTADIS is well suited to the study of ordinal

classification problems. Second, UTADIS is notbased on statistical assumptions that often causeproblems to the application of statistical methods,3

such as the normality of the variables or the groupdispersion matrices (e.g. DA) and is not sensitiveto multicollinearity or outliers (e.g. logit analysis).Third, it can easily incorporate qualitative data.

The rest of the paper is as follows: Section 2presents the sample and the variables used in thestudy, while Section 3 outlines the UTADIStechnique. Section 4 discusses the empirical results,and Section 5 concludes the study.

2. SAMPLE AND VARIABLES

2.1. SampleThe data set consists of those banks that hadavailable financial and non-financial data and

1Demirguc-Kunt et al . (2006) also used ratings tomeasure bank soundness. However, they used theratings of Moodys and focused on the relation betweencompliance with Basel core principles and bank sound-ness and did not develop a classification model.2

One could argue that we could limit the heterogeneityin the sample by decreasing the number of countries andincreasing the number of banks in the sample. However,this is not possible since we have considered all thebanks rated by Fitch, with available data in Bankscope,and by definition, we can only consider rated banks.Hence, decreasing the number of countries is not analternative as this approach would limit the number ofbanks in the sample resulting in an inefficient estimationof the model.

3Barniv and McDonald (1999) summarized some of theproblems related to discriminant, logit and probit thatwere mentioned in previous studies. Logit and Probit aresensitive to: (a) data properties, such as departure fromnormality of financial variables (Frecka and Hopwood,

1983; Richardson and Davidson, 1984; Hopwood et al.,1988); (b) overall small sample size (Noreen, 1988; Stoneand Rasp, 1991); (c) multicollinearity (Aldrich andNelson, 1984; Stone and Rasp, 1991). The basicassumptions of discriminant analysis (DA) such asnormality, symmetry and equal covariance matricesare also usually violated. Hopwood et al. (1988) pointedout that DA is generally sensitive to departure fromnormality and both logit and probit analyses aresensitive to extreme non-normality.

C. GAGANIS ET AL.104

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Fitch individual bank ratings in Bankscopedatabase.4 The ratings correspond to October2004, while the bank-specific characteristics corre-spond to end of 2003 or March of 2004 dependingon the date of publication of the annual report.The above selection criteria yielded a sample of894 banks operating in 79 countries.

Table I presents the definitions of Fitch indivi-dual bank ratings along with the coding used in thepresent study. The ratings are based on AE scaleand represent Fitchs view on the likelihood that the

bank would fail, and therefore require support toprevent it from defaulting. Fitch may also assignthe following intermediate ratings: A/B, B/C, C/D

and D/E. As mentioned earlier, the purpose of thepresent study is not to explain or replicate theratings of Fitch, but rather to use them as the basisfor the development of a general model to assess thesoundness of banks. We, therefore, classify thebanks into three broad groups. The first consists ofbanks with ratings A and B (i.e. very strong orstrong banks), the second with banks rated C (i.e.adequate banks), and the third with banks rated Dand E (i.e. banks with weaknesses or seriousproblems). Table II presents the number of banks

in sample by country and group.

2.2. VariablesTable III presents the variables used in themodels as criteria of banks soundness. Creditagencies, auditors and bank regulators tend toevaluate banks performance on the basis ofthe CAMEL model that stands for the acronymsof Capital, Asset quality, Management, Earningsand Liquidity. We follow the same approachand select financial variables that proxy for thefour of the five dimensions, as well as size.

Management has not been included in the analysisdue to its qualitative nature and the subjectiveanalysis that is required. Credit agencies point outthat during their rating they consider variousnon-financial characteristics such as the environ-ment in which banks operate, ownership andfranchise power. Consequently, we use additionalnon-financial variables to proxy for thesecharacteristics.

Table I. Definitions of Fitchs bank individual ratings

Fitch

rating

Coded in the

present study

Definition

A Group 1 A very strong bank. Characteristics may include outstanding profitability and balance sheet

integrity, franchise, management, operating environment or prospectsB Group 1 A strong bank. There are no major concerns regarding the bank. Characteristics may include

strong profitability and balance sheet integrity, franchise, management, operating environment or

prospects

C Group 2 An adequate bank, which, however, possesses one or more troublesome aspects. There may be

some concerns regarding its profitability and balance sheet integrity, franchise, management,

operating environment or prospects

D Group 3 A bank, which has weaknesses of internal and/or external origin. There are concerns regarding its

profitability and balance sheet integrity, franchise, management, operating environment or

prospects. Banks in emerging markets are necessarily faced with a greater number of potential

deficiencies of external origin

E Group 3 A bank with very serious problems, which either requires or is likely to require external support

Note: Fitch also uses the following intermediate assignments among the major five categories: A/B, B/C, C/D, D/E.

4Using Bankscope has two main advantages. First, ithas information for a very large number of banks,accounting for about 90% of total assets in each country(Claessenset al., 2001). Second, and most important, thefinancial information at the bank level is presented instandardized formats, after adjusting for differences inaccounting and reporting standards. The data compiledby Bankscope use financial statements and notes foundin audited annual reports. Each country in the Bank-

scope database has its own data template, thus allowingfor differences in the reporting and accounting conven-tions. The data are then converted to a global formatusing a globally standardized template derived from thecountry-specific templates. The global format alsoprovides standard financial ratios, which can becompared across banks and between countries. There-fore, Bankscope is the most comprehensive databasethat allows cross-country comparisons (Claessens et al.,2001).

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TableII.

Banksbycoun

tryandriskgroup

Group1

Group2

Group3

Group1

Group2

Group3

Group1

Group2

Group3

Argentina

0

0

4

HongKong

10

8

0

Philippines

0

6

8

Australia

8

1

0

Hungary

0

1

1

Poland

0

2

4

Austria

2

0

0

Iceland

0

2

0

Portugal

6

3

0

Azerbaijan

0

0

1

India

0

6

20

Qatar

1

2

0

Bahrain

1

1

0

Indonesia

0

5

7

Romania

0

0

3

Bangladesh

0

0

3

Ireland

6

0

0

RussianFederation

0

2

23

Belarus

0

0

2

Israel

0

2

1

SaudiArabia

7

0

1

Belgium

7

0

0

Italy

11

15

1

Singapore

4

0

0

Benin

0

1

0

Japan

1

6

17

Slovenia

1

5

0

Brazil

0

9

1

Jordan

2

1

0

SouthAfrica

5

3

0

Bulgaria

0

2

1

Kazakhstan

0

1

5

Spain

44

4

1

Canada

6

1

0

Kenya

0

0

4

SriLanka

0

0

5

Chile

3

1

0

Korea(Repu

blicof)

2

7

2

Sweden

7

0

0

China

0

0

14

Kuwait

2

6

0

Switzerland

5

1

0

Croatia

0

1

0

Latvia

0

1

1

Taiwan

5

6

10

Cyprus

0

3

1

Lebanon

0

2

0

Thailand

0

4

6

CzechRepublic

0

2

0

Lithuania

0

1

3

Tunisia

0

0

2

Denmark

5

0

0

Luxembourg

2

0

0

Turkey

0

5

19

DominicanRepublic

0

0

2

Malaysia

2

5

4

Ukraine

0

0

4

Egypt

0

1

1

Malta

0

1

1

UnitedArabEmirates

1

5

0

ElSalvador

0

1

4

Mexico

2

6

1

UnitedKingdom

42

4

0

Estonia

1

0

0

Netherlands

9

2

1

USA

219

22

4

Finland

3

0

0

NewZealand

3

0

0

Venezuela

0

0

8

France

13

9

0

Norway

8

0

0

Vietnam

0

0

4

Georgia(Rep.of)

0

0

2

Oman

0

4

0

Total

466

214

214

Germany

7

19

2

Pakistan

0

0

4

Greece

3

2

0

Panama

0

4

1



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2.2.1. Financial variables. CAP is the equity tototal assets ratio that serves as a measure of capitalstrength.5 PROVIS is the loan loss provisions tonet interest revenue ratio that is a measure of assetquality. ROAA is the return on average assets thatis a classical measure of profitability. EXPENSESis the cost-to-income ratio6 that reveals theefficiency in managing expenses. LIQ correspondsto the liquid assets7 to customer and short-termfunding ratio that shows the percentage ofcustomer and short-term funding that could bemet if they were withdrawn suddenly. SIZE is the

logarithm of total assets and is a measure of size.

2.2.2. Non-financial variables. SUBS is the numberof subsidiaries that is used as a proxy for thediversification of business and franchise. OWNERSis the number of institutional shareholders, andLIST is a dummy variable indicating whether the

bank is listed on a stock exchange (LIST 1) or not(LIST 0). Both variables are used as proxies ofcorporate governance and ownership. Our lastvariable is a dummy variable indicating whetherthe banks operate in a developed (MARKET 1)

or developing (MARKET 0) market.

3. METHODOLOGY

The most common approach to address multi-criteria classification problems is to develop acriteria aggregation model based on absolute

judgements, which provides a rule for the classi-fication of the alternatives on the basis of theircomparison with some reference profiles (cut-offpoints) that distinguish the classes. Following thisapproach, the objective of the UTADIS method isto develop a classification model in the form of anadditive value function.

Vx Xn

i1

wivixi

where x x1; x2;. . .; xn is the vector of decisionattributes (financial ratios), w1; w2;. . . ; wn areattributes weights defined such that wi50 andP

iwi 1, and v1; v2;. . . ; vn are the attributesmarginal value functions normalized in [0, 1].Each marginal value function, vi, is used to assessthe partial performance of each bank in attribute

xiin an increasing 01 scale.Given the global values of the banks as defined

by the estimated additive value function, theirclassification into q groups C1; C2;. . . ; Cq can beperformed in a straightforward way through theintroduction of q1 value cut-off pointst1; t2;. . . ; tq1, such that

Vxi5t1 , bank ibelongs to group C1

t24Vxi5t1 , bank ibelongs to group C2

.

.

.

.

.

.

Vxi5t

q1 , bank ibelongs to group C

q

The estimation of the additive value functionand the cut-off thresholds is performed throughlinear programming techniques. The objective ofthe method is to develop the additive value modelso that the above classification rules can reproducethe predetermined grouping of the banks asaccurately as possible. Therefore, a linear pro-gramming formulation is employed to minimize

Table III. List of criteria (variables)

Financial

CAP Equity/total assets

PROVIS Loan loss provisions/net interest revenue

ROAA Return on average assets

EXPENSES Cost-to-income ratioLIQ Liquid assets/customer and short-term funding

SIZE Logarithm of total assets

Non-financial

SUBS The number of subsidiaries

OWNERS The number of institutional shareholders

LIST Dummy variable indicating whether the bank

is listed (LIST 1) in a stock exchange or not

(LIST 0)

MARKET Dummy variable indicating whether the bank

is operating in a developed (MARKET 1) or

developing (MARKET 0) market

5Probably, the employment of a risk-weighted ratiosuch as the Tier 1 ratio would be more appropriate.However, due to too many missing values for Tier 1, werely on EQAS that is considered one of the basic ratios

whose use dates back to the 1900s, and is still being usedin many recent studies in banking.6Cost refers to overheads that are the expenses forrunning business, such as staff salaries and benefits, rentexpenses, equipment expenses and other administrativeexpenses.7These are generally short-term assets that can be easilyconverted into cash, such as cash itself, deposits with thecentral bank, treasury bills, other government securitiesand interbank deposits among others.



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the sum of all violations of the above classificationrules for all the banks in the training sample.Detailed description of the mathematical program-ming formulation can be found in the works ofZopounidis and Doumpos (1999) and Doumpos

and Zopounidis (2002).

4. EMPIRICAL RESULTS

Table IV shows descriptive statistics while distin-guishing between the three groups of banks, aswell as between developed and developing mar-

kets. We also present the results of a Kruskal

Table IV. Descriptive statistics and KruskalWallis test (total sample): (A) Continuous variables and (B)Categorical variables (no.)

1 2 3

Mean Std. Deviation Mean Std. Deviation Mean Std. Deviation KruskalWallis

chi-square

(A)

SIZE

Total 4.388 0.669 3.843 0.689 3.535 0.865 188.992

***

Developed 4.398 0.678 4.010 0.764 4.462 0.885 26.279***

Developing 4.221 0.506 3.650 0.529 3.328 0.713 50.538***

PROVIS

Total 13.664 15.556 23.103 21.500 32.198 39.085 85.516***

Developed 13.568 15.889 25.612 22.730 52.776 55.357 61.67***

Developing 15.172 8.877 20.190 19.691 27.612 32.936 7.58**

CAP

Total 8.159 3.690 9.297 4.874 9.443 6.099 6.51**

Developed 8.019 3.654 7.970 4.612 5.543 3.962 27.919***

Developing 10.353 3.615 10.838 4.735 10.312 6.159 4.127

ROAA

Total 1.189 0.740 1.124 1.043 1.231 1.609 3.652

Developed 1.152 0.715 0.589 0.669 0.061 0.772 121.639***

Developing 1.763 0.899 1.746 1.056 1.492 1.632 11.268***

EXPENSES

Total 57.054 13.241 57.105 15.577 57.891 18.599 0.551

Developed 57.625 12.921 62.432 14.597 57.708 17.537 11.503***

Developing 48.131 15.148 50.918 14.406 57.932 18.876 12.365***

LIQ

Total 19.588 19.473 28.024 21.329 30.632 19.763 82.258***

Developed 18.666 19.198 25.675 23.702 15.611 17.039 11.591***

Developing 34.014 18.322 30.752 17.923 33.979 18.785 3.098

OWNERS

Total 5.711 7.255 5.171 6.074 5.537 5.690 9.226**

Developed 5.768 7.368 5.736 7.230 5.205 6.127 2.742

Developing 4.821 5.193 4.515 4.310 5.611 5.604 2.021

SUBS

Total 91.983 195.844 32.808 85.374 17.551 68.655 114.935***

Developed 96.572 200.980 49.574 112.187 64.359 152.735 6.737**

Developing 20.214 33.317 13.333 22.096 7.120 8.649 14.017***

(B)

LIST 1 2 3

Total 466 214 214

Developed 438 115 39

Developing 28 99 175

Notes: ***Significant at the 1% level; **significant at the 5% level; *significant at the 10% level; variables are defined in Table III.



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Wallis test to assess the means differences acrossthe groups.

SIZE, PROVIS and SUBS are the only variablesthat are statistically significant in all cases.However, while higher size results in higher

soundness in the cases of the total sample andthe developing markets sub-sample, the results aremixed in the case of the developed markets sub-sample, with an average SIZE equal to 4.398(Group 1), 4.010 (Group 2) and 4.462 (Group 3).Lower PROVIS results in higher soundness whichcan be attributed to the perception that lowerprovisions correspond to lower probability ofnon-performing loans and higher asset quality.As in the case of SIZE, the impact of SUBS onsoundness depends on the status of the market.CAP and LIQ are significant in the case of thetotal sample and in developed countries, although

not in developing countries. ROAA and EX-PENSES on the other hand are significant in thecase of the two sub-samples, although not inthe case of the total sample. Higher ROAA resultsin higher soundness, however, the results aremixed with respect to EXPENSES. Finally, OWN-ERS is significant only in the case of the totalsample.

Table V presents the average weights of thecriteria over the 10 replications. The two mostimportant criteria are PROVIS and CAP, withweights equal to 20 and 19.76%, respectively.Hence, as in previous studies (Poon et al., 1999;

Poon and Firth, 2005; Pasiouras et al., 2006) andconsistent with the univariate results, lower assetquality (in terms of loan portfolio) results in lowersoundness. As for CAP, our finding is consistentwith the view that capital is important for banksfor several reasons. For example, capital serves asthe last line of defence against the risk of banksinsolvency, as any losses a bank suffers could bepotentially written off against capital. Even in thecase that insolvency becomes unavoidable, capitalprotects to some degree depositors, creditors andinvestors (Le Bras and Andrews, 2004). Further-more, as mentioned by Theodore (1999) capital

allows the leveraging of a banks growth anddiversification, and a tight solvency position wouldbe an obstacle to do so.

The average weight of MARKET equals15.66% showing that the country where thebanks operate has an important impact on theirclassification. In other words, we find that operat-ing in a developed market results in highersoundness. EXPENSES and ROAA also carry

an average weight above 10%, indicating thathigher efficiency in expenses management andhigher profitability result in higher bank sound-ness. The least important variables are SIZE andLIST. Especially, the latter carries an averageweight equal to 0%, indicating that it makes nodifference whether the bank is listed in a stockexchange or not.

Table VI presents the average classificationaccuracies over the 10 replications. Panel A

corresponds to the training sample and Panel Bto the validation sample. For benchmarkingpurposes, two additional models are developedthrough DA and ordinary logistic regression(OLR) using the same input variables and 10-foldcross-validation process for estimating and testingthe models.

UTADIS obtains the highest overall classifica-tion accuracy in the training sample that equals

Table V. Weights of criteria (variables) in the UTADISmodel (averages of 10 replications)

Criteria (variables) Weights (%)

PROVIS 20.00

CAP 19.76MARKET 15.66

EXPENSES 10.31

ROAA 10.21

LIQ 9.08

OWNERS 8.79

SUBS 4.58

SIZE 1.60

LIST 0.00

Note: Variables are defined in Table III.

Table VI. Classification results (averages over 10replications): (A) Training and (B) Validation

Group

1 (%) 2 (%) 3 (%) Overall (%)

(A)

UTADIS 84.10 53.89 74.54 70.84

OLR 92.63 24.02 73.37 63.34

DA 90.17 27.1 79.78 65.68

(B)

UTADIS 83.47 50.49 72.75 68.91

OLR 92.63 23.33 72.68 62.88

DA 90.48 26.22 78.47 65.06

Notes: UTADIS, UTilites Additives DIScriminantes; OLR,

ordinary logistic regression; DA, discriminant analysis.



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70.84%, while the corresponding figures for DAand OLR are 65.58 and 63.34%, respectively. Allthe three models classify correctly a high propor-tion of banks from Group 1 that ranges between84.19% (UTADIS) and 92.63% (OLR), followed

by banks in Group 3, but only a relatively smallerproportion of banks in Group 2 that is between24.02% (OLR) and 53.89% (UTADIS). The poorperformance in terms of classifying banks intointermediate groups has been observed in paststudies as well (e.g. Pasiouras et al., 2007) and canbe attributed to the fact that banks falling in thiscategory might be closely related either to banks inthe lower band of Group 1 or the upper band ofGroup 3, hence making their correct classificationa difficult task. The results of a t-test, used toassess the differences in the classification accura-cies among the methods, indicate that the accura-

cies achieved by UTADIS are significantlydifferent from the ones obtained by DA andOLR at the 1% level.

Turning to the accuracies in the validation dataset, the highest overall accuracy (68.91%) is againachieved by UTADIS, with group accuracies equalto 83.47% (Group 1), 50.49% (Group 2) and72.75% (Group 3). OLR and DA classify correct62.88 and 65.06% of the banks in sample (i.e.overall). As in the case of the training sample, allthree models classify correctly a higher proportionof banks from Groups 1 and 3, and a relativelylower proportion of banks from Group 2. The

difference between UTADIS and DA is nowstatistically significant at the 5% level, whereasthe one between UTADIS and OLR remainssignificant at the 1% level.

5. CONCLUSIONS

In this paper, we developed a multicriteria model,through UTADIS, to classify banks into threegroups on the basis of their soundness. The sampleconsisted of 894 banks from 79 countries. Themodel was developed through a 10-fold cross-

validation procedure using six financial and fournon-financial variables, while the ratings of Fitchformed the basis for assessing the soundness ofbanks.

The results indicate that asset quality (asmeasured by loan loss provisions), capitalizationand the market where banks operate are the mostimportant criteria (in terms of weights) in classify-ing the banks. Profitability and efficiency in

expenses management are also important attri-butes, whereas size and listing in a stock exchangeare the least important ones.

UTADIS classified correctly 70.84 and 68.91%of the banks in training and validation samples

accordingly, and was more efficient than modelsdeveloped through DA and OLR for benchmark-ing purposes. Furthermore, the differences in theclassification accuracies achieved by UTADIS andthe ones obtained by DA and OLS were statisti-cally significant in both the training and thevalidation samples. Finally, all the techniquesexperienced difficulties in classifying banks inintermediate situation.

The present study could be extended in variousways, such as the classification of banks into moregroups, and the inclusion of additional bank-specific variables into the model. It would also be

worthwhile to incorporate further country-specificvariables into the analysis, reflecting the regulatoryand economic environment in the markets wherebanks operate. Finally, one could benchmark thedeveloped model against the alternative classifica-tion techniques or develop integrated models.

ACKNOWLEDGEMENTS

We would like to thank two reviewers, andparticipants at the 18th International Conferenceon Multiple Criteria Decision Making (2006) for

helpful comments and suggestions that helped usimprove earlier versions of the paper circulatedunder the title An assessment of banks credit-worthiness: a multicriteria approach.

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A Multicriteria Decision Framework for Measuring Banks’ Soundness Around the World

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