The AHP technique application is usually for determining criteria priorities according to respondents, but no statistical query exists for instance, whether the experts’ views significantly change with respect to some characteristics, the criteria priorities significantly differentiate with each other. This research objective is along with determining the priorities of subcriteria in the evaluation of bank failure/bankruptcy risk, to show generating priority series from experts’ views for each criteria for carrying out statistical tests with respect to expert subgroups, and then produce information for researchers/decision makers. The research utilises the usage of conducting statistical hypothesis testing on generated priority series and CAMELS approach to bank failure. This study investigates and determines the subcriteria priorities of CAMELS dimensions, and uses the data of study Pekkaya & Erol (2016) for statistical tests by generating priority series of CAMELS dimensions. Since no similar academic study, which uses statistical tests and generates priority series in bank failure/bankruptcy literature via similar approach, is observed; this study can be accepted as paving the way of the usage of AHP technique. The obtained priority values of subcriteria with main criteria of CAMELS dimensions can be used to improve the early warning system for bank failure.
JanA. and MarimuthuM., Bankruptcy profile of foreign versus domestic Islamic banks of Malaysia: A post crisis period analysis, Int Journal of Economics and Financial Issues6(1) (2016), 332–346.
2.
SantoniA. and ArbiaB., Assessing rating agencies’ ability to predict bank bankruptcy – the lace financial case, Economic Notes by Banca Monte dei Paschi di Siena SpA42(2) (2013), 203–215.
3.
SarkerA.A., Camels rating system in the context of Islamic banking a proposed ‘s’ for shariah framework, Journal of Islamic Economics Banking and Finance2(2) (2006), 1–26.
4.
AkyazanA.E., Bankacılık Düzenleme ve Denetleme Kurumu’nun bankalara el koyma ve denetleme yetkisi, TBB Dergisi63 (2006), 146–172.
GaganisC., PasiourasF. and ZopounidisC., A multicriteria decision framework for measuring banks’ soundness around the World, Journal of Multi-Criteria Decision Analysis14(1–3) (2006), 103–111.
7.
HamzaçebiC. and PekkayaM., Determining of stock investments with grey relational analysis, Expert Systems with Appl38(8) (2011), 9186–9195.
8.
-CincaC.S. and -NietoB.G., Partial least square discriminant analysis for bankruptcy prediction, Decision Support Systems54 (2013), 1245–1255.
9.
ÖztunaD., ElhanA.H. and TüccarE., Investigation of four different normality tests in terms of type 1 error rate and power under different distributions, Turk J Med Sci36(3) (2006), 171–176.
10.
AltmanE.I., HaldemanR.G. and NarayananP., ZETA Analysis, A new model to identify bankruptcy risk of corporations, Journal of Banking and Finance1(1) (1977), 29–54.
11.
FerrouhiE.M., Moroccan banks analysis using CAMEL model, Int J of Economics and Financial Issues4(3) (2014), 622–627.
12.
EcerF., Türkiye’deki özel bankaların finansal performanslarının karşılaştırılması: 2008–2011 dönemi, AIBU Sosyal Bilimler Enstitüsü Dergisi13(2) (2013), 171–189.
13.
DoddF.J., DoneganH.A. and McMasterT.B.M., A statistical approach to consistency in AHP, Mathl Comput Modelling18(6) (1993), 19–22.
14.
LanineG. and VennetR.V., Failure prediction in the Russian bank sector with logit and trait recognition models, Expert Sys with App30(3) (2006), 463–478.
15.
Gökalp and Füsun, Comparing the financial performance of banks in Turkey by using promethee method, Ege Strategic Research Journal6(1) (2015), 63–82.
16.
Akhisarİ. and KarpakB., AHP as an early warning system: An application in commercial banks in Turkey, Berlin: Springer Verlag, 2010, pp. 223–233.
17.
Egeİ., TopaloğluE.E. and KarakozakÖ.. CAMELS performans değerleme modeli: Türkiye’deki mevduat bankalari üzerine ampirik bir uygulama, Niğde Üniversitesi IIBF Dergisi8(4) (2015), 109–126.
18.
HilmanI., The bank bankruptcy prediction models based on financial risk (An empirical study on Indonesian banking crises), Int J of Business Economics and Law4(2) (2014), 1–16.
19.
OlmedaI. and FernandezE., Hybrid classifiers for financial multicriteria decision making: The case of bankruptcy prediction, Computational Economics10(4) (1997), 317–335.
20.
ChatziI.G., DiakomihalisM.N. and ChytisE.T., Performance of the Greek banking sector pre and throughout the financial crisis, Journal of Risk & Control2(1) (2015), 45–69.
21.
OhlsonJ.A., Financial ratios and the probabilistic prediction of bankruptcy, J of Accounting Research18 (1980), 109–131.
22.
BellovaryJ., GiacominoD. and AkersM., A review of bankruptcy prediction studies: 1930 to present, Journal of Financial Education33(1) (2007), 1–42.
MinJ.H. and LeeY.-C., Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters, Expert Systems with App28(4) (2005), 603–614.
25.
JinJ.Y., KanagaretnamK. and LoboG.J., Ability of accounting and audit quality variables to predict bank failure during the financial crisis, Journal of Banking & Finance35(11) (2011), 2811–2819.
26.
DoumposM. and ZopounidisC., A multicriteria decision support system for bank rating, Decision Support Systems50 (2010), 55–63.
27.
KhaddafiM., Falahuddin, HeikalM. and NandariA., Analysis Z-score to predict bankruptcy in banks listed in Indonesia Stock Exchange, Int J of Economics and Financial Issues7(3) (2017), 326–330.
28.
PekkayaM. and Erol-DemirF., Determining the priorities of criteria in assessing the bankruptcy risk of the banks via AHP, Journal of Management Economics and Business, ICAFR 16 Special Issue (2016), 40–45.
29.
PekkayaM. and Erol-DemirF., Determining the Priorities of CAMELS Dimensions Based on Bank Performance. DincerH., HaciogluÜ., YükselS. (eds) Global Approaches in Financial Economics, Banking, and Finance. Cham: Springer, (2018), 445–463.
30.
PekkayaM. and AktoganM., Dizüstü bilgisayar seçimi: DEA, VIKOR ve TOPSIS ile karşılaştırmalı bir analiz, The Int J of Economic and Social Research10(1) (2014), 107–125.
31.
PekkayaM. and ÇolakN., Üniversite öğrencilerinin meslek seçimini etkileyen faktörlerin önem derecelerinin AHP ile belirlenmesi, Int J of Social Science6(2) (2013), 797–818.
32.
PekkayaM. and BaşaranS., Konaklama işletmeleri hizmet kalitesi boyutlari önem derecelerinin AHP ile belirlenmesi ve işletmelerin hizmet kalitesine göre TOPSIS ile sıralanması, Mali Ufuklar Dergisi5(15) (2011), 111–136.
33.
PekkayaM. and AslanB., OSB yer seçiminde dikkate alınan kriterler arası etkileşimin ve kriter önem derecelerinin belirlenmesi, Int Journal of Economic and Administrative Studies, 18. EYİ Special Issue (2018), 293–308.
34.
PekkayaM. and İmamoğluÖ.P., Hastane hizmet kalitesinde servqual boyutlarının önem derecelerinin belirlenmesi, J of Management Economics and Business, ICMEB17 Special Issue (2017), 607–616.
35.
StanisicM., StefanovicD., ArezinaN. and MizdrakovicV., Analysis of auditor’s reports and bankruptcy risk in banking sector in the republic of Serbia, Amfiteatru Economic15(34) (2013), 431–441.
36.
VilenM., Predicting failures of large U.S. commercial banks. Master’s Thesis, Aalto University School of Economics, 2010.
37.
OdomM.D. and ShardaR., Neural networks for bankruptcy prediction. International Joint Conference on Neural Networks, (1990) 163–168. https://www.researchgate.net/publication/224755073, Accessed 15.03.2016.
38.
KumarP. and Kavita, Prediction of bankruptcy risk in Indian banks: An application of Altman’s model, Journal for Studies in Management and Planning1(10) (2015), 217–236.
39.
WankeP., HassanM.K. and GaviãoL.O., Islamic banking and performance in the Asean banking industry: A TOPSIS approach with probabilistic weights, International Journal of Business and Society18(S1) (2017), 129–150.
40.
GinevičiusR. and PodviezkoA., The evaluation of financial stability and soundness of Lithuanian banks, Ekonomska Istraživanja-Economic Research26(2) (2013), 191–208.
41.
GhasempourS. and SalamiM., Ranking Iranian private banks based on the CAMELS model using the AHP hybrid approach and TOPSIS, Int Journal of Academic Research in Accounting, Finance and Management Sciences6(4) (2016), 52–62.
42.
ShapiroS.S. and WilkM.B., An analysis of variance test for normality, Biometrika52(3–4) (1965), 591–611.
43.
SaatyT.L., Decision making with the analytic hierarchy process, Int Journal of Services Sciences1(1) (2008), 83–98.
44.
SaatyT.L., The analytic hierarchy process, NY: McGraw-Hill, 1980.
45.
KwakW., ShiY. and Kou.G., Bankruptcy prediction for Korean firms after the 1997 financial crisis: using a multiple criteria linear programming data mining approach, Rev Quant Finan Acc38(4) (2012), 441–453.
DemyanykY. and HasanI., Financial crises and bank failures: A review of prediction methods, Omega38(5) (2010), 315–324.
48.
GüneysuY., ErB. and Arİ.M., Türkiye’deki ticari bankalarin performanslarinin AHS ve GIA yöntemleri ile incelenmesi, KTU SBE Sosyal Bilimler Dergisi9 (2015), 71–93.
ZhangZ., KouX. and DongQ., Additive consistency analysis and improvement for hesitant fuzzy preference relations, Expert Systems With Applications98 (2018), 118–128.
