Analyze and compare the impact of systematical factors such as national risk and banking sector risk on bank’s credit rating between developed markets and emerging markets. Analyze and compare the impact of specific features includes bank size, form of ownership and financial ratios on bank’s credit rating between developed markets and emerging markets.
1 ABSTRACT Credit rating agencies such as Fitch, Standard & Poor’s and Moody’s not mention the difference in impact of affecting factors on credit ratings of commercial banks between developed markets and emerging markets However, some researchers have pointed out there is difference in affecting of finacial ratios to credit rating of commercial banks between developed markets and emerging markets Thesis’s objective is to investigate the difference in impacting of systematical factors such as national risk, banking system risk in country where the banks locate and specific factors of banks as ownership structure, bank size and finacial ratios on bank credit ratings between developed markets and emerging markets First, we use One way anova analysis and choosing indepent variables for ordered logit model method to indentify factor affecting to bank credit ratings in developed markets and emerging markets The results of the thesis indicate that systematical factors have a stronger impact on bank’s credit ratings in emerging markets than developed markets Meanwhile, financial ratios have less impact on bank’s credit ratings in emerging than developed markets Moreover, the thesis shows the existence of the difference in affecting of ownership structure to bank’s credit rating between developed markets and emerging markets Basing on the empirical results, we have some policy implications for central banks of emerging markets to raise the bank’s credit ratings in their countries We also imply some methods for commercial banks in emerging markets to enchance their credit ratings CHAPTER 1: INTRODUCTION 1.1 Study background Investors and depositors have a great concern about bank’s credit rating However, credit rating agencies not mention details the way and level of impacting of affecting factors on bank’s credit ratings Besides, some empirical studies have indicated there is difference in level of impacting of financial ratios on the credit ratings of commercial banks in developed markets and emerging markets So it is essential to identify the difference in impacting of affecting factors on bank’s credit rating in developed markets and emerging markets 1.2 Research problem Due to the study background above, we carry out the study to solve the detail research objectives following: Identify the difference in impacting of systematical factors such as national risk, banking sector risk and bank specific features such as bank size, form of ownership and financial ratios on bank’s credit rating in developed and emerging markets 1.3 Research questions Firstly, is there difference in impacting of systematical factors as national risk and banking sector risk on bank’s credit rating between developed markets and emerging markets Secondly, is there difference in affecting of specific features as bank scale, form of ownership and financial ratios to bank’s credit rating between developed markets and emerging markets 1.4 Research objectives (1): Analyze and compare the impact of systematical factors such as national risk and banking sector risk on bank’s credit rating between developed markets and emerging markets (2): Analyze and compare the impact of specific features includes bank size, form of ownership and financial ratios on bank’s credit rating between developed markets and emerging markets 1.5 Scope of this study The thesis focus on analyzing bank’s credit ratings and affecting factors to bank’s credit ratings at developed markets and emerging markets in the period from 2010 to 2015 1.6 Academic and empirical meaning of this study 1.6.1 Academic meaning First, the thesis identifies the affecting factors to bank’s credit ratings at developed markets and emerging markets Second, this study indicates the difference in the impact of systematical factors and specific feature of commercial banks to their credit ratings between developed markets and emerging markets 1.6.2 Empirical meaning First, identifying affecting factors and the impact level of these factors on bank’s credit ratings helps banking governors in emerging markets define the credit risk of commercial banks Moreover, the empirical results of the thesis supply more reference foundation for banking governors in emerging markets to issue regulations for ensuring the safety of commercial banks and enhancing the bank’s credit ratings in these countries Second, defining the affecting factors on bank’s credit ratings helps commercial banks to choose suitable solutions to raise their credit ratings 1.7 Contribution of this thesis The contribution of this study to the empirical literatures relating to bank’s credit ratings is that this study clarifies the difference in the impacts of national risk, banking sector risk, bank size, form of ownership and bank’s financial ratios to bank’s credit rating between developed markets and emerging markets 1.8 Structure of this thesis Chapter “Introduction” Chapter “Bank’s credit ratings in developed markets and emerging markets” Chapter “Methodologies” Chapter “Empirical results and analysis” Chapter “Conclusions and policy implications” CHAPTER 2: BANK CREDIT RATINGS IN DEVELOPED MARKETS AND EMERGING MARKETS 2.1 The overview of bank’s credit ratings 2.1.1 Concepts of bank’s credit ratings Bank’s credit ratings issued by credit rating agencies are ordinal measures that should not only reflect the current positions of banks but also provide information about their future financial positions (Bellotti et al, 2011a) 2.1.2 Bank’s credit rating methodologies 2.1.2.1 The Uniform Financial Institutions Rating System - UFIRS This system is adopted by the Federal Financial Institutions Examination Council in 1979 At first, this system is applied in United State After that this system is used by many countries due to the recommendation of the Federal Reserve 2.1.2.2 Bank’s credit rating methodologies of credit rating agencies Fitch evaluates the bank’s credit rating through phases: Phase 1: Accessing bank’s viability rating – VR bases on factors: operating environments, bank size, management capability, risk management and financial positions of commercial banks Phase 2: Accessing bank’s final credit ratings by combining bank’s viability ratings and supports from government and group The same as Fitch, Standard & Poor’s evaluates bank’s credit rating within steps: Step 1: identify bank’s stand alone credit profile bases on factors include: economic risk, industry risk of country where banks locate; business position; capital and earnings; risk position; funding and liquidity Step 2: evaluate bank’s final credit rating by combining bank’s stand alone credit profile and external supports from government and group 2.2 The affecting factors on bank’s credit rating According to The Uniform Financial Institutions Rating System and bank’s credit rating methodologies of credit rating agencies, we realize that bank’s credit ratings are affected by the following factors: economic risk, industry risk of country where banks locate, external supports from government and group and some specific features of banks 2.2.1 The affecting of macro factors on bank’s credit ratings Banking operations are very sensitive to macro factors’ varieties Especially, changes in economic policies or political systems have strong affects on bank’s credit ratings in these countries 2.2.2 The affecting of government supports and group supports on bank’s credit ratings Fitch (2104) supposes that government supports to belonging commercial banks help to improve these banks’ credit ratings Moreover, supports of big and prestige groups have positive impacts on banks’ credit ratings According to Moody’s (1999), groups utilize their scale advantages, risk diversification capabilities and management experience to help belonging commercial banks 2.2.3 The affecting of specific features on banks’ credit ratings According to Standard & Poor’s (2011a), specific features of commercial banks have impacts on bank’s credit rating included: bank size and business position; asset quality; capital and earnings; funding and liquidity Credit rating agencies analyze these factors to identify bank’s stand alone credit profiles After that, credit rating agencies combine bank’s stand alone credit profiles, economic risks and supports from governments or groups to determine bank’s credit rating 2.3 Economic features and commercial bank characteristics in developed markets 2.3.1 Economic features in developed markets First, developed countries have a high level of per capita GNP Second, developed countries have post-industrial economies Third, developed countries have a high standard of living 2.3.2 Commercial bank characteristics in developed markets First, banking system in developed countries has a high degree of competition Moreover, commercial banks in developed markets have a higher level of services diversification than commercial banks in emerging markets Finally, the regulatory frameworks controlling the banking operations in developed countries are better than emerging markets 2.4 Economic features and commercial bank characteristics in emerging markets 2.4.1 Economic features in emerging markets First, emerging markets are countries being in transition process from a closed and less developed economy to an opened and developed economy Second, instability of financial system in emerging markets is an important feature discussed by a lot of researchers Third, financial liberalization is taking place in emerging markets to over come the instability of financial systems Final, GDP growth rates in emerging markets are usually higher than developed markets 2.4.2 Bank characteristics in emerging markets First, asset and loan growth rates of commercial banks in emerging markets are at high level Second, Suarez (2001) indicates that equity capital of commercial banks in emerging markets not present the financial capability of commercial banks as in developed markets Third, earning capability of commercial banks, presented by net profit/average total assets ratio, in emerging markets is higher than commercial banks’ in developed markets Final, quality of financial information issued by commercial in emerging markets is not reliable (Vives, 2006) In these countries, issuing financial information of commercial banks has a lot of problems due to slow intuitional reforms 2.5 The impact of information asymmetry on bank’s credit rating in emerging markets 2.5.1 Concept of information asymmetry Information asymmetry occurs when one party of a financial transaction have more sufficient information than the other party And this may lead to moral hazard or adverse selection 2.5.2 The reasons cause the impaction of information asymmetry on bank’s credit rating in emerging markets Information asymmetry between credit rating agencies and credit rated banks always occurs in credit rating process The reason make information asymmetry have a strong impact on bank’s credit rating in emerging market may due to the essence of bank’s credit ratings and the quality of bank’s financial information in these countries 2.5.3 The impact of information asymmetry on bank’s credit ratings in emerging markets Most of bank’s credit ratings in emerging markets are unsolicited rating These credit ratings base almost on public information of rated banks So that the credit rating agencies can not access the credibility and accuracy of the information especially when publishing information frameworks and accounting standards are not strict in emerging markets In this case, the credit rating agencies focus on evaluating the bank’s operation environment and skip accessing the specific financial ratios of rated banks So that the information asymmetry causes the the difference in the impact of economic risks and specific features of commercial banks on bank’s credit rating between developed markets and emerging markets 2.6 Literature reviews Empirical researches relating bank’s credit rating can be divided into strands: The first one presented by studies that search and try to identify the reliability of rating assignments The second strand is focused on empirical researches that investigate the prediction models for bank’s credit ratings 2.6.1 Reliability of rating assignments Researches of Poon and Firth (2005), Poon et al (2009), Shen et al (2012) 2.6.2 Prediction models for bank’s credit ratings 2.6.2.1 The studies applied statistical techniques Poon et al (1999), Matousek and Stewart (2009), Caporale et al (2012) 2.6.2.2 The studies applied artificial intelligent Boyacioglu et al (2009), Ioannidis et al (2010), Bellotti et al (2011a, 2011b), Chen (2012) 2.7 Research gaps and thesis’ analyzing framework 2.7.1 Research gaps We notify that the previous studies have not mentioned the differences in impact of some factors include: economic risk, industry risk and bank’s ownership form on bank’s credit ratings between developed markets and emerging markets Moreover, the number of financial ratios used in these studies is limited 2.7.2 Thesis analyzing framework We have groups of factor in our research model The first one presents systematical factors such as economic risk and industry risk of the countries where banks locate The other group presents the specific features of commercial banks including bank’s ownership form, bank size and financial ratios We apply One way – ANOVA analyzing on bank’s financial ratios and indepenent variables selection method for ordered logit model to indentify the factors impacting bank’s credit ratings in developed markets and emerging markets.The next step, we access the model’s reliability and test the model’s assumptions Finnaly,we analyse the impact of affecting factors and the difference in impact of these factor on bank’s credit ratings between developed markets and emerging markets CHAPTER 3: METHODOLOGIES 3.1 Research model 3.1.1 Ordered logic model The objectives of the thesis are identifying the different in impact of affecting factors on bank’s credit rating between developed markets and emerging markets and interpreting the impact of these factors on bank’s credit ratings So that, we apply ordered logic model as our main analyzing model in this study Because ordered logic model is suitable for presenting the result of object classifying process into ordinary ratings (Greene, 2002) Otherwise, basing on the direction of variables’’ coefficients we can identify the impact directions of corresponding factors on bank’s credit ratings Meanwhile, we can not achieve these objectives with non linear models such as neutral network or support vector machines Moreover, we can use interacting variables in ordered logic model to access the difference in impact of affecting factors on bank’s credit ratings between developed markets and emerging markets The ordered logic model is presented as following: y* is a dependent variable and unobserved We can only observer Y = if y* ≤ = if < y* ≤ µ1 = if µ1 < y* ≤ µ2 … = J if µj-1 < y* µ1 , µ2 ,… µj-1 are thresholds calculated by the models β is coefficients presenting the impact of independent variables on the dependent variable Ɛ is a stochastic error term Ɛ has a standard distribution, a average value and variance equaling 3.1.2 Definition and measurement of dependent variable The dependent variable of the model is bank’s credit ratings issued by Fitch The dependent variable is given symbol yi and coded from to 3.1.3 Definition and measurement of independent variable The independent variables of model are classified into groups: The first group presents the systematic factors affecting bank’s operation environment The other group have some subgroups presenting bank’s specific features such as form of ownership, bank’ size and financial ratios 3.2 The research data The thesis’ research data is crossed-sectional data including bank’s credit ratings, bank’s financial ratios and macro economic factors affecting bank’s operation environment The thesis’ research data is divided into data sets The first data set has 296 observations of bank’s credit ratings and bank’s financial ratios in developed markets The second data set has 282 observations of bank’s credit ratings and bank’s financial ratios in emerging markets The list of developed markets and emerging markets is referent to World Economic Outlook 2014 (IMF, 2014) We apply systematical sample selection method with steps to select observations for these data sets The bank’s credit ratings are Fitch’s bank’s credit rating assignments from 2013 to 2015 The bank’s financial ratios between 2010 and 2014 are provided by Bank scope 3.3 Research hypothesis Hypothesis (H1): there are differences in impact of economic risk and industry risk of countries where banks locate on bank’s credit ratings between developed markets and emerging markets Hypothesis (H2):there are differences in impact of international financial group ownership on bank’s credit ratings between developed markets and emerging markets Hyppothesis (H3):there are differences in impact of government ownership on bank’s credit ratings between developed markets and emerging markets Hyppothesis (H4):there are differences in impact of bank’s total asset size on bank’s credit ratings between developed markets and emerging markets Hyppothesis (H5):there are differences in impact of financial ratios on bank’s credit ratings between developed markets and emerging markets 3.4 Analyzing data process of the thesis The data analyzing process of the thesis to achieve the thesis’ objectives and answer the research question of the thesis is described by the following diagram: Diagram 3.1: Thesis’ analyzing data process Apply One way Anova analyzing on bank’s financial ratios Apply variable selection process for Ordered logic model Identify the affecting factors on bank’s credit rating Access the creditability and assumptions of the research model Merge data sets and add interacting variables Access the differences in impact of affecting factors on bank’s credit ratings Source: Author’s inference from literature review Step 1, to identify the differences in impact of affecting factors on credit ratings between developed markets and emerging markets we must indicate the specific factors affecting on bank’s credit ratings To achieve this purpose, we separately apply one way anova analyzing process and variable selection process for ordered legit model on the data sets of developed markets and emerging markets Step 2, we apply BIC ratios (Bayesian information criteria) to compare the crediability of model inferring from the above proccess to random models Besides that, we also test the model’s assumptions such as muticollinearity, heteroskedasticity and missing essential variables Step 3, to achieve the first and the second research objectives, we merge the data set of commercial banks in developed markets and the data set of commercial banks in emerging markets We also add a dummy variable named Emer This proxy takes value if banks locate in developed markets and value in case banks locate in emerging markets After that, we apply interact proxies between Emer variable and each variable presents systematical factors and bank’s specific factors Finally, we regress again the Ordered logit model with all variables indentified from previous analyzing steps and these interact proxies In case the cofficients of the interact variables are significant that mean there are difference in impact of corresponding variables on bank’s credit ratings between developed markets and emerging markets 10 CHAPTER 4: EMPIRICAL RESULTS AND ANALYSIS 4.1 One way Anova analyzing bank’s financial ratios We separately apply one way Anova analyzing for each data set in the thesis Table 4.1: Average values of bank’s financial ratios classified by bank’s credit ratings in emerging markets Bank’s credit rating Number of Obs Standard deviation Average Min Max LnAss:Logarit bank’s total assets Average Standard deviation Min Max AssGrow:Average grow rate of bank’s assets over years B 70 8.1763 1.3779 5.4189 10.8731 0.2477 0.2719 -0.0512 1.2881 BB 64 8.9984 1.3439 6.3988 13.1649 0.1615 0.1277 -0.0920 0.5908 BBB 116 9.9381 1.6938 5.8197 13.4052 0.1601 0.1167 -0.1883 0.6149 A 32 11.1366 1.9862 6.3651 14.9549 0.1009 0.0645 -0.0326 0.2870 Total 282 9.4235 1.8310 5.4189 14.9549 0.1755 0.1729 -0.1883 1.2881 CreGrow:Average grow rate of loan over years LoanLoss_Ln:Overdue loan/total loan B 70 0.2679 0.3014 -0.1466 1.4153 9.2476 14.2697 0.0600 89.9940 BB 64 0.1717 0.1271 -0.1175 0.6352 6.1456 7.2884 0.2160 37.2630 BBB 116 0.3186 1.5222 -0.1357 16.5153 4.8128 6.4004 0.0000 33.9010 A Total 32 282 0.1160 0.2497 0.0727 0.9901 -0.0319 -0.1466 0.3488 16.5153 3.9422 6.1173 4.1475 9.1790 0.4840 0.0000 19.0650 89.9940 LoanLoss_Equ:Overdue loan/Equity B 70 9.0775 13.1440 0.5800 LoanPro_Loan:Loan provision/Average total loan 86.0830 77.8620 172.4143 1.3510 985.2000 BB 64 5.9166 7.1224 0.4070 40.8250 33.3714 50.3338 3.2010 335.6260 BBB 116 5.1344 10.4250 0.0000 100.0000 30.5691 62.7480 0.0000 592.5900 A Total 32 282 4.6897 6.2403 6.3130 10.2703 0.4030 0.0000 27.4330 100.0000 23.6996 42.1649 17.3031 99.7057 3.8960 0.0000 62.9140 985.2000 Equ_Ass:Equity/Total assets Equ_Loan:Equity/Net loan B 70 13.0284 7.5463 4.8960 54.4000 12.5905 9.9021 -35.8200 46.3100 BB 64 11.4510 4.6231 4.6550 29.9590 11.3953 4.4502 4.5650 27.6880 BBB 116 11.4910 7.0204 2.2510 45.6580 12.1437 10.6638 2.7870 87.1290 A 32 10.0933 6.3050 1.2650 36.4300 9.7345 7.1818 1.3450 43.6430 Total 282 11.7050 6.6405 1.2650 54.4000 11.8114 9.0263 -35.8200 87.1290 Equ_ShortCap:Equity/Short term capital Equ_Debt:Equity/Total debts B 70 27.2697 22.0246 7.4000 117.8660 19.8259 17.1355 5.6050 122.1600 BB 64 22.0198 10.9775 9.9440 71.3420 16.0915 10.6321 5.7420 76.8130 BBB 116 28.5163 67.7129 3.0640 717.1900 30.4765 63.2117 3.1470 514.0000 A 32 18.6040 15.5689 1.6960 76.2060 19.0323 26.8788 5.2040 153.1840 Total 282 25.6077 45.4093 1.6960 717.1900 23.2694 43.0184 3.1470 514.0000 IntIn_Loan:Interest income/Average total loan IntIn_Ass:Interest income/Total earning interest assets B 70 19.1616 17.7580 -41.8370 86.6200 15.5154 10.0329 3.8030 67.3100 BB 64 16.9513 14.6495 4.9450 116.6560 13.7887 5.9801 5.1230 39.7260 BBB 116 37.3368 90.0853 1.5400 520.3250 17.7913 39.6251 2.4600 413.7670 A 32 18.4976 32.5349 5.3740 191.8260 11.9574 10.6335 1.3040 64.2230 Total 282 26.0610 60.4427 -41.8370 520.3250 15.6560 26.3065 1.3040 413.7670 19 -0.2973 0.2780 Exp_Int -0.0845 0.2310 NetLoan_Debt Number of obs = 226 Number of obs = 282 LR chi2(16) = 295.4400 LR chi2(9) = 353.8200 Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 Pseudo R2 = 0.5036 Pseudo R2 = 0.4845 Log likelihood = -145.6301 Log likelihood = -188.2233 Source: Author’s caculating from thesis’ data sets Basing on the regression results of ordered logit models on sub data sets and the initial data set in emerging markets presented in table 4.7, we calculate the frequencies of variables having significant cofficients in above models Table 4.8: The frequency of variables having significant cofficients in ordered logit models in emerging markets Variable Country_rating Bicra Government Group LnAss AssGrow CreGrow LoanLoss_Ln LoanLoss_Equ LoanPro_Loan Equ_Ass Equ_Loan Equ_ShortCap Equ_Debt IntIn_Loan Frequency (%) Variable Frequency(%) 100 IntIn_Ass 100 IntEx_Cap 100 NIM 100 NetIntIn_Ass 100 OthIn_Ass 83 100 NonIntEx_Ass 67 ROAA 17 83 ROAE 17 Exp_Int 33 17 NetLoan_Ass 0 NetLoan_ShortCap 0 NetLoan_Debt 0 LiAss_ShortCap 83 LiAss_Debt 17 Source: Author’s caculating from thesis’ data sets The empirical results in table 4.7 and 4.8 show that Country_rating, Bicra, Government, Group, LnAss, AssGrow, LoanLoss_Ln, Equ_Debt OthIn_Ass are variable having significant cofficients in the model on the initial data set Moreover, these proxies have the frequencies greater than 80% in ordered logit models on sub data sets and initial data set Besides that, the results of one way anova analyzing presented in table 4.1 also indicate that LnAss, AssGrow, LoanLoss_Ln, Equ_Debt and OthIn_Ass having different average values in each bank’s credit ratings So that, we can conclude that these proxies have impacts on the bank’s credit ratings in emerging markets We regress the ordered logit models with these variables on the initial data set in emerging markets 20 Table 4.9: The ordered logit model with selected variables on initial data set in emerging markets Standard Variable Coefficient P-Value Confident interval Deviation Min Max 1.8164 0.2897 0.0000 1.2487 2.3842 Country_rating 0.8881 0.1857 0.0000 0.5241 1.2520 Bicra 1.1377 0.3722 0.0020 0.4082 1.8672 Government 3.9542 0.5847 0.0000 2.8082 5.1003 Group 0.5979 0.1049 0.0000 0.3923 0.8035 LnAss -4.5942 1.4117 0.0010 -7.3612 -1.8273 AssGrow -0.0832 0.0220 0.0000 -0.1264 -0.0400 LoanLoss_Ln 0.0157 0.0038 0.0000 0.0083 0.0232 Equ_Debt -0.1744 0.0536 0.0010 -0.2794 -0.0695 OthIn_Ass 16.6202 19.3397 24.3097 /cut1 /cut2 /cut3 1.7691 1.9176 2.1752 13.1528 15.5812 20.0465 20.0876 23.0982 28.5730 Number of obs = 282 LR chi2(9) = 353.8200 Prob > chi2 = 0.0000 Pseudo R2 = 0.4845 Log likelihood = -188.2233 Source: Author’s caculating from thesis’ data sets We apply the same analyzing process with the variables having impact on bank’s credit ratings in developed markets Table 4.12: The ordered logit model with selected variables on initial data set Variable Country_rating Government Group LnAss LoanLoss_Ln Equ_Ass Equ_Loan IntIn_Loan NIM ROAE Exp_Int Coefficient 1.3263 2.2145 1.3725 0.6562 -0.1043 0.1149 0.0131 0.1019 -0.5620 0.0121 -0.0232 in developed markets Standard P-Value Deviation 0.1564 0.4154 0.3041 0.0939 0.0233 0.0485 0.0059 0.0740 0.1659 0.0050 0.0061 0.0000 0.0000 0.0000 0.0000 0.0000 0.0180 0.0270 0.1690 0.0010 0.0150 0.0000 Confident interval Min 1.0197 1.4003 0.7765 0.4721 -0.1500 0.0197 0.0015 -0.0432 -0.8871 0.0023 -0.0352 Max 1.6328 3.0287 1.9684 0.8403 -0.0586 0.2100 0.0247 0.2470 -0.2368 0.0218 -0.0113 21 NetLoan_Ass /cut1 /cut2 /cut3 /cut4 /cut5 0.0194 11.2014 14.3689 17.1884 21.2567 23.7446 0.0087 1.8856 1.9165 2.0324 2.1849 2.2434 0.0260 0.0024 7.5057 10.6126 13.2049 16.9744 19.3476 0.0365 14.8970 18.1251 21.1718 25.5390 28.1415 Number of obs = 296 LR chi2(12) = 287.7800 Prob > chi2 = 0.0000 Pseudo R2 = 0.3545 Log likelihood = -262.0579 Source: Author’s caculating from thesis’ data sets 4.2.2 The result of accessing model’s crediability We apply the BIC (Bayesian information criteria) to compare the crediability of the selected model to random models The results of BIC show that the selected models are the finest models 4.3 Test of the ordered logit model’s assumptions 4.3.1 Test of multicollinearity in the models To access the affect of high multicollinearity on the variables in the model, we calculate VIF ratios of each proxies of the model The variables cause the high multicollinearity in the model if their VIF ratios greater than 10 We calculate the VIF ratios of the variables of the ordered logic model in developed markets and in the emerging markets The results of VIF ratios show that the VIF ratios of all variables in models are below 10 So that we can conclude that the ordered logic models on the data sets in developed market and in emerging markets are not affected by the multicollinearity 4.3.2 Test of heteroskedasticity in the models One of the most important assumptions of models is that there is no heteroskedasticity in the model The violation of this assumption can impact the standard deviations and p-values of variables in the models We add option Robust in the regression command to regress the model without heteroskedasticity assumption and compare to the above regression results The results of model’s regression without heteroskedasticity assumption show that there changes in standard deviations and p-value of the variables in the models But the differences are not much and not change p-value and the direction of cofficients So that, the thesis’models are not affected by the heteroskedasticity 4.3.3 Test of missing essential variables in the models The test of missing essential variables in the models, proposed by Chen et al (2015), is applied in this study The test’s results show that there is no missing essential variables in the thesis’ models 22 4.4 Access the marginal affects of the model’s variables We calculate the marginal affects of the variables having significant p-values in 4.2.1 The results show that Country_rating, Government and Group have strong marginal affects on the banks’ classification Besides, that marginal affects of these variables in the data set of emerging markets are greater these affects in the developed markets 4.5 Analyzing the differences in impact of affecting factors on bank’s credit ratings between developed markets and emerging markets As presented in 3.4 to indentify the differences in impact of affecting factors on bank’s credit rating between developed markets and emerging markets, we merge the data set in developed markets and the data set in emerging markets together Moreover, we add Emer, a dummy variable, in the model This proxy take value if banks locate in emerging markets Otherwise, it takes value After that, we create interact proxies between Emer and each variables in the model We add Emer and these interact proxies in the model The model’ resgression result show that the cofficients of Country_rating_Emer and Emer are significant and have directions as expected Besides, the cofficient of Bicra_Emer is significant but Bicra’s is not The empirical results also indicate that the cofficients of Government_Emer and Group_Emer are significant The cofficient of Government_Emer is negative But the cofficient of Group_Emer is positive Moreover, we notice that LnAss, AssGrow, LoanLoss_Ln, NIM, ROAE, Exp_Int and the interact proxies between these variables and Emer have significant cofficients and oppositve directions 4.6 Result analyzing 4.6.1 Analyzing the empirical model of affecting factors on bank’s credit ratings in emerging markets 4.6.1.1 The impact of systematical factors on bank’s credit ratings in emerging markets The systematical factors include economic risk and industry risks of countries where banks locate have positive affects on bank’s credit ratings Besides that, the coefficients of these variables are significant at 1% level These results are similar to bank’s rating criteria of international rating agencies These results are also coincident with the empirical results of Bellotti et al (2011a, 2011b) and Caporale et al (2012) 4.6.1.2 The impact of ownership factor on bank’s credit rating in emerging markets From the regression results showed in table 4.9, we notice that ownership factor has a strong impact on bank’s credit ratings Government variable has a positive impact on bank’s credit ratings This indicates that the commercial banks belonging to government of countries where banks locate has a larger change to receive better ratings Similarly, Group variable has a positive affect on bank’s credit ratings This show that the commercial banks belonging big financial groups have a better changes to be classified into A or BBB rating than the others These results are agreed with bank’s rating criteria of international rating agencies Because these agencies suppose that governments or international financial groups tend to support to their belonging commercial banks 4.6.1.3 The impact of scale factor on bank’s credit ratings in emerging markets 23 Bank’s size, presented by the total asset of the banks, has positive affect on bank’s credit ratings This implies that the bigger size the banks have the better change they have to receive the better ratings Goddard et at (2004) explain that big commercial banks have economic scale advantages They benefit from their market power to generate an above average profit rate 4.6.1.4 The impact of financial ratios on bank’s credit ratings in emerging markets The empirical results show that the average assets growth rate over years and the overdue loan/Total loan ratio have negative impacts on bank’s credit ratings These variable’s coefficients are significant at 1% level This indicates that banks having a rapid assets growth rate may be classified into low credit ratings This is agreed with the empirical results of Köhler (2015) Besides, the impact of the overdue loan/Total loan ratio coincides with the bank rating criteria of banking regulatory authorities Moreover, it is also proved by Caporale et al (2012) Next, among capital capacity ratios, we notice that the equity/total debt ratio has positive affect on bank’s credit rating at 1% significant level This implies that banks having high equity/total debt ratios can receive better bank’s credit ratings Pasiouras and Kosmidou (2007) explain that a strong equity help to increase bank’s creditworthiness, reduce capital expense These banks have more capability to expend their business and deal with operation risks Finally, among earning ratios, we find that the other operation income/Average total asset (OthIn_Ass) has negative impact on bank’s credit ratings Berger et al (2010) indicate that bank’s diversification in China cause an increase in operation expenditure and reduce bank’s profitability 4.6.2 Analyzing the empirical model of affecting factors on bank’s credit ratings in developed markets 4.6.2.1 The impact of systematical factors on bank’s credit ratings in developed markets Among systematical factors, we notice that the economic risk factor has positive impact on bank’s credit rating at 1% significant level The coefficient of Bicra, industry risk, is not significant in the model 4.6.2.2 The impact of ownership factor on bank’s credit rating in developed markets The results indicate that all independent variables presenting these factors have positive impact on bank’s credit rating at 1% significant level This result is the same as the result in the ordered logic model in emerging markets 4.6.2.3 The impact of scale factor on bank’s credit ratings in developed markets The scale factor has positive impact on bank’s credit ratings This result is also the same as the result in the ordered logic model in emerging markets 4.6.2.4 The impact of financial ratios on bank’s credit ratings in developed markets Among bank’s asset quality ratios, we notice that the overdue debt/total debt ratio has negative impact on bank’s credit rating at 1% significant level Next, the model’s results show that the equity/total asset ratio and the equity/total net debt ratio have positive impact on bank’s credit rating at 5% significant level This result is coincident with the result of the empirical model in emerging market and empirical studies presented above In the group of earning ratios, we notice that there are ratios include NIM ratio, ROAE ratio and the total expenditure/total income ratio having significant coefficients NIM ratio has negative impact on bank’s credit ratings This result is the same as the result of Matousek and Stewart (2009) In the emerging markets, the 24 levels of deposit interest rate and lending interest rate are lower than these of developed markets So that the negative impact of NIM on bank’s credit ratings may be caused by the difference in risk and level of interest rates We also notice that the net loan/total assets ratio has positive impact on bank’s credit ratings This result is agreed with many previous empirical studies Matousek and Stewart (2009) indicate that the liquidity assets/total assets has negative impact on bank’s credit ratings Because the bank which face difficuties in capital mobilization must maintain more liquidity assets This also reduce the bank’s profitabilies 4.6.3 Analyzing the difference in impact of affecting factors on bank’s credit rating between developed markets and emerging markets 4.6.3.1 The difference in impact of systematical factors on bank’s credit ratings between developed markets and emerging markets The empirical results of the models indicate that credit rating of the country where banks locate have a stronger impact on bank’s credit rating in emerging markets than in developed markets Among the factors, the industry risk only has impact on bank’s credit ratings in emerging markets Liu and Ferri (2001) also conclude that economy risk strongly impact on corporation’s credit ratings in emerging markets But this factor does not impact on the credit ratings of corporations in developed markets Williams et al (2013) prove that the countries’ credit ratings are the ceilings of corporations’ credit ratings in these countries 4.6.3.2 The difference in impact of ownership factor on bank’s credit ratings between developed markets and emerging markets The thesis’ results show that international financial group’s ownership has stronger impact on bank’s credit rating in emerging markets than in developed markets Meanwhile, the positive impact of government ownership on bank’s credit ratings in emerging markets is lower than in the developed markets Mirzaei et al (2013) indicate that commercial banks belonging to international financial groups have a better profitability in emerging markets than in developed markets The reason is when these banks penetrate in the emerging markets, they can use their advantages in banking technologies and innovation services to compete with domestic commercial banks in order to attain a better profitability In contrast, when these banks enter the developed markets their profitability may reduce due to competion with domestic banks in these countries 4.6.3.3 The difference in impact of scale factor and financial ratios on bank’s credit ratings between developed markets and emerging markets The thesis’s result suggest that there is no difference in impact of scale factor on bank’s credit ratings between developed markets and emerging markets Otherwise, the average asset growth rate over years has negative impact on bank’s credit rating in emerging markets But this ratio has no impact on bank’s credit ratings in developed markets The reason of this is discussed in detail in section 4.6.1 Besides, the empirical results show that the impact of the overdue loan/total loan ratio on bank’s credit ratings is lower in the emerging markets than in developed markets The cause of this may come from information asymmetry as explained in previous sections Suarez (2001) explain that bank’s quality assets ratios not reflect the true credit risk of commercial banks due to difference in bank’s accounting standards Moreover, bank financial reports in emerging markets are not reliable due to inexact classification of overdue loans 25 Next, among the capital capacity ratios, we notice that there is no difference in impact of the equity/total assets ratio and the equity/net loan ratio on bank’s credit rating between developed markets and emerging markets Shen et al (2012) explain that the rating agencies access carefully the impact of capital capacity ratios on bank’s credit ratings in both high information asymmetry countries and low information asymmetry countries Although these ratios are usually not transparent in countries with high information asymmetry But the ratings agencies have no other choice and have to classify banks with high capital capacity ratios into high credit ratings Moreover, the impact of bank’s earning ratios (include the total expenditure/total income ratio, Net interest margin and net return/average total equity ratio) on bank’s credit ratings also reduce in emerging markets The information asymmetry in these countries is also the cause of this problem Finally, we notice there is no difference in impact of the net loan/total assest ratio on bank’s credit rating between developed markets and emerging markets 26 CHAPTER 5: CONCLUSION AND POLICY IMPLICATIONS 5.1 Conclusion Bank’s credit ratings issued by credit rating agencies provide investors and banking governors with precious information to access bank’s financial situation Moreover, the bank’s credit ratings affect strongly on bank’s capital mobilization capacity on international capital markets Rating agencies such as Fitch, Standard & Poor’s and Moody’s present their banking credit rating methodologies Although these agencies not mention in detail about the affecting factors and the impact direction of these factors on bank’s credit ratings in both developed markets and emerging markets Besides, the previous empirical studies relating to bank’s credit ratings are not coincident in the set of affecting factors and the impact directions of these factors on bank’s credit ratings So we apply one way anova analyzing method and variables selection process for ordered logic model on the data sets of commercial banks in developed markets and emerging markets separately We also create and access the impact of interact proxies on merge data set to indicate the difference in impact of affecting factors on bank’s credit rating between developed markets and emerging markets The thesis’ results identify the affecting factors on bank’s credit ratings in developed markets and emerging markets Besides that, the thesis indicates the difference in impact of these factors on bank’s credit ratings between developed markets and emerging markets The empirical results of the thesis help us to answer the research questions mentioned in chapter as detail First, there is difference in impact of systematical factors such as economic risk and industry risk on bank’s credit ratings between developed markets and emerging markets Second, there is difference in impact of bank’s specific factors include bank’s scale, ownership structure and financial ratios on bank’s credit rating between developed markets and emerging markets 5.2 Policy implications 5.2.1 Policy implication for banking governors Beside the regular financial ratios used to access bank’s financial situation such as bank’s total assets, the overdue loan/total debt ratio, the equity/total assets ratio, … the central banks in emerging markets should focus on the asset growth rate over years Because the bank’s with rapid asset growth rate possibly face to high operation risks Moreover, the bank’s earning ratios include ROAE, ROAA or NIM does not reflect bank’s financial situations or bank’s credit risks Instead of that, the central banks may focus on the other operation income/average total assets ratio Because banks with this ratio exceeding the average level may face with high potential risks Moreover, the thesis’ results imply that the central banks of emerging markets should improve the banking industry environment to enhance bank’s credit ratings in these countries Some of these solutions are improving the transparence of bank’s financial reports, establishing competitive banking environment and stimulating banking reforms 27 Besides, the thesis’ results indicate that the impacts of bank’s earning ratios on bank’s credit ratings reduce in emerging markets So that, the central banks of emerging markets should apply the overdue loan classification regulations and bank’s financial reports standard which are compatible with international standards in order to make these ratios reflect exactly the bank’s financial situation Finally, the thesis’ results indicate that capital factors have positive impact on bank’s credit ratings However capital scale of banks in emerging markets is too small to compare with banks in developed markets So that to enhance the capital scale of commercial banks, the central banks in emerging markets should set up a regulation framework for commercial banks to attract capital through domestic and international stock markets 5.2.2 Policy implication for commercial banks The empirical results of the thesis show that the ownership of international financial groups has positive impact on bank’s credit rating in emerging markets So the attraction capital from these groups not only helps to enhance bank’s credit ratings but also create a chance for these banks to apply effective operation and administration models Besides, to improve bank’s credit ratings, the bank administrator in emerging markets must not only focus on core financial ratios (such as the overdue loan/total loan ratio and the equity/total assets ratio) but also maintain a suitable asset growth rate Because the thesis’ results show that the asset growth rate has negative impact on bank’s credit ratings in emerging markets Finally, commercial banks in emerging markets should focus on traditional operation such as capital mobilization and lending to enhance their credit ratings 5.3 Limitation and suggest for further research In this study, we focus on identification the difference in impact of systematical and bank’s specific factors on bank’s credit ratings between developed markets and emerging markets However, in the bank’s credit rating process, the rating agencies also access the administration capability of bank’s leader boards, bank culture, bank’s risk appetite and bank’s diversification So the next studies should focus on the impact of these factors on bank’s credit ratings or the difference in impact of these factors on bank’s credit ratings between developed markets and emerging markets Moreover, the empirical results of this study are based on the model of affecting factors on bank’s credit ratings in developed markets and emerging markets So the policy implications of this study may not be suitable with Vietnamese banking industry The next researches should establish the model on Vietnamese commercial banks to imply specific policy for State Bank of Vietnam and Vietnamese commercial banks 28 REFERENCES &&& -1 Altman, E I., 1968 Financial ratios, discriminant analysis and the prediction of corporate bankruptcy The journal of finance, 23: 289-609 Alsakka, R et al., 2014 The sovereign – bank rating channel and rating agencies’ downgrades during the European debt crisis Journal of International Money and Finance, 1: 1-23 Bellotti, T et al., 2011a A note comparing support vector machines and ordered choice models’ predictions of international banks’ ratings Decision Support Systems, 51: 682-687 Bellotti, T et al., 2011b Are rating agencies’ assignments opaque? Evidence from international banks Expert Systems with Applications, 38: 4206-4214 Berger, N.A et al., 2010 The effects of focus versus diversification on bank performance: Evidence from Chinese banks Journal of Banking & Finance, 34: 1417-1435 Berger, N.A et al., 2007 Bank ownership and efficiency in China: what lies ahead in the world's largest nation? Bank of Finland Research 15- 26 Bank of Finland, Finland, 2007 Berger, N.A and Bouwman, C.H.S., 2013 How does capital affect bank performance during financial crises? Journal of Financial Economics, 109: 146-176 Boritz, J.E and Kennedy, D.B., 1995 Effectiveness of Neural Network Types for Prediction of Business Failure Expert Systems with Applications, 9: 503-512 Boyacioglu, M.A et al., 2009 Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey Expert Systems with Applications, 36: 3355-3366 10 Bouvard, M et al., 2011 Transparency in the financial system: rollover risk and crises, Working papers 11 Borensztein, E et al., 2013 Sovereign ceiling ‘lite’? The impact of sovereign rating on corporate ratings Journal of Banking and finance, 37: 4014 - 4024 12 Boyd, J.D and Runkle, D.E., 1993 Size and performance of banking firms: Testing the predictions of theory Journal of Monetary Economics, 31: 47-67 13 Caprio, G and Klingebiel, D., 2003 “Episodes of systematic and borderline financial crises” Available at: < http:// siteresources.worldbank.org/ INTRES/Resources/ 469232 -1107449512766/6480831108140788422/ 23456_Table_on_systemic_and_non-systemic_banking crises January 212003.pdf > [Accessed date 15 April 2014] 14 Caporale, G.M et al., 2012 Ratings assignments: Lessons from international banks Journal of International Money and Finance, 31: 1593-1606 15 Canbas, S et al., 2005 Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case European Journal of Operational Research, 166: 528-546 16 Cetorelli, N., 2001 Competition among banks: Good or bad? Federal Reserve Bank of Chicago 29 17 Chen, Y.S., 2012 Classifying credit ratings for Asian banks using integrating feature selection and the CPDA-based rough sets approach Knowledge-Based Systems, 26: 259-270 18 Chen, X et al., 2015 Logistic Regression with Stata Institute for Digital Research and Education, UCLA, Los Angeles 19 Christopoulos, A.G et al., 2011 Could Lehman Brothers’ Collapse Be Anticipated? An Examination Using CAMELS Rating System International Business Research, 4: 11-19 20 Claessens S and Laeven, L., 2003 What Drives Bank Competition? Some International Evidence Journal of Money, Credit and Banking, 36: 563-583 21 Demyanyk, Y and Hasan, I., 2010 Financial crises and bank failures: A review of prediction methods Omega, 38: 315-324 22 Demirguc, K.A and Huizinga, H., (2013) Are banks too to fail or too big to save? International evidence from equity prices and CDS spreads Journal of Banking and Finance, 37: 875-894 23 Ederington, L.H., 1985 Classification Models and Bond Ratings The Financial Review, 20: 237-262 24 Ederington, L.H et al., 1987 The Information Content of Bond Ratings The Journal of Financial Research, 10: 211-226 25 Economy Watch (2016) Emerging Markets [Online] Available at [Accessed date 15 September 2016] 26 Elliott, R.J et al., 2014 A Double HMM approach to Altman Z-scores and credit ratings Expert Systems with Applications, 41: 1553-1560 27 Fang, Y., Hasan, I and Marton, K 2014 Institutional development and bank stability: Evidence from transition countries Journal of Banking & Finance, 39: 160 – 176 28 Falavigna, G., 2012 Financial ratings with scarce information: A neural network approach Expert Systems with Applications, 39: 1784 - 1792 29 Fitch, 2003 Bank ratings methodology, New York 30 Fitch, 2014 Global Financial Institutions Rating Criteria, New York 31 Fethi, M.D and Pasiouras, F., 2010 Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey European Journal of Operational Research, 204: 189-198 32 Fons, J.,S., 1998 Improving Transparency in Asian Banking Systems Moody's Investors Service 33 Fu, X , Lin, Y and Molyneux, P., 2014 Bank competition and financial stability in Asia Pacific 2014 Journal of Banking & Finance, 38: 64 -77 34 Fu, M., X and Heffernan, S., 2009 The effects of reform on China’s bank structure and performance Journal of Banking & Finance, 33 (1): 39 – 52 30 35 Gasparino, C., 1996 Bond-rating Firms may be Required to Disclose When Work is Unsolicited The Wall Street Journal (July 11) 36 Golin, J., 2001 The Bank Credit Analysis Handbook: A Guide for Analysts, Bankers and Investors Singapore :John Wiley & Sons 37 Goddard, J et al., 2004 The profitability of European banks: a cross-sectional and dynamic panel analysis Manchester School, 72 (3): 363– 381 38 Greene, W H., 2002 Econometric analysis (Vol 5) New Jersey: Prentice Hall 39 Griffiths, B and Beynon, M.J, 2005 Expositing stages of VPRS analysis in an expert system: Application with bank credit ratings Expert Systems with Applications, 29: 879-888 40 Haan, D.J and Poghosyan, T., 2012 Size and earnings volatility of US bank holding companies Journal of Banking and Finance, 36: 3008-3016 41 Hammer, P.L., Kogan, A and Lejeune, M.A., 2012 A logical analysis of banks’ financial strength ratings Expert Systems with Applications, 39: 7808-7821 42 Harington, H., 1997 Not Moody – Just Angry The Banker (February, 22–23) 43 Horrigan, J.O., 1966 The dertermination of long-term credit standing with financial ratios Journal of accouting research, 4: 25-36 44 Hoàng Trọng và Chu Nguyễn Mộng Ngọc, 2008 Phân tích liệu nghiên cứu với SPSS Tp.HCM: Nhà xuất Hồng Đức 45 Hulisi Öğüt, M M D et al., 2012 Prediction of bank financial strength ratings: The case of Turkey Economic Modelling, 29: 632 – 640 46 Ioannidis, C et al., 2010 Assessing bank soundness with classification techniques Omega, 38: 345-357 47 Iannotta, G., Nocera, G and Sironi, A., 2010 The impact of government ownership on bank’s rating: Evidence from the European Banking Industry Working papers 48 Imbierowicz, B and Rauch, C., 2014 The relationship between liquidity risk and credit risk in banks Journal of Banking & Finance, 40: 242–256 49 IMF, 2014 Recovery Strengthens, Remains Uneven World Economy Outlook April 2014 [Online] Available at [Accessed date 20 May 2015] 50 IMF, 2016 Frequently asked question World Economy Outlook [Online] Available at [Accessed date 10 Octorber 2016] 51 Investopedia, 2017 Developed economy criteria [Online] Available at: [Accessed date 22 June 2016] 52 Jardin, P.D., 2010 Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy Neurocomputing, 73: 2047-2060 31 53 Jo, H and Han, I., 1996 Integration of Case-Based Forecasting, Neural Network, and Discriminant Analysis for Bankruptcy Prediction Expert Systems with Applications, 11: 415-422 54 Keffala, M., R., 2015 How using derivatives affects bank stability in emerging countries? Evidence from the recent financial crisis Research in International Business and Finance, 35: 75–87 55 Kiema,I and Jokivoulle, E., 2014 Does a leverage ratio requirement increase bank stability? Journal of Banking & Finance, 39: 240-254 56 Kumar, P.R and Ravi, V., 2007 Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review European Journal of Operational Research, 180: 1-28 57 Lacher, R.C et al., 1991 A neural network for classifying the financial health of a firm European Journal of Operational Research, 85: 53-65 58 Lang, L and So, R., 2002 Bank ownership structure and economic performance Working papers 59 Lassoued, N et al., 2016 The impact of state and foreign ownership on banking risk: Evidence from the MENA countries Research in International Business and Finance, 36: 167-178 60 Liu, L.G and Ferri, G., 2001 How Do Global Credit Rating Agencies Rate Firms from Developing Countries? ADB Institute Research Paper, 26 61 Long, J.S and Freese, J., 2001 Regression Models for Categorical Dependent variable Using Stata New York: Stata Press 62 Martin, D., 1977 Early warning of bank failure A logit regression approach Journal of Banking and Finance, 1: 249-276 63 Manzoni, K., 2004 Modeling Eurobond credit ratings and forecasting downgrade probability International Review of Financial Analysis, 13: 277-300 64 Mariathasan, M and Merrouche, O , 2012 The manipulation of basel risk-weights Evidence from 2007-10 Department of economics Oxford: University of Oxford 65 Manso, G., 2013 Feedback effects of creditratings Journal of Financial Economics, 109: 535-548 66 Malhotra, R and Malhotra, D.K., 2003 Evaluating consumer loans using neural networks Omega, 31: 83-96 67 Matousek, R and Stewart, C., 2009 A note on ratings of international bank Journal of Financial Regulation and Compliance, 17: 146-155 68 Köhler, M., 2015 Which banks are more risky? The impact of business models onbank stability Journal of Financial Stability, 16: 195–212 69 Martin, T.W and Cherney, M., 2014 S&P Increases Unsolicited Ratings The Wall street journal, Dec, 2014 70 Mizena, P and Tsoukas, S., 2012 Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model International Journal of Forecasting, 28: 273-287 32 71 Mishkin, F.S., 1999 Lessons from the Asian crisis Journal of International Money and Finance, 18: 709–723 72 Mirzaei, A et al., 2014 Does market structure matter on banks’ profitability and stability? Emerging vs advanced economies Journal of Banking & Finance, 37: 2920-2937 73 Montgomery, H., 2003 The Role of Foreign Banks in Post-crisis Asia: The Importance of Method of Entry ADB Institute Research Paper, 51 74 Moody’s., 2009 Moody ’s Rating Symbols & Definitions, New York 75 Moody’s Ratings Investors in which Service the (Moody’s), Issuer has 1999 Not Designation Participated of Unsolicited Moody’s Special Comment (November, 1–4) 76 Moody’s Investors Service (Moody’s), 1999 Rating methodology: Bank credit risk in Emerging Markets (July) 77 Nath, R at el., 1997 Determining the saliency of input variable in neural network classifiers Computer Operational Research, 24: 767-773 78 Nafziger, E., W., 2006 Economic Development 4th Ed New York: Cambridge University Press 79 Niemann, H et al., 2008 Improving performance of corporate rating prediction models by reducing financial ratio heterogeneity Journal of Banking & Finance, 32: 434-446 80 Nilsen, J and Rovelli, R., 2001 Investor risk aversion and financial fragility in emerging economies Journal of International Financial Markets Instutions & Money, 11: 443–474 81 Nguyen, M et al., 2012 Bank market power and revenue diversification: Evidence from selected ASEAN countries Journal of Asian Economics, 23: 688–700 82 Ohlson, J.A., 1980 Financial ratios and the probabilistics prediction of bankruptcy Journal of Accouting Research, 18: 109-131 83 Orsenigo, C and Vercellis, C., 2013 Linear versus nonlinear dimensionality reduction for banks’ credit rating prediction Knowledge-Based Systems, 47: 14-22 84 Pasiouras, F and Kosmidou, K., 2007 Factors influencing the profitability of domestic and foreign commercial banks in the European Union Research in International Business and Finance, 21: 222–237 85 Poon, W.P.H et al., 1999 A multivariate analysis of the determinants of Moody’s bank financial strength ratings Journal of International Financial Markets, Institutions and Money, 9: 267-283 86 Poon, W.P.H and Firth, M., 2005 Are Unsolicited Credit Ratings Lower? International Evidence From Bank Ratings Journal of Business Finance & Accounting, 32: 306-686 87 Poon, W.P.H et al., 2009 Do Solicitations Matter in Bank Credit Ratings? Results from a Study of 72 Countries Journal of money credit and banking, 35: 340-365 88 Powell, A., 2004 Basel II and developing countries: Sailing thourgh the sea of standards World Bank Policy Research Working Paper 3387, 9/2004 33 89 Purda, L.D., 2003 Consistency of Global Credit Ratings: An Analysis of Firm versus Country-Specific Factors Working papers 90 Romana, A and Sargu, A.C., 2013 Analysing the Financial Soundness of the Commercial Banks in Romania: An Approach Based on the Camels Framework Procedia Economics and Finance, 6: 703-712 91 Roy, P.V., and Unsolicited 2005 Is Bank There Ratings a Difference and, if so, in Why? Treatment Working Between Paper Solicited (ECARES, Universite Libre de Bruxelles) 92 Salvador, C et al., 2014 Impact of the subprime crisis on bank rating: The effect of the hardening of rating policies and worsening of solvency Journal of Financial Stability, 11: 13-31 93 Shen, C.H et al., 2012 Asymmetric benchmarking in bank credit rating Journal of International Financial Markets, Institutions & Money, 22: 171-193 94 Smirlock, M., 1985 Evidence of the non-relationship between concentration and profitability in banking Journal of Money, Credit and Banking, 17: 69–83 95 Standard & Poor's, 2009 Standard & Poor's Credit Rating Definitions, New York 96 Standard & Poor's, 2011a Banks: Rating Methodology And Assumptions, New York 97 Standard & Poor's, 2011b Banking Industry Country Risk Assessment Methodology And Assumptions, New York 98 Standard and Poor’s Ratings Services, 2000 S&P Refines its ‘pi’ Ratings on Japanese Companies S&P’s News Release (15/11), 1–5 99 Suarez, L R., 2001 Rating banks in emerging markets: what rating agencies should learn from financial indicators Wooking Paper 100 The Federal Financial Institutions Examination Council (FFIEC)., 1979 Uniform financial institutions rating system, Washington, D.C 101 Vives, X., 2006 Banking and regulation in emerging markets: the role of external discipline Occasional paper no 06/15 University of Navarra 102 Watson, J., 2001 How to Determine a Sample Size: Tipsheet #60 University Park, PA: Penn State Cooperative Extension, New York 103.Williams, G., Alaskka, R and Gwilym, O.A., 2013 The impact of sovereign rating actions on bank ratings in emerging markets Journal of Banking & Finance, 37: 563–577 104.Wikipedia, 2017 Developed country [Online] Available [Accessed date 22 June 2017] 105.Wong, B.K and Selvi, Y., 1998 Neural network applications in Finance: A review and analysis of literature (1990±1996) Information & Management, 34: 129-139 at ... credit ratings in emerging markets Most of bank’s credit ratings in emerging markets are unsolicited rating These credit ratings base almost on public information of rated banks So that the credit. .. model The objectives of the thesis are identifying the different in impact of affecting factors on bank’s credit rating between developed markets and emerging markets and interpreting the impact of. .. banking governors in emerging markets to issue regulations for ensuring the safety of commercial banks and enhancing the bank’s credit ratings in these countries 3 Second, defining the affecting