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.
Trang 1ABSTRACT
Credit rating agencies such as Fitch, Standard & Poor’s and Moody’s do 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
Trang 2CHAPTER 1: INTRODUCTION 1.1 Study background
Investors and depositors have a great concern about bank’s credit rating However, credit rating agencies
do 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
1.5 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
Trang 3Second, 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 1 “Introduction”
Chapter 2 “Bank’s credit ratings in developed markets and emerging markets”
Chapter 3 “Methodologies”
Chapter 4 “Empirical results and analysis”
Chapter 5 “Conclusions and policy implications”
Trang 4CHAPTER 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 2 phases:
Phase 1: Accessing bank’s viability rating – VR bases on 5 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 2 steps:
Step 1: identify bank’s stand alone credit profile bases on 6 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
Trang 5According 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 do 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
Trang 6Information 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 2 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 2 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
Trang 7CHAPTER 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
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 0 average value and variance equaling 1
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 1 to 9
3.1.3 Definition and measurement of independent variable
The independent variables of model are classified into 2 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 2 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
Trang 8emerging markets is referent to World Economic Outlook 2014 (IMF, 2014) We apply systematical sample selection method with 2 steps to select observations for these 2 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 1 (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 2 (H2):there are differences in impact of international financial group ownership on bank’s
credit ratings between developed markets and emerging markets
Hyppothesis 3 (H3):there are differences in impact of government ownership on bank’s credit ratings
between developed markets and emerging markets
Hyppothesis 4 (H4):there are differences in impact of bank’s total asset size on bank’s credit ratings
between developed markets and emerging markets
Hyppothesis 5 (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
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
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 2 data sets and add interacting variables
Access the differences in impact
of affecting factors on bank’s credit ratings
Trang 9Step 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 1 value if banks locate in developed markets and 0 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
Trang 10CHAPTER 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
LnAss:Logarit bank’s total assets AssGrow:Average grow rate of bank’s assets over 3 years
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 3 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 32 4.6897 6.3130 0.4030 27.4330 23.6996 17.3031 3.8960 62.9140 Total 282 6.2403 10.2703 0.0000 100.0000 42.1649 99.7057 0.0000 985.2000
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
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
Trang 11Bank’s
credit
rating
Number
of Obs Average
Standard
Standard
IntEx_Cap:Interest expense/Total interest bearing capital
NIM:Net interest margin
B 70 14.8549 7.8491 3.5600 54.4100 15.9974 8.3957 4.7900 52.1300
BB 64 11.2155 9.3518 2.9000 70.8000 11.3424 9.2202 0.6000 66.7300 BBB 116 9.8933 7.3001 3.2300 69.0200 9.9942 7.3708 -5.1700 68.0100
Total 282 11.0839 7.9578 2.9000 70.8000 11.4420 8.2354 -5.1700 68.0100
NetIntIn_Ass:Net interest income/Average total assets OthIn_Ass:Other operation income/Average total assets
B 70 13.8780 7.6415 3.7000 61.9300 6.0989 3.3288 1.6300 22.4300
BB 64 12.2491 18.7090 3.5600 139.9600 4.3123 2.4593 1.0200 10.7000 BBB 116 9.1547 6.9236 3.1200 67.9000 4.3609 2.7360 0.1900 16.5400
Total 282 10.6972 10.9169 3.1200 139.9600 4.5683 2.8806 0.1900 22.4300
NonIntEx_Ass: Non interest expense/Average total assets ROAA:Return on Average total assets
BB 64 4.4777 2.8568 1.2300 14.4600 5.6268 6.8561 1.1943 52.5063 BBB 116 4.7868 3.2113 0.0000 16.6100 4.7438 5.8730 0.7413 61.2250
Total 282 4.8309 3.1961 0.0000 16.6100 5.3739 5.8792 0.6933 61.2250
B 70 6.0141 4.7440 0.7347 32.3683 6.1262 5.3752 -0.4880 32.5820
BB 64 4.8491 5.1862 1.1607 39.2023 4.8953 5.0337 1.0670 36.9770 BBB 116 4.3054 5.5214 0.4237 58.0370 4.3207 5.4613 0.5420 58.1850
Total 282 4.7120 5.0017 0.4237 58.0370 4.7574 5.1023 -0.4880 58.1850
NetLoan_Ass:Net loan/Total asset NetLoan_ShortCap:Net loan/ Total shorterm capital
B 70 3.5223 5.8696 -0.6480 35.7410 6.4434 4.7629 1.0610 23.1070
BB 64 1.9295 1.7895 -1.7550 9.8840 4.8889 5.9204 0.9370 44.4720 BBB 116 1.5113 1.2835 -2.7600 7.3990 3.9355 4.3881 0.1740 40.4890
Total 282 2.0855 3.2642 -2.7600 35.7410 4.6243 4.8026 0.1740 44.4720
NetLoan_Debt:Net loan/Total debts LiAss_ShortCap:Liquidity assets/Total shortterm capital
B 70 7.0169 5.4266 0.0300 29.0060 1.6594 4.1657 -9.4630 23.8630
BB 64 5.0173 5.8099 0.9400 43.5840 1.2533 1.7253 -6.5410 7.7230 BBB 116 4.0887 3.9986 0.2640 33.3720 1.3015 2.1374 -10.6100 13.1910
Total 282 4.8560 4.8536 0.0300 43.5840 1.3840 2.6187 -10.6100 23.8630
LiAss_Debt:Liquidity asset/Total debts
B 70 13.8842 12.0269 -12.1550 58.9220
BB 64 11.4613 12.6789 -74.5380 30.8730
BBB 116 13.2433 9.6325 -31.4700 44.0260
A 32 15.4400 5.7954 4.2120 28.6060
Total 282 13.2472 10.7057 -74.5380 58.9220
Source:Author’s caculating from thesis’ data sets
Trang 12Table 4.2: The result of homogeneity of variance test and One way Anova analyzing on bank’s
financial ratios in emerging markets
Variance
P-value of homogeneity
of variance test
P-value of One way anova analyzing
Variance
P-value of homogeneity
of variance test
P-value of One way anova analyzing
Source: Author’s caculating from thesis’ data sets
According to the average values of bank’s financial ratios in each bank’s credit ratings and the P-value
of One way Anova analyzing showed in table 4.1 and 4.2, we can conclude that LnAss, AssGrow, LoanLoss_Ln, LoanLoss_Equ, LoanPro_Loan, Equ_Debt, IntIn_Loan, IntEx_Cap, NIM, NetIntIn_Ass, OthIn_Ass, NonIntEx_Ass, NetLoan_Ass, NetLoan_ShortCap và NetLoan_Debt have different average values
in each bank’s credit ratings
Meanwhile, the P-values of homogeniety of variance tests on CreGrow, Equ_Ass, Equ_Loan, Equ_ShortCap, IntIn_Ass, ROAA, ROAE, Exp_Int and LiAss_Debt are not significant (>10%) so the assumption about homogeneity of variance on these variables are invalid So that we take a nonparameter Kruskal – Wallis test on these proxies
Table 4.3: The results of Krukal – Wallis test on bank’s financial ratios in emerging markets
Variable P-value of Kruskal –
Wallis test Variable
P-value of Kruskal – Wallis test
Source: Author’s caculating from thesis’ data sets
According to the results of One way anova analyzing and Kruskal – Wallis test, we can conclude that LnAss, AssGrow, CreGrow, LoanLoss_Ln, LoanLoss_Equ, LoanPro_Loan, Equ_Ass, Equ_Loan, Equ_ShortCap, Equ_Debt, IntIn_Loan, IntIn_Ass, IntEx_Cap, NIM, NetIntIn_Ass, OthIn_Ass, NonIntEx_Ass, ROAA, ROAE, Exp_Int, NetLoan_Ass, NetLoan_ShortCap and NetLoan_Debt have different average values in
Trang 13each bank’s credit ratings in emerging markets In contrast, the average values of LiAss_ShortCap and LiAss_Debt are not different in each bank’s credit ratings
We apply one way anova analyzing on bank’s financial ratios in developed markets
Table 4.4: Average values of bank’s financial ratios classified by bank’s credit ratings
LnAss:Logarit bank’s total assets AssGrow:Average grow rate of bank’s assets
over 3 years
B 8 11.4577 6713 10.0440 12.3110 0.0017 0.0920 -0.0945 0.1964
BB 25 10.7804 1.5263 5.2490 12.8230 0.0339 0.1207 -0.1151 0.3602 BBB 52 10.4119 1.7275 6.6830 13.8380 0.0218 0.0884 -0.1968 0.3364
A 152 11.4706 1.7368 7.3850 14.7240 0.0143 0.1047 -0.4042 0.5461
AA 48 11.8124 1.6266 7.7130 14.2390 0.0557 0.0472 -0.0594 0.1627 AAA 11 11.9109 1.3402 10.1530 14.4920 0.0322 0.0577 -0.0453 0.1623 Total 296 11.2977 1.7253 5.2490 14.7240 0.0243 0.0951 -0.4042 0.5461
CreGrow:Average grow rate of loan
B 8 0.0098 0.1309 -0.1826 0.2727 24.7640 13.5608 0.8800 44.8630
BB 25 -0.0093 0.1130 -0.1708 0.2753 13.2698 8.8345 0.2180 37.9730 BBB 52 0.0361 0.1170 -0.1491 0.5168 6.5633 8.5192 0.0410 44.6330
A 152 0.0443 0.1859 -0.4088 1.4227 3.6221 4.6317 0.0000 44.6490
AA 48 0.0558 0.0648 -0.0759 0.3202 1.7663 1.7369 0.0090 8.1780 AAA 11 0.0800 0.1715 -0.0318 0.5829 1.0203 1.6900 0.0000 5.8160 Total 296 0.0406 0.1534 -0.4088 1.4227 5.1274 7.4089 0.0000 44.8630
LoanLoss_Equ:Overdue loan/Equity LoanPro_Loan:Loan provision/Average total loan
B 8 306.8720 186.7417 28.7310 690.0910 2.8763 1.8082 0.0100 5.9100
BB 25 130.6217 89.4631 4.7470 381.3450 1.6124 1.1304 -0.3100 4.5700 BBB 52 59.6979 86.3918 0.2420 414.3900 0.9592 1.6735 -0.2900 10.6800
A 152 37.0226 55.7724 0.0000 464.9560 0.4932 1.0274 -0.4900 10.8700
AA 48 23.2292 63.8459 0.0400 433.0000 0.3258 0.3950 -0.0700 1.8300 AAA 11 73.3240 232.5974 0.0000 774.5700 0.0900 0.2509 -0.1800 0.6500 Total 296 55.3169 96.9361 0.0000 774.5700 0.6919 1.2257 -0.4900 10.8700
B 8 5.8810 3.1806 2.1000 11.3540 9.2836 4.4860 3.0860 16.1920
BB 25 6.6860 3.7176 0.2520 16.6180 12.9957 10.5310 0.6050 53.5830 BBB 52 8.4437 3.8266 1.4640 23.1450 19.7328 37.4397 3.2320 276.9500
A 152 7.1024 3.4055 1.1450 20.4280 16.7668 22.8025 1.9740 198.1360
AA 48 8.0283 2.9046 0.0060 16.0840 20.1603 29.4661 0.0090 151.4190 AAA 11 8.3670 6.5926 0.6530 20.1630 17.4925 15.9759 0.7620 57.3420 Total 296 7.4670 3.6177 0.0060 23.1450 17.3444 25.8997 0.0090 276.9500
Trang 14B 8 7.1263 3.6462 2.3740 13.6510 6.3853 3.6620 2.1450 12.8410
BB 25 10.2912 6.1303 0.2880 29.5010 7.4736 4.4889 0.2610 19.9290 BBB 52 12.6163 5.6406 1.7230 33.6150 9.5709 4.9256 1.4860 30.1150
A 152 14.0868 19.5748 2.0100 171.4090 7.9145 4.1861 1.1620 26.2490
AA 48 12.0495 8.4045 0.0080 63.6210 8.9642 3.5694 0.0060 19.7250 AAA 11 23.8286 21.0270 5.8390 60.7440 9.7279 8.2934 0.6570 25.2560 Total 296 13.3514 15.4219 0.0080 171.4090 8.3645 4.4883 0.0060 30.1150
IntIn_Loan:Interest income/Average total loan IntIn_Ass:Interest income/Total earning interest assets
B 8 4.1388 1.2694 2.5400 6.3800 3.9250 1.4714 1.5800 5.8500
BB 25 3.6928 2.5023 1.5200 11.8800 3.9576 2.5821 0.8700 11.6800 BBB 52 3.7648 1.5208 0.0900 9.3700 3.5096 1.3593 1.0600 8.8300
A 152 3.9412 1.5957 0.7200 10.9100 3.4638 1.5443 0.1900 9.9400
AA 48 3.8719 1.4801 1.7300 7.4400 3.3244 1.4050 1.2300 6.3300 AAA 11 5.2309 6.7548 0.0000 24.6100 3.0355 1.2925 1.0800 4.5000 Total 296 3.9313 2.0545 0.0000 24.6100 3.4875 1.5942 0.1900 11.6800
IntEx_Cap:Interest expense/Total interest
B 8 2.1900 0.4121 1.7300 2.9500 1.7205 1.1500 -0.1980 3.3640
BB 25 2.4952 2.2075 0.7400 11.6300 1.6064 1.1379 0.1360 5.2070 BBB 52 1.8075 0.9530 0.2800 4.5900 1.8282 0.9111 0.3460 4.6810
A 152 1.9437 1.3209 0.0300 7.3800 1.6478 1.3925 -0.0370 10.3710
AA 48 1.5529 1.1935 0.1500 4.8000 1.8591 0.7391 0.7560 3.7010 AAA 11 1.9327 1.4846 0.3300 4.4600 1.2684 1.2307 0.3440 3.5380 Total 296 1.9092 1.3441 0.0300 11.6300 1.6981 1.1946 -0.1980 10.3710
NetIntIn_Ass:Net interest income/Average total assets OthIn_Ass:Other operation income/Average
total assets
B 8 1.5258 1.0146 -0.1950 2.9270 0.6049 0.4338 -0.0780 1.2280
BB 25 1.4659 1.0459 0.1340 4.5890 1.2422 0.8313 -0.2310 3.4960 BBB 52 1.7070 0.8400 0.3420 4.3510 0.7673 0.6303 -1.3640 2.7220
A 152 1.4897 1.1239 -0.0310 8.2190 1.2219 2.3249 -0.1810 21.1570
AA 48 1.6610 0.6182 0.7230 3.0580 1.1403 0.9118 0.0070 4.1040 AAA 11 1.2160 1.1900 0.3390 3.3870 0.2618 0.2869 -0.0980 0.8160 Total 296 1.5444 1.0019 -0.1950 8.2190 1.0782 1.7586 -1.3640 21.1570
NonIntEx_Ass: Non interest expense/Average
B 8 3.7551 1.7927 0.0520 6.1600 0.3375 2.4722 -2.7960 4.4290
BB 25 2.7567 1.4319 0.1470 6.3080 -0.2208 1.0151 -3.4080 1.2870 BBB 52 2.1230 1.9067 0.1950 13.9980 0.1170 1.8315 -11.2020 1.8410
A 152 2.0315 2.1603 0.0160 16.3690 0.4275 1.1498 -9.8850 6.6490
AA 48 1.7656 0.9176 0.1470 4.6780 0.7194 0.7254 -3.2490 2.0640 AAA 11 0.4705 0.4257 -0.0390 1.2000 1.0657 0.9620 0.0120 2.4610 Total 296 2.0543 1.9050 -0.0390 16.3690 0.3868 1.2965 -11.2020 6.6490
Trang 15Bank’s
credit
rating
Number of
Standard
Standard
B 8 -0.3178 61.2013 -88.0050 81.8900 74.4116 35.5491 15.1590 133.5020
BB 25 -9.7504 34.8778 -150.1230 28.1520 66.1058 18.6566 24.3430 102.1420 BBB 52 2.6575 17.4151 -68.9970 23.8510 57.2777 20.5838 14.7550 145.2210
A 152 4.9554 11.9207 -74.3900 57.2300 64.3338 24.0560 9.4820 200.0000
AA 48 15.9109 46.3791 -53.1800 321.7950 54.1946 15.2158 14.5110 101.8980 AAA 11 51.0976 132.3486 0.0910 449.3400 29.6526 16.8780 7.4290 62.7300 Total 296 6.6585 36.9528 -150.1230 449.3400 60.5832 23.0990 7.4290 200.0000
NetLoan_Ass:Net loan/Total asset NetLoan_ShortCap:Net loan/ Total shorterm capital
B 8 63.1024 7.9104 46.6140 70.1220 77.4719 11.1113 60.0990 95.6470
BB 25 57.7573 14.9626 4.0300 73.1660 87.2260 24.5990 4.6560 126.1080 BBB 52 63.8140 20.3189 2.5610 91.2770 95.8951 36.5762 3.0680 193.1080
A 152 57.4293 21.0607 5.1490 92.0240 98.7197 66.7776 9.6570 488.7750
AA 48 62.4362 21.3904 5.5320 97.0130 94.5649 48.3778 6.8200 283.3760 AAA 11 61.8165 25.8793 6.7990 94.1260 209.4969 203.4991 38.1260 766.4160 Total 296 59.7069 20.5274 2.5610 97.0130 100.1214 69.4688 3.0680 766.4160
NetLoan_Debt:Net loan/Total debts LiAss_ShortCap:Liquidity assets/Total short-term
capital
B 8 69.6515 9.0654 51.4010 81.3050 10.0890 7.1723 3.7010 24.2990
BB 25 66.5232 17.6029 4.1020 84.0970 15.3523 19.1648 2.5500 89.0300 BBB 52 73.8234 23.1714 2.6510 104.0820 25.3165 30.2986 1.4260 159.0970
A 152 66.5928 23.9872 7.4470 147.4430 38.4403 42.6849 0.3020 391.6890
AA 48 72.3578 24.9982 6.5170 133.5730 23.0709 15.9403 0.8560 61.5100 AAA 11 70.0112 31.3312 7.2010 112.6920 67.9500 104.4219 7.7610 377.7170 Total 296 69.0017 23.5972 2.6510 147.4430 32.0229 40.7363 0.3020 391.6890
LiAss_Debt:Liquidity asset/Total debts
B 8 8.8375 5.9283 3.1700 21.4620
BB 25 12.5558 17.1223 1.7010 78.4480
BBB 52 17.4041 16.4145 1.3710 86.9570
A 152 24.0424 19.1224 0.0630 111.3150
AA 48 18.5450 12.3313 0.4960 54.2490
AAA 11 22.5233 20.4505 2.5690 71.3440
Total 296 20.5472 17.7427 0.0630 111.3150
Source: Author’s caculating from thesis’ data sets
Trang 16Table 4.5: The result of homogeneity of variance test and One way Anova analyzing on bank’s
financial ratios in developed markets
Variance
P-value of homogeneity
of variance test
P-value of One way anova analyzing
Variance
P-value of homogeneity
of variance test
P-value of One way anova analyzing
Source: Author’s caculating from thesis’ data sets
The same as the above analyzing, basing on average values of bank’s financial ratios in each bank’s credit ratings and the p-value of one way anova analyzing presented in table 4.4 and 4.5, we can conclude that LnAss, LoanLoss_Ln, LoanLoss_Equ, LoanPro_Loan, Equ_Ass, Equ_Debt, ROAA, ROAE, NetLoan_Ass, NetLoan_ShortCap, LiAss_ShortCap và LiAss_Debt have different average values in each bank’s credit ratings
Meanwhile, the P-values of homogeniety of variance tests on CreGrow, Equ_Loan, IntIn_Ass, NIM, NetIntIn_Ass, OthIn_Ass, NonIntEx_Ass and Exp_Int are not significant (>10%) so the assumption about homogeneity of variance on these variables are invalid So that we take a nonparameter Kruskal – Wallis test on these proxies
Table 4.6: The results of Krukal – Wallis test on bank’s financial ratios in emerging markets Variable P-value of Kruskal –
Wallis test Variable
P-value of Kruskal – Wallis test
Source: Author’s caculating from thesis’ data sets
The results of Krukal – Wallis test presented in table 4.6 show that CreGrow, NIM, NetIntIn_Ass, OthIn_Ass, NonIntEx_Ass và Exp_Int have different average values in each bank’s credit ratings
Basing on the results of One way anova analyzing and Kruskal – Wallis test, we can conclude that LnAss, CreGrow, LoanLoss_Ln, LoanLoss_Equ, LoanPro_Loan, Equ_Ass, Equ_Debt, NIM, NetIntIn_Ass,