RỦI RO HỆ THỐNG NGÂN HÀNG

UNIVERSITY OF ECONOMICS AND LAW FINANCE AND BANKING INTERNSHIP REPORT The approach of Z-score model to analyze ACB’s risks (Asia Commercial Bank) Advisor: Ph.D LE TRUNG THANH Student: HUYNH THU AN Class: K10404B Student ID: K104040561 Ho Chi Minh City, April 7th, 2014 Page of 19 ACKNOWLEDGEMENT An endeavor over period can be successful only with the advice and support of well-wisher I take this opportunity to express my gratitude and appreciation to all those who encourage me to complete my internship report I am deeply indebted to PH.D LE TRUNG THANH who introduced me to the approach of quantitative finance, reviewed my mistakes as well as listened and responded all my questions I express my profound and sincerely thank to ASIA COMMERCIAL BANK- NGUYEN VAN TROI BRANCH which created a great condition to assist me in completing the report I also extend thank to my best friends for their support and encouragement I sincerely thank all! Page of 19 Comment of Internship Organization Ho Chi Minh City, April th, 2014 Signature of Branch Director Page of 19 Comment of Advisor Ho Chi Minh City, April 7th, 2014 Signature of Advisor CONTENTS Page of 19 Page ABSTRACT In this study, ACB’s data are used in Vietnam in the period 2008-2012 and quantitative methods are adopted to determine the impact of specific factors on insolvency risk These factors are LLR -Loans loss reserve ratio, NIR- Net interest income ratio, LEV Leverage Ratio, LDR- Loan to Deposit Ratios, and LAD – Liquidity assets to deposit ratios Using NIR instead of NIM helps to complete the research in the past The study also confirmed the increase of equity as prerequisites to protect banks from insolvency risk INTRODUCTION At the moment, the financial crisis in 2008 still affects our economy, resulting in the sign of slow growth As a consequence, many companies were out of business Acting as veins in the economy, banks are also facing difficulties, which disbursement and unfreezing the capital flow to businesses The competition among banks is even more intense and banks are suffering many risks, such as liquidity risk, credit risk, interest risk, etc Thus, bank managers should pay more attention to a particular risk but also all risks affecting the operation of banks That leads to the decision whether a bank continues to operate or merge with another bank Within the past years, several banks have lost the brand-name on the market forever which shows the view of the State Bank of Vietnam: "Mergers and consolidation are an indispensable trend to enhance the competitiveness of the bank." On December,15 th 2011, consolidation project of banks: Sai Gon Commercial Bank , Tin Nghia Bank and Ficombank were approved by the General Meeting of Shareholders and named Saigon Commercial Bank ( SCB) Habubank officially took the name away when it was merged into Sai Gon - Ha Noi Bank ( SHB ) on August ,28 th 2012 On October, 4th , PVcomBank officially came into operation , meaning that the Western Bank lost an official name On November, 18th 2013, the State Bank of Vietnam decided to merge Dai A Bank with Ho Chi Minh Develop Bank (HD Bank) Student: HUYNH THU AN - Advisor: LE TRUNG THANH Page Asian Commercial Bank (ACB) is one of the group which was establish on June, 4th, 1993 Since December, 31st, 2012, ACB's capital has been 9,376,965,060,000 VND During more than 20 years of development, the bank has gained remarkable achievements: The best bank of Viet Nam in 2011,2012; The strongest bank in 2010 But over the last few years, the bank has been in trouble because of many competitors such as Techcombank, Sacombank, Eximbank, etc In addition, there are more unexpected information that impacts the internal development of the bank Applying former research is very essential in assessing all activity risks of the ACB and comparing with G12 banks After this research, I can identify the basic factors affecting the insolvency risk of ACB, analyze and explain them based on the real condition From there, I compare and review the overall difference between ACB’s risk and G12’s risk This research will help bank managers have an objective and accurate view to plan strategies for sustainable development LITERATURE REVIEW A number of researchers have attempted to discriminate between financial characteristics of successful firms and those facing failure The objective has been developed a model that uses financial ratios to predict which firms have the greatest likelihood of becoming insolvent in the near future Altman is perhaps the best known of these researchers The Z-score model, commonly referred to as the Altman Z-Score, was developed by Professor Edward I, Altman in 1968 The Z-Score is constructed from six basic accounting values and one market-based value These seven values are combined into five ratios which are the pillars that comprise the Z-score The five pillars are combined to result in a company’s Z-score (Altman 2002) Z –score model of Altman has accurately estimated 66 % of bankrupt companies and 78 % of companies that did not go bankrupt for a previous year period Thanks to the fairly accurate prediction of this model should only be used some popularity in many countries around the world However, this model does not indicate the expected time of bankruptcy because the bankruptcy of the business depends on the economic situation and the crisis Student: HUYNH THU AN - Advisor: LE TRUNG THANH Page Banks are a non-manufacturing sector so the application of Z-score model is somewhat different It is the study of Boyd and Graham (1986) Define bank insolvency as a state where (CAR+ROA) ≤ 0, with CAR the bank’s capital-asset ratio and ROA its return on assets Then if ROA is a normal distributed random variable such as, Boyd and Graham (1986) noted that the probability of insolvency can be given as: Where the Z–score is defined as >0 and is the cumulative distribution function (CDF) of standard normal distribution N (0, 1) According to Cihak (2008), the model that is used to show relationship between insolvency risk and other bank-specific risk is described: (1) where i = 1,…, N indexes banks, Xj, j = 1,…,J , denote macroeconomic variables which are identical across banks and affect all the banks in the same fashion through while Zik, k = 1, …, K, denote bank-specific variables with corresponding pooled effects Lagged z is included into specification as an attempt to capture capital reserves built in previous period All the specifications are estimated applying the ordinary least squares with robust White errors Given a relatively large number of cross sections within each country in this sample we not estimate the fixed effects specification and restrict the analysis to pooled intercepts only METHODOLOGY A regression model which based on following variables will be built below: Zit = β0 + βi *Xit + eit Zit: z score of bank i at time t, measured by Xit: (i=1, 2, 5): stand for independent variables Input: X1: LLR - Loans loss reserve ratio X2: LLP - Loan Loss Provision Student: HUYNH THU AN - Advisor: LE TRUNG THANH Page X3: LEV –Leverage Ratio X4: NIR – Net interest income ratio X5: LDR – Loan to Deposit Ratios X6: LAD – Liquidity assets to deposit ratios Output: , ,,, , and their correlations with insolvency risk measured by Z-score Then the detailed model will be presented by the equation below: = According to many researches, there are many variables which have a lot to with insolvency risk Some of those factors are LLR, LLP, LEV, NIR, LDR, LAD that stand for credit risk, interest risk, and liquidity risk Credit risk which is related to asset portfolio represented by LLR variable (Loan Loss Reserve) Interest risk is shown by the NIR variable (Net Income Ratio) Both LDR and LAD from asset portfolio and capital reflect the liquidity supply and demand LEV represents leverage of bank LLP is bad debt expense, which can be combined with LLR to evaluate credit risk LLR: Loans loss reserve ratio Whalen’s study (1988) shows that loan loss reserve to total loans ratio moves with insolvency risk The increase of bad debt makes loan loss reserve increase However, Halling’s study (2006) said that they are in inverse correlation with each other Banks which have good financial condition usually increase loan loss reserve Banks facing financial difficulties will reduce the amount of loan loss reserve to the lowest I expect that the correlation with Z-score will get -/+ and +/- with insolvency risk LLP – Loan Loss Provision According to Whalen (1988), loan loss provision to total assets has positive correlation with risks but it has no statistical significance Halling (2006) supposed that loan loss provision to income from an active business moves with risk However, due to changes in the process of regression, this ratio doesn’t have significance I expect that the correlation with Z-score will get - and + with insolvency risk LEV – Leverage Ratio From the research of Logan (2001), leverage ratio which means total loan to total equity (D/E) has negative correlation with bank insolvency risk The higher this leverage ratio is, the higher possibility of risk we can get In the developing financial system, he Student: HUYNH THU AN - Advisor: LE TRUNG THANH Page 10 realized that weak banks must raise capital so that the shareholders continue to fund them According to Montgomery (2004), leverage ratio which means equity to total loans ratio (E/D) moves with insolvency risk However, it has no statistical significance The results of Jordan (2011) with leverage ratio are calculated by Tier capital to total asset ratio This leverage ratio has inverse correlation to risk Therefore, I expect that the correlation with Zscore will get +/- and -/+ with insolvency risk NIR – Net interest income ratio Logan‘s research (2001) gives the empirical result that the net interest income to total assets moves with the risk of insolvency Halling (2006) has the same result as Logan While the results of Jordan (2011) show that non-interest income to interest income ratio moves with bank insolvency risk, or diversification of income makes banks riskier because those banks could not keep their traditional customers Therefore, I expect that the correlation with Z-score will get - and + with insolvency risk LDR – Loan to Deposit Ratios According to Montgomery Research (2004), total loan to total deposits ratio has positive correlation with insolvency risk This is because when banks face difficulties, they will concentrate on growing the credit to get more profit and they tend to lend with higher interest rates for the customers who are not qualified enough As a result, it causes bad debt in banks Therefore, I expect that the correlation with Z-score will get - and + with insolvency risk LAD – Liquidity assets to deposit ratios Montgomery’s research (2004) shows that the ratios between liquid assets and total deposit have negative relationship with insolvency risk If banks had more liquidity assets, they could reduce liquidity risk Therefore, I expect that the correlation with Z-score will get + and - with insolvency risk This is summarized in the following Table 1: Table 3.1: The summarization of risky variables and their relation to insolvency risk Student: HUYNH THU AN - Advisor: LE TRUNG THANH Page 11 Original number Variable LLR - Loans loss reserve ratio Formula Researc Relation -hers -ship with Zcore Relation -ship with To Insolvency risk Whalen (1988) - + + - - + Logan (2001 + - Montgo mery (2004) - + Halling (2006) LLP - Loan Loss Provision Whalen (1988) Halling (2006) LEV – Leverage Ratio NIR – Net interest income ratio Logan (2001) Halling (2006) - + LDR – Loan to Deposit Ratios Montgo mery (2004) - + LAD – Liquidity assets to deposit ratios Montgo mery (2004) + - start the regression, I use a quantitative research model which is called the multi- variable Student: HUYNH THU AN - Advisor: LE TRUNG THANH Page 12 regression equation It is based on Marco‘s study (2004), Cihak (2008) and Whalen’s research (1988) in which the dependent variable and the independent variable will be qualified From empirical studies results, variables which affect the insolvency risk such as instability, bankruptcy that will be prior The basic rule is to keep the essence of variable and formula unchanged Unsuitable variables for Viet Nam‘s condition will be adjusted due to scientific arguments The advantages and disadvantages of this regression will be analyzed to clarify how the specific risks affect on insolvency risk RESULT Table 5.1: Result of ACB’s model Estimate Std Error t value Intercept 7.4901 7.4934 1.000 LLR -140.8788 43.4063 -3.246 LLP 15.6948 30.0979 0.521 LEV 101.7979 47.5832 2.139 NIR 19.1267 8.6543 2.210 LDR -2.1540 7.5242 -0.286 LAD -0.6142 5.6063 -0.110 Residual standard error: 2.137 Multiple R-squared: 0.7718, Adjusted R-squared: 0.5437 F-statistic: 3.383 , P-value: 0.08184 For the table (*) and (**) denote significance at 10% and 1% respectively Pr(>|t|) 0.3561 0.0176 * 0.6207 0.0762 * 0.0691 * 0.7843 0.9163 Result: = The following steps will be presented to test the hypothesis about model 1: The null and alternative hypotheses are: Student: HUYNH THU AN - Advisor: LE TRUNG THANH Page 13 Assuming that variables X and Y are normally distributed, I will use the t distribution to perform this test about the linear correlation coefficient According to the result of model 1, P (F – statistic) < 0.1  H0 is denied, therefore, ACB’s model is accepted With the same method, the statistical significance of the independent variables in model will be tested with =0.1, I have: • LLR: The coefficient is 8788, so for every unit increase in LLR, a unit decrease in Z-score is predicted, holding all other variables constant Besides, P-value = 0.0176 < 0.1 => the coefficient for LLR is statistically significant • LLP: The coefficient is so for every unit increase in LLP a unit increase in Zscore is predicted, holding all other variables constant Besides, P-value = 0.6207 > 0.1 => the coefficient for LLP is not statistically significant • LEV: The coefficient is so for every unit increase in LEV a unit increase in Z- score is predicted, holding all other variables constant Besides, P-value=0.0762 < => the coefficient for LEV is statistically significant • NIR: The coefficient is so for every unit increase in NIR a unit increase in ZScore is predicted, holding all other variables constant Besides, P-value=0.0691> => the coefficient for NIR is statistically significant • LDR: The coefficient is so for every unit increase in LRD a unit decrease in Z- Score is predicted, holding all other variables constant Besides, P-value = 0.7843> 0.1 => the coefficient for LDR is not statistically significant • LAD: The coefficient is so for every unit increase in LAD a unit decrease in Z- score is predicted, holding all other variables constant Besides, P-value = 0.9163> 0.1 => the coefficient for LDR is not statistically significant After the results of model 1, unsuitable variables such as LLP, LDR, LAD will be eliminated as table below: Table 5.2: Result of ACB’s model Intercept Estimate 6.890 Std Error 2.044 t value 3.371 Pr(>|t|) 0.008238** Student: HUYNH THU AN - Advisor: LE TRUNG THANH Page 14 LLR -126.055 26.030 -4.843 0.000918** LEV 92.740 32.386 2.864 0.018670* NIR 19.621 6.204 3.163 0.011499* Residual standard error: 1.795 Multiple R-squared: 0.7886, Adjusted R-squared: 0.6782 F-statistic: 9.429 , P-value: 0.003864 For the table (*) and (**) denote significance at 10% and 1% respectively Result: = The same above : According to the result of model 2, P (F – statistic) < 0.1  H0 is denied, therefore, ACB’s model is accepted • LLR: The coefficient is, so for every unit increase in LLR, a unit decrease in Zscore is predicted, holding all other variables constant Besides, P-value = 0.000918 • < 0.1 => the coefficient for LLR is statistically significant LEV: The coefficient is so for every unit increase in LEV a unit increase in Zscore is predicted, holding all other variables constant Besides, P-value = 0.018670 • < 0.1 => the coefficient for LEV is statistically significant NIR: The coefficient is so for every unit increase in LDR a unit increase in Zscore is predicted, holding all other variables constant Besides, P-value = 0.011499 < 0.1 => the coefficient for LDR is statistically significant Loan Loss Reserve: Analyzing LLR supports the explanation of the credit risk impacting on banking operations In a positive correlation with the Z-score, LLR of ACB focus on reserving for loans which means insolvency risk decreases Since 2011, the restructuring of the bank required banks to increase emphasis on reserving for loan loss As the result, LLR has gone up for recent years We can see the increase of LLR year by year in making banking system healthy until 2015 Moreover, according to KPMG, business continued to suffer difficulties in 2012 Therefore, businesses which were already facing difficulties in 2011 experienced worsening conditions in 2012 while businesses which were operating satisfactorily in 2011 faces a big Student: HUYNH THU AN - Advisor: LE TRUNG THANH Page 15 challenge in 2012 As a result, banks had to account for more reserve as the quality of loan portfolio worsened This supports the research of Halling (2006) Net Interest Income Ratio: It has significance and positive relationship in analyzing the insolvency risk of ACB In comparison to the research of Logan (2001), Halling’s research (2006) used NIR instead of NIM In Vietnam market, 70 % of the money resources is taken from banks With the developing financial market and competitive credit field, the main activity of banks is lending activity Within years from 2008 to 2012, the proportion of net interest income is rather high in ACB’s income Especially in 2012, the main source of income which offset for other income loss is net interest income This chart can explain what I mention Student: HUYNH THU AN - Advisor: LE TRUNG THANH Page 16 Diagram 5.2: Net interest income to total income of ACB Therefore, if the main income decreased, the bank would be losing customers, decreasing profitability and increasing bankruptcy risk KPMG’s survey : Despite being an effective operational performance indicator, NIR does not fully reflect banking sector profitability.A bank’ s profitability is affected by its unit profile, etc the nature of its activities, the composition of its customer base and its funding strategies No two banks are the same and this is especially true across the Vietnamese banking sector No one end of the range, the widest and most favorable NIR’s are generally found at banks with traditional lending and deposit businesses On the other hand, some state owned bank are able to operate effectively with lower NIR’s because of the size of the operations Besides, NIR did not take into account the service fees and other non-interest income and operating expenses, such as personnel and assets costs, or credit loss expenses and therefore could not fully reflect banking sector profitability This cause the discorrelation between NIR and Z-score Leverge Ratio: LEV= Equity/total deposits , which has negative relationship with bank risk However, from the research of Logan (2001), leverage ratio which means total loan to total equity (D/E) has negative correlation with bank insolvency risk The more capital banks mobilize the more risks they can get Thus ,increasing equity in this situation means the banks are facing trouble Due to the undisciplined market in Viet Nam, it is difficult to raise capital Therefore, we can not apply Logan’s result to this report The fact that banks raise capital does not mean they have less insolvency risk, but they try to decrease risks Loan to Deposit Ratios: Student: HUYNH THU AN - Advisor: LE TRUNG THANH Page 17 LDR = Total loans /total short-term deposits Completely contrary to the expectations, LDR has negative relationship with risk and statistical significance This also differs from Montgomery’s research (2004 ) that the percentage of total outstanding loans to deposits related to the insolvency risk positively LDR does not change in specific rules year by year Liquidity assets to deposit ratios: LAD: This ratio indicates that increasing liquidity asset will reduce the bank's risk Short-term assets which meet the liquid needs of the bank are cash, gold, precious stones, money at the central bank, government securities, etc Banks prefer to hold less liquid assets and increase loan rates This helps bank to gain more profit but insolvency risk also goes up for this reason CONCLUSION AND IMPLICATION In light of the recent credit market turmoil, reassessing popular models of corporate bankruptcy prediction is well-spent time Application of z-score model along with the previous study showed the impact of variables representing different risks to insolvency risk of ACB However, because of limited ability, my study was not far-reaching It only covered a short period from 2008 to 2012 so the results could not be entirely convinced Asset and liability management (ALM) is to make the measurement of interest rate risk, liquidity risk perfect and use derivative Banks need to select an appropriate model and focus on liquidity risk, interest rate risk, analyzing trends as well as specific ratio, paying attention to net interest income For liquidity risk, it should be assigned three levels: short -term liquidity, stress liquidity situations and mobilizing capital for liquidity For interest rate risk, banks should focus on the measurement, calculating the sensitivity of net interest income from first market, secondary market and valuable papers, analyzing the sensitivity of equity Results of the study will assist banks to select suitable models and appropriate tools Student: HUYNH THU AN - Advisor: LE TRUNG THANH Page 18 For interest rate risk: In case of ACB, it shows that interest income to total assets has negative relationship with risk of banks When interest income increases, risk decreases This will help the bank managers have appropriate strategies Using interest sensitive gap as well as duration gap to manage interest risk will avoid the insolvency risk Besides, management should pay attention to ACB’s other investments because of high risk For example, loss from trading in foreign currencies and gold were 1863 billion in 2012, while losses from investment securities were 273 billion For liquidity risk: In short, ACB should concentrate on the percentage of total loans to short term deposit, care about the fluctuations of deposit and capital structure for liquidity and operational efficiency We can invest in valuable papers for two objectives: reserving liquidity and reducing the income fluctuation from the steady income of valuable papers in the mid-term When the bond market is developing, two goals not need separating Once the bond market developed and the derivatives are popular, they need separating in order to increase the efficiency of cash flow for banks and the economy Planning the investment depends on the business strategy of individual bank, the strength segment, such as small and medium-sized enterprise (SME), consumer lending, private household, etc For credit risk: LLR had implications for banks ACB as well as it showed the importance of credit risk management In current period, not only ACB but also all banks need to supervise step by step in loans processing, appraise the detail of loan documents and establish the reserve for loan loss For example, reserving loans of ACB continued to increase during the period 2008-2012 from 502 billion to 1502 billion, goes with the size of loans from 102000 billion up 62000 billion REFERENCES: Documents: Bellovary J., Giacomino D., Akers M (2007), “A Review Bankrupt Prediction Studies: 1930 To Present”, Journal of Finance Education,Vol 33 Student: HUYNH THU AN - Advisor: LE TRUNG THANH Page 19 Cihak M., Hesse H (2008), Islamic Banks And Financial Stability: An Empirical Analysis, IMF working paper Federic S Mishkin (2010), the Economics of Money, Banking & Financial Market, 9th Edition Foos D., Norden L., Weber M (2010), “Loan Growth And Riskiness Of Banks”, Journal of Banking and Finance, Vol 34, p 2929-2940 Gary Whalen & James B Thomson (1988), “Using Financial Data To Indentify Changes In Bank Condition”, SSRN Halling M., Hayden E (2006), “Bank failure Predicttion: A Two-Step Survival Time Approach”, SSRN Jordan D J., Rice D., Sanchez J., Walker C., Work D H (2011), “Predicting Bank Failures: Evidence From 2007 To 2010”, SSRN KPMG, Viet Nam banking survey 2013 Logan A (2001), “The UK’s small bank’s crisis of the early 1990s: what were the leading indicators of failure”, Banking of England 10 Montgomery H., Tran B H., Santoso W., Besar D (2004), Coordinate failure 11 Pham Thanh Duong, “Analyzing bank’s risk in operation”, 4/2013- Paper No.19, Development and Integration Magazine Website: http:// acb.com.vn http://cafef.vn/su-kien/230-hop-nhat-hdbank-daiabank.chn http://m.vietnamnt.vn/vn/kinh-te/143383/nhung-ngan-hang-viet-bien-mat-vinh- vien.html Student: HUYNH THU AN - Advisor: LE TRUNG THANH [...]... decreasing profitability and increasing bankruptcy risk KPMG’s survey : Despite being an effective operational performance indicator, NIR does not fully reflect banking sector profitability.A bank’ s profitability is affected by its unit profile, etc the nature of its activities, the composition of its customer base and its funding strategies No two banks are the same and this is especially true across the... Logan (2001), Halling’s research (2006) used NIR instead of NIM In Vietnam market, 70 % of the money resources is taken from banks With the developing financial market and competitive credit field, the main activity of banks is lending activity Within 5 years from 2008 to 2012, the proportion of net interest income is rather high in ACB’s income Especially in 2012, the main source of income which offset... This will help the bank managers have appropriate strategies Using interest sensitive gap as well as duration gap to manage interest risk will avoid the insolvency risk Besides, management should pay attention to ACB’s other investments because of high risk For example, loss from trading in foreign currencies and gold were 1863 billion in 2012, while losses from investment securities were 273 billion... period, not only ACB but also all banks need to supervise step by step in loans processing, appraise the detail of loan documents and establish the reserve for loan loss For example, reserving loans of ACB continued to increase during the period 2008-2012 from 502 billion to 1502 billion, goes with the size of loans from 102000 billion up 62000 billion REFERENCES: Documents: 1 Bellovary J., Giacomino... “Loan Growth And Riskiness Of Banks”, Journal of Banking and Finance, Vol 34, p 2929-2940 5 Gary Whalen & James B Thomson (1988), “Using Financial Data To Indentify Changes In Bank Condition”, SSRN 6 Halling M., Hayden E (2006), “Bank failure Predicttion: A Two-Step Survival Time Approach”, SSRN 7 Jordan D J., Rice D., Sanchez J., Walker C., Work D H (2011), “Predicting Bank Failures: Evidence From 2007... because of limited ability, my study was not far-reaching It only covered a short period from 2008 to 2012 so the results could not be entirely convinced Asset and liability management (ALM) is to make the measurement of interest rate risk, liquidity risk perfect and use derivative Banks need to select an appropriate model and focus on liquidity risk, interest rate risk, analyzing trends as well as... liquidity For interest rate risk, banks should focus on the measurement, calculating the sensitivity of net interest income from first market, secondary market and valuable papers, analyzing the sensitivity of equity Results of the study will assist banks to select suitable models and appropriate tools Student: HUYNH THU AN - Advisor: LE TRUNG THANH Page 18 For interest rate risk: In case of ACB, it shows... risks affect on insolvency risk 4 RESULT Table 5.1: Result of ACB’s model 1 Estimate Std Error t value Intercept 7.4901 7.4934 1.000 LLR -140.8788 43.4063 -3.246 LLP 15.6948 30.0979 0.521 LEV 101.7979 47.5832 2.139 NIR 19.1267 8.6543 2.210 LDR -2.1540 7.5242 -0.286 LAD -0.6142 5.6063 -0.110 Residual standard error: 2.137 Multiple R-squared: 0.7718, Adjusted R-squared: 0.5437 F-statistic: 3.383 , P-value:... eliminated as table below: Table 5.2: Result of ACB’s model 2 Intercept Estimate 6.890 Std Error 2.044 t value 3.371 Pr(>|t|) 0.008238** Student: HUYNH THU AN - Advisor: LE TRUNG THANH Page 14 LLR -126.055 26.030 -4.843 0.000918** LEV 92.740 32.386 2.864 0.018670* NIR 19.621 6.204 3.163 0.011499* Residual standard error: 1.795 Multiple R-squared: 0.7886, Adjusted R-squared: 0.6782 F-statistic: 9.429 , P-value:... expenses, such as personnel and assets costs, or credit loss expenses and therefore could not fully reflect banking sector profitability This cause the discorrelation between NIR and Z-score Leverge Ratio: LEV= Equity/total deposits , which has negative relationship with bank risk However, from the research of Logan (2001), leverage ratio which means total loan to total equity (D/E) has negative correlation ... as prerequisites to protect banks from insolvency risk INTRODUCTION At the moment, the financial crisis in 2008 still affects our economy, resulting in the sign of slow growth As a consequence,... where (CAR+ROA) ≤ 0, with CAR the bank’s capital-asset ratio and ROA its return on assets Then if ROA is a normal distributed random variable such as, Boyd and Graham (1986) noted that the probability... PH.D LE TRUNG THANH who introduced me to the approach of quantitative finance, reviewed my mistakes as well as listened and responded all my questions I express my profound and sincerely thank