The effect of credit risk on the financial performance of vietnamese commercial banks a comparison of vietnamese listed and unlisted banks

ABSTRACT The study aims to determine the effect of credit risk on the financial performance of commercial banks in Vietnam: A comparison of Vietnamese listed and unlisted banks.. COMMITM

INTRODUCTION

Reason for choosing the topic

In recent years, the relationship between credit risk and banks' performance has been a topic of interest to many scholars since credit is the main revenue- generating activity and carries significant risks Banks generate income through credit creation, which carries enormous risks for both the lender and the borrower Failure by a trading partner to fulfill their contractual obligations at maturity may significantly imperil the bank’s operations Additionally, a bank with high credit risk has high bankruptcy risk that jeopardizes depositors as well as economic stability and growth (Ekinci & Poyraz, 2019)

1.1.2 The necessity of the research

Firstly, banks face many risks, including credit risk, operational risk, liquidity risk, nominal risk, market risk, and legal risk, which may have a negative impact on the profitability of commercial banks According to Ekinci and Poyraz

(2019), the primary source of income in the banking sector consists of granting loans, so credit risk is one of the most significant risks that affects the banks' performance

Second, according to VnEconomy 1 , in Vietnam, the non-performing loans (NPL) rate has been observed to continue to rise by the end of the third quarter of

2023 and has now been the highest since 2015 In addition, credit risk at Vietnamese listed banks has increased by 53% compared to the start of 2023 Increases in NPL rates are often associated with the failure of the bank’s credit

1 VnEconomy, “Dự báo rủi ro tín dụng năm 2024,” accessed December 16, 2024, at: https://vneconomy.vn/du-bao-rui-ro-tin-dung-nam-2024.htm policy (Isanzu, 2017) Therefore, there is no doubt that banks need to manage credit risk mainly from NPL as it is vital to their performance

Third, updated until the April of 2023 2 , the Vietnamese banking industry has about 26 commercial banks in regular operation, of which 19 are officially listed on the stock exchange and 7 are not officially listed This characteristic is why the article studies the effect of credit risk on financial performance and assesses how this effect differs between listed formally and unlisted banks in Vietnam, an area that previous studies still need to include

Based on the above reasons, the author conducted a study on the topic “The effect of credit risk on the financial performance of Vietnamese commercial banks: A comparison of Vietnamese listed and unlisted banks” during the 2011-

2023 period as a graduation thesis From there, the article proposes appropriate management measures to help commercial banks achieve the following objectives: mitigating credit risk while also assisting in developing appropriate structures to protect the financial performance of the banking sector.

Objectives of the research

This study aims to investigate the effect of credit risk on the financial performance of Vietnamese listed and unlisted banks and provide policy and managerial implications for safeguarding the banking industry's financial performance

Focusing on the overall objective, the study is conducted to achieve the following specific goals:

Firstly, a research model will be evaluated to investigate the impact of credit risk on the financial performance of Vietnamese commercial banks

2 VnEconomy, “Càng nhiều ngân hàng lên sàn niêm yết càng minh bạch,” accessed December 16,

2024, at: https://vneconomy.vn/cang-nhieu-ngan-hang-len-san-niem-yet-cang-minh-bach.htm

Secondly, the direction and magnitude of credit risk's influence on banks’ profitability should be assessed, and the differences between Vietnamese listed and unlisted banks should be compared

Thirdly, corporate governance strategies and policy measures should be proposed to limit credit risk and boost bank profitability.

Research questions

To achieve the research objectives mentioned above, the thesis will concentrate specifically on the following aspects:

(1) What research model will be employed to analyze the effect of credit risk on the financial performance of commercial banks?

(2) How does credit risk influence the profitability of Vietnamese commercial banks? How does this influence differ between officially listed and unlisted banks?

(3) What policy implications or recommendations should be provided to limit credit risk and boost profitability in Vietnamese commercial banks?

Research subjects and scope

The subject of the study is the effect of credit risk on the financial performance of Vietnamese commercial banks

The study used data collected from the annual consolidated financial statements of 26 joint-stock commercial banks in Vietnam, of which 19 banks are officially listed on the HOSE and HNX, and 7 banks are not officially listed, with the time-scope from 2011 to 2023.

Research methodology

The thesis applies quantitative research methods, drawing on the secondary data from the FiinProX software and the annual consolidated financial statements of

26 joint-stock commercial banks from 2011 to 2023 To analyze the panel data, the author employs the multivariable regression models, including Pooled ordinary least squares (Pooled OLS), fixed effects (FEM), and random effects (REM) The study exploits Stata 17.0 software for estimation and model selection When heteroscedasticity and autocorrelation are detected, the research utilizes the feasible generalized least squares (FGLS) for adjustment Afterward, the Generalized Method of Moments (GMM) controls endogeneity effects, ensuring the study achieves high accuracy in its results On top of that, the outcomes of the models will be compared to determine whether to accept or reject the proposed hypotheses.

Contribution of the research

The theoretical basis and overview of previous domestic and foreign studies on the impact of credit risk on the financial performance of commercial banks are used as the foundation for this study The main objective is to determine the impact of credit risk on the financial performance of Vietnamese commercial banks In addition, this study addresses a significant gap in previous studies While previous studies mainly consider the overall impact of credit risk on bank performance, this study clarifies the difference in impact between listed and unlisted banks From there, the author concludes and gives appropriate management implications These are valuable implications for bank managers or policymakers, helping them have a new perspective on promoting the efficiency of bank business operations and minimizing credit risks.

Structure of the research

Chapter 1 provides an overview of the research topic and critical aspects of the study, including the reason for choosing the topic, research objectives, research subjects and scope, research methodology, research structure, and the research contributions to science and practice.

THEORETICAL BASIS AND LITERATURE REVIEW

Concept of commercial banks

Banks serve as profit-seeking intermediaries connecting borrowers and lenders within economies (Kwashie et al., 2022) Commercial Banks (CBs) are financial institutions that offer various services, including accepting deposits, issuing money in multiple forms, providing loans, processing transactions, and creating credit, as Poudel (2012) documented

According to the Institution Laws of 2024, CBs are authorized to engage in all banking activities and other business activities with the aim of profit The activities of CBs include (1) receiving demand deposits, term deposits, savings deposits, and other types of deposits; (2) issuing certificates of deposit; (3) granting credit such as lending, discounting, rediscounting, providing bank guarantees, issuing credit cards, domestic factoring, international factoring for banks licensed to conduct international payments, letter of credit, and other forms of credit by regulations set by the Governor of the State Bank; (4) opening payment accounts for customers; (5) providing payment facilities; and (6) providing payment services via account

Difference between the listed and unlisted banks

Firstly, in terms of law, a listed bank is an issuing organization that has completed all legal procedures to list its shares for public trading on the HOSE or HNX, with the approval of the State Securities Commission Meanwhile, an unlisted bank still operates in the form of a joint stock company but has not listed its shares for public trading, so it is not directly regulated by the regulations on information disclosure for listed enterprises

Secondly, in terms of transparency, listed banks are required to strictly comply with the regulations on information disclosure under the Securities Law They must publicly disclose quarterly financial statements, audited annual financial statements, annual reports, resolutions of the Board of Directors, as well as events that significantly affect their operations In contrast, unlisted banks are only required to disclose information at the minimum level required by the State Bank, resulting in a significantly lower level of transparency

Thirdly, in terms of capital mobilization, listed banks have many advantages thanks to their ability to issue shares or bonds to the public, thereby accessing a large group of domestic and foreign investors The fact that shares are traded on the stock exchange also helps to improve liquidity and the ability to value businesses Meanwhile, unlisted banks face many limitations in capital mobilization because they cannot issue to the public, and can only mobilize capital from existing shareholders or strategic investors through private issuance.

Theoretical basis of credit risk

According to Rose and Hudgins (2007), credit risk is the probability that some of a financial institution’s assets, especially its loans, will decline in value and perhaps become worthless Because financial firms tend to hold little owners’ capital relative to the aggregate value of their assets, only a tiny percentage of total loans needs to turn bad to push them to the brink of failure

Ekinci and Poyraz (2019) highlight that credit creation is a vital source of income for banks, although it comes with significant risks for lenders and borrowers According to the Basel Committee on Banking Supervision (2006), credit risk is the potential for a bank borrower or counterparty to fail to meet its obligations under negotiated terms This risk arises from various factors, including the potential for default or non–payment, shifts in market conditions, or adverse economic developments Credit risk is an internal factor that significantly impacts a bank’s profitability

Moreover, credit risk can cause severe asset losses for banks When credit risk is high and not controlled promptly, it can lead to negative consequences, including loan defaults, increased operational costs, reduced profits, and potential damage to the bank’s reputation Therefore, it is crucial for banks to proactively manage these risks to safeguard their financial health and ensure long-term success

Non-performing loans are the substandard, overdue debt ratio, showing the quality of bank loans and their risk This indicator represents the amount of money customers have borrowed and cannot pay the bank on time according to contractual obligations The debt indicator is comprised of the total debt of groups 3, 4, and 5, so it dramatically affects the operations and profits of the bank When the non- performing loans ratio is high, the bank faces more risks, and the recovery of loans is complicated, affecting the quality of assets held and the efficiency of the bank's operations Research by Muriithi et al (2016) have shown a significant negative relationship between non-performing loans and the performance of commercial banks in both the short and long term Banks with high non-performing loans will operate less effectively However, the study by Dang Hoang Nhat Tam and Pham Thi Tuan Linh (2020) has shown that the NPL ratio positively impacts bank performance This means that when faced with credit risk, banks may increase their default risk premiums beyond the actual risk, which increases their earnings

Provision for credit losses is a financial indicator used by credit institutions to ensure the safety and assess the risk of their loans and investments This ratio represents the percentage of money that credit institutions must set aside to handle debts and assets that are likely not to be repaid When the credit growth rate is unreasonable, credit provisions will increase Additionally, when customers pay late or cannot pay their debts, the provisions offset inadequate income, thereby reducing profits A higher credit risk provision ratio shows that the credit institution is placing a higher level of risk on its debts and investments, thus reducing the bank's performance (Huynh Thi Huong Thao, 2019) Research by Pham Huu Hong Thai

(2014) and Kolapo et al (2012) all argued that the ratio of loan loss provision negatively impacts banks' performance

The Loan to Deposit ratio is measured as the ratio of customer loans to total deposits For commercial banks, lending activities contribute mainly to revenue A low LDR indicates that the bank is holding many deposits and not using all its money supply to lend Conversely, a high LDR ratio may suggest that the bank uses a significant portion of its capital for lending The stronger the development of customer lending activities, the better the bank's performance Muriithi et al (2016), Dang Hoang Nhat Tam and Pham Thi Tuan Linh (2020), and Afolabi et al (2020) all argue that the ratio of loans to total deposits has a positive relationship with the bank's performance.

Theoretical framework

The “bad luck” theory of Berger and DeYoung (1997) explained that when commercial banks grant credit to customers, bad debts increase due to the impact of unfavorable economic conditions beyond the control of the bank, such as inflation, unemployment, economic recession, etc At that time, commercial banks must establish aside a sum of money to reserve for possible credit risks and incur additional costs in handling issues related to credit risks, such as customer monitoring and urging costs or indirect costs such as protecting the bank's reputation and prestige, leading to a decrease in bank profits Therefore, the financial performance of commercial banks can be negatively affected by credit risks, even leading to bank losses, reducing equity

Theoretical basis of the financial performance of the commercial banks

to closely monitor and control loans after granting them to customers Therefore, the high credit risk ratio causes commercial banks to incur costs of handling overdue loans, causing banks' profitability to decline

2.4 Theoretical basis of the financial performance of the commercial banks

2.4.1 Concept of the financial performance of commercial banks

Financial performance plays a crucial role in the existence and growth of each bank because it reflects the nation's financial stability The economic performance of commercial banks is measured by the profit they generate from their operations over a certain period Theoretically, an increase in credit risk can lead to higher costs for the bank, which may negatively impact its assets' liquidity and solvency This, in turn, can reduce the banks’ performance (Pham Thi Kieu Khanh and Pham Thi Bich Duyen, 2017)

The financial performance of a commercial bank is the optimal combination that minimizes input factors such as financial resources, facilities, human resources, and other factors in financial intermediation activities Commercial banks can perform their financial intermediation functions by combining these input factors through capital mobilization, credit granting, investment, and providing other services to create maximum outputs From these outputs, commercial banks will seek profits; however, optimization must be evaluated through the ratio between profits earned and total resources Evaluating this ratio will show that the bank uses resources in a balanced manner to create maximum profits and maintain stable operations and growth

ROA is an important indicator of a bank's profitability It measures the ability of bank management to generate income by using the commercial bank's assets at their discretion ROA shows how much profit, on average, each asset used in business activities will generate after tax This indicator evaluates the efficiency of revenue and cost management and reflects the ability to convert assets into the bank's net profit In other words, the larger this indicator is, the more profitable the bank is

ROE evaluates the ability to generate profit from a shareholder's capital The higher this ratio, the more effectively the bank can use investment capital, benefitting shareholders more When ROE decreases, it shows that the bank is operating inefficiently and needs to take appropriate adjustment measures In addition, the ROE index shows how much profit the business will earn for each dong invested by the owner A business with a stable ROE index at a high level can be considered a good sign of the effective use of capital However, when the ROE is too high, it should be reconsidered because the bank may want to increase profits and invest in riskier portfolios

The net interest margin measures a bank's net interest income It is calculated as the difference between the interest income it receives and the interest expense it pays, divided by the bank's total earning assets In other words, NIM is a measure of efficiency that assesses the bank's ability to generate profit from a given cash flow.

Factors affecting the financial performance of commercial banks

Measuring the capital adequacy ratio will assess the risk in banks' operations Central banks in countries often prescribe the minimum capital adequacy ratio to protect lenders and depositors, thereby helping to ensure the financial system's safety In addition, administrators have used this ratio to assess banks' solvency and risk level in paying debts when due Research by Noman et al (2015), Yeasin

(2022), and Poudel (2012) has shown that bank profits will decrease when the capital adequacy ratio is too high On the other hand, according to Isanzu (2017), the capital adequacy ratio is believed to increase the bank's strength, enhance its ability to withstand losses from impaired loans and ensure its continued effective operation

Bank size is calculated using the natural logarithm of total assets This is considered an important indicator when assessing a bank's financial health Typically, large commercial banks have many branches, which gives them an advantage over small commercial banks in capital mobilization, credit granting, and payment services provision Therefore, large banks often have higher revenue and profit than small banks In addition, large banks with a long history of operation and their brand frequently attract more customers due to their high safety and reasonable information security Consequently, the financial performance of large banks will be higher than that of small banks Boahene et al (2012), Munangi and Sibindi

(2020), and Al-Eitan and Bani-Khalid (2019) pointed out that bank size has a positive relationship with banks’ performance

Leverage is an index measured by the ratio of total debts to total assets When leverage is higher than expected, the bank may face many risks because the source of mobilization may come from loans from credit institutions If the bank's investment activities are uncontrolled, it will lead to risks and reduce its financial performance Research by Akhtar et al (2011), Shahid et al (2019) has shown that financial leverage has an inverse relationship with the performance of commercial banks

GDP is an essential ratio in measuring the growth rate of a country When the economy develops positively, businesses have the conditions to establish production and boost consumption, thereby increasing revenue, profit, or profitability GDP growth creates favorable conditions for banks by creating demand for loans and other financial services, helping commercial banks increase profits and improve their financial performance According to Ongore and Kusa

(2013) and Kwashie et al (2022), GDP has a positive relationship with bank financial performance However, in contrast to the above two studies, the study of Nguyen Chi Duc and Nguyen Duc Trong (2023) shows that GDP has a negative relationship with financial performance

Inflation is a continuous increase in the price of goods and services, leading to the country's currency depreciation When inflation increases, banks often increase lending interest rates, so businesses limit borrowing to save costs, and consumers spend less to balance their finances This reduces the demand for capital and other financial services, negatively affecting the profits and performance of commercial banks According to Rahman et al (2015), and Odusanya et al (2018), an inflation increase will reduce commercial banks' performance On the contrary, Huynh Thi Huong Thao (2019) showed that inflation positively impacts banks' financial performance When inflation increases, deposit and lending rates will increase if bank managers can adjust interest rates so that revenue increases faster than costs As a result, bank profits will increase In addition, lending rates increase when inflation increases, and banks will earn higher profits by providing more loans to potential borrowers at these higher interest rates.

RESEARCH METHOD

Research process

To conduct research effectively and achieve meaningful outcomes, the author has outlined the following process:

Research data

Step 1: Review the theoretical background and relevant prior studies to build a solid foundation

Step 6: Provide a general conclusion and make recommendations based on the findings

Step 4: Conduct OLS, FEM, and REM regression, while addressing any model deficiencies using FGLS, then test for endogeneity in the model and apply GMM regression

Step 3: Perform descriptive statistics, correlation analysis, and test for multicollinearity

Step 2: Develop the research model and determine the appropriate research methodology

Step 5: Analyze and discuss the results, comparing the outcomes of the different model estimations

The data used in this study is secondary data from the annual audited consolidated financial statements of 26 commercial banks in Vietnam that were publicly disclosed from 2011 to 2023 The author used Fiinpro software to collect and synthesize all data for the study This is a reliable financial channel used by many organizations and individuals In addition, the author collected macro data from the World Bank website.

Research model

Based on previous studies such as Nguyen Chi Duc and Nguyen Duc Trong (2023); Evoney and Margaretha (2024), Afolabi et al (2020); Shahid et al (2019); Mithila and Kengatharan (2024), the author will build a research model as follows:

In addition, the author proposes a research model on the influence of the independent variable ROA under the interaction of the dummy variable (LIS) Proposed research model:

In which: i and t: Commercial bank i = 1,2,…26 in year t = 1,2,…12 β 0 : A constant; β 1 β 2 β 10 are the regression coefficients or slope coefficients of the corresponding independent variables;

T: includes bank-specific factors (CAR, NPL, LLP, LDR, SIZE, and LEV) ε: Error term

LLP: Loan Loss Provision ratio

LDR: Loan to Deposit ratio

GDP: Vietnam’s GDP growth rate

LIS: has a value of 1 if the bank is officially listed on the Vietnamese stock market (specifically HOSE and HNX) and a value of 0 otherwise.

Description of variables and research hypotheses development

Return on assets (ROA) is an essential indicator for evaluating the overall performance of a commercial bank This indicator reflects the bank's ability to utilize its assets to generate profits A high return on assets (ROA) usually shows that the bank manages its assets effectively and can create profits The bank needs a sustainable profit growth strategy to achieve a high ROA In addition, the bank's ability to reduce costs and optimize capital structure to enhance ROA must be

Bank size Capital Adequacy ratio

Dummy variable (LIS) assessed This study only employs ROA as the dependent variable to measure financial performance instead of ROE and NIM because of its advantages over other measures According to Kwashie et al (2022), ROA is the best indicator for determining the financial performance of commercial banks as it shows how effective the banks’ management is in generating profit from limited resources Besides, ROE and NIM have been criticized for failing to account for how much shareholder wealth is maximized

Non-performing loans include loans that customers have overdue for 90 days or more This is a warning sign that the customer's ability to manage and pay debt is having problems If the customer shows signs of financial weakness, uncertain payment ability, or doubts about fulfilling debt repayment obligations, their loan can be classified as bad debt This is an essential criterion that financial institutions use to assess the level of risk in managing outstanding debt and setting aside doubtful debt provisions Higher non-performing loans lead to lower profitability for banks, as increases in NPL require banks to set aside more provisions for expected credit losses, thereby reducing banks’ profit (Ekinci & Poyraz, 2019) Many studies support this view, including Evoney and Margaretha

(2024), Kwashie et al (2022), Afolabi et al (2020), Al-Eitan and Bani-Khalid

(2019), and Dao Thi Huong and Nguyen The Anh (2023)

The formula for measuring non-performing loans in this thesis:

Hypothesis 1: Non-performing loans have a negative impact on financial performance

(2) Loan loss provision ratio (LLP)

Excessive provisioning for credit risk can decrease bank profitability and operating efficiency In addition, a high-risk provision ratio indicates that the bank has many bad debts, which can lead to a loss of confidence among investors and customers, thereby reducing the bank's operating efficiency Studies by Huynh Thi Huong Thao (2019), Kolapo et al (2012), and Pham Huu Hong Thai (2014) have indicated that LLP negatively affects the bank's operating efficiency The formula for measuring capital adequacy ratio loans in this thesis:

Hypothesis 2: Loan loss provision ratio has a negative impact on financial performance

(3) Loan to Deposit ratio (LDR)

The Loan to Deposit ratio provides insight into how well a bank utilizes its funds and can reflect the stability of its credit operations A low LDR may indicate that a bank is holding a significant portion of its deposits and is not using all of its money supply to make loans Conversely, a high LDR may indicate that a bank uses many funds to make loans According to Muriithi et al (2016), Dang Hoang Nhat Tam and Pham Thi Tuan Linh (2020), and Afolabi et al (2020), LDR positively affects the financial performance of commercial banks

The formula for measuring Loan to Deposit ratio in this thesis:

Hypothesis 3: Loan to Deposit ratio has a positive impact on financial performance

Bank size is one of the significant indicators affecting commercial banks' financial performance Larger banks typically have a competitive advantage because they offer a broader range of products and services, reach more customers, and take advantage of economies of scale Larger banks are generally considered to be more stable and secure due to their greater capitalization and greater resilience to economic shocks and financial risks (Ekinci & Poyraz, 2019) Studies carried out by Kwashie et al (2022) and Al-Eitan and Bani-Khalid (2019) depict that bank size significantly positively affects financial performance

The bank size is defined using the natural logarithm of the bank’s total assets value The formula for measuring bank size in this thesis:

Hypothesis 4: Bank size has a positive impact on financial performance

The capital adequacy ratio plays a vital role in maintaining a bank's stability, but keeping this ratio high can negatively impact the bank's business performance

A high CAR requires the bank to retain more capital instead of using it for lending or investing to generate profits, reducing the banks’ performance Research by Noman et al (2015), Yeasin (2022), and Poudel (2012) also depict that CAR negatively affects the bank's performance

The formula for measuring capital adequacy ratio loans in this thesis:

CAR Tier 1 Capital Tier 2 Capital

Hypothesis 5: Capital Adequacy ratio has a negative impact on financial performance

A high ratio of total loans to total assets may indicate that the bank may face increased bad debts if loans are not adequately assessed or if customers default If this ratio is not managed carefully, it will cause serious risks affecting the bank’s performance and sustainability The study by Akhtar et al (2011), Shahid et al

(2019) have shown that LEV has a negative relationship with bank profitability

The formula for measuring leverage in this thesis:

Hypothesis 6: Leverage has a negative impact on financial performance

GDP growth is a standard indicator for measuring a country's economic growth rate When GDP increases, the economy develops well, and investment activities occur firmly On the contrary, the country's economy is considered a recession if GDP decreases Therefore, GDP has a positive impact on the efficiency of banking business activities, which is similar to the research of Ongore and Kusa

Hypothesis 7: GDP growth rate has a positive impact on financial performance

Inflation is the rate of the general increase in prices of goods and services in the economy Money's purchasing power decreases when inflation increases, affecting nominal and actual interest rates Therefore, maintaining inflation at its natural level (0 to less than 10%) is an important goal of countries because the inflation rate is considered an important indicator to assess the stability of a country The increase in inflation is accompanied by an increase in deposit interest rates, which reduces the business profits of banks This is similar to the study of Rahman et al (2015) and Odusanya et al (2018)

Hypothesis 8: Inflation rate has a negative impact on financial performance

The dummy variable LIS has a value of 1 if the bank is officially listed on the Vietnamese stock market (specifically HOSE and HNX) and a value of 0 otherwise The study uses the dummy variable LIS to examine and compare the impact of credit risk on the financial performance of listed and unlisted banks on the Vietnamese stock market

Table 3.1 Variables used in the research model

Nguyen Chi Duc and Nguyen Duc Trong (2023); Ekinci and Poyraz (2019); Afolabi et al (2020); Shahid et al (2019); Huynh Thi Huong Thao (2019)

Nguyen Chi Duc and Nguyen Duc Trong (2023); Evoney and Margaretha (2024), Afolabi et al (2020); Shahid et al (2019);

Nguyen Thanh Dat and Thi Thi My Duyen (2021); Dang Hoang Nhat Tam and Pham Thi Tuan Linh (2020)

Loan loss provision ratio LLP

(-) (+) Huynh Thi Huong Thao (2019); Afolabi et al (2020);

Isanzu (2017); Dang Hoang Nhat Tam and Pham Thi Tuan Linh (2020); Nguyen Thanh Dat and Thi Thi My Duyen (2021)

Loan to Deposit ratio LDR (+)

Dang Hoang Nhat Tam and Pham Thi Tuan Linh (2020);

Nguyen Chi Duc and Nguyen Duc Trong (2023); Kolapo et al

(2012); Afolabi et al (2020); Muriithi et al (2016); Kwashie et al (2022)

Bank size SIZE (+) Dang Hoang Nhat Tam and Pham Thi Tuan Linh (2020);

Nguyen Chi Duc and Nguyen Duc Trong (2023); Munangi and Sibindi (2020); Al-Eitan and Bani-Khalid (2019)

Shahid et al (2019); Muriithi et al (2016); Yeasin (2022) Munangi and Sibindi (2020); Isanzu (2017); Rahman et al

(+) (-) Boahene et al (2012); Pham Huu Hong Thai (2014)

Munangi and Sibindi (2020); Shahid et al (2019); Akhtar et al

(+) (-) Ongore and Kusa (2013); Kwashie et al (2022)

Dang Hoang Nhat Tam and Pham Thi Tuan Linh (2020; Nguyen Chi Duc and Nguyen Duc Trong (2023)

(-) Ekinci and Poyraz (2019); Huynh Thi Huong Thao (2019) Rahman et al (2015); (Odusanya et al., 2018) (-)

RESEARCH RESULTS AND DISCUSSION

Descriptive statistic result from the variables

The panel research data includes 338 observations of financial indicators calculated through secondary data collected from audited consolidated financial statements of 26 Vietnamese Joint Stock Commercial Banks from 2011 to 2023 The values presented include the number of observations, average values, standard deviations, minimum values, and maximum values of the variables in the model

Variable Observations Mean Standard deviation Min Max

Source: Extracted from Stata software 17.0

From the statistical results in Table 4.1, it can be seen that the dependent variable - Return on asset (ROA) of 26 banks from 2011 to 2023 generally has a low average value, only about 0.96%, showing that the profitability of most commercial banks compared to total assets is not high The smallest ROA value is -5.599% (Tien Phong Bank in 2011), and the most significant value is 3.65% (Techcombank in 2021) Moreover, there is little difference between the banks in the sample under consideration when the standard order level reaches 0.82%

Capital Adequacy Ratio (CAR) has an average value of 13.39% and a standard deviation of 4.47% The results show that most Vietnamese commercial banks have met the minimum capital requirements, indicating that banks are operating safely and stably in terms of capital The smallest CAR value is 7.98% (Southeast Asia Commercial Joint Stock Bank in 2011), and the largest CAR is 40.15% (Tien Phong Bank in 2012)

Non-performing loans (NPL) in the review period of 26 commercial banks had an average value of 2.25% and a standard deviation of 2.09% The results show that the NPL ratio still has the potential to appear during operations and does not differ much between banks Specifically, the smallest and largest NPL ratios were 0.47% and 29.76%, respectively, from Techcombank in 2020 and National Citizen Commercial Joint Stock Bank in 2023

The loan loss provision ratio (LLP) has an average value of 1.14% and a standard deviation of 0.87%, showing that banks maintain an appropriate provisioning ratio, control credit risks, and ensure financial safety The smallest LLP value is 0 (Viet A Commercial Joint Stock Bank in 2011), and the largest LLP is 5.41% (Vietnam Prosperity Bank in 2021)

The Loan to Deposit ratio (LDR) has an average value of 90.27% and a standard deviation of 18.91% The results show that banks generally use most of their customers' deposits for lending, maximizing mobilized capital to increase profits However, there is a significant difference in LDR ratios between commercial banks The smallest LDR value is 37.19% (Vietnam Maritime Commercial Joint Stock Bank in 2014), and the largest is 180.5% (Bac A Bank in

Bank size (SIZE) has an average value of 14.1441 and a standard deviation of 0.5119, showing a negligible difference between banks and a positive trend in expanding the size and increasing the total asset value of banks SaiGonBank had the smallest size of 13.1669 in 2013, and the Joint Stock Commercial Bank for Investment and Development of Vietnam had the largest size of 15.3619 in 2023

Leverage (LEV) has an average value of 90.82% and a standard deviation of 3.64% This result shows that most of the banks' assets are used for lending, and the banks are maximizing the use of assets to generate profits from credit activities In addition, the LEV ratio of the banks in the survey sample has a high level of uniformity, with no significant difference between banks, in which the lowest LEV ratio is 76.16% (SaiGonBank in 2013) The highest LEV ratio is 95.96% (Joint Stock Commercial Bank for Investment and Development of Vietnam in 2017)

GDP growth rate (GDP) from 2011 to 2022 reached an average value of 6% with a standard deviation of 1.62%, with the smallest and largest values of 2.6% and 8.1%, respectively, in 2021 and 2022

The inflation rate (INF) from 2011 to 2022 reached an average of 4.85% with a standard deviation of 4.47%, with minimum and maximum values of 0.63% and 18.58% in 2015 and 2011, respectively.

Correlation analysis

The author conducts a correlation matrix test between variables in the model to evaluate and examine the level of correlation between variables The correlation coefficient shows the positive or negative relationship between variables The necessary condition for the model to not be multicollinear is that the correlation coefficient between pairs of variables has an absolute value of less than 0.8

ROA CAR NPL LLP LDR SIZE LEV GDP INF ROA 1.0000

Source: Extracted from Stata software 17.0

The results of the correlation analysis between the variables in the research model show that the variables are correlated with each other within the allowable range, all of which are less than 0.8 Specifically, the correlation value ranges from -0.7099 to 0.4503.

Multicollinearity test through the VIF coefficient

To check whether multicollinearity occurs between variables in the research model, the author uses the Pooled OLS model regression method to obtain the VIF index results, thereby checking and evaluating multicollinearity between variables

Source: Extracted from Stata software 17.0

The results of multicollinearity testing through regression using the Pooled OLS method show that the VIF index of all independent variables is suitable under the allowable conditions; VIF is less than 10 Specifically, the average VIF index of all independent variables in the model is 1.55; the independent variable with the highest VIF is the LEV variable, reaching 2.55; the independent variable with the smallest VIF value is GDP 1.03 Thereby, the results are consistent with the assumption in the classical linear regression model and the necessary condition for regression: "Independent variables must not have a linear relationship with each other," so the model does not violate multicollinearity occurring between independent variables and no variables will be eliminated from the model.

Regression model estimation

After testing the model, the author continues to estimate the regression methods according to the Pooled OLS, FEM, and REM models, with the dependent variable being ROA and the independent variables within the bank including CAR, NPL, LLP, LDR LEV, SIZE, along with two macro factors: GDP and INF

Table 4.4 Regression result using Pooled OLS, FEM, REM

Loan loss provision ratio (LLP) -0.0956** -0.230*** -0.191***

Loan to deposit ratio (LDR) 0.00931*** 0.00722*** 0.00925***

Note: ***, **, * denote for significant at 1%, 5%, and 10% level, respectively

Source: Extracted from Stata software 17.0

Pooled OLS estimation results: Pooled OLS model estimation results show that the independent variables explain 47.5% of the change in ROA In which the independent variables NPL, LLP, LDR, SIZE, and LEV are statistically significant in the model with ROA as the dependent variable Specifically, at the 1% significance level, LDR has a regression coefficient of 0.00931, SIZE has a coefficient of 0.00944, and positively impacts ROA On the contrary, LEV at the 1% significance level has a regression coefficient of -0.127 At the 5% significance level, NPL has a coefficient of -0.0318, and LLP has a coefficient of -0.0956, which shows that LEV, NPL, and LLP have a negative impact on ROA In addition, the variables CAR, GDP, and INF are insignificant in this model

FEM estimation results: The FEM model estimation results show that the independent variables explain 50.8% of the change in ROA In particular, the variables NPL, LLP, LDR, SIZE, LEV, and INF all affect the dependent variable ROA Specifically, the regression coefficient results show that LDR, SIZE, and INF positively impact ROA at the 1% statistical significance level Meanwhile, LLP and LEV at the 1% statistical significance level and NPL at the 5% significance level have a negative impact on ROA In addition, the GDP variable is not significant in the FEM model Through this, it can be seen that with the FEM model, the author receives more statistically significant variables than with the Pooled OLS model

REM estimation results: Similar to the FEM regression model, in the REM model, the author received six statistically significant variables: NPL, LLP, LDR, SIZE, LEV, and INF At the same statistical significance level of 1%, LDR and SIZE positively correlate to Return On Asset (ROA), whereas LLP and LEV correlate negatively In addition, at the same statistical significance level of 10%, the variable INF positively impacts the variable ROA, and the variable NPL negatively impacts the variable ROA The GDP variable is not statistically significant in the estimation model

4.4.1 Selection test between Pooled OLS and FEM model

The author conducts F-test to examine the suitability of the research data between the Pooled OLS model and FEM for the research With the hypothesis:

: The Pooled OLS model is the appropriate model

: The FEM model is the appropriate model

Table 4.5 F-test results for Pooled OLS and FEM models

Source: Extracted from Stata software 17.0

The results from Table 4.5 show that the P-value is less than 5%, thus rejecting the hypothesis and concluding that the FEM model is more suitable for the study than the Pooled OLS model

4.4.2 Selection test between FEM and REM model

Next, the author considers in choosing the FEM and REM models, which model is more suitable for the data of the research by performing the Hausman test with the following hypothesis:

Difference in coefficients not systematic

Source: Extracted from Stata software 17.0

The results show P-Value = 0.0902 > 5%, and is accepted The conclusion is that choosing the REM model will suit research data with ROA as the dependent variable

Through the two test results above in choosing between Pooled OLS, FEM, and REM models, the author decides the REM fixed effects model is the most suitable model for the variables of the study.

Model defect testing

For the estimation results from the selected REM model to be accurate without violating heteroscedasticity problems, reducing the estimation results' efficiency and accuracy The author will conduct a test of heteroscedasticity in the research model by using the Breusch and Pagan Lagrangian multiplier test; the hypothesis is:

: The model does not have the phenomenon of heteroscedasticity

: The model has the phenomenon of heteroscedasticity

Breusch and Pagan Lagrangian multiplier test

Source: Extracted from Stata software 17.0

With P-Value = 0.0000 < 5%, reject the hypothesis and conclude that the REM model has heteroscedasticity Therefore, with the study's data, the REM model with ROA as the dependent variable has violated the heteroscedasticity defect

The author used the Wooldridge test to test the autocorrelation phenomenon in the research model Specifically, the hypothesis is put forward as follows:

Source: Extracted from Stata software 17.0

With P-Value=0.0000 < 5%, reject and conclude that the model has a correlation phenomenon and needs fixing.

Regression results with FGLS

The research data's REM model violates heteroscedasticity and autocorrelation Accordingly, the author will use the FGLS method to regress the GLS model to overcome the defects or violations in the research data model with ROA as the dependent variable

Loan loss provision ratio (LLP) -0.103***

Loan to Deposit ratio (LDR) 0.00193

Note: ***, **, * denote for significant at 1%, 5%, and 10% level, respectively

Source: Extracted from Stata software 17.0

Compared with the previous REM model, the FGLS estimation model has overcome the defects in the REM model The author received five independent variables with statistical significance, with the model's ROA variable being the dependent variable Specifically, at the 1% statistical significance level, the regression coefficients of the SIZE and INF variables are 0.00977 and 0.0287, respectively, showing a positive impact on the performance of commercial banks

On the contrary, at the same 1% significance level, the regression coefficients of the NPL variable are -0.0421, the LLP variable is -0.103, and the LEV variable is -0.112, demonstrating a negative impact on the performance of commercial banks with ROA as the dependent variable In addition, the CAR, LDR, and GDP variables are not statistically significant in the FGLS model.

GMM regression model method

After checking and correcting the autocorrelation and heteroskedasticity in the regression model using the FGLS method, the author continues to check whether the model has endogenous variables If the model has endogenous variables, the FGLS method above cannot correct this phenomenon; the author needs to use a more advanced process, the Generalized Method of Moments (GMM), to handle it To determine whether the study's regression model has endogenous phenomena, the author uses the Durbin Wu Hausman test to check for each independent variable The hypothesis is:

If P-Value > 5%, accept : the variable is exogenous Conversely, if

P-Value < 5% reject : the variable is endogenous in the model

Table 4.10 Endogenous variable test results

Source: Extracted from Stata software 17.0

Based on Table 4.10, it can be concluded that in the model with ROA as the dependent variable, the variable SIZE is an endogenous variable that interacts with other variables in the model Accordingly, the author will estimate using the GMM method to eliminate the endogeneity problem in the research data model

In addition to running the GMM method with independent variables, the author uses the dummy variable LIS to compare the differences between Vietnamese listed and unlisted banks on the stock market The author multiplies the

CAR P = 0.3250 > 5% Accept hypothesis : CAR is an exogenous variable NPL P = 0.0911 > 5% Accept hypothesis : NPL is an exogenous variable LLP P = 0.7778 > 5% Accept hypothesis : LLP is an exogenous variable LDR P = 0.1167 > 5% Accept hypothesis : LDR is an exogenous variable SIZE P = 0.0000 < 5% Reject hypothesis : SIZE is an endogenous variable LEV P = 0.2338 > 5% Accept hypothesis : LEV is an exogenous variable dummy variable LIS with the independent variables to create CAR*LIS, NPL*LIS, LLP*LIS, LDR*LIS, SIZE*LIS, and LEV*LIS Model (1) in Table 4.10 presents the estimation results of the independent variables affecting ROA; the remaining models consider the interaction between the model's dummy variable (LIS) and the independent variables.

Note: ***, **, * denote for significant at 1%, 5%, and 10% level, respectively

Source: Regression results from Stata 17.0

The results in Table 4.11 suggest that the above GMM regression model is statistically significant and reliable because it satisfies the necessary conditions All

7 models satisfy the condition that the number of instrument groups is less than the number of specific groups The AR(2), Sargan, and Hansen tests all have P-values greater than 10%, proving that the above GMM regression model is appropriate

The author obtained 7 statistically significant variables and one lagged variable - L.ROA, as a result of the P test Specifically, at the 1% significance level, L.ROA, NPL, LEV, and GDP affect ROA; at the 5% significance level, the variables CAR, LDR, and SIZE impact ROA; finally, at the 10% significance level, the variable INF has a correlation impact on ROA

Thereby, compared with the estimation by the FGLS method, the model estimated by GMM regression has better results and receives an additional lag variable of ROA as an independent variable and impacts ROA Moreover, the GMM method also helps to eliminate the endogeneity problem of variables occurring in the research model Accordingly, the author draws the following model after estimating by the GMM general moment method:

Variable Hypothesis Regression coefficient β Conclusion

Note: ***, **, * denote for significant at 1%, 5%, and 10% level, respectively

Source: Regression results from Stata 17.0

CONCLUSIONS AND RECOMMENDATIONS

Conclusions

Seeing the limitations of credit risk and the importance of controlling and managing credit risk well in commercial banks, in the research paper, the author started from the theoretical basis and, at the same time, reviewed previous research papers at home and abr oad with the same research topic, then proposed and selected the factors of credit risk affecting the performance of banks Specifically, it includes factors such as capital adequacy ratio, non-performing loans, loan loss provision ratio, loan to deposit ratio, bank size, leverage, GDP growth rate, and inflation rate

To see the specific impact of credit risk on the performance of commercial banks, the author based on the data table collected from the audited consolidated financial statements of 26 commercial banks in Vietnam in the period of 2011 -

2023 and used Stata 17.0 software to regress and test the research model: descriptive statistics, correlation matrix and multicollinearity between variables in the model Then, the author successively regressed according to the estimation methods: ordinary least squares (OLS), fixed effects (FEM), random effects (REM), FGLS, generalized moment (GMM) and performed in parallel with tests such as F- test, Hausman test, Breusch and Pagan Lagrangian multiplier test, Wooldridge test and Durbin-Wu Hausman test to give the estimation results that are both suitable for the research data, accurate and highly effective

The results of the GMM estimation method are as follows: the lagged variable (L.ROA), loan to deposit ratio (LDR), bank size (SIZE), and GDP growth rate (GDP) have a positive impact on the performance of commercial banks and vice versa, capital adequacy ratio (CAR), non-performing loans (NPL), loan loss provision ratio (LLP), leverage (LEV), and inflation rate (INF) have a negative impact on the performance of banks In addition, the author also uses a dummy variable (LIS) to compare the differences between listed and unlisted banks on the stock exchange Specifically, when a bank is listed on the stock exchange, the negative impacts of CAR, NPL, and LLP will be weaker than those of unlisted banks The study not only stops at studying the impact of credit risk on the performance of banks but also explores more deeply the differences between listed and unlisted banks on the stock exchange.

Recommendations

Through the discussion and analysis results in the content sections in Chapter

4, the author will propose recommendations on policies to control and minimize credit risks and improve the operational efficiency of Vietnamese joint stock commercial banks:

5.2.1 Optimizing CAR for enhanced banks’ performance

According to the research results, a high capital adequacy ratio reduces bank efficiency, so banks must apply appropriate management strategies to balance capital adequacy and profitability Banks should maintain CAR optimally, sufficient to comply with regulations but not too high to avoid wasting capital In addition, banks need to reassess their risky asset portfolio regularly, optimize capital structure, and k

5.2.2 Controlling NPL effectively to enhance banks’ performance

Non-performing loans must be effectively controlled to increase the business efficiency of commercial banks To control NPL, commercial banks need to focus on improving credit quality and customer quality, focusing on credit assessment and appraisal, and developing separate policies for specific industries In addition, banks need to strengthen management and supervision of the situation before and after disbursement to ensure the implementation and strict control of the credit granting process, helping banks avoid arising risks and, simultaneously, have timely handling measures when risks occur Moreover, commercial banks must scientifically screen customers, have specific and continuously updated processes, and focus on controlling the lending process

5.2.3 Controlling LLP to protect reputation and efficiency

The credit risk provision ratio is too high, causing banks to lose prestige in the eyes of customers and investors Therefore, banks need to focus on strictly controlling credit quality They also need to improve their credit appraisal process, ensure the approval and funding are carried out carefully, and prioritize customers with good credit histories, feasible projects, and stable financial capacity In addition, banks must focus more on credit risk management, reviewing lending activities, and updating and supplementing terms in the customer credit appraisal process

5.2.4 Optimizing LDR to enhance performance and ensure stability

Based on the results of the above model research, the increase in the customer loan to total deposit ratio helps improve banks’ performance, so banks need to take full advantage of this advantage while still maintaining financial stability and safety Banks need to strictly manage the structure of mobilized capital and lending to ensure stable cash flow, meeting customers' capital withdrawal needs without putting pressure on liquidity In addition, banks need to maintain the LDR ratio reasonably, not exceeding the safety threshold prescribed by regulatory agencies In addition, banks need to focus on developing programs to care for and maintain loyal customers, creating long-term connections, thereby developing sustainably in capital mobilization and lending activities

5.2.5 Expanding operational scale to maximize profitability

Banks must increase their scale by expanding transaction points and branches in potential locations according to their competitive advantages To increase the scope of operations, banks need to focus on increasing internal capital and prioritizing retained profits - an internal capital source that positively impacts commercial banks' financial goals When expanding the scope of operations, commercial banks will increase the opportunity to provide products, increase market share, and attract customers with superior financial products that can bring more profits

Banks need to minimize the negative impact of the ratio of total outstanding loans to total assets by enhancing debt collection activities and restructuring bad debts This helps reduce the bad debt ratio, reduce credit risk, and restore value from unpaid debts, thereby improving profits Develop debt collection and restructuring strategies, including increased monitoring and negotiation with customers with bad debts to find solutions suitable for each situation

5.2.7 Enhancing operational efficiency by adapting to macroeconomic fluctuations

The results of the research model show that the GDP growth rate has a positive impact on operational efficiency and vice versa; the inflation rate has a negative impact on the operational efficiency of commercial banks To increase profits, banks need to increase observation, analysis, and forecasting of fluctuations in macroeconomic factors, especially GDP growth rate and inflation rate, so that they can have policies on adjusting lending interest rates and deposit interest rates following changes in the general trend of the economy, through which banks will have a comprehensive view to develop policies and business plans of the bank under the economic situation, thereby increasing the operational efficiency of commercial banks.

Recommendations for listed banks

The research results show that, in listed banks, the negative impact of CAR, NPL, and LLP on financial performance is significantly reduced compared to unlisted banks

From this result, some important management implications can be drawn: First, listing on the stock exchange is not only a step to improve access to capital but also plays an essential role in enhancing risk management efficiency and improving business performance Listed banks tend to apply higher governance standards, are more transparent, and can build comprehensive risk management systems, thereby better controlling the negative impact of maintaining high CAR, high NPL ratio or high provisioning costs

Second, unlisted banks should consider formal listing as a long-term strategy to improve governance capacity, enhance operational efficiency, and minimize negative impacts from credit and capital risk factors

Finally, regulators should also encourage the listing process of commercial banks through appropriate support policies, as this not only improves the quality of banking operations but also contributes to the stability and sustainable development of the entire financial and banking system.

Limitations of the study

Although the study has achieved particular research objectives and results, due to limited time, resources, and capacity, the thesis will have some limitations:

Firstly, the research data is limited The study selected representatives of 26 joint stock commercial banks from 2011 - 2023 out of 49 banks 3 (as of January

2024) operating in Vietnam Thus, the study is only representative and does not fully reflect the nature and characteristics of the entire banking system in Vietnam

Secondly, the study was conducted only to assess the impact of credit risk on bank performance, which was measured by return on assets (ROA) In reality, many other quantities can be considered when evaluating banks’ performance

Third, in the research model, the author only proposed eight independent variables that need to be considered in determining the impact of credit risk on

3 WIKIPEDIA, “Danh sách ngân hàng tại Việt Nam,” accessed December 16, 2024, at: https://vi.wikipedia.org/wiki/Danh_s%C3%A1ch_ng%C3%A2n_h%C3%A0ng_t%E1%BA%A1i_Vi%E1%BB%87t_Nam#:~:text=B%E1%BA%A1n%20c%C3%B3%20th%E1%BB%83%20gi%C3%BAp%20Wikipe dia,h%C3%A0ng%20100%25%20v%E1%BB%91n%20n%C6%B0%E1%BB%9Bc%20ngo%C3%A0i banks’ performance Therefore, it does not truly reflect all the causes and factors of credit risk that can affect and impact the performance of commercial banks

From the model estimation results obtained in chapter 4, in this chapter, the research results are summarized, and based on that, recommendations and proposals are made to help banks in Vietnam minimize and prevent credit risks, thereby increasing the operational efficiency of commercial banks In addition, the author presents some limitations of the thesis, thereby helping to provide directions for future research

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APPENDIX Appendix 1: List of 26 Vietnamese commercial banks

1 ABB An Binh Commercial Joint Stock Bank Viet Nam

2 ACB Asia Commercial Joint Stock Bank

3 BAB Bac A Commercial Joint Stock Bank

4 BID Joint stock Commercial Bank for Investment and Development of Viet Nam

5 BVB Viet Capital Commercial Joint Stock Bank

6 CTG Vietnam Joint Stock Commercial Bank for Industry and Trade

7 EIB Vietnam Export Import Commercial Joint Stock Bank

8 HDB Ho Chi Minh City Development Joint Stock Commercial Bank

9 KLB Kien Long Commercial Joint Stock Bank

10 LPB Fortune Vietnam Joint Stock Commercial Bank

11 MBB Military Commercial Joint Stock Bank

12 MSB Vietnam Maritime Commercial Joint Stock Bank

13 NAB Nam A Bank Commercial Joint Stock Bank

14 NVB National Citizen Commercial Joint Stock Bank

15 OCB Orient Commercial Joint Stock Bank

16 PGB Prosperity and Growth Commercial Joint Stock Bank

17 SGB Saigon Bank For Industry And Trade

18 SHB Saigon – Hanoi Commercial Joint Stock Bank

19 SSB Southeast Asia Commercial Joint Stock Bank

20 STB Saigon Thuong Tin Commercial Joint Stock Bank

21 TCB Vietnam Technological And Commercial Joint Stock Bank

22 TPB Tien Phong Commercial Joint Stock Bank

23 VAB Vietnam Asia Commercial Joint Stock Bank

24 VCB Bank for Foreign Trade of Vietnam

25 VIB Vietnam International Commercial Joint Stock Bank

26 VPB Vietnam Prosperity Joint Stock Commercial Bank

Appendix 2: Descriptive statistics of variables

Appendix 3: Correlation Analysis with ROA Model

Appendix 4: Multicollinearity test of the model

Appendix 5: Pooled OLS Regression Analysis

Appendix 6: Fixed Effects Regression Model

Appendix 7: Random Effects Regression Model

Appendix 8: F-test for Choosing Between Pooled OLS and FEM

Appendix 9: Hausman test for choosing between FEM and REM

Appendix 10: Heteroscedasticity test for REM model

Appendix 12: FGLS Model Regression Results

Appendix 13: Test for endogeneity with the CAR variable

Appendix 14: Test for endogeneity with the NPL variable

Appendix 15: Test for endogeneity with the LLP variable

Appendix 16: Test for endogeneity with the LDR variable

Appendix 17: Test for endogeneity with the SIZE variable

Appendix 18: Test for endogeneity with the LEV variable

Appendix 19: GMM Model Regression Results

Appendix 20: Endogenous results with interaction variables CAR*LIS

Appendix 21: Endogenous results with interaction variables NPL*LIS

Appendix 22: Endogenous results with interaction variables LLP*LIS

Appendix 23: Endogenous results with interaction variables LDR*LIS

Appendix 24: Endogenous results with interaction variables SIZE*LIS

Appendix 25: Endogenous results with interaction variables LEV*LIS

Appendix 26: Results of the regression model of interaction variables

(3.28) (5.11) (3.34) (4.36) (4.54) (4.39) (4.45) L.ROA 0.393*** 0.447*** 0.323*** 0.458*** 0.470*** 0.468*** 0.470*** ROA ROA ROA ROA ROA ROA ROA

(1) (2) (3) (4) (5) (6) (7) esttab gmm gmmcar gmmnpl gmmllp gmmldr gmmsize gmmlev, r2 star (* 0.1 ** 0.05 *** 0.01)

Year ROA CAR NPL LLP LDR SIZE LEV LIS GDP INF

ABB 2011 0.0077 0.1490 0.0279 0.0286 0.9835 13.6185 0.8863 0 0.0640 0.1858 ABB 2012 0.0091 0.1428 0.0284 0.0091 0.6527 13.6629 0.8935 0 0.0550 0.0921 ABB 2013 0.0027 0.1782 0.0763 0.0145 0.6363 13.7606 0.9003 0 0.0560 0.0660 ABB 2014 0.0019 0.1490 0.0451 0.0165 0.5758 13.8291 0.9153 0 0.0640 0.0409 ABB 2015 0.0014 0.1620 0.0242 0.0215 0.6504 13.8087 0.9101 0 0.0700 0.0063 ABB 2016 0.0035 0.1350 0.0256 0.0165 0.7724 13.8702 0.9212 0 0.0670 0.0266 ABB 2017 0.0062 0.1260 0.0277 0.0103 0.8274 13.9269 0.9276 0 0.0690 0.0353 ABB 2018 0.0082 0.1280 0.0189 0.0060 0.8382 13.9542 0.9237 0 0.0750 0.0354 ABB 2019 0.0104 0.1110 0.0231 0.0087 0.8164 14.0110 0.9235 0 0.0740 0.0279 ABB 2020 0.0102 0.0900 0.0209 0.0081 0.8729 14.0658 0.9234 0 0.0290 0.0323 ABB 2021 0.0131 0.1280 0.0234 0.0108 1.0169 14.0826 0.9030 0 0.0260 0.0184 ABB 2022 0.0108 0.1247 0.0288 0.0095 0.9749 14.1144 0.8997 0 0.0810 0.0315 ABB 2023 0.0031 0.1100 0.0291 0.0153 0.9807 14.2098 0.9168 0 0.0500 0.0325 ACB 2011 0.0132 0.0925 0.0089 0.0029 0.7229 14.4487 0.9574 1 0.0640 0.1858 ACB 2012 0.0034 0.1350 0.0250 0.0051 0.8210 14.2463 0.9284 1 0.0550 0.0921 ACB 2013 0.0048 0.1453 0.0303 0.0080 0.7761 14.2217 0.9249 1 0.0560 0.0660 ACB 2014 0.0055 0.1480 0.0218 0.0076 0.7524 14.2543 0.9310 1 0.0640 0.0409 ACB 2015 0.0054 0.1280 0.0131 0.0065 0.7738 14.3042 0.9365 1 0.0700 0.0063 ACB 2016 0.0061 0.1319 0.0087 0.0075 0.7892 14.3686 0.9398 1 0.0670 0.0266 ACB 2017 0.0082 0.1149 0.0070 0.0129 0.8224 14.4538 0.9436 1 0.0690 0.0353 ACB 2018 0.0167 0.1281 0.0073 0.0040 0.8538 14.5176 0.9362 1 0.0750 0.0354 ACB 2019 0.0169 0.1091 0.0054 0.0010 0.8720 14.5838 0.9276 1 0.0740 0.0279 ACB 2020 0.0186 0.1106 0.0059 0.0030 0.8819 14.6479 0.9203 1 0.0290 0.0323

ACB 2021 0.0198 0.1123 0.0077 0.0092 0.9526 14.7224 0.9149 1 0.0260 0.0184 ACB 2022 0.0241 0.1280 0.0074 0.0002 0.9994 14.7838 0.9039 1 0.0810 0.0315 ACB 2023 0.0242 0.1250 0.0121 0.0037 1.0101 14.8566 0.9013 1 0.0500 0.0325 BAB 2011 0.0060 0.1822 0.0064 0.0021 1.8050 13.4106 0.8739 1 0.0640 0.1858 BAB 2012 0.0012 0.1246 0.0566 0.0054 0.7690 13.5281 0.9067 1 0.0550 0.0921 BAB 2013 0.0046 0.1000 0.0232 0.0125 0.6959 13.7016 0.9342 1 0.0560 0.0660 BAB 2014 0.0051 0.1207 0.0215 0.0077 0.7868 13.7573 0.9279 1 0.0640 0.0409 BAB 2015 0.0060 0.1119 0.0070 0.0041 0.7894 13.8025 0.9210 1 0.0700 0.0063 BAB 2016 0.0072 0.1296 0.0081 0.0013 0.8131 13.8805 0.9235 1 0.0670 0.0266 BAB 2017 0.0072 0.1114 0.0063 0.0053 0.8750 13.9628 0.9305 1 0.0690 0.0353 BAB 2018 0.0072 0.1115 0.0076 0.0043 0.8821 13.9869 0.9270 1 0.0750 0.0354 BAB 2019 0.0073 0.1022 0.0069 0.0021 0.9576 14.0330 0.9276 1 0.0740 0.0279 BAB 2020 0.0052 0.0839 0.0079 0.0032 0.9190 14.0689 0.9286 1 0.0290 0.0323 BAB 2021 0.0061 0.0924 0.0077 0.0029 0.9054 14.0784 0.9244 1 0.0260 0.0184 BAB 2022 0.0067 0.0895 0.0055 0.0012 0.9713 14.1099 0.9239 1 0.0810 0.0315 BAB 2023 0.0061 0.0856 0.0092 0.0015 0.8428 14.1825 0.9286 1 0.0500 0.0325 BID 2011 0.0083 0.1107 0.0276 0.0155 1.2222 14.6083 0.9394 1 0.0640 0.1858 BID 2012 0.0058 0.0900 0.0270 0.0104 1.1216 14.6856 0.9449 1 0.0550 0.0921 BID 2013 0.0078 0.1010 0.0226 0.0166 1.1538 14.7391 0.9411 1 0.0560 0.0660 BID 2014 0.0083 0.0900 0.0203 0.0157 1.0119 14.8131 0.9483 1 0.0640 0.0409 BID 2015 0.0085 0.0900 0.0168 0.0095 1.0598 14.9297 0.9502 1 0.0700 0.0063 BID 2016 0.0067 0.0900 0.0199 0.0127 0.9968 15.0028 0.9562 1 0.0670 0.0266 BID 2017 0.0063 0.0900 0.0162 0.0171 1.0080 15.0800 0.9594 1 0.0690 0.0353 BID 2018 0.0059 0.0902 0.0190 0.0191 0.9991 15.1182 0.9585 1 0.0750 0.0354 BID 2019 0.0061 0.0877 0.0175 0.0180 1.0025 15.1732 0.9479 1 0.0740 0.0279

BID 2020 0.0048 0.0861 0.0176 0.0192 0.9899 15.1809 0.9475 1 0.0290 0.0323 BID 2021 0.0066 0.0897 0.0100 0.0218 0.9813 15.2459 0.9510 1 0.0260 0.0184 BID 2022 0.0095 0.0934 0.0119 0.0157 1.0330 15.3265 0.9509 1 0.0810 0.0315 BID 2023 0.0099 0.0860 0.0126 0.0114 1.0428 15.3619 0.9466 1 0.0500 0.0325 BVB 2011 0.0214 0.3440 0.0270 0.0024 0.8373 13.2296 0.8055 0 0.0640 0.1858 BVB 2012 0.0110 0.2748 0.0190 0.0054 0.7556 13.3154 0.8419 0 0.0550 0.0921 BVB 2013 0.0047 0.2010 0.0411 0.0055 0.8332 13.3628 0.8606 0 0.0560 0.0660 BVB 2014 0.0066 0.1320 0.0154 0.0030 0.8844 13.4113 0.8715 0 0.0640 0.0409 BVB 2015 0.0019 0.1570 0.0120 0.0034 0.8518 13.4627 0.8858 0 0.0700 0.0063 BVB 2016 0.0001 0.0854 0.0127 0.0033 0.8531 13.5103 0.8978 0 0.0670 0.0266 BVB 2017 0.0009 0.1106 0.0378 0.0036 0.9263 13.6010 0.9162 0 0.0690 0.0353 BVB 2018 0.0022 0.1076 0.0206 0.0043 0.8864 13.6679 0.9261 0 0.0750 0.0354 BVB 2019 0.0026 0.0854 0.0251 0.0032 0.9652 13.7144 0.9279 0 0.0740 0.0279 BVB 2020 0.0028 0.0927 0.0279 0.0087 0.9628 13.7861 0.9363 0 0.0290 0.0323 BVB 2021 0.0036 0.1086 0.0253 0.0080 1.0253 13.8837 0.9394 0 0.0260 0.0184 BVB 2022 0.0047 0.1315 0.0279 0.0044 1.0146 13.8980 0.9367 0 0.0810 0.0315 BVB 2023 0.0007 0.1133 0.0331 0.0048 1.0110 13.9439 0.9335 0 0.0500 0.0325 CTG 2011 0.0151 0.1057 0.0075 0.0167 1.1406 14.6633 0.9377 1 0.0640 0.1858 CTG 2012 0.0128 0.1033 0.0147 0.0131 1.1531 14.7020 0.9328 1 0.0550 0.0921 CTG 2013 0.0108 0.1320 0.0100 0.0110 1.0324 14.7607 0.9058 1 0.0560 0.0660 CTG 2014 0.0093 0.1040 0.0112 0.0089 1.0370 14.8204 0.9164 1 0.0640 0.0409 CTG 2015 0.0079 0.1060 0.0092 0.0087 1.0915 14.8918 0.9280 1 0.0700 0.0063 CTG 2016 0.0078 0.1040 0.0105 0.0076 1.0106 14.9771 0.9364 1 0.0670 0.0266 CTG 2017 0.0073 0.0986 0.0114 0.0106 1.0501 15.0394 0.9418 1 0.0690 0.0353 CTG 2018 0.0047 0.0896 0.0159 0.0090 1.0474 15.0661 0.9422 1 0.0750 0.0354

CTG 2019 0.0079 0.0900 0.0116 0.0139 1.0476 15.0937 0.9377 1 0.0740 0.0279 CTG 2020 0.0107 0.0860 0.0094 0.0120 1.0252 15.1276 0.9363 1 0.0290 0.0323 CTG 2021 0.0099 0.0914 0.0126 0.0163 0.9732 15.1851 0.9389 1 0.0260 0.0184 CTG 2022 0.0102 0.0898 0.0124 0.0187 1.0205 15.2574 0.9401 1 0.0810 0.0315 CTG 2023 0.0104 0.0931 0.0113 0.0170 1.0443 15.3081 0.9381 1 0.0500 0.0325 EIB 2011 0.0193 0.1294 0.0161 0.0036 1.3916 14.2638 0.9112 1 0.0640 0.1858 EIB 2012 0.0121 0.1454 0.0132 0.0032 1.0634 14.2309 0.9071 1 0.0550 0.0921 EIB 2013 0.0039 0.1447 0.0198 0.0036 1.0488 14.2300 0.9136 1 0.0560 0.0660 EIB 2014 0.0021 0.1316 0.0246 0.0095 0.8597 14.2045 0.9181 1 0.0640 0.0409 EIB 2015 0.0003 0.1652 0.0186 0.0169 0.8611 14.0964 0.8947 1 0.0700 0.0063 EIB 2016 0.0024 0.1712 0.0295 0.0125 0.8490 14.1099 0.8956 1 0.0670 0.0266 EIB 2017 0.0059 0.1598 0.0227 0.0060 0.8620 14.1743 0.9046 1 0.0690 0.0353 EIB 2018 0.0044 0.1500 0.0185 0.0070 0.8766 14.1837 0.9025 1 0.0750 0.0354 EIB 2019 0.0054 0.1381 0.0171 0.0061 0.8132 14.2241 0.9060 1 0.0740 0.0279 EIB 2020 0.0065 0.1181 0.0252 0.0066 0.7525 14.2053 0.8952 1 0.0290 0.0323 EIB 2021 0.0059 0.1229 0.0196 0.0086 0.8348 14.2197 0.8928 1 0.0260 0.0184 EIB 2022 0.0168 0.1464 0.0180 0.0008 0.8781 14.2673 0.8893 1 0.0810 0.0315 EIB 2023 0.0112 0.1343 0.0265 0.0049 0.8984 14.3041 0.8886 1 0.0500 0.0325 HDB 2011 0.0107 0.1646 0.0211 0.0062 0.7254 13.6535 0.9212 1 0.0640 0.1858 HDB 2012 0.0067 0.1401 0.0235 0.0141 0.6172 13.7225 0.8978 1 0.0550 0.0921 HDB 2013 0.0031 0.1320 0.0367 0.0044 0.7058 13.9356 0.9003 1 0.0560 0.0660 HDB 2014 0.0051 0.1320 0.0142 0.0109 0.6399 13.9979 0.9076 1 0.0640 0.0409 HDB 2015 0.0061 0.1340 0.0132 0.0165 0.7587 14.0273 0.9076 1 0.0700 0.0063 HDB 2016 0.0071 0.1253 0.0146 0.0121 0.7960 14.1769 0.9338 1 0.0670 0.0266 HDB 2017 0.0115 0.1350 0.0152 0.0097 0.8669 14.2772 0.9220 1 0.0690 0.0353

HDB 2018 0.0158 0.1210 0.0153 0.0081 0.9615 14.3346 0.9221 1 0.0750 0.0354 HDB 2019 0.0180 0.1120 0.0136 0.0088 1.1611 14.3607 0.9112 1 0.0740 0.0279 HDB 2020 0.0169 0.1210 0.0132 0.0100 1.0212 14.5040 0.9226 1 0.0290 0.0323 HDB 2021 0.0186 0.1440 0.0165 0.0113 1.1087 14.5736 0.9178 1 0.0260 0.0184 HDB 2022 0.0208 0.1340 0.0167 0.0116 1.2227 14.6194 0.9063 1 0.0810 0.0315 HDB 2023 0.0203 0.1260 0.0179 0.0124 0.9262 14.7798 0.9230 1 0.0500 0.0325 KLB 2011 0.0259 0.1500 0.0277 0.0043 1.0327 13.2516 0.8064 0 0.0640 0.1858 KLB 2012 0.0193 0.3342 0.0293 0.0075 0.9100 13.2691 0.8146 0 0.0550 0.0921 KLB 2013 0.0157 0.2074 0.0247 0.0068 0.9117 13.3299 0.8374 0 0.0560 0.0660 KLB 2014 0.0079 0.1838 0.0195 0.0058 0.8163 13.3637 0.8544 0 0.0640 0.0409 KLB 2015 0.0068 0.1977 0.0113 0.0040 0.8076 13.4035 0.8668 0 0.0700 0.0063 KLB 2016 0.0043 0.1635 0.0106 0.0047 0.8636 13.4836 0.8895 0 0.0670 0.0266 KLB 2017 0.0060 0.1578 0.0084 0.0028 0.9449 13.5720 0.9049 0 0.0690 0.0353 KLB 2018 0.0058 0.1662 0.0094 0.0013 1.0091 13.6264 0.9114 0 0.0750 0.0354 KLB 2019 0.0014 0.1342 0.0102 0.0022 1.0170 13.7084 0.9258 0 0.0740 0.0279 KLB 2020 0.0023 0.1200 0.0542 0.0001 0.8262 13.7580 0.9316 0 0.0290 0.0323 KLB 2021 0.0109 0.1002 0.0189 0.0021 0.7469 13.9234 0.9442 0 0.0260 0.0184 KLB 2022 0.0064 0.0852 0.0189 0.0106 0.8564 13.9333 0.9396 0 0.0810 0.0315 KLB 2023 0.0066 0.0973 0.0193 0.0081 0.9101 13.9394 0.9338 0 0.0500 0.0325 LPB 2011 0.0214 0.3231 0.0214 0.0059 0.4972 13.7492 0.8825 1 0.0640 0.1858 LPB 2012 0.0142 0.1290 0.0271 0.0134 0.5562 13.8223 0.8887 1 0.0550 0.0921 LPB 2013 0.0077 0.1491 0.0248 0.0096 0.5319 13.9009 0.9086 1 0.0560 0.0660 LPB 2014 0.0052 0.1060 0.0110 0.0055 0.5306 14.0035 0.9267 1 0.0640 0.0409 LPB 2015 0.0034 0.1010 0.0097 0.0089 0.7235 14.0318 0.9294 1 0.0700 0.0063 LPB 2016 0.0085 0.1323 0.0111 0.0062 0.7179 14.1519 0.9413 1 0.0670 0.0266

LPB 2017 0.0090 0.1028 0.0107 0.0052 0.7844 14.2133 0.9426 1 0.0690 0.0353 LPB 2018 0.0057 0.1085 0.0141 0.0052 0.9539 14.2433 0.9417 1 0.0750 0.0354 LPB 2019 0.0085 0.0835 0.0144 0.0031 1.0269 14.3055 0.9377 1 0.0740 0.0279 LPB 2020 0.0084 0.1100 0.0143 0.0040 1.0120 14.3844 0.9413 1 0.0290 0.0323 LPB 2021 0.0108 0.1126 0.0137 0.0063 1.1591 14.4612 0.9419 1 0.0260 0.0184 LPB 2022 0.0146 0.1236 0.0146 0.0135 1.0909 14.5155 0.9266 1 0.0810 0.0315 LPB 2023 0.0157 0.1224 0.0134 0.0103 1.1602 14.5830 0.9109 1 0.0500 0.0325 MBB 2011 0.0154 0.1546 0.0159 0.0109 0.6594 14.1425 0.9258 1 0.0640 0.1858 MBB 2012 0.0148 0.1115 0.0184 0.0272 0.6325 14.2446 0.9230 1 0.0550 0.0921 MBB 2013 0.0128 0.1100 0.0245 0.0216 0.6447 14.2562 0.9129 1 0.0560 0.0660 MBB 2014 0.0131 0.1007 0.0273 0.0201 0.6000 14.3021 0.9145 1 0.0640 0.0409 MBB 2015 0.0119 0.1170 0.0161 0.0173 0.6683 14.3445 0.8951 1 0.0700 0.0063 MBB 2016 0.0121 0.1250 0.0132 0.0135 0.7738 14.4087 0.8962 1 0.0670 0.0266 MBB 2017 0.0122 0.1200 0.0120 0.0177 0.8365 14.4968 0.9057 1 0.0690 0.0353 MBB 2018 0.0183 0.1090 0.0133 0.0141 0.8947 14.5591 0.9057 1 0.0750 0.0354 MBB 2019 0.0209 0.1068 0.0116 0.0195 0.9179 14.6144 0.9031 1 0.0740 0.0279 MBB 2020 0.0190 0.1042 0.0109 0.0205 0.9593 14.6946 0.8988 1 0.0290 0.0323 MBB 2021 0.0240 0.1128 0.0090 0.0221 0.9451 14.7833 0.8971 1 0.0260 0.0184 MBB 2022 0.0272 0.1153 0.0109 0.0175 1.0383 14.8625 0.8907 1 0.0810 0.0315 MBB 2023 0.0252 0.1075 0.0160 0.0100 1.0767 14.9754 0.8977 1 0.0500 0.0325 MSB 2011 0.0069 0.1467 0.0270 0.0032 0.6060 14.0583 0.9169 1 0.0640 0.1858 MSB 2012 0.0020 0.1131 0.0265 0.0176 0.4857 14.0411 0.9173 1 0.0550 0.0921 MSB 2013 0.0030 0.1056 0.0271 0.0119 0.4185 14.0299 0.9121 1 0.0560 0.0660 MSB 2014 0.0014 0.1570 0.0516 0.0307 0.3719 14.0186 0.9095 1 0.0640 0.0409 MSB 2015 0.0011 0.2453 0.0341 0.0188 0.4486 14.0183 0.8695 1 0.0700 0.0063

MSB 2016 0.0011 0.2359 0.0236 0.0188 0.4486 14.0183 0.8695 1 0.0670 0.0266 MSB 2017 0.0011 0.1948 0.0223 0.0281 0.6370 14.0501 0.8777 1 0.0690 0.0353 MSB 2018 0.0069 0.1217 0.0301 0.0152 0.7676 14.1392 0.8997 1 0.0750 0.0354 MSB 2019 0.0071 0.1109 0.0204 0.0145 0.7864 14.1958 0.9053 1 0.0740 0.0279 MSB 2020 0.0121 0.1060 0.0196 0.0135 0.9066 14.2472 0.9045 1 0.0290 0.0323 MSB 2021 0.0212 0.1152 0.0174 0.0154 1.0734 14.3089 0.8918 1 0.0260 0.0184 MSB 2022 0.0222 0.1230 0.0171 0.0040 1.0301 14.3279 0.8747 1 0.0810 0.0315 MSB 2023 0.0194 0.1276 0.0287 0.0108 1.1269 14.4265 0.8828 1 0.0500 0.0325 NAB 2011 0.0143 0.2029 0.0284 0.0059 0.9689 13.2796 0.8266 0 0.0640 0.1858 NAB 2012 0.0103 0.2144 0.0271 0.0118 0.7847 13.2043 0.7953 0 0.0550 0.0921 NAB 2013 0.0060 0.1347 0.0148 0.0066 0.8458 13.4591 0.8868 0 0.0560 0.0660 NAB 2014 0.0057 0.1066 0.0147 0.0049 0.7806 13.5716 0.9107 0 0.0640 0.0409 NAB 2015 0.0053 0.1292 0.0091 0.0117 0.8563 13.5499 0.9037 0 0.0700 0.0063 NAB 2016 0.0008 0.1118 0.0294 0.0201 0.7054 13.6320 0.9199 0 0.0670 0.0266 NAB 2017 0.0049 0.1263 0.0195 0.0143 0.9118 13.7359 0.9326 0 0.0690 0.0353 NAB 2018 0.0091 0.1115 0.0154 0.0020 0.9378 13.8754 0.9436 0 0.0750 0.0354 NAB 2019 0.0086 0.0966 0.0197 0.0002 0.9548 13.9763 0.9476 0 0.0740 0.0279 NAB 2020 0.0070 0.0957 0.0083 0.0063 0.9076 14.1281 0.9509 0 0.0290 0.0323 NAB 2021 0.0100 0.0946 0.0157 0.0076 0.8902 14.1854 0.9476 0 0.0260 0.0184 NAB 2022 0.0109 0.0892 0.0135 0.0072 0.9564 14.2494 0.9288 0 0.0810 0.0315 NAB 2023 0.0135 0.1116 0.0211 0.0060 0.9726 14.3220 0.9274 0 0.0500 0.0325 NVB 2011 0.0078 0.1804 0.0292 0.0054 0.8713 13.3521 0.8570 1 0.0640 0.1858 NVB 2012 0.0001 0.1909 0.0564 0.0069 1.0499 13.3342 0.8524 1 0.0550 0.0921 NVB 2013 0.0007 0.1603 0.0607 0.0018 0.7333 13.4635 0.8898 1 0.0560 0.0660 NVB 2014 0.0002 0.1083 0.0252 0.0030 0.6809 13.5663 0.9128 1 0.0640 0.0409

NVB 2015 0.0002 0.1108 0.0215 0.0051 0.6004 13.6833 0.9333 1 0.0700 0.0063 NVB 2016 0.0002 0.1058 0.0148 0.0078 0.6066 13.8389 0.9532 1 0.0670 0.0266 NVB 2017 0.0003 0.0927 0.0153 0.0073 0.7023 13.8564 0.9552 1 0.0690 0.0353 NVB 2018 0.0005 0.0958 0.0167 0.0038 0.7566 13.8599 0.9554 1 0.0750 0.0354 NVB 2019 0.0006 0.0967 0.0193 0.0085 0.6415 13.9052 0.9464 1 0.0740 0.0279 NVB 2020 0.0000 0.0944 0.0151 0.0210 0.5592 13.9523 0.9524 1 0.0290 0.0323 NVB 2021 0.0000 0.0983 0.0300 0.0180 0.6450 13.8680 0.9422 1 0.0260 0.0184 NVB 2022 0.0000 0.1063 0.1793 0.0065 0.6688 13.9535 0.9358 1 0.0810 0.0315 NVB 2023 -0.0072 0.0922 0.2976 0.0027 0.7202 13.9834 0.9471 1 0.0500 0.0325 OCB 2011 0.0134 0.2488 0.0279 0.0055 1.4139 13.4052 0.8524 1 0.0640 0.1858 OCB 2012 0.0087 0.2800 0.0280 0.0146 1.1288 13.4381 0.8607 1 0.0550 0.0921 OCB 2013 0.0080 0.2241 0.0290 0.0148 1.0556 13.5158 0.8791 1 0.0560 0.0660 OCB 2014 0.0061 0.1710 0.0300 0.0141 0.8981 13.5921 0.8972 1 0.0640 0.0409 OCB 2015 0.0047 0.2620 0.0194 0.0132 0.9386 13.6941 0.9145 1 0.0700 0.0063 OCB 2016 0.0068 0.1710 0.0175 0.0086 0.8942 13.8049 0.9261 1 0.0670 0.0266 OCB 2017 0.0110 0.2800 0.0179 0.0053 0.9056 13.9258 0.9272 1 0.0690 0.0353 OCB 2018 0.0191 0.1204 0.0229 0.0168 0.9330 13.9998 0.9120 1 0.0750 0.0354 OCB 2019 0.0237 0.1119 0.0184 0.0131 1.0282 14.0725 0.9026 1 0.0740 0.0279 OCB 2020 0.0261 0.1300 0.0169 0.0142 1.0237 14.1834 0.8857 1 0.0290 0.0323 OCB 2021 0.0261 0.1234 0.0132 0.0098 1.0329 14.2660 0.8818 1 0.0260 0.0184 OCB 2022 0.0185 0.1284 0.0222 0.0089 1.1722 14.2878 0.8697 1 0.0810 0.0315 OCB 2023 0.0152 0.1330 0.0265 0.0111 1.1688 14.3804 0.8812 1 0.0500 0.0325 PGB 2011 0.0263 0.2488 0.0206 0.0101 1.1086 13.2451 0.8526 0 0.0640 0.1858 PGB 2012 0.0130 0.2260 0.0844 0.0173 1.1180 13.2845 0.8341 0 0.0550 0.0921 PGB 2013 0.0017 0.1910 0.0298 0.0116 1.0004 13.3958 0.8710 0 0.0560 0.0660

PGB 2014 0.0052 0.1710 0.0248 0.0071 0.8058 13.4113 0.8705 0 0.0640 0.0409 PGB 2015 0.0016 0.2140 0.0275 0.0132 0.9418 13.3924 0.8634 0 0.0700 0.0063 PGB 2016 0.0050 0.1810 0.0247 0.0116 0.9583 13.3949 0.8592 0 0.0670 0.0266 PGB 2017 0.0024 0.1489 0.0334 0.0215 0.9363 13.4668 0.8785 0 0.0690 0.0353 PGB 2018 0.0043 0.1455 0.0306 0.0235 0.9446 13.4757 0.8767 0 0.0750 0.0354 PGB 2019 0.0024 0.1389 0.0316 0.0230 0.9334 13.4993 0.8809 0 0.0740 0.0279 PGB 2020 0.0050 0.1224 0.0244 0.0109 0.8934 13.5581 0.8913 0 0.0290 0.0323 PGB 2021 0.0067 0.1235 0.0252 0.0065 0.9795 13.6077 0.8968 0 0.0260 0.0184 PGB 2022 0.0090 0.1146 0.0256 0.0089 0.9293 13.6901 0.9064 0 0.0810 0.0315 PGB 2023 0.0054 0.1199 0.0285 0.0066 0.9890 13.7442 0.9123 0 0.0500 0.0325 SGB 2011 0.0189 0.2283 0.0475 0.0179 1.2524 13.1865 0.7849 0 0.0640 0.1858 SGB 2012 0.0197 0.2394 0.0293 0.0254 1.0392 13.1718 0.7617 0 0.0550 0.0921 SGB 2013 0.0117 0.2405 0.0224 0.0146 0.9877 13.1669 0.7616 0 0.0560 0.0660 SGB 2014 0.0119 0.2203 0.0208 0.0217 0.9484 13.1993 0.7797 0 0.0640 0.0409 SGB 2015 0.0026 0.1998 0.0188 0.0231 0.8836 13.2492 0.8089 0 0.0700 0.0063 SGB 2016 0.0076 0.2336 0.0263 0.0108 0.8846 13.2799 0.8155 0 0.0670 0.0266 SGB 2017 0.0027 0.1900 0.0298 0.0200 0.9499 13.3288 0.8397 0 0.0690 0.0353 SGB 2018 0.0020 0.2216 0.0219 0.0251 0.9314 13.3091 0.8314 0 0.0750 0.0354 SGB 2019 0.0067 0.1839 0.0187 0.0135 0.9291 13.3582 0.8439 0 0.0740 0.0279 SGB 2020 0.0042 0.1800 0.0144 0.0103 0.8477 13.3792 0.8488 0 0.0290 0.0323 SGB 2021 0.0051 0.1786 0.0197 0.0094 0.9114 13.3911 0.8493 0 0.0260 0.0184 SGB 2022 0.0073 0.1700 0.0212 0.0134 0.9129 13.4425 0.8592 0 0.0810 0.0315 SGB 2023 0.0090 0.1774 0.0203 0.0135 0.8476 13.4983 0.8708 0 0.0500 0.0325 SHB 2011 0.0123 0.1337 0.0223 0.0035 0.8383 13.8512 0.9179 1 0.0640 0.1858 SHB 2012 0.0180 0.1418 0.0881 0.0099 0.7338 14.0665 0.9184 1 0.0550 0.0921

SHB 2013 0.0065 0.1238 0.0406 0.0064 0.8430 14.1572 0.9279 1 0.0560 0.0660 SHB 2014 0.0051 0.1133 0.0202 0.0060 0.8447 14.2280 0.9380 1 0.0640 0.0409 SHB 2015 0.0043 0.1140 0.0172 0.0064 0.8831 14.3111 0.9450 1 0.0700 0.0063 SHB 2016 0.0041 0.1300 0.0187 0.0080 0.9748 14.3816 0.9450 1 0.0670 0.0266 SHB 2017 0.0058 0.1130 0.0233 0.0082 1.0174 14.4564 0.9486 1 0.0690 0.0353 SHB 2018 0.0055 0.1179 0.0240 0.0066 0.9634 14.5096 0.9495 1 0.0750 0.0354 SHB 2019 0.0070 0.1201 0.0191 0.0091 1.0229 14.5626 0.9493 1 0.0740 0.0279 SHB 2020 0.0067 0.1010 0.0183 0.0152 1.0068 14.6156 0.9418 1 0.0290 0.0323 SHB 2021 0.0109 0.1186 0.0169 0.0207 1.1076 14.7047 0.9299 1 0.0260 0.0184 SHB 2022 0.0147 0.1222 0.0281 0.0136 1.0662 14.7343 0.9209 1 0.0810 0.0315 SHB 2023 0.0125 0.1220 0.0302 0.0161 0.9798 14.7997 0.9205 1 0.0500 0.0325 SSB 2011 0.0016 0.0798 0.0275 0.0035 0.5717 14.0047 0.9452 1 0.0640 0.1858 SSB 2012 0.0006 0.1550 0.0298 0.0090 0.5309 13.8755 0.9256 1 0.0550 0.0921 SSB 2013 0.0020 0.1429 0.0284 0.0035 0.5784 13.9024 0.9283 1 0.0560 0.0660 SSB 2014 0.0011 0.1761 0.0286 0.0063 0.7121 13.9041 0.9291 1 0.0640 0.0409 SSB 2015 0.0011 0.1755 0.0143 0.0022 0.7507 13.9282 0.9319 1 0.0700 0.0063 SSB 2016 0.0012 0.1559 0.0170 0.0110 0.8178 14.0144 0.9431 1 0.0670 0.0266 SSB 2017 0.0027 0.1755 0.0084 0.0077 0.8811 14.0969 0.9506 1 0.0690 0.0353 SSB 2018 0.0037 0.1260 0.0151 0.0066 0.9948 14.1476 0.9409 1 0.0750 0.0354 SSB 2019 0.0074 0.1212 0.0231 0.0186 1.0302 14.1970 0.9306 1 0.0740 0.0279 SSB 2020 0.0081 0.1150 0.0186 0.0062 0.9611 14.2558 0.9241 1 0.0290 0.0323 SSB 2021 0.0133 0.1168 0.0165 0.0097 1.1622 14.3257 0.9118 1 0.0260 0.0184 SSB 2022 0.0183 0.1466 0.0160 0.0076 1.3324 14.3644 0.8866 1 0.0810 0.0315 SSB 2023 0.0148 0.1361 0.0194 0.0067 1.2410 14.4251 0.8862 1 0.0500 0.0325 STB 2011 0.0136 0.1166 0.0058 0.0049 1.0725 14.1507 0.8972 1 0.0640 0.1858

STB 2012 0.0068 0.0953 0.0205 0.0138 0.8965 14.1822 0.9099 1 0.0550 0.0921 STB 2013 0.0142 0.1022 0.0146 0.0039 0.8399 14.2078 0.8943 1 0.0560 0.0660 STB 2014 0.0126 0.0987 0.0119 0.0075 0.7851 14.2783 0.9048 1 0.0640 0.0409 STB 2015 0.0027 0.0951 0.0580 0.0121 0.7123 14.4654 0.9244 1 0.0700 0.0063 STB 2016 0.0003 0.0961 0.0691 0.0035 0.6818 14.5212 0.9332 1 0.0670 0.0266 STB 2017 0.0034 0.1130 0.0467 0.0037 0.6970 14.5664 0.9369 1 0.0690 0.0353 STB 2018 0.0046 0.1188 0.0213 0.0062 0.7345 14.6086 0.9393 1 0.0750 0.0354 STB 2019 0.0057 0.1153 0.0194 0.0073 0.7385 14.6567 0.9410 1 0.0740 0.0279 STB 2020 0.0057 0.0953 0.0170 0.0089 0.7951 14.6924 0.9412 1 0.0290 0.0323 STB 2021 0.0067 0.0993 0.0150 0.0092 0.9077 14.7169 0.9343 1 0.0260 0.0184 STB 2022 0.0091 0.0949 0.0098 0.0202 0.9646 14.7723 0.9347 1 0.0810 0.0315 STB 2023 0.0122 0.0911 0.0228 0.0076 0.9452 14.8289 0.9322 1 0.0500 0.0325 TCB 2011 0.0191 0.1143 0.0283 0.0054 0.7158 14.2566 0.9307 1 0.0640 0.1858 TCB 2012 0.0042 0.1206 0.0270 0.0212 0.6124 14.2551 0.9261 1 0.0550 0.0921 TCB 2013 0.0039 0.1403 0.0365 0.0201 0.5857 14.2011 0.9124 1 0.0560 0.0660 TCB 2014 0.0065 0.1565 0.0238 0.0284 0.6098 14.2453 0.9148 1 0.0640 0.0409 TCB 2015 0.0083 0.1470 0.0166 0.0323 0.7887 14.2833 0.9143 1 0.0700 0.0063 TCB 2016 0.0147 0.1312 0.0158 0.0257 0.8222 14.3717 0.9168 1 0.0670 0.0266 TCB 2017 0.0255 0.1268 0.0161 0.0224 0.9408 14.4304 0.9000 1 0.0690 0.0353 TCB 2018 0.0287 0.1430 0.0175 0.0115 0.7941 14.5065 0.8387 1 0.0750 0.0354 TCB 2019 0.0290 0.1550 0.0133 0.0040 0.9979 14.5840 0.8382 1 0.0740 0.0279 TCB 2020 0.0306 0.1600 0.0047 0.0094 1.0002 14.6431 0.8303 1 0.0290 0.0323 TCB 2021 0.0365 0.1500 0.0066 0.0077 1.1035 14.7549 0.8364 1 0.0260 0.0184 TCB 2022 0.0322 0.1520 0.0072 0.0046 1.1733 14.8445 0.8377 1 0.0810 0.0315 TCB 2023 0.0235 0.1435 0.0116 0.0076 1.1407 14.9292 0.8451 1 0.0500 0.0325

TPB 2011 -0.0599 0.1192 0.0067 0.0255 0.5870 13.3959 0.9328 1 0.0640 0.1858 TPB 2012 0.0058 0.4015 0.0366 0.0119 0.6562 13.1796 0.7805 1 0.0550 0.0921 TPB 2013 0.0162 0.1981 0.0233 0.0071 0.8321 13.5063 0.8847 1 0.0560 0.0660 TPB 2014 0.0128 0.1504 0.0122 0.0025 0.9175 13.7116 0.9177 1 0.0640 0.0409 TPB 2015 0.0088 0.1213 0.0081 0.0048 0.7148 13.8821 0.9370 1 0.0700 0.0063 TPB 2016 0.0062 0.0979 0.0075 0.0058 0.8468 14.0266 0.9466 1 0.0670 0.0266 TPB 2017 0.0084 0.0934 0.0110 0.0073 0.9022 14.0938 0.9462 1 0.0690 0.0353 TPB 2018 0.0139 0.1024 0.0112 0.0068 1.0138 14.1341 0.9220 1 0.0750 0.0354 TPB 2019 0.0206 0.1070 0.0129 0.0136 1.0347 14.2160 0.9205 1 0.0740 0.0279 TPB 2020 0.0189 0.1300 0.0118 0.0149 1.0353 14.3145 0.9188 1 0.0290 0.0323 TPB 2021 0.0193 0.1339 0.0082 0.0206 1.0119 14.4666 0.9113 1 0.0260 0.0184 TPB 2022 0.0201 0.1260 0.0084 0.0115 0.8258 14.5167 0.9019 1 0.0810 0.0315 TPB 2023 0.0130 0.1239 0.0205 0.0192 0.9856 14.5522 0.9082 1 0.0500 0.0325 VAB 2011 0.0106 0.1192 0.0256 0.0000 1.5977 13.3524 0.8412 0 0.0640 0.1858 VAB 2012 0.0070 0.1290 0.0465 0.0005 0.8595 13.3911 0.8564 0 0.0550 0.0921 VAB 2013 0.0023 0.1149 0.0288 0.0021 0.7644 13.4319 0.8673 0 0.0560 0.0660 VAB 2014 0.0015 0.1149 0.0233 0.0008 0.7999 13.5513 0.8978 0 0.0640 0.0409 VAB 2015 0.0021 0.1910 0.0226 0.0158 0.8293 13.6220 0.9064 0 0.0700 0.0063 VAB 2016 0.0019 0.1577 0.0214 0.0119 0.9449 13.7886 0.9346 0 0.0670 0.0266 VAB 2017 0.0016 0.1024 0.0268 0.0090 0.9949 13.8091 0.9361 0 0.0690 0.0353 VAB 2018 0.0017 0.1009 0.0137 0.0123 0.9166 13.8530 0.9406 0 0.0750 0.0354 VAB 2019 0.0028 0.1076 0.0118 0.0086 0.8987 13.8834 0.9419 0 0.0740 0.0279 VAB 2020 0.0041 0.0843 0.0230 0.0157 0.8162 13.9372 0.9338 0 0.0290 0.0323 VAB 2021 0.0070 0.0905 0.0189 0.0087 0.8045 14.0045 0.9369 0 0.0260 0.0184 VAB 2022 0.0086 0.0912 0.0153 0.0010 0.8903 14.0218 0.9309 0 0.0810 0.0315

VAB 2023 0.0068 0.0930 0.0159 0.0099 0.7966 14.0500 0.9287 0 0.0500 0.0325 VCB 2011 0.0125 0.1114 0.0203 0.0166 0.9225 14.5643 0.9215 1 0.0640 0.1858 VCB 2012 0.0113 0.1463 0.0240 0.0138 0.8479 14.6175 0.8994 1 0.0550 0.0921 VCB 2013 0.0099 0.1313 0.0273 0.0128 0.8256 14.6712 0.9093 1 0.0560 0.0660 VCB 2014 0.0088 0.1161 0.0231 0.0142 0.7658 14.7612 0.9247 1 0.0640 0.0409 VCB 2015 0.0085 0.1104 0.0179 0.0157 0.7736 14.8289 0.9330 1 0.0700 0.0063 VCB 2016 0.0094 0.1113 0.0146 0.0138 0.7804 14.8965 0.9389 1 0.0670 0.0266 VCB 2017 0.0100 0.1163 0.0111 0.0114 0.7670 15.0151 0.9492 1 0.0690 0.0353 VCB 2018 0.0139 0.1214 0.0097 0.0117 0.7879 15.0310 0.9421 1 0.0750 0.0354 VCB 2019 0.0162 0.0934 0.0078 0.0090 0.7913 15.0874 0.9338 1 0.0740 0.0279 VCB 2020 0.0145 0.0956 0.0062 0.0119 0.8137 15.1226 0.9291 1 0.0290 0.0323 VCB 2021 0.0161 0.0931 0.0063 0.0119 0.8462 15.1508 0.9228 1 0.0260 0.0184 VCB 2022 0.0185 0.0995 0.0068 0.0083 0.9209 15.2586 0.9252 1 0.0810 0.0315 VCB 2023 0.0181 0.1139 0.0098 0.0036 0.9102 15.2647 0.9103 1 0.0500 0.0325 VIB 2011 0.0067 0.1114 0.0269 0.0224 0.9852 13.9866 0.9158 1 0.0640 0.1858 VIB 2012 0.0064 0.1910 0.0262 0.0220 0.8675 13.8131 0.8703 1 0.0550 0.0921 VIB 2013 0.0007 0.1730 0.0282 0.0247 0.8150 13.8858 0.8962 1 0.0560 0.0660 VIB 2014 0.0066 0.1770 0.0251 0.0311 0.7783 13.9067 0.8946 1 0.0640 0.0409 VIB 2015 0.0063 0.1800 0.0207 0.0107 0.8963 13.9259 0.8979 1 0.0700 0.0063 VIB 2016 0.0059 0.1330 0.0258 0.0101 1.0155 14.0192 0.9164 1 0.0670 0.0266 VIB 2017 0.0099 0.1307 0.0264 0.0044 1.1680 14.0905 0.9286 1 0.0690 0.0353 VIB 2018 0.0167 0.1290 0.0252 0.0068 1.1329 14.1435 0.9233 1 0.0750 0.0354 VIB 2019 0.0202 0.0970 0.0196 0.0049 1.0559 14.2661 0.9272 1 0.0740 0.0279 VIB 2020 0.0216 0.1012 0.0174 0.0056 1.1275 14.3886 0.9265 1 0.0290 0.0323 VIB 2021 0.0231 0.1169 0.0232 0.0079 1.1610 14.4907 0.9215 1 0.0260 0.0184