Luận văn Nợ xấu của hệ thống ngân hàng thương mại Việt Nam

lập dự phòng, cần xây dựng chuẩn kế toán Việt Nam theo chuẩn mực kế toán quốc tế IAS 39, xây dựng hướng dẫn thống nhất xác định giá trị tài sản đảm bảo cho các TCTD trong việc tính toán trích lập dự phòng. 5.4 Giới hạn và hướng nghiên cứu tiếp theo Bên cạnh các đóng góp mới, luận án còn có một số hạn chế. Mặc dù hạn chế đó không ảnh hưởng đến ý nghĩa của luận án nhưng cần được xem xét nghiêm túc. Thứ nhất, chưa thu thập được nợ xấu cụ thể của từng NHTM bán cho VAMC để làm rõ thực chất nợ xấu của các NHTM Việt Nam. Thứ hai, nợ xấu mang tính hệ thống của NHTM Việt Nam chịu tác động của các nhân tố khác như cấu trúc sở hữu chéo giữa các ngân hàng nhưng luận án chưa đề cập do chưa tiếp cận được dữ liệu. 25 DANH MỤC CÁC CÔNG TRÌNH CÔNG BỐ KẾT QUẢ NGHIÊN CỨU 1. Nguyen Thi Hong Vinh & Le Phan Thi Dieu Thao 2016, Effects of Bank Capital on Profitability and Credit Risk: The Case of Vietnam’s Commercial Banks, Journal of Economic Development, Vol.23 (Issue 4), 117 - 137. 2. Nguyen Thi Hong Vinh 2016, The Impact of Non-performing Loans on Bank Profitbility and Lending Behavior: The Case of Vietnam, Policies and Sustainable Economic Development, International Conference of University of Economics HoChiMinh City, 474 - 488. 3. Nguyễn Thị Hồng Vinh 2016, Tác động của mức độ cạnh tranh đến khả năng sinh lời và rủi ro của hệ thống ngân hàng Việt Nam, Công nghệ Ngân hàng số 122, 20-29. 4. Nguyen Thi Hong Vinh 2015, Bad debt and Cost Efficiency in Vietnamese Commercial Banks, Journal of Economic Development, Vol.22 (Issue 1), 125 – 140. 5. Nguyễn Thị Hồng Vinh 2015, Yếu tố tác động đến nợ xấu các ngân hàng thương mại Việt Nam, Phát triển Kinh tế Vol. 26 (Issue 11), 80-98. 6. Nguyễn Minh Sáng & Nguyễn Thị Hồng Vinh 2015, Nghiên cứu tác động của sử dụng nguồn lực đến hiệu quả kinh doanh của các ngân hàng thương mại Việt Nam và Thái Lan, CT-1301-1 – Đề tài nghiên cứu khoa học cấp cơ sở. 7. Nguyễn Thị Hồng Vinh 2012, Đo lường hiệu quả kỹ thuật và chỉ số Malmquist của ngân hàng thương mại Việt Nam, Công nghệ Ngân hàng, số 74, 16-22. Ministry of Education and Training State Bank of Vietnam Banking University Ho Chi Minh City ***** NGUYEN THI HONG VINH NON- PERFORMING LOANS IN THE VIETNAMESE BANKING SYSTEM Major: Finance and Banking Code: 62.34.02.01 SUMMARY OF PHD THESIS HO CHI MINH CITY, 2017 1 ABSTRACT The aims of this study is to investigate the determinants of Vietnamese banks non performing loans, using empirical framework that incorporates the related literature and theoretical hypothesis. In addition, this study also examines the impact of non-performing loans of profitability, capital and lending behavior. We applies the Generalized Method of Moments technique for dynamic panels using bank-level data for the Vietnamese commercial banks over the period 2005 to 2015. The findings show that the cost efficiency of Vietnamese commercial banks is 69,3%. This result suggests that banks still waste the banks’ input resources. This study finds the evidence of cost efficiency, credit growth, equity, loan to deposit, market concentration, GDP have a negative relationship to NPLs, and loan loss provision, size, ownership, inflation rate, exchange rates, and interest rates and real estate prices have a positive effect on NPLs. We also find some evidences that non-performing loans has statistically significant negative effect on Vietnamese commercial banks’ profitability, equity and lending behavior. These findings will be helpful for bank managers and policy makers to solve the non performing loans and improve the performance and lending behavior of Vietnamese commercial banks. Key words: Non performing loans (NPLs), Vietnamese commercial banks, bank-specific and macroeconomic determinants. 2 CHAPTER 1 INTRODUCTION 1.1. Non performing loans of Vietnamese commercial banks In the period from 2005 to 2015, the rising of NPL does not only increase the vulnerability of the banking sectors but also is a limiting factors to lending activities of commercial banks. The ratio of NPLs in Vietnam sharply increased in the year of 2012. SBV reported that the ratio of NPLs to total loans was 4.3% by the third quarter of 2012. IMF and World Bank (2014) estimate the ratio of NPLs for Vietnam banking sector was 12 % by the end of 2012. Meanwhile, Moody (2014) shows the ratio of NPLs to total assets in Vietnam was 15% by February of 2014. Researchers have shown that NPLs is caused by many determinants, but most studies suggest efficiency, credit growth, scale, capital adequacy, macroeconomic factors such as economic growth, inflation, interest rates, exchange rates, and house prices are the main factors affecting NPLs 1.2. Relevant research status and research issues Recently, the topic of determinants of NPLs attracts a considerable attention. There have been plenty of studies and researches explain the causes, consequences and solutions of the NPLs. These studies focus on finding out the causes of bad debt and then finding solutions to manage unexpectedly high NPLs ratios. The studies highlight the role of specific factors such as bank efficiency, operational safety, financial capacity and credit growth related to NPLs (Louzis and Altmann 2012, Klein 2013). Studies of macroeconomic factors such as macroeconomic conditions in general, based on financial accelerator theory and bank lending channel theory, while studying the determinants of banks focus on the change in NPLs of banks due to the change of particular factors of the banks. One of the gaps of current researches is that the causes of NPLs have not been fully verified as well as the impact of NPLs on the behavior of commercial banks in Vietnam. Therefore, the thesis seeks evidence to fill to the gaps of previous studies. 1.3. Research objectives and questions Firstly, the objective of this study is to identify the determinants of non-performing loans in the Vietnamese banking system. Secondly, this study also investigates the impact of NPLs on cost efficiency, profitability, equity and loan growth of Vietnamese commercial banks. In order to achieve these research objectives, this study goes to answer these questions. 1. What cause the remarkable rising of non-performing loans in Vietnam? Are they bank- specific or macro economic determinants of NPL in Vietnam? How is the trend and magnitude of the determinants? 2. How non-performing loans mutually affects banks’ cost efficiency, profitability, equity and loan growth of Vietnamese commercial banks? 3 1.4. The scope of this study This study examines the determinants and impact of NPLs of Vietnamese commercial banks in the period from 2005 to 2015. 1.5. Research methodologies and data 1.5.1. Methodologies In the first step, the study uses DEA model to measure cost efficiency of commercial banks. The next step, we apply the two-step dynamic panel data approach suggested by Arellano and Bover (1995) and Blundell and Bond (2000) and also uses dynamic panel Generalized Method of Moments technique to address potential endogeneity, heteroskedasticity, and autocorrelation problems in the data (Doytch and Uctum, 2011). 1.5.2. Data This study analyzes a panel dataset comprising 34 Vietnamese commercial banks over the period 2005–2015. The panel data set is extracted from non-consolidated income statements and balance sheets of these banks, and it consists of 357 observations. The macroeconomic data come from IMF – IFS website. 1.6. The structure of this study Chapter 1. Introduction Chapter 2. Theoretical framework and Literature review Chapter 3. Methodologies Chapter 4. Empirical evidences from Vietnam Chapter 5. Conclusions and policy implications 4 CHAPTER 2 THEORETICAL FRAMEWORK AND LITERATURE REVIEW 2.1. Theoretical framework 2.1.1. Non-performing loans The definition of NPLs from international institutions related to three factors: (i) Full repayment is in doubt due to inadequate protection (e.g., obligor net worth or collateral) and/or interest or principal or both are more than 90 days overdue. (IMF, 2004); (ii) decline in borrowers' ability to repay; And (iii) Debts classified into three Group such as: substandard, doubtful and loss. In this study, NPLs are those which are principal or interest or both are overdue more than 90 days overdue and in doubt borrower’s ability to repay. NPLs are debts, which have been classified as those in Groups 3, 4 and 5 stipulated in Article 6 or Article 7 of Decision 493/2005 / QD-NHNN. The ratio of bad debts to the total outstanding debt is used to assess the credit quality of credit institutions. 2.1.2. Classification of debts and methods of non-performing loans assessment Bholat (2016) states that classification of debts has not uniform international accounting standards. Approaching debts classification is considered as a manager's responsibility or a monitoring report. In Vietnam, according to Decision 493/2005 / QD-NHNN, SBV allows credit institutions to carry out debts classification by the quantitative and qualitative method. However, most banks carry out their debts classification by quantitative method and qualitative factors have not yet been considered, except for three large banks such as Agribank, BIDV and VCB. Based on the above Decision, credit institutions shall carry out the debts classification as follows: Group 1 standard debts, Group 2 debts, which need special attention; Group 3 sub-standard debts; Group 4 doubtful debts; and Group 5 potentially irrecoverable debts. Bad debts (NPLs) are debts, which have been classified as those in Groups 3, 4 and 5. 2.1.3. Literature review on determinants of non-performing loans 2.1.3.1. Macroeconomic determinants of non-performing loans The financial accelerator theory- discussed in Bernanke and Gertler (1989), Bernanke and Gilchrist (1999), and Kiyotaki and Moore (1997)- suggests that a small change in the financial market can make a difference in the economy and create a feedback cycle. Theory explains explaining bank lending behaviour and its relationship with the cyclical fluctuations in the economy. When asset values are hit by a temporary shock, a direct effect occurs because the changes in collateral values cause changes in obtained credit. On the bank side, when the central bank raises interest rates, the value of the bank's reserves is affected by the decline of stock prices. The bank lending channel theory refers that bank debt is effected by monetary policy through an imperfect market. Monetary policy affects on the supply of intermediated credit, particularly bank loans. A restrictive monetary policy leads to a drop of banks’ reserves and typically insured 5 deposits. Only banks that have a larger share of liquid assets or that are bigger are able to shield their lending relationships. Bernanke and Gertler suggests two channels through borrower balance sheet channel and lending channel theory. Monetary transmission mechanism theory mentions the process by which asset prices and general economic conditions are affected as a result of monetary policy decisions. Such decisions are intended to influence the aggregate demand, interest rates, and amounts of money and credit in order to affect overall economic performance. The traditional monetary transmission mechanism occurs through interest rate channels, which affect interest rates, costs of borrowing, levels of investment, and aggregate demand. Additionally, aggregate demand can be effected through friction in the credit markets, known as the credit view. In short, the monetary transmission mechanism can be defined as the link between monetary policy and aggregate demand (Blinder Maccini, 1991; Chirinko, 1993; Boldin, 1994). In summary, these above theories indicate that macroeconomic policy affects the banking lending channel in the economy, thereby affecting the quality of bank loans or NPLs. 2.1.3.2. Bank-specific determinants of non-performing loans Loan growth. Keenton (1009) explains the impact of loan growth through the shift of factors in the relationship between loan growth and NPLs. First, supply shifts implies that an increase in banks’ willlingness to lend by reducing credit standards requirements. The reduction in the credit standard increases the chances that some borrowers will eventually default on their loans. Thus, the outward supply shift not only raises total lending but also increases the likelihood of future loan losses. Second, demand shift notes that an increased demand for credit unrelated to borrowers. underlying creditworthiness will tend to boost loan growth and raise credit standards, reducing the likelihood of future loan losses. This is explained by the need to change the capital structure of the business or investment project. This change in capital structure will help to improve cash flow, thus, the borrower's ability to repay loans will be better, ensuring future loan quality. Third, Productivity shift shows an overall increase in the productivity of borrowers’ investment projects will also tend to boost loan growth and reduce the likelihood of future loan losses, although credit standards may decline in this case. The increase in labor productivity also indicate the good sign of the borrower. Cost efficiency and profitability. Bad management hypothesis proposed by Berger and DeYoung (1997) suggests that the efficiency banks are better at managing their credit risk. This hypothesis also argues that low cost efficiency is a signal of poor management practices, thus implying that as a result of poor loan underwriting, monitoring and control, NPLs are likely to increase. Capital adequacy. According to moral hazard hypothesis, Keeton and Morris (1987) find that low capitalization of banks leads to an increase in non-performing loans. In essence, low- capitalized banks are more risky, then they invest more in risky assets, which causes NPLs to increase because if the risk occurs, the lender is the one who suffers the most. Bank size. Size effect hypothesis mentions the effect of bank size on asset quality. Bank size is negatively related to NPLs. 6 2.1.3.3. Industry-specific determinants of non-performing loans Industry competition. The risk-shifting Paradigm refers that when the level of competition among banks is higher, the banking sector will be less risky or more stable. Accordingly, large commercial banks will receive greater assistance from regulators and lead to business operations risk. On the contrary, the other view that the moral hazard hypothesis is that the banking system will become more volatile and fragile if competition levels increase. For the large commercial banks select customers may be more careful, credit portfolio will more secure. In addition, these commercial banks have enough capacity to diversify their asset portfolio to minimize risk. Besides, a banking system with a few large banks will be easier to manage than a system with more small banks. Owership. The Berle Mean theory implies that concentration ownership will increase the operational efficiency of the business, including financial institutions (Shehzad và ctg, 2010). The more centralized the ownership will be, the more prudent banks will be through tight control over loans. 2.1.4. Literature review on the impact of non-performing loans on bank behavior 2.1.4.1. Impact of non-performing loans on bank performance efficiency The rising of non-performing loans can lead to efficiency problem for banking sector. The bad luck hypothesis suggests that loans become overdue, banks will increase operating cost to deal with NPLs. These costs increase as bad debts rise. Bad management hypothesis mentions that the high efficiency banks will be able to manage credit risk better than low efficiency banks. This is considered as a part of the bank's core competencies. 2.1.4.2. Impact of non-performing loans on bank capital adequacy The moral hazard hypothesis referred the relationship between NPLs and bank captialization. High NPLs increase the uncertainty of the bank capital status and thus limit their access to capital mobilization. This in turn raises banks' lending rates and thus contributes to reducing loan growth. Increases in credit risk during recession cause a deterioration in the bank capital ratio and hence banks face much higher capital needs to fulfil regulatory requirements. 2.1.4.3. Impact of non-performing loans on bank loan growth The financial accelerator effect also refers to the effects of NPLs on banks lending behavior. This theory relates to borrowers’ equity position (or net worth) which influences their access to credit. This also explains bank lending behavior and its relationship with the cyclical fluctuations in the economy. A net worth of a firm is improved and the greater it is, the lower the external finance premium as lenders assume less risk when lending to high net worth agents during business upturn. An adverse shock that lowers borrowers’ current cash flows leads to a decline in their net worth and raises external finance premium. The increase in borrowers’ cost of financing will discourage their desires to undertake more investment projects and consequently affect the demand for credit, and amplifying the effect of the initial shocks. 2.2. Previous empirical studies 2.2.1. Previous empirical studies on determinants of non-performing loans 7 - Loan growth (Clair 1992; Keeton 1999; Louzis 2012; Le 2016 and Jimenez 2006). - Bank size (Louzis và ctg, 2010; Salad và Saurina, 2002; Jimenez, Salad, and Saurina, 2006). - Cost efficiency and profitability (Berger và Humphrey, 1992; Wheelock and Wilson, 1995; Karim et al., 2010). - Capital adequacy and operational safety (Salas 2002; Le 2016). - Economic growth (Salad and Saurina 2002; Klein 2013; Park and Zhang 2012). - Inflation and interest rates (Salad and Saurina 2002; Klein 2013; Pestova 2011) - Exchange rate (Castro 2012; Beck 2013; Pestova 2011 and Washington 2014). - Real estate market (Nkusu 2011; Fainstein, 2011). - Industry competition (Lee và Hsieh 2013 and Jimenez 2007) - Ownership (Iannotta et al., 2007; Shehzad et al., 2010). 2.2.2. Previous empirical studies on the impact of non-performing loans on bank behavior Impact of NPLs on bank performance efficiency (Alkhar, 2011; Ponce, 2011; Le 2016; and Phạm Hữu Hồng Thái 2013,...). Impact of NPLs on bank capital (Le 2016; Lee and Hsieh 2013) Impact of NPls on bank loan growth (Le, 2016; Cucinelli, 2015; Stolz and Wedow 2009; Wangai et al., 2012). 8 CHAPTER 3 METHODOLOGIES 3.1. Research models 3.1.1. Research models of determinants of non-performing loans The first model is applied to address the first objective of this study which is to analyze the causes of NPLs of commercial banks in Vietnam. Following the earlier literature discussion (Louzis et al. 2012, Salas and Sarina (2002), Klein 2013 and Le 2016 on banking and macroeconomic related studies), a dynamic approach is adopted in order to account for the time persistence in the NPLs structure. The relationships between determinants and NPLs can be specified as follows: NPLit = αNPLit−1 + βMt + λ1Ht + π1 Fit + ηt + ε1,it, |α| ≤ 1 (3.1) where t and i denote time period and banks, respectively, 𝜂𝑖𝑡 is an unobserved bank-specific effect, ε1,it is the idiosyncratic error term. To test for the persistence of NPLs, we use lagged NPLs (i.e., NPL t -1) as an explanatory variable and we expect a positive and significant sign. The vector of explanatory variables includes bank-specific variables (Fit), industry-specific variables (Hit) and macroeconomic factor (Mt). 3.1.2. Research models of impact of non-performing loans on bank behavior The second model is used to answer the second objective which is to investigate impact of NPLs on bank behavior. Following the earlier literature discussion (Le 2016, Goddard et al. 2011 và Girardone et al. 2004), the three equations are set up as follows: 𝐸𝐹𝑖𝑡 = 𝛾2𝐸𝐹𝑖𝑡−1 + 𝜑2𝑀𝑡 + 𝜆2𝑁𝑃𝐿𝑖𝑡 + 𝜋2𝐹𝑖𝑡 + 𝜀2,𝑖𝑡 (3.13) 𝐸𝑇𝐴𝑖𝑡 = 𝛾3𝐸𝑇𝐴𝑖𝑡−1 + 𝜑3𝑀𝑡 + 𝜆3𝑁𝑃𝐿𝑖𝑡 + 𝜋3 𝐹𝑖𝑡 + 𝜀3,𝑖𝑡 (3.14) 𝐿𝐺𝑅𝑖𝑡 = 𝛾4𝐿𝑂𝐴𝑁𝑖𝑡−1 + 𝜑4𝑀𝑡 + 𝜆4𝑁𝑃𝐿𝑖𝑡 + 𝜋4 𝐹𝑖𝑡 + 𝜀4,𝑖𝑡 (3.15) where t and i denote time period and banks, respectively, 𝜂𝑖𝑡 is an unobserved bank-specific effect, ε1,it is the idiosyncratic error term. 𝐸𝐹𝑖𝑡 is efficiency of banks, 𝐸𝑇𝐴𝑖𝑡 presented bank’s capital and 𝐿𝐺𝑅𝑖𝑡 is proxied by bank’s loan growth. 9 Table 3.1. Summary of explanatory variables Classification Variable Expected sign Non-performing loans Ratio of non-performing loan to total loans Efficiency The lagged of NPL L.NPL (+) Profitability ROA (-) Cost efficiency CE (-) Loan growth Percentage change in gross loan LGR (+) Bank size Logarithms total asset TA (+) Capitalization Ratio of equity on total assets ETA (-) Liquidity Ratio of loan to customer deposit LDR (+) Ability to offset risk Loan loss Provision LLR (+) Industry competition Concentration of 4 largest banks CR4 (-) HHI Index HHI (-) Ownership ownership ratio OWN (-) Macroeconomic Variables Real GDP annual growth rate GDP (-) Inflation, average consumer price INF (+) Lending interest rates INT (+) Exchange rate EXI (+) Housing price growth index ESI (+) 3.2. Research methodologies 3.2.1. Measuring bank cost efficiency by using Data Envelopment Analysis (DEA) To measure banks’ cost efficiency, the study uses Data Envelopment Analysis, a non- parametric technique originally developed by Charnes Cooper & Rhodes (1978). Because this method requires very few assumptions of the production function, this helps to avoid the arbitrary assumption of effective boundaries. The efficiency of a firm consists of two components: Technical Efficiency (TE), which reflects the ability of a firm to obtain maximal output from a given set of inputs, and Allocative Efficiency (AE), which reflects the ability of a firm to use the inputs in optimal proportions, given their respective prices. These two measures are then combined to provide a measure of total economic efficiency. Two another terms are used to measure efficiency of a firm are Scale efficiency and Cost efficiency (Coelli, 2005). Berger and Humphrey (1997) suggest there are two main approaches to the choices of how to measure the flow of services provided by financial institutions: the production and intermediation approaches. The approach of input and output definition used in this study is a variation of the intermediation approach which assumes that financial firms act as an intermediary between savers and investors. Accordingly, deposits are treated as an input in the process of generating output such as interest, non-interest income. Following the earlier researches (Cevdet et al., 2000; Matthews and Tripe, 2002; Nguyễn Việt Hùng, 2007), outputs in this study are defined to include interest and similar income and non-interest income and three kinds of inputs: labor, fixed assets, and deposit from customers. 10 3.2.2. Dynamic panel Generalized Method of Moments This study applies the two-step dynamic panel data approach suggested by Arellano and Bover (1995) and Blundell and Bond (2000) and also uses dynamic panel GMM technique to address potential endogeneity, heteroskedasticity, and autocorrelation problems in the data. By using GMM estimation, it allows for instrumenting of the endogenous variables and provides consistent estimates. The paper use the lags of right hand side variables in the equations as instruments. In this estimation, the Hansen J-test is used to test the validity of instrument sets and the Arellano-Bond test is applied to check the absence of second-order serial correlation in the first differenced residuals. 3.3. Descriptions of Data This study analyzes a panel dataset comprising 34 Vietnamese commercial banks over the period 2005–2015. The panel data set is extracted from non-consolidated income statements and balance sheets of these banks. Among 34 commercial banks, there 5 State owned banks and 29 joint stock commercial banks. The sample size of 34 out of 35 joint stock banks is now representative of the JSBs in Vietnam. The macroeconomic data come from IMF – IFS website. 3.4. Summary This research applies GMM panel model to examine the factors affecting NPLs and the impact of NPLs on the efficiency, capital adequacy and credit growth of the commercial banks in Vietnam. The thesis also measures cost effectiveness by DEA method. With the models and data presented in this section, the next chapter uses the above models to present empirical research. CHAPTER 4. EMPIRICAL EVIDENCES FROM VIETNAM 4.1. Descriptive statistics The data used in the GMM model is arranged in panel data. Statistical description is presented in Table 4.1. 11 Table 4.1. Descriptive statistics of variables Trung bình Giá trị nhỏ nhất Giá trị lớn nhất Độ lệch chuẩn Số quan sát NPL 2.172 0.000 14.856 1.683 357 ROA 1.137 0.000 4.19 0.799 357 CE 0.693 0.228 1 0.233 357 TA 17.343 11.884 20.562 1.648 357 LGR 53.375 -40.811 1131.728 109.780 357 ETA 12.566 0.514 71.206 9.971 357 LDR 66.910 15.333 206.2 27.322 357 LLR 1.150 0.000 3.885 0.715 357 HHI 0.099 0.0715 0.170602 0.0306 357 CR4 0.561 0.456 0.796148 0.105 357 GDP 6.304 5.250 8.440 0.913 357 INF 9.501 0.630 23.120 5.978 357 LNEXI 9.823 9.671 9.984 0.123 357 IR 11.878 7.500 16.95 2.700 357 ESI 9.584 -1.620 20.5 6.519 357 Source: financial report of Vietnamese commercial banks, own estimations 12 Table 4.2. Testing the stationary by Fisher với lag=1 Variable ADF Test PP Test Prb>chi 2 Prb>chi 2 No trend Trend No trend Trend NPL 0,000*** 0,000*** 0,000*** 0,002*** GDP 0,026** 1,000 0,000*** 0,998 IR 0,888 0,990 0,188 1,000 ∆.IR 0,000*** 0,000*** 0,000*** 0,000*** EXI 0,000*** 0,241 0,915 1,000 ESI 0,691 0,002*** 0,003** 0,575 HHI 0,000*** 1,000 0,000*** 1,000 INF 0,020** 0,880 0,000*** 0,013 ROA 0,000*** 0,000*** 0,000*** 0,000*** CE 0,021** 0,000*** 0,000*** 0,000*** LDR 0,002*** 0,007*** 0,000*** 0,000*** LGR 0,602 0,000*** 0,000*** 0,000*** ETA 0,000*** 0,000*** 0,000*** 0,000*** TA 0,000*** 0,000*** 0,000*** 0,000*** LLR 0,831 0,327 0,449 0,418 ∆.LLR 0,000*** 0,000*** 0,000*** 0,000*** ***, **, * * and ** denote significance at the 10 %, 5 %and 1% levels. Source: own estimations Results of testing the stationary and cointegration of variables in Table 4.2 and 4.3. In the study model, all independent variables are co-dependent with the dependent variable. 13 Table 4.3. Westerlund panel cointegration test Gt Gα Pt Pα Biến phụ thuộc:NPL Các biến độc lập GDP 0,000*** 0,000*** 0,000*** 0,106 IR 0,000*** 0,000*** 0,000*** 0,000*** EXI 0,000*** 0,000*** 0,000*** 0,000*** ESI 0,000*** 0,000*** 0,000*** 0,000*** INF 0,000*** 0,000*** 0,000*** 0,000*** HHI 0,000*** 0,000*** 0,000*** 0,000*** ROA 0,000*** 0,000*** 0,000*** 0,000*** CE 0,000*** 0,000*** 0,000*** 0,000*** LDR 0,000*** 0,000*** 0,000*** 0,000*** LGR 0,000*** 0,000*** 0,000*** 0,122 ETA 0,000*** 0,000*** 0,000*** 0,000*** TA 0,000*** 0,000*** 0,000*** 0,000*** LLR 0,000*** 0,000*** 0,000*** 0,988 ***, **, * * and ** denote significance at the 10 %, 5 %and 1% levels. Source: own estimation 4.2. Estimation results for determinants of non-performing loans in Vietnam The estimation results for the determinant of NPLs of Vietnamese commercial banks are presented in Table 4.4. 14 Table 4.4. GMM estimation results for the determinant of NPLs of Vietnamese banks NPL Model 1 Model 2 Model 3 Model 4 L.NPL 0,3312*** (0,0042) 0,3801*** (0,0045) 0,3033*** (0,0182) 0,4147*** (0,0225) Bank-specific characteristics ROA -0,2335*** (0,0104) -0,4860*** (0,0121) -0,2680*** (0,0887) -0,2665** (0,0196) CE -0,1649** (0,1778) -0,1908** (0,2011) -0,2510** (0,1893) -0,2680* (0,2582) ETA -0,0227*** (0,0060) -0,0098* (0,0073) -0,0270** (0,0114) -0,1053*** (0,0214) LGR -0,0018*** (0,0003) -0,0012*** (0,0002) -0,0005*** (0,0064) -0,0047*** (0,0014) TA 0,1405** (0,065) 0,1146* (0,1078) 0,0968** (0,3987) 0,3664*** (0,1802) LDR -0,0044*** (0,0016) -0,0016*** (0,0064) -0,0018* (0,0008) -0,0034* (0,0031) LLR 0,0111*** (0,004) 0,0192** (0,0021) 0,093*** (0,0160) 0,0219*** (0,0117) Own1 - 0,1158*** (0,4256) Own2 0,0605*** (0,6212) Own3 0,0347** (0,0899) Industry competition HHI -0,553*** (0,2428) CR4 -0,628* (0,9957) -0,273** (0,0738) -0,5421*** (0,1367) Macroeconomic variables GDP -0,399*** (0,0708) -0,3931*** (0,0624) -0,4589*** (0,0545) -0,7546*** (0,0462) INF 0,0188** (0,0061) 0,0447*** (0,0054) EXI 0,2059*** (0,4102) 0,3210*** (0,4019) 0,5124*** (0,1103) 0,4397*** (0,1217) IR 0,1083*** (0,0204) ESI 0,0683*** (0,0038) 15 CONS. -0,6293*** (0,0110) -1,774*** (0,0257) -0,5672*** (0,4357) -1,4959*** (0,3802) Obs. 323 323 323 323 No. of banks 34 34 34 34 No. of instruments 19 22 23 21 Pro>chi2 0,000 0,000 0,000 0,000 Hansen test 0,488 0,574 0,559 0,625 AR(1) 0,009 0,031 0,015 0,008 AR(2) 0,594 0,775 0,535 0,612 ***, **, * * and ** denote significance at the 10 %, 5 %and 1% levels, respectively5% và 10%. Standard errors in parentheses. Source: own estimations Our findings indicate that factors such as bank efficiency, equity, credit growth and economic growth are the main factors that have a significant and negatively related to NPLs of Vietnamese commercial banks. Meanwhile, lagged NPLs, bank size, loans to deposit, capital and inflation, exchange rates, interest rates and real estate prices have the significant and positive impact on NPLs. 4.4. Estimation results for impact of non-performing loans on bank behavior 4.4.1. Estimation results for impact of non-performing loans on bank performance efficiency The results of Table 4.5 show the significant impact of NPLs on bank performance and support the hypothesis developed in Chapter 3: The rising of NPLs reduces cost efficiency as well as profitability of banks. Table 4.5. GMM estimation results for impact of NPLs on bank performance efficiency Dependent Variable ROA CE Model 1 Model 2 Model 3 Model 4 L.ROA 0,2432*** (0,0302) 0,2542*** (0,0347) L.CE 0,2997*** (0,0968) 0,372*** (0,0251) Bank-specific characteristics NPL -0,1579*** (0,0331) -0,1904*** (0,0315) -0,1221* (0,0343) - 0,1803*** (0,0427) ETA 0,0117*** (0,0033) 0,0061** (0,0332) -0,0118*** (0,0032) - 0,0186*** (0,0202) LGR 0,0019*** (0,0005) 0,0006** (0,0005) 0,0051** (0,0002) 0,0053** (0,0008) TA -0,2989** (0,0606) -0,3067** (0,0699) 0,0315*** (0,0366) 0,0781*** (0,2306) LDR 0,0009*** (0,0020) 0,0008* (0,0002) 0,0004*** (0,0000) 0,0046*** (0,0027) 16 Own1 0,1079** (0,4648) -0,2425** (0,1319) Own2 -0,0896* (0,1395) 0,1946*** (0,0874) Own3 -0,0736* (0,3354) 0,0237** (0,2619) HHI 0,2264*** (0,0321) 0,319** (0,1922) CR4 0,4198*** (0,3381) 0,1292*** (0,7801) Macroeconomic variables GDP 0,0323*** (0,0187) 0,0432*** (0,0279) 0,0441*** (0,0188) 0,0639*** (0,0387) INF 0,0004*** (0,0022) 0,0005 (0,0030) 0,0229* (0014) 0,0003*** (0,0045) LNER 0,1456** (0,0251) 0,2721** (0,3026) -0,11607*** (0,0329) - 0,1473*** (0,2446) CONS. -1,248*** (0,096) -0,5806*** (0,2319) -0,7255** (0,5712) -0,7714 (0,6018) Obs. 323 323 323 323 No. of banks 34 34 34 34 No. of instruments 22 24 22 22 Pro>chi2 0,000 0,000 0,000 0,000 Hansen test 0,503 0,304 0,456 0,46 AR(1) 0,007 0,016 0,005 0,002 AR(2) 0,390 0,242 0,742 0,627 ***, **, * * and ** denote significance at the 10 %, 5 %and 1% levels, respectively5% và 10%. Standard errors in parentheses. Source: own estimations. 17 4.4.2. Estimation results for impact of non-performing loans on capital adequacy Our findings show that there is a negative coefficient of NPLs and ETA and be significant. This result supports the bank lending channel theories. The result is also consistent with Lee and Hsieh (2013), Le (2016) and Alfon (2005). Table 4.6. GMM estimation results for impact of NPLs on capital edequacy Dependent variable ETA Model 1 Model 2 L.ETA 0,3906*** (0,0945) 0,3314*** (0,0863) Bank-specific characteristics NPL -0,1812*** (0,2499) -0,1750*** (0,2461) ROA 0,1718*** (0,7270) 0,1061*** (0,7523) CE -0,1659*** (0,1013) -0,1035*** (0,1232) LGR 0,0174*** (0,0024) 0,0147*** (0,0027) TA -0,2680*** (0,5645) -0,3767*** (0,7296) LDR 0,0025*** (0,0000) 0,0031*** (0,0000) OWN1 0,2002** (0,5815) OWN2 -0,2564*** (0,7532) OWN3 -0,1227* (0,1062) Industry Competition HHI 0,4246*** (0,1109) CR4 0,1235*** (0,4785) Macroeconomic variables GDP 0,1574** (0,2140) 0,1899*** (0,2273) INF 0,0172*** (0,0227) 0,0042*** (0,0235) LnER -0,1162** (0,2879) -0,1405** (0,5816) CONS -0,6528*** (0,2772) -0,6840** (0,3112) Obs. 323 323 No. of banks 34 34 No. of instruments 25 27 18 Pro>chi2 0,000 0,000 Hansen test 0,399 0,527 AR(1) 0,008 0,036 AR(2) 0,471 0,510 Source: own estimations ***, **, * * and ** denote significance at the 10 %, 5 %and 1% levels, respectively5% và 10%. Standard errors in parentheses 4.4.3. Estimation results for impact of non-performing loans on bank loan growth Table 4.7 exhibits the empirical results for non-performing loans and banks lending behavior (LGR). As regards NPLs variables, results show, in both cases, a negative impact on bank lending behavior with 1% level. Table 4.7. GMM estimation results for impact of NPLs on loan growth Dependent variable LGR Model 1 Model 2 L.LGR 0,1829*** (0,0267) 0,1654*** (0,0018) Bank-specific characteristics NPL -0,2116*** (0,1143) -0,2383*** (0,1129) ROA 0,0490*** (0,1080) 0,0515*** (0,1301) CE 0,0012*** (0,0235) 0,0013*** (0,0284) ETA 0,5244*** (0,1506) 0,0533*** (0,1755) TA -0,2616*** (0,7638) -0,0050*** (0,3612) LDR 0,0253* (0,1353) 0,004*** (0,1264) OWN1 -0,1241** (0,1730) OWN2 0,1433* (0,3352) OWN3 0,1384* (0,2770) Industry-specific HHI 0,1192*** (0,4272) CR4 0,2210*** (0,1612) Macroeconomic variables GDP 0,0390*** (0,4286) 0,0008*** (0,4063) INF -0,0021*** (0,3479) -0,0035*** (0,2370) 19 LnER -0,0150*** (0,6077) -0,0173*** (0,0481) CONS. -0,0221*** (0,5611) -0,4953*** (0,4350) Obs. 323 323 No. of banks 34 34 No. of instruments 21 27 Pro>chi2 0,000 0,000 Hansen test 0,522 0,328 AR(1) 0,039 0,047 AR(2) 0,468 0,523 In summary, the study first examines the relationship between NPLs and cost efficiency of Vietnamese commercial banks. This reverse relationship shows that inefficient cost management is one of the most important causes of NPLs of Vietnamese commercial banks. In addition, the study finds that other factors such as equity, credit growth and economic growth are the main factors that have a negative impact on NPLs of the banking sector in Vietnam. Meanwhile, past NPLs, bank size, loan to deposit and inflation rate, exchange rates, interest rate and real estate prices have the positive impact on NPLs. Besides that, NPLs is one of the most critical factors negatively affecting profitability and cost efficiency, capital as well as loan growth with significance at 1% level. ***, **, * * and ** denote significance at the 10 %, 5 %and 1% levels, respectively5% và 10%. Standard errors in parentheses. Source: own estimations 20 CHAPTER 5 CONCLUSIONS AND POLICY IMPLICATIONS 5.1. Main findings of the study The first objective of the study is to examine determinants of NPLs of Vietnamese commercial banks. The empirical results show that the specific factors that affect NPLs such as bank efficiency, equity, credit growth and are the main factors that have negative impact on NPLs of Vietnamese commercial banks. Meanwhile, NPLs in the past, the size of banks, loan to deposit have positive impact on NPLs. Macro economic determinants such as economic growth, inflation, interest rate, exchange rates, real estate prices also have a significant relationship to NPLs. The thesis measures cost efficiency of Vietnamese commercial banks, the results show that the average cost e efficiency of the studied period is 69,3. The study first examines the relationship between NPLs and cost efficiency of Vietnamese commercial banks. This reverse relationship shows that inefficient cost management is one of the most important causes of NPLs of Vietnamese commercial banks. The second objective is to assess the impact of NPLs on the bank performance of Vietnamese commercial banks. The results find that NPLs have a negative impact on the ROA and cost efficiency CE. This is explained by the poor credit quality that reduces interest income and increases the cost of provisioning. Bad management leads to many risky activities and the rising of NPLs. In contrast, banks with high profitability are banks that have good ability to control NPLs or control business expenses so NPLs ratio decreases. In addition, NPLs also has a negative impact on capital adequacy (ETA) as well as loan growth of 1%. Increased NPLs, coupled with a decline in collateral value, will increase the caution and lead to tightening credit expansion and credit growth decreases. Moreover, high non-performing loans will also impact negatively on bank capital and limited access to financing by banks. 5.2. Solutions relate to bank-specific characteristics 5.2.1. Enhance bank efficiency solutions First, to improve efficiency and profitability, commercial banks need to enhance their competitive advantage in the market by maintaining their market share expanding service network, improving financial capacity through capital mobilization and use of capital more efficiently. Second, banks can reduce the impact of NPL persistence in the future by focusing on risk management with the rising of total asset size over time. Third, banks can improve the cost efficiency of Vietnamese commercial banks by controlling interest expenses, labor costs and capital costs. 5.2.2. Enhance financial capacity and expand the rational scale solutions Vietnamese commercial banks need increase the ratio of equity to total assets according to the appropriate route and suitable methods, especially concern the specific situation of individual commercial bank. This helps to avoid pressure on maintaining profitability for investors. In 21 addition, banks should continue to improve the credit process to ensure a balance between the maximum credit approval limits of bank representatives and development capabilities of bank credit. This will help to minimize the consequences of moral hazard for low equity banks. Banks also need to ensure minimum capital adequacy ratios in accordance with international standards and regulations of the State Bank of Vietnam and Basel 3 standards. 5.2.3. Improve operational safety or liquidity The study results show that loan to deposit has negative relationship to NPLs, therefore, banks should focus on improving the operational safety or liquidity of the banking sector. In addition, banks need to restructure their deposit products by increasing the proportion of medium- and long- term deposits to improve liquidity and reduce liquidity costs. This leads to reduce lending rate. 5.2.4. Reasonable loan growth solution The decline of labor productivity or demand of economy can lead to the decreasing of loan growth. A good economic environment can help to improve aggregate demand. This will support firms to expand their business. This leads to the improvement of demand for credit. In the case of the stability of macro economy, the productivity of credit supply of the banking system, Vietnam need improve consumer demand. 5.2.5. Solutions relate to Industry Competition characteristics The empirical result finds that industry competition index has negative impact to NPLs. This means Vietnam has a unique character to its banking system. The competition-fragility view or the moral hazard hypothesis is supported in the Vietnamese case. Deregulation has a negative impact on the structure of Vietnam’s banking sector. Therefore, Vietnam’s authority should monitor banks’ portfolio risk during the deregulation process. 5.3. Policy recommendations related to macroeconomics 5.3.1. Reform of macroeconomic policies The results also show the macroeconomic growth rate are important factors that influence NPLs. This implies that SBV need to stimulate the economy, support to the private sector in their production and access to loans. This will help increase the repayment capacity of firms and decrease NPLs. In addition, because inflation has the positive impact on NPLs, the SBV should control consumer prices to curb inflation. 5.3.2. Reform the financial monitoring system The negative relationship between NPL and the level of competition also suggests that the regulator should apply closer monitoring to prohibit those banks from gambling in excessively risky undertakings. 5.3.3. Reform regulatory framework for banking supervision 22 First, Vietnam need to develop appropriate monitoring mechanisms for real ownership, and review the percentage of ownership shares for individuals and organizations referred to limit the over-involvement of the delegation to the governance. Second, in terms of operating safety ratios, there should be a roadmap guiding the market risk, operational risk and interest rate risk in determining risk weightings for assets. Third, in terms of debt classification and provisioning, it is necessary to make the Vietnamese accounting standard in accordance with International Accounting Standard IAS 39. In addition, the authority need to provide uniform guidelines for the determination of the collateral value for credit institutions in measuring of provisions. 5.4. Limitations and further research directions Although the thesis has some new contributions as mentioned, it still has limitations. First, we could have not collected the NPLs selling to VAMC of Vietnamese banks. Second, NPLs of Vietnamese banks may be effected by cross-ownership structure between banks, but the thesis does not mentioned due to inaccessible data. We could not classify the banks to their size as well as bank’s non-performing loans classification. Further study will examine the determinants on NPLs by classifying bank size and different level of banks’ growth on the market. 23 LIST OF AUTHOR’S PUBLICATION 1. Nguyen Thi Hong Vinh & Le Phan Thi Dieu Thao 2016, Effects of Bank Capital on Profitability and Credit Risk: The Case of Vietnam’s Commercial Banks, Journal of Economic Development, Vol.23 (Issue 4), 117 - 137. 2. Nguyen Thi Hong Vinh 2016, The Impact of Non-performing Loans on Bank Profitbility and Lending Behavior: The Case of Vietnam, Policies and Sustainable Economic Development, International Conference of University of Economics HoChiMinh City, 474 - 488. 3. Nguyễn Thị Hồng Vinh 2016, Tác động của mức độ cạnh tranh đến khả năng sinh lời và rủi ro của hệ thống ngân hàng Việt Nam, Công nghệ Ngân hàng số 122, 20-29. 4. Nguyen Thi Hong Vinh 2015, Bad debt and Cost Efficiency in Vietnamese Commercial Banks, Journal of Economic Development, Vol.22 (Issue 1), 125 – 140. 5. Nguyễn Thị Hồng Vinh 2015, Yếu tố tác động đến nợ xấu các ngân hàng thương mại Việt Nam, Phát triển Kinh tế Vol. 26 (Issue 11), 80-98. 6. Nguyễn Minh Sáng & Nguyễn Thị Hồng Vinh 2015, Nghiên cứu tác động của sử dụng nguồn lực đến hiệu quả kinh doanh của các ngân hàng thương mại Việt Nam và Thái Lan, CT-1301-1 – Đề tài nghiên cứu khoa học cấp cơ sở. 7. Nguyễn Thị Hồng Vinh 2012, Đo lường hiệu quả kỹ thuật và chỉ số Malmquist của ngân hàng thương mại Việt Nam, Công nghệ Ngân hàng, số 74, 16-22. THÔNG TIN TÓM TẮT VỀ NHỮNG ĐÓNG GÓP MỚI CỦA LUẬN ÁN TIẾN SĨ Tên luận án: Nợ xấu của hệ thống ngân hàng thương mại Việt Nam Chuyên ngành: Tài chính - Ngân hàng Mã số: 62.34.02.01 Nghiên cứu sinh: Nguyễn Thị Hồng Vinh Người hướng dẫn luận án: PGS.,TS. Lê Phan Thị Diệu Thảo PGS.,TS. Hạ Thị Thiều Dao Cơ sở đào tạo: Trường Đại Học Ngân Hàng TP. Hồ Chí Minh Luận án này đánh giá các nguyên nhân gây nên nợ xấu của các NHTM Việt Nam bao gồm các yếu tố vĩ mô, yếu tố đặc thù ngành và yếu tố đặc thù ngân hàng. Thêm vào đó, luận án này còn kiểm tra tác động của nợ xấu đến hiệu quả chi phí, hiệu quả lợi nhuận, an toàn vốn và tăng trưởng tín dụng của các NHTM Việt Nam. So với các nghiên cứu trước cùng chủ đề mà luận án đã tham khảo, luận án có những đóng góp mới như sau: Thứ nhất, luận án lần đầu tiên kiểm định mối quan hệ giữa nợ xấu và hiệu quả chi phí của các NHTM Việt Nam. Mối quan hệ ngược chiều này cho thấy việc kiểm soát chi phí kém hiệu quả là một trong những nguyên nhân quan trọng dẫn đến nợ xấu của các NHTM Việt Nam. Để tăng cường hiệu quả hoạt động của mình, các NHTM cần cắt giảm các chi phí đầu vào, từ đó sẽ giúp kiểm soát chặt chẽ hơn các khoản vay và làm giảm các khoản nợ xấu. Đồng thời, luận án phân tích các nguyên nhân của nợ xấu của các NHTM Việt Nam bằng phương pháp định lượng dưới nhiều góc độ: yếu tố đặc thù ngân hàng, yếu tố đặc thù ngành, các yếu tố kinh tế vĩ mô trong đó có tính đến ảnh hưởng của độ trễ của nợ xấu, được ước lượng thông qua mô hình ước lượng dữ liệu bảng động moment tổng quát GMM. Thứ hai, luận án lần đầu tiên nghiên cứu sâu về tác động của nợ xấu đến hoạt động ngân hàng trên mẫu các NHTM Việt Nam và chỉ ra được liệu nợ xấu có ảnh hưởng quan trọng như thế nào đến kết quả kinh doanh, hiệu quả chi phí, an toàn vốn hay tăng trưởng tín dụng. Hàm ý chính sách quan trọng từ kết quả nghiên cứu này là để tăng hiệu quả ngân hàng, nhà quản lý nên tăng cường việc giám sát và theo dõi rủi ro của các khoản nợ. Nghiên cứu thực nghiệm trên các NHTM Việt Nam cho ra các kết quả nổi bật như sau: (i) Việc cải thiện hiệu quả ngân hàng, tăng mức vốn hóa, tăng trưởng tín dụng, tăng trưởng kinh tế, kiểm soát mức độ cạnh tranh thị trường trong Ngành sẽ giúp giảm nợ xấu; (ii) Việc giảm dự phòng rủi ro, quy mô ngân hàng, mức độ kiểm soát của chủ sở hữu, lạm phát, lãi suất, giá nhà sẽ làm giảm nợ xấu; (iii) Nợ xấu gia tăng tác động tiêu cực đến hiệu quả ngân hàng, an toàn vốn và tăng trưởng tín dụng. Người hướng dẫn Nghiên cứu sinh ký tên A SUMMARY OF INFORMATION ON NEW CONTRIBURIONS OF THE THESIS Title of the thesis: Non-performing loans in the Vietnamese banking system Major: Finance and Banking Code: 62.34.02.01 PhD candidate: Nguyen Thi Hong Vinh Academic advisor: 1. Assoc. Prof. Dr. Le Phan Thi Dieu Thao 2. Assoc. Prof. Dr. Ha Thi Thieu Dao Training institution: Banking University Hochiminh City This thesis investigates the determinants of Vietnamese banks non performing loans (NPLs), using empirical framework that incorporates the related literature and theoretical hypothesis. In addition, this study also examines the impact of NPLs on Vietnamese banks’ profitability, capital and lending behavior. Throughout the whole sample, the thesis contributes to existing empirical researches in some ways. Firstly, it is the first study which examines the relationship between NPLs and cost efficiency of Vietnamese commercial banks. This reverse relationship shows that cost inefficiency is one of the most important causes of NPLs of Vietnamese commercial banks. The finding also suggests banks should reduce the input expenses, control loans tightly and limit the NPLs in order to improve banks’ efficiency. In addition, we investigate the bank-specific, industry - specific, macroeconomic determinants of NPLs by using dynamic panel Generalized Method of Moments techniques to analyze the panel data, which are designed to check the persistence of NPLs. Secondly, it is also the first study which examines the impact of NPLs on banking behavior for Vietnamese banking sector. The findings show that NPLs has a significantly effect on banks’ profitability, cost efficiency, capital and lending behaviors. The crucial policy implication of this study is that the bank managers should apply closer screening and monitoring of the risk of loan default in order to maximize profits. Some important empirical results are as follows: (i) Enhance banking efficiency, increasing capitalization, credit growth, economic growth, and controlling market competition will lead the decline of NPLs; (ii) Reduce risk provisions, bank size, owner control, inflation, interest rates, house prices will reduce NPLs; and (iii) the rising of NPLs has a negative effects on banks’ performance, capital adequacy and loan growth. Academic supervisors PhD candidate