FACTORS DETERMINING NET INTEREST MARGINS OF THE COMMERCIAL BANKS IN VIETNAM In Partial Fulfillment of the Requirements of the Degree of MASTER OF BUSINESS ADMINISTRATION In finance By Ms. Do Thi Thanh Huyen ID: MBA03015 International University - Vietnam National University HCMC February 2013 -i- THESIS FACTORS DETERMINING NET INTEREST MARGINS IN THE COMMERCIAL BANKS IN VIETNAM In Partial Fulfillment of the Requirements of the Degree of MASTER OF BUSINESS ADMINISTRATION In Finance by Ms. Do Thi Thanh Huyen ID: MBA03015 International University - Vietnam National University HCMC February 2013 Under the guidance and approval of the committee, and approved by all its members, this thesis has been accepted in partial fulfillment of the requirements for the degree. Approved: ---------------------------------------------Chairperson ---------------------------------------------Committee member ---------------------------------------------Committee member --------------------------------------Committee member --------------------------------------Committee member --------------------------------------Committee member - ii - Acknowledge To complete this thesis, I have been benefited from the following people: I would like to express my appreciation and say thank my supervisor, Dr. Nguyen Kim Thu for her careful guidance and support me to complete this thesis. I also would like to thank all lecturers for teaching me, giving me interesting knowledge and all office staffs for their support me during two years at International University. - iii - Plagiarism Statements I would like to declare that, apart from the acknowledged references, this thesis either does not use language, ideas, or other original material from anyone; or has not been previously submitted to any other educational and research programs or institutions. I fully understand that any writings in this thesis contradicted to the above statement will automatically lead to the rejection from the MBA program at the International University – Vietnam National University Hochiminh City. - ii - Copyright Statement This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognize that its copyright rests with its author and that no quotation from the thesis and no information derived from it may be published without the author’s prior consent. © Đỗ Thị Thanh Huyền/ MBA03015/2013 - iii - Table of Contents Acknowledge .. ......................................................................................................... i Plagiarism Statements .............................................................................................. ii Copyright Statement ............................................................................................... iii Table of Contents .................................................................................................... iv List of Abbreviations .............................................................................................. vi List of Tables . ....................................................................................................... vii List of Figures.... .................................................................................................. viii Abstract ............. .................................................................................................... ix CHAPTER 1. INTRODUCTION ..................................................................................................1 1. Background. ...................................................................................................1 2. Research objectives ........................................................................................2 3. Research method ............................................................................................2 4. Scope and limitation of the study....................................................................2 5. Research structure ..........................................................................................3 CHAPTER 2............................................................................................................4 OVERVIEW OF VIETNAMESE BANKING SYSTEM ......................................4 1. Growth of Vietnamese banking system .......................................................4 2. Vietnamese commercial banks performance:……………………………..6 CHAPTER 3 ...........................................................................................................9 LITERATURE REVIEW .......................................................................................9 1. Previous international studies .........................................................................9 2. Previous researches in Vietnam ....................................................................13 CHAPTER 4.......................................................................................................... 14 DATA AND METHODOLOGY .......................................................................... 14 1. Sampling design ........................................................................................... 14 2. Data collection methods ................................................................................ 14 3. Variables ...................................................................................................... 14 4. Framework: ..................................................................................................17 CHAPTER 5………………………………………………………………………..18 FINDINGS ............................................................................................................ 18 1. Descriptive statistics…………………………………………………………18 2. Empirical results .......................................................................................... 19 - iv - CONCLUSIONS ...................................................................................................25 1. Summary of the thesis .................................................................................. 25 2. Limitations ...................................................................................................25 3. Main implications......................................................................................... 26 4. Suggestion for future research ...................................................................... 26 REFERENCES .....................................................................................................28 -v- List of Abbreviations ABBank An Binh Commercial Joint Stock Bank ACB Asia Commercial Joint Stock Bank Agribank Vietnam Bank for Agriculture and Rural Development Baovietbank Bao Viet Joint Stock Commercial Bank BIDV Bank of Investment and Development of Vietnam BVSC Bao Viet Securities Company Eximbank Export and Import Joint Stock Commercial Bank JSCBs Joint-stock commercial banks MHB Mekong Housing Joint Stock Commercial Bank NIM Net interest margins ROA Return on Assets ROE Return on Equity Sacombank Saigon Thuong Tin Commercial Joint Stock Bank SBV The State Bank Of Vietnam SOCBs State-owned commercial banks Southern Bank Southern Bank VCBS Vietcombank Securities Co., LTD. VIB Vietnam International Joint Stock Bank Vietcombank Joint Stock Commercial Bank for Foreign Trade of Vietnam VietinBank Vietnam Bank for Industry and Trade - vi - List of Tables Table 1. Descriptive statistics of all variables of entire sample................................ 19 Table 2. Descriptive statistics of all variables of SOCBs ......................................... 19 Table 3. Descriptive statistics of all variables of JSCBs .......................................... 19 Table 4. Correlation of independent variables ......................................................... 20 Table 5. Redundant Fixed Effect Tests……………………………………………21 Table 6. Hausman Test result………………………………………………………21 Table 7. Fixed effect model ………………………………………………………22 - vii - List of Figures Figure 1. Number of commercial banks in Vietnam from 2006 to 2012 .....................4 Figure 2. Total assets of SOCBs and JSCBs from 2008 to 2011 ................................5 Figure 3. Interest rate stipulated by SBV from 2/2008 to 12/2011 .............................6 Figure 4. Bad debt ratios of Vietnamese banking system from 2007 to June 2012 .....7 Figure 5. The percentage of bad debt according to bank types at 31/3/2012...............8 - viii - Abstract This study investigates the factors determining the net interest margins of 33 Vietnamese commercial banks during the period 2008-2011. Based on the literature reviews, market power, managerial risk aversion, interest rate risk, credit risk, management quality and implied payment are the independent variables in the model. Fixed effects model will be chosen to run regression of panel data. The empirical analysis points out that managerial risk aversion, credit risk, management quality and implied payment are statistically significant in explaining bank’s net interest margins. Among four significant variables said above, only management quality has negative relationship with net interest margins. Additional, there is no evidence to conclude that both market power and interest rate risk are significant to net interest margins. Keywords: net interest margins, Vietnamese commercial banks. - ix - -x- CHAPTER 1 INTRODUCTION 1. Background. Becoming the fiftieth member of the World Trade Organization is the Vietnam’s remarkable economic event in 2007. The Vietnamese economy has accessed to the global market and has gained many achievements. The public data released by World Bank showed that Vietnam GDP growth rate rose from 8.23% in 2006 to a peak 8.46% in 2007 and the inflation rate was only 8.30% in this same year. Enterprises have many opportunities not only to develop their domestic market but also to expand into international market. However, five years later, the economy was falling down so fast and there has been no signal for complete recovery. In 2011, GDP growth rate stood at 5.89%, lower than 6.78% in 2010, and 6.31% in 2008. The inflation rate soared to 23.12% in 2008, much higher than previous year and was recorded at 18.67% in 2011. Unstable macroeconomic environment makes the business of enterprises in general and banking system in particular become more difficult than ever before. Thus, Vietnamese commercial banks not only find the way to survive, to face to the competition pressures from the foreign financial institutions, to meet many international standard regulations; but also take an important role in saving enterprises and economic recovery. Bank acts as “an intermediary between the demanders and suppliers of funds.” (Ho and Saunders, 1981, p.583). In recent years, Vietnamese commercial banks seem to perform this function inefficiently. Companies who are in tremendous need of capital must suffer high lending interest rates. Although the State Bank of Vietnam (SBV) imposed a ceiling deposit interest rate in the hope of dragging down lending interest rates, the access to banking loans remains harsh for companies. In the mean time, the interest rate spreads (i.e., the difference between lending interest rates and deposit interest rates) brings huge profits to commercial banks. This is the largest component of a bank’s net interest income and leads to the ratio net interest margins (NIM), which measures the return on bank’s earning assets, is high. Accordingly, commercial banks have been criticized to have maintained high net interest margin and no difficulty sharing with companies. Despite net interest margins being one of the major determinants of bank profits, little is known of the determinants of Vietnamese trading bank interest margins. Why Vietnamese commercial banks need to maintain high NIM or which variables have strong impact on NIM become the interesting questions for all those who care about bank sector in Vietnam. Therefore, in this context, the study will help to explain the queries above. It is likely that the NIM reflects the costs such as the implied interest payments that the banks have to offer to its customers. Those non-interest expenses must be accounted for in the net interest margins. Besides, the high credit risk also partly contributes to the high net interest margin, as credit risk premium increases in the current economic downturn. Consequently, the principal objective of this research is to empirically test the model of bank interest margin determination in the context of Vietnamese banking system. Based on the findings of the research, the SBV would be able to have effective solutions (instead of the administrative measures) to reduce the lending rates. 2. Research objectives Based on the above-mentioned problems, this research is formulated towards the following objectives:  To identify the factors determining the net interest margins of 33 Vietnamese commercial banks.  To examine the impact of determinants on 33 Vietnamese commercial banks during the period 2008-2011.  To give some recommendations to the State Bank of Vietnam (SBV) and commercial banks. 3. Research method This research will use quantitative method and cover 33 Vietnamese commercial banks operating during the period from 2008 to 2011. There are various sources to collect data: banks audited financial statements, public data of World Bank, annual reports of SBV, and market researches of some securities companies in Vietnam. All data are published on the official websites of those above-mentioned institutions. The Eviews software version 6 is used to run regression the data. 4. Scope and limitation of the study This study is limited to 33 Vietnamese commercial banks. Foreign commercial banks and foreign bank branches are beyond the scope of this study. This study is also limited to the period from 2008 to 2011. From the analysis mentioned above, this is the period of time in which there have been numerous fluctuations in the operations of the Vietnamese banking system and of the whole -2- economy. This is also the period in which banking operation and the interest rate policy of the SBV received special attention from businesses. 5. Research structure This research includes five chapters and conclusions. Chapter 1 gives the background and justifies the reasons of conducting this study. Chapter 2 provides an overview of the banking system in Vietnam, and then review key theories and empirical studies related to the model development of net interest margins in chapter 3. Chapter 4 discusses the model used in this research and explains the relationship between dependent and independent variables. Chapter 5 discusses the results of the regression. Finally, the conclusion will summary all results mentioned in chapter 5, also the implications, limitations and suggestion for future researches. -3- CHAPTER 2 OVERVIEW OF VIETNAMESE BANKING SYSTEM This chapter will look around the operation of Vietnamese banking system in recent years. The achievements banks get, as well as the problems they are facing to will be considered herein. These characteristics of Vietnamese banking system help in understanding and explaining for later analysis in the next chapters. 1. Growth of Vietnamese banking system Though the Vietnamese banking system is quite young compared with others in the world, it has been growing very fast. The number of commercial banks increases dramatically, rising from 8 banks in 1991 to 85 banks in 2007 and 98 banks in 2012. Although VCB, Vietinbank, MHB were equitized, SBV still sort them into group State-owned commercial banks (SOCBs). Therefore, of those 98 banks, there are 50 branches of foreign banks, 5 foreign banks, 4 joint-venture banks, 34 jointstock commercial banks (JSCBs), and 5 SOCBs. In the period of 2006-2012, the Vietnamese banking system has increasingly attracted foreign attention. The number of foreign banks in Vietnam has increased by 77 percent in this period (see Figure 1). 120 100 5 5 80 4 5 Joint-venture banks 5 44 60 5 5 31 45 53 53 55 41 Joint-stock commercial banks 40 20 0 Branchs of foreign banks and wholly foreign-owned banks 37 34 5 5 40 39 37 37 34 5 5 5 5 5 State-owned commercial banks 2006 2007 2008 2009 2010 2011 2012 Figure 1. Number of commercial banks in Vietnam from 2006 to 2012 -4- Source: website of SBV (www.sbv.gov.vn), BVSC (www.bvsc.com.vn), and VCBS (www.vcbs.com.vn). In addition, commercial banks in Vietnam have grown in both total assets and equity. As of the end of 2012, website of SBV updated figures about the total assets of the commercial banking system as VND 5,085,850 billion, up by 3.84% compared to VND 4,897,774 billion at the end of 2011 (Source: The report of the National Financial Supervisory Commission 2012). However, there exist differences among two major banking groups in the growth rates of total assets. While SOCBs are gradually losing their leader position, JSCBs have an enormous increase in asset growth rate. For instance, the asset growth rate of ACB, HDB, and Eximbank in 2011 were 37.91%, 98.91% and 40.01% respectively, while this rate of the two SOCBs - Agribank and BIDV- are 4.68% and 10.78%, respectively (Source: author’s calculation) 5,000,000 4,500,000 4,000,000 3,500,000 3,000,000 Financial institutions assets 2,500,000 SOCBs (5 banks) 2,000,000 JSCBs (31 banks) 1,500,000 1,000,000 500,000 0 2008 2009 2010 2011 Figure 2. Total assets of SOCBs and JSCBs from 2008 to 2011 (unit: billion) (Source: Financial reports of 5 SOCBs and 31 JSCBs from 2008 to 2011) In terms of equity capital, to satisfy the requirement of the SBV on commercial banks’ minimum chartered capital of VND 3,000 billion, stated in Decree 141/2006/ND-CP in 2010, chartered capital of most banks except BaoViet Bank and PG Bank, reached VND 3,000 billion at the end of 2011. Some SOCBs have the equity capital far above the required level, such as BIDV with capital of 28,251 billion VND, Agribank with capital of 21,103 billion VND and Vietinbank with capital of 20,230 billion VND. By raising the chartered capital, banks will enhance their competitiveness and maintain the Capital Adequacy Ratio (CAR) of 9 percent regulated by the Decree 13/2010/TT-NHNN. 2. Vietnamese commercial banks performance: With some remarkable changes as said above, Vietnamese banking sector was expected to develop strongly or at least to be stable. However, recently, commercial banks have to face with problems related to interest rate race and bad debts. To curb high inflation rates, the SBV implemented the tight monetary policy in 2008 with a series of solutions. Firstly, VND base deposit rate was applied, from 12% to 14% per year at the first half year of 2008. Next, other tools of monetary policy were used simultaneously, such as higher reserve requirement and the issuance of VND 20,300 billion of compulsory SBV bills to withdraw money out of circulation. Facing with increasing difficulty in capital mobilization, banks rushed to raise interest rates and provided promotion to encourage deposits from individuals and organizations. Some small-sized banks adjusted their VND mobilizing interest rates up to 18% to 19% per annum. Large-sized banks also push their rates to high point to keep customer’s feet. Besides that, being controlled maximum 150% of the base interest rate, or within a cap of 18% per annum, banks add more extra fees into VND lending interest rate to cover the high mobilizing interest rate. Therefore, both real mobilizing interest rate and lending interest rate increased very high. From 2009 until now, SBV continuously enacts many decrees to stop the interest rate race and keep it stable. Base interest rate Refinancing interest rate Discount interest rate Figure 3. Interest rate stipulated by SBV from 2/2008 to 12/2011 (Source: SBV’s website http:// www.sbv.gov.vn) -6- Dec-11 Oct-11 Aug-11 Jun-11 Apr-11 Feb-11 Dec-10 Oct-10 Aug-10 Jun-10 Apr-10 Feb-10 Dec-09 Oct-09 Aug-09 Jun-09 Apr-09 Feb-09 Dec-08 Oct-08 Aug-08 Jun-08 Apr-08 Feb-08 16 14 12 10 8 6 4 2 0 With regard to bad debts, this is really the urgent problems of Vietnamese banking sector. Before 2008, Vietnam’s credit growth was too hot. This growth was later reduced by the SBV’s tight monetary policy. However, in 2010, although the global economy has not recovered completely from the 2008 financial crisis, it had to suffer from the consequences of the debt crisis in the Euro zone in the second quarter of 2010. Enterprises in Vietnam were also deeply influenced by the crisis, and numerous enterprises had to do bankrupt or stop their operation. As a result, banks find it extremely hard to collect their loans made in previous periods. The high lending interest rate made it harder for companies to repay their debt. Consequently, banks’ bad debts are growing. According to Chief Inspector of the State Bank of Vietnam, Mr.Nguyen Huu Nghia’s statement dated 12/7/2012, the bad debts which was calculated by SBV was 8.6% of total outstanding loans, much higher than 4.47% reported by the credit institutions. 5.0% 4.47% 4.0% 3.5% 3.0% 2.0% 3.2% 2.2% 2.0% 3.6% 2.5% 1.0% 0.0% 2007 2008 2009 2010 2011 Q1.2012 Q2.2012 Figure 4. Bad debt ratios of Vietnamese banking system from 2007 to June 2012 (Source: Report of Vietnamese banking sector Q2.2012, Vietcombank Securities Company – VCBS) It is the fact that the percentage of bad debts in the state-owned banks is much more than the others. They occupied 50% of bad debt ratio of whole credit market. The next positions were commercial joint stock banks (27.8%), foreign banks (17.5%) and the remains belonged to other financial institutions (4.2%). Most of state-owned banks serve many customers who are state-owned corporations with inefficient operations. It could be seen as one of the reason explaining why the state-owned banks stood on top on bad debts ratio. -7- 4.20% State-owned banks 17.50% Commercial joint stock banks 50.5% 27.80% Foreign banks Others Figure 5. The percentage of bad debt according to bank types at 31/3/2012 (Source: VnEconomy’s website www.vneconomy.com) -8- CHAPTER 3 LITERATURE REVIEW 1. Previous international studies: Determining the bank’s net interest margins is the attractive subject and researched in many countries. Ho and Saunders (1981) are considered as the pioneers in this subject. In their research, they viewed banks as the dealers in the credit market, providing the services to depositors and loaners. Because the mismatching in maturity of the deposits and bank loans, banks must face to two kinds of risks: reinvestment risk and refinancing risk when a change in the short-term rate of interest and a bank’s unmatched portfolio of the deposits and loans, it will face to interest rate risk. For instance, having a long-term deposit, but no new loan demand, bank will invest funds temporarily in money market. In this case, it will get trouble in reinvestment risk if short-term rate fall. Or having a new loan demand but no inflow of deposit, bank has to borrow funds from money market. Refinancing risk happens as short-term rate raise. Hence, banks will determine the optimal interest spread in order to cover the uncertainty in transactions and interest rate risk. Based on this reasoning, the study by Ho and Saunders (1981) defined the pure spread (s) as a function below = Where measures the elasticity of demand and supply in the markets in which the bank operates. Bank faces relatively inelastic demand and supply (high ) it may be able to exercise monopoly power, and earn a producer's rent by demanding a greater spread than it could get if banking markets were competitive. The second term in the model implies that, other things equal, the greater the degree of risk aversion (measured by R), the larger the size of transactions (measured by Q), and the greater the variance of interest rates (measured by ), the larger bank margins are. The quarterly data from 1976 to 1979 of over 100 US commercial banks, and cross-section regression were used in this study. Although not explicitly considered in above equation, the research conducted an empirical study on the determinants of actual bank margins (M), which comprise a pure spread (s) due to underlying -9- transaction uncertainty, plus mark-up for implicit interest expense (IR), the opportunity cost of required reserves (OR), and default premiums on loans (DP). It was found that the pure spread and the implied interest expense were statistically significant, which implied that the main determinants of the size of actual bank margins were transactions uncertainty and markups to cover implicit payments to depositors. Then, they tested whether these estimated spreads depend on interest rate volatility and market structure or not. Later studies on bank net interest margins have added more independent variables to the model of Ho and Saunders. The next article done by R.W.McShane and I.G.Sharpe (1985) continued to contribute new discoveries on NIM and its determinants. In the case of Australian trading banks, the authors assumed the uncertainty coming from the instantaneous short-term money market rate, wider than that from the deposit and loan rates as stated in theory of Ho and Saunders (1981). In addition, the factors chosen in this approach were the bank’s power in the loan and deposit markets, interest rate volatility, and risk aversion, uncertainty of the instantaneous money market risk-free interest rate and average size of transactions. After running regression with the sample of 22 banks, from 1962 to 1982, they found that there existed a stable non-linear relationship between NIM and those abovementioned factors. Moreover, upon the hypothesis of differences between business and personal sectors, they found that in the Australian context, the more personal business sector, the greater market power and the higher margins are. Angbazo (1997) developed his model from the previous papers by adding some risk factors. He concentrated on building a function in which NIM was a function of those risks and bank specific variables. His sample consisted 286 US commercial banks with assets equal or over USD 1 billion from 1989 to 1993 and was estimated by generalized least squares (GLS). The regression’s results showed the relationship between NIM and determinants by entire sample and by each bank group. While the default risk proxy was significantly positive, the interest rate risk was negative and significant to NIM. Three other proxies including capital base, management quality, and non-interest bearing assets were significantly positive. For result tested by each bank group, the author realized that the sensitivity of above variables on NIM of each group was different. For instance, money-center and local banks’ NIM has relationship with defaults risk, but regional and super-regional banks did not. In the second part of his study, he continued to test whether off-balance sheet - 10 - had effect on on-balance sheet portfolio risk and NIM or not. His analysis evidenced that off-balance sheet indirectly led to higher NIM. Joaquin Maudos and Juan Fernandez de Guevara (2004) indentified the factors affecting to the interest margin in banking sectors in some Europe countries: Germany, France, United Kingdom, Italy, and Spain from 1993 to 2000. The researchers introduced some more variables such as: market power to capture competitive conditions, operating costs, ect. Most of his variables were significant and explained as follows. In European banks, the degree of concentration reduced competition pressure and thus market power increased. Market interest volatility was found to have a small effect on NIM. Like other result from many previous researches, Joaquin Maudos and Juan Fernandez de Guevara also tested there was a positive sign between NIM and implicit payment. One more important discovery in this paper was that a decrease in level of average production costs would lead to reduction in NIM. Another research of Australian banks was provided by Barry William (2007), who investigated the NIM of 22 domestic banks and 21 foreign subsidiary banks operating from 1989 to 2001. Not only testing the application of the Ho and Saunders (1981) model with the core variables- managerial aversion and interest rate risk, he also considered the extended models like Angbazo (1997) and Joaquin Maudos and Juan Fernandez de Guevara (2004) such as operating cost, liquidity, management quality, credit risk, interaction between interest rate risk and credit risk, bank operation size, implied payment, implied taxes and control variables, in order to have a framework for Australian banking sector. After running descriptive statistics, he found that the foreign banks had lower NIM, lower level of retail, but higher levels of average capital than domestic Australian banks. Four major banks called the Big Four banks have the lower operating cost, higher management quality, while the others were very active in their retail banking. Consistent to Ho and Saunders (1981), regression presented that interest rate volatility and management risk aversion related positively on NIM. While McShane and Sharpe proposed NIM and market, power to be positive, William found this relationship was negative in case of whole sample, but positive consistently in case of NIM of big four banks in Australia. Continuously, the higher management quality was, the lower NIM was. However, variable liquidity, implied taxes was found to have no relationship, and credit risk was negative significantly to NIM. - 11 - Anthony Q.Q.Aboagye, S.K.Aknoena, T.O.Antwi-Asare and A.F.Gockel (2008) explained the bank’s optimal spread between lending rate and deposits rate in Ghana. Like other authors, their research relied on Ho and Saunders (1981) framework and the model of Joaquin Maudos and Juan Fernandez de Guevara (2004). The determinants here included the bank specific (the competitive structure of markets, average operating costs, extent of risk aversion, volatility of money market rates, riskiness of a bank’s loan portfolio, covariance of interaction between interest rate risk and credit risk and the average size of credit and deposit operations), industry characteristics (banking industries structure, the Lerner index, the concentration, opportunity cost of non-earning bank reserves) and macroeconomic variables (expected inflation and money supply). The quarterly data of 17 Ghanaian banks was collected. Their finding were: the decrease in market power, bank concentration, bank total assists, bank equity, inflation and bank staff expenses, capital expenditure and administrative expenses over total assets would decrease NIM, while the bank liquidity, central bank lending rate, bank management efficiency would increase NIM. Ahmet Ugur and Hakan Erkus (2010) investigate the net interest margins of both domestic and foreign banks in Turkey. Firstly, they run regression to find the effect of bank specific factors on the bank spreads, including NIM, bank size, risk aversion, loan quality, liquidity risk, bank market share, operating costs, personnel expenses, and management quality. Then, the constant term in the first model called “pure spread” would become the dependent variable in the second regression. In this regression, the independent variables were volatility of interest rates, the ratio of budget deficit to GDP, GDP growth rate, inflation rate and two crisis dummy variables to capture the effect of the financial crisis in Turkey in two years. The authors run the descriptive statistics and the panel data random effect model. They found that the foreign bank had higher internet margin due to their higher operating costs. However, they also had higher personnel expenses and management quality, while their market shares were smaller than domestic banks. While NIM and market share had a negative sign, the bank size, operating costs, and risk adverse affect positively on NIM. The liquidity ratio was not significant factor in the model. For the second model, Ahmet Ugur and Hkan Erkus (2010) realized that only inflation rate significantly affected on the pure spread. - 12 - 2. Previous researches in Vietnam Ngo Dang Thanh (2010) evaluated the efficiency of Vietnamese banking system by using Data Envelopment Analysis- DEA. This method calculated the limited production capacity based on the inputs and compares the current output to evaluate the effective use of resources. The author focused on analyzing 22 commercial banks in Vietnam in the year of 2008 and chose the inputs as wages, interest and similar expenses, and other expenses; while the outputs were total asset, interest and similar income and other incomes. The result showed the way for Vietnamese banking system to increase its efficiency is managing their lending activities. Nguyen Thuy Duong and Tran Hai Yen (2011) analyzed the determinants of credit growth of commercial banks in Vietnam in 2011. The dataset was collected from some banks Vietnam in quarter 1, 2, 3 of 2011. They assumed that the credit growth depends on state ownership, foreign ownership, ROE, liquidity, deposit, spread between loan interest and deposit interest. Based on the regression model, the changes in credit growth have positive relationship with deposit and liquidity. Vice versa, when the spreads increases, the credit growth decreases. In addition, Vietnamese commercial banks or foreign banks in Vietnam are equally affected, so two variables state ownership and foreign ownership are not significant. In summary, the topic “determinants to net interest margins” is one of the greatest interesting issues in many countries: Europe, Emerging market. They mostly based on the basic model of Ho and Saunders (1981) and developed their own model, which are suitable to practice in their countries. Whereas, Vietnam does not have many studies of this problem. Most of them just evaluate banks performance by using financial ratios, or only focus on some specific activities of bank, such as determinants of credit growth, financial structure and bank performances. After examining the theoretical framework and related empirical studies on bank interest margins such as Angbazo (1997), Barry William (2007), this paper selects several factors which are thought to be more appropriate to the determination of net interest margins in Vietnamese banking system. - 13 - CHAPTER 4 DATA AND METHODOLOGY In this chapter, the research model will be built upon selected independent variables and dependent variable NIM. Before going to this important part, the sample and data collection methods will be introduced. 1. Sampling design This study investigates the factors determining NIM of Vietnamese commercial banks for the period 2008-2011. Thus, sample collected includes 5 SOCBs (Vietcombank, BIDV, Agribank, Vietinbank, and MHB) and 28 JSCBs (see Appendix A). The requirement is that those banks must have annual financial reports from 2007-2011 in order to calculate the financial ratios which need average figures of previous year and current year. 2. Data collection methods The secondary data will be used in this research. Firstly, the audited annual financial statements during the period 2007-2011 will be collected from websites of 33 Vietnamese commercial banks. Secondly, data of 2-year treasury bonds are listed on Hanoi Stock Exchange and Sacombank Securities Company. The other data will be gathered from reports of Vietcombank Securities Company, SBV website, ect. The raw data will be calculated to present for all variables in the model. 3. Variables Net interest margins (NIM): it is the dependent variable in the model and measured by net interest income divided by average earnings assets Market power (MPO): The greater the market power of the bank is, the greater the interest spreads banks receive (McShane and Sharpe, 1985). To measure this variable, McShane and Sharpe (1985) use two alternative calculations, such as: deposit market share of bank A over deposit market share of entire banking system, or total assets of bank A over total assets of all financial institutions. While Barry William (2007) employed that market power as aspect of competition and is very important to NIM. He applied three ways to definite market power. The first definition considers bank market share as a percent of total Australian bank assets. The second one is a percentage of bank and non-bank financial institution assets; and the third is a percent of bank, non-bank financial institution assets, and securitization vehicle assets. Which one should be chosen in this paper? McShane and Sharpe - 14 - (1985) preferred the narrow market share measure than the others and also agreed all method would give similar conclusions. Therefore, the variance market power of each banks in study will calculate by total assets of bank I to total assets of all financial institutions in Vietnam. The relationship between this factor and NIM are expected to be positive. Bank, which has more market power, will increase NIM. Managerial risk aversion (MRV): taking high-risk investment, it will be expected high return. However, a depository institution often face to sudden risks such as liquidity risk, credit risk, operational risk, foreign exchange risk, and others, the overall safety and soundness is more important. Marginal risk aversion helps commercial banks to protest their performance from failure; and meet requirements of the international regulation, particularly capital adequacy ratio of Basel I, II, and III. Moreover, with high risk averse, bank also must recover their higher cost of equity financing (Ahmet Agur and Hakan Erkun, 2010). In this study, because the data for calculating capital adequacy ratio in Vietnam is not completely published. The ratio applied is shareholders funds divided by total assets ((McShane and Sharpe, (1985), Barry William (2007)). Interest rate volatility (IRR): The impact of interest rate volatility on bank’s NIM is supported in many literatures. Along with mismatching in maturities of bank loans and deposits, the interest rate volatility causes the interest rate risk. In the researches of Ho and Saunders (1981) or Saunders and Cornett (2009), they analysis that there are two kinds of interest rate risk which banks must face to. With shortterm borrowing but long-term lending, banks cannot avoid the refinancing risk if interest rates increase in the future. In this case, banks must pay more for new liabilities or higher re-borrowing cost for the next period, while the long-terms lending continuously keeps the low return. Similarly, when the maturity of deposit is longer than the maturity of loan, banks will face to reinvestment risk if there is a decrease in interest rate. This means that banks must bear the lower interest rate or lower return earned on new lending, while the payment for the old liabilities still based on the old interest rates. Besides that, Perter S.Rose (2009) also mentioned about price risk, explaining that market value of bonds invested by banks will decrease due to falling interest rate. So, the sudden changes in interest rate will affect on banks’ income from loans, securities and on bank’s cost from borrowing. In other words, NIM will change, involving the decrease or increase in bank’s net profit. Barry William (2007) used the standard deviation of daily 90-day bank bill rate or 5- 15 - year Treasury bond rate to measure interest rate volatility on NIM. While Maudos and Guevara (2004) used three alternative types of financial tools: the three-month interest rate in inter-bank market, treasury bonds with three-year maturity, treasury bond with ten year maturity period. They also found the positive impact of this variable upon three above measures, or in another words, NIM has positive correlation with short, medium and long-term interest rate. Another method of calculating interest rate risk provided by Mark J.Flannery and Christopher M.James (1984), which run AR model and time series models testing the effect interest rate changes on common stock returns of financial institutions. Following the maturity mismatch hypothesis mentioned on paper of Mark J.Flannery and Christopher M.James (1984), Angbazo (1997) applied the net short term by using the account short-term liabilities minus short-term assets and all divided book value of total equity capital. For Vietnamese banks, data to calculate maturity gap as Angbazo (1997) is unavailable, and data of daily treasury bonds in Vietnamese securities market is also missing. Therefore, in this study, interest rate risk will be measured by the standard deviation of 2-year treasury bonds auctioned off by government in each year 2008-2011. Like the first and second variables, interest rate risk is expected positive to NIM. Credit risk (CRR): credit risk is the risk which banks loan is very difficult to collect back from the customers. Most of previous researchers like Angbazo (1997) and Barry William (2007) agreed that banks holding more risky loans would require higher NIM. Provision for loans loss divided by total gross loans will measure credit risk. Management quality (MQU): The variable will be presented by the ratio operating cost to gross income. High management quality is proved through the ability of managers and bank staff in maintaining the growth of revenues regardless of the cost. In other words, a bank with efficient management will hold assets that are more profitable and pursue low-cost capital in order to raise NIM higher. The management quality and bank net interest margins are therefore expected to be negative (Barry William, 2007). Implied payment (IP): Ho and Saunders (1981), Barry William (2007) also measured these variables as total noninterest expense minus total noninterest revenue and all divided by total earning assets. This calculation will be applied in study with - 16 - expectation that more implied payment, more extra interest expense will reflect in actual NIM of a bank (Ho and Saunders, 1981). 4. Framework: The data used herein are panel data and run regression by Eviews software. The basic model will be formed as follows; =∝ + .( + ) + .( ) + .( ) + ( .( ) + .( ) ) + In which, i = 1, 33 banks and t=year 2008, 2009, 2010, 2011. To run regression of panel data, there are two approaches considered: Fixed effects model (FEM) and Random effect model (REM). Hausman test will help to choose which model is better in this case (Damodar N.Gujarati, 2004). - 17 - CHAPTER 5 FINDINGS 1. Descriptive Statistics Before estimating the regressions, the descriptive statistics will be shown on table 1, table 2, and table 3. In the first table, the mean of NIM of entire banks is 3.48% over the period from 2008 to 2011. The maximum value and minimum value are 9.17%, 0.33% respectively. These values are belongs to Western Bank and Southern Bank, indicating that there is a great varies in this ratio across banks in group JSCBs . Whereas, the difference between banks in-group SOCBs is not much like that. This is confirmed by descriptive statistics by bank type in table 2 and table 3. While mean in NIM between two groups is nearly equal, the standard deviation of JSCBs is more than twice SOCBs. As mentioned in chapter 2, SOCBs have high total assets in whole banking systems. Therefore, it is no surprise when the maximum value in market power 18% comes from SOCBs, and also the maximum value of entire sample. While one bank of JSCBs hold the lowest total asset 0.08%. Next, table 2 shows the mean of managerial risk aversion ratio of SOCBs is 5.6% and standard deviation 1.2%, much lower than that of JSCBs. As said in the overview section of Vietnamese banking sector, besides being at the head of the total assets, SOCBs such as Agribank, Vietcombank also are banks with suffering the high bad debts. Table 3 presents JSCBs’ credit risk ratio is just 0.8%, while the other group is 1.07%. Management quality of SOCBs amounts to 53.44% on average, which is 7% higher than overall. And the last variable in this study is the implied payment ratio. Table 1 reports the implied payment ratio from a minimum -3%, average of 0.9%, and to maximum of 6.5%. Again, table 3 indicates that the big distance from smallest to biggest value of this ratio is from JSCBs. - 18 - Table 1. Descriptive statistics of all variables of entire sample NIM MPO MRV IRR CR 1.59957 0.008512 MQU IP Mean 0.034796 0.023464 0.127016 Median 0.033094 0.009872 0.101146 1.044289 0.006057 0.434885 0.009657 Maximum 0.091717 0.180134 0.413903 3.866855 0.050683 0.46493 0.009436 0.88775 0.065762 Minimum 0.003364 0.000832 0.029051 0.442844 0.000000 0.121708 Std. Dev. 0.014562 0.033245 0.079794 1.337226 0.008229 -0.03203 0.1442 0.01205 Table 2. Descriptive statistics of all variables of SOCBs NIM MPO MRV IRR 1.59957 CR MQU IP Mean 0.033721 0.084756 0.056135 0.01119 0.534027 0.013012 Median 0.031451 0.087524 0.059808 1.044289 0.010693 0.480391 0.013604 Maximum 0.05143 Minimum 0.018983 0.009654 0.029051 0.442844 0.000167 0.299686 0.001618 Std. Dev. 0.008618 0.045629 0.012439 1.366758 0.006851 0.165646 0.006764 0.180134 0.078094 3.866855 0.023174 0.88775 0.024096 Table 3. Descriptive statistics of all variables of JSCBs NIM MPO MRV IRR CR MQU IP Mean 0.034987 0.012519 0.139673 Median 0.033305 0.008456 0.112289 1.044289 0.005895 0.425467 0.009088 Maximum 0.091717 0.057377 0.413903 3.866855 0.050683 0.881677 0.065762 Minimum 0.003364 0.000832 0.042556 0.442844 0.000000 0.121708 Std. Dev. 0.015405 0.012255 0.080131 1.338138 0.008388 0.137222 0.012681 2. 1.59957 0.008034 0.452591 0.008797 Empirical results The first step to check the mutlcollinearity of all independent in this research. In table, the correlation between dependent variables is low. It means that all factors are not found to be highly correlated each others. Therefore, these variables will be kept to continue run regression. - 19 - -0.03203 Table 4. Correlation of independent variables MPO MRV IRR CR MQU MPO 1 MRV -0.463 IRR 0.002701 0.19648 1 CR 0.215263 -0.1563 -0.1386 MQU 0.009331 -0.10402 0.167561 -0.20852 IP -0.05767 IP 1 1 1 0.197478 0.036459 0.009022 0.408758 1 The regression between dependent variables and independent variable is often run by Ordinary Least Squares (OLS). However, the simple assumptions of OLS that the intercept value being the same for each individual over times is not suitable in reality. Therefore, it is necessary to use other regression techniques for panel data: Fix effects model (FEM) and random effects model (REM). The next step is running the regression by FEM with cross section fix effects and then testing whether FEM is necessary or not. The null hypothesis is that there is no cross section fixed effect in the data and the alternative is that there is cross section fixed effect. Eviews table shows the result of p=0.00< 0.05, so the null hypothesis is rejected or the alternative is accepted. It means that there is cross section fixed effect. Table 5. Redundant Fixed Effect Tests Redundant Fixed Effects Tests Equation: Untitled Test cross-section fixed effects Effects Test Statistic Cross-section F Cross-section Chi-square d.f. Prob. 4.322227 (32,93) 0.0000 120.273744 32 0.0000 - 20 - The Hausman test (1978) will determine the method FEM or REM should be used. Again, the null hypothesis is that FEM and REM are equal. If this hypothesis is not rejected, the study should choose REM to estimate the model. Conversely, the choice belongs to FEM. Table 6. Hausman Test result Correlated Random Effects - Hausman Test Equation: Untitled Test cross-section random effects Chi-Sq. Test Summary Statistic Chi-Sq. d.f. Cross-section random 15.392837 6 Prob. 0.0174 The result of Hausman test gives Prob=0.0174 < 0.05, which means that the null hypothesis is rejected – the FEM and REM is not equal. Therefore the suitable technique used herein is FEM. The FEM regression will be showed again to analysis the model of NIM in Vietnamese banking system. Table 7. Fixed effect model Dependent Variable: NIM Method: Panel Least Squares Date: 04/08/13 Time: 10:30 Sample: 1 132 Periods included: 4 Cross-sections included: 33 Total panel (balanced) observations: 132 Variable Coefficient Std. Error t-Statistic Prob. C 0.037340 0.004726 7.900660 0.0000 MPO 0.053076 0.096778 0.548431 0.5847 MRV 0.083040 0.014543 5.709755 0.0000 - 21 - IRR -0.000441 CR 0.353095 MQU -0.055701 IP 0.981254 0.000455 -0.968619 0.3352 0.122738 2.876816 0.0050 0.006367 -8.748141 0.0000 0.075782 0.0000 12.94840 Effects Specification Cross-section fixed (dummy variables) R-squared 0.873346 Mean dependent var 0.034796 Adjusted R-squared 0.821595 S.D. dependent var 0.014562 S.E. of regression 0.006151 Akaike info criterion -7.103782 Sum squared resid 0.003518 Schwarz criterion Log likelihood 507.8496 Hannan-Quinn criter. -6.757675 F-statistic 16.87587 Durbin-Watson stat Prob(F-statistic) 0.000000 -6.252045 2.098570 The value for the adjusted R-squared is high 82.15% which evidences that this model has high explanatory power or 82.15% of the variation in the dependent variable could be explained by the independent ones. As predicted, market power is one of significant in explaining NIM. Surprisingly, it is really positive but insignificant in explaining the changes in NIM of banks in Vietnam. There are some ideas supporting to this result such as Roman Horvath (2009), Jesús Gustavo Garza-García (2010). In Vietnam, a bank with big total assets are often known as large banks and have high market power, such as SOCBs or some JSCBs like ACB, ABBank, ect.. Looking in NIM of SOCBs in descriptive statistics, it is recognized that although their average market power (measured by their total asset to total assets of all financial institutions) are much higher than the JSCBs, their NIM are similar to the others. Besides that, in the context of Vietnam, SOCBs is affected by government policy than the others. For example, during the period of difficult economy, Agribank, MHB, Vietcombank had to comply with the direction of the SBV on the adjustment of lending interest rates, thereby affecting their NIM. Furthermore, according to the views of the member of the Board of Directors of ABBank Dr. Nguyen Tri Hieu, the banks accept breakeven to share the difficulties with the customers. But so doing, they will get more and more customers trust. This - 22 - is also the same views of Barry William (2007), he said that banks would sacrifice their NIM to get size targets. So, the market power measured by a percentage in total asset has not significantly explained NIM of Vietnamese commercial banks in this period. As seen in the table, managerial risk aversion measured by shareholder’s equity to total assets is found as a statistically significant factor in model. The correlation between dependent variable and this determinant is positive, or saying in another way, if others unchanged, 1% increase in managerial risk aversion leads to 8.3% increase in NIM. This result is strongly supported by many empirical papers. If a bank chooses to adopt a low capital ratio, it implies a high risk preference (McShane and Sharpe, 1985). When banks with more risk aversion or high capital ratio, they need higher NIM in order to cover their high cost of equity (Ahmet Ugur and Hakan Erkus, 2010). Besides that, banks with higher shareholder’s equity could provide initial resources for remaining active when the bank start-up, create the trust for customers, to deal with and hedge banking business. Therefore, facing to many risk makes Vietnamese commercial banks need to keep more equity to protest their business. The variable interest rate risk measured by standard deviation of 2-year treasury bond is insignificant to NIM. As explaining in chapter 4, Vietnam does not allow to get full data for this study. Otherwise, some observers and experts of bond market of Vietnam, who are working for famous securities companies like Hanoi Stock Exchange or Sacombank Security Company, also evaluated that in Vietnam market, it is hard to find out the impact of treasury bond rate on interest rate of bank’s loans or deposit. The regression indicates that the relationship between NIM and credit risk is significant at level 10% and the coefficient is very high. As expected that banks are more risky loans select higher NIM (Angbazo, 1997), when credit risk increases 1% in the case of others unchanged, NIM will increase 35,3%. The next factor affecting the NIM of Vietnamese banking systems is management quality, which is presented by the operating cost to total income. According to the regression result, this is evidenced to say that management quality is significant but negative to NIM. This relationship is consistent to expected hypothesis and findings provided by Barry William (1997), Saunders and Schumacher, 2000. It is understood that with others unchanged, if the proportion of - 23 - operating cost over total income decreased 1%, the NIM will be increase 5.57%. In this case, good management quality will minimum costs such as operating costs, management cost, advertising, promotional, maximum the asset effectiveness to be reduced lending cost to customers. The last variable considered herein is implied payment, which has a significant and positive impact on NIM. As analysis in chapter 2 of overview of Vietnamese banks within the period 2008-2011, although SBV prescribed interest rate ceiling, the real deposit interest rate was not lower than it was. The interest rate race happened recent year and frequently repeated in future. In the context of the low interest rate, banks, which have the most attractive promotions, customers will choose. All forms of promotion will help depositors to get higher interest rate than the prescribed ceiling rates. Moreover, loan customers will be the persons suffering those costs. Most of commercial banks often promise to offer a suitable loan rate to customers, but additional costs, such as advisory service charges, appraisal fees records, disbursements, fees, records management, etc. Therefore, NIM of banks in Vietnam will be kept stable or even higher due to large difference between high lending rate and deposit rate. - 24 - CHAPTER 6 CONCLUSIONS 1. Summary of the thesis The determinants to net interest margin of banks within a country or across many countries are the highly valuable documents for doing research in Vietnam. From the basis dealer model of Ho and Saunders (1981) and many developed versions like McShane and Sharpe (1985), Angbazo (1997) and Barry William (2007), a model of NIM is examined for banking system in Vietnam. Annual financial data of 33 Vietnamese commercial banks are collected from 2007 to 2011, creating a panel data of 132 observations. And the regression estimated by Eviews, giving the relationship results of all variables. First of all, managerial risk aversion, credit risk, management quality and implied payment are found as the statistically significant to NIM. Those results are consistent to all papers of authors mentioned above. Except management quality impact negatively on NIM, the others are positive explanations. Among them, implied payment is seen as the variable which has the highest coefficient with dependent variable. While market power and interest rate risk have no evidences to be as significant determinants. One of the reasons is the ability in collecting information to calculate those variables. 2. Limitations During the process of doing this research, there are some limitations. The first problem is collecting data. As mentioned in chapter 2, there are many kinds of banks and the number of commercial banks increases dramatically. However, most of new banks were born in 2008 and 2009, so it is too hard to collect data of whole banking system or data for a long period. Also, it is very difficult to get financial statements of foreign banks, branches of foreign banks in Vietnam. Therefore, this study chooses 33 Vietnamese commercial banks which satisfy the criteria of full data from the year 2007 to 2011. The second limitation is the ability of measuring variables in the model. For example, the variable interest rate risk is calculated easily in many countries like US, Australia, Turkey, and ect. But in Vietnam, to get the daily interest rate of government treasury bond is unable. Therefore, the study must use the interest rate of government treasury bond auctioned monthly, announced in Hanoi Stock Exchange and recorded by the expert of Sacombank securities company. It is also - 25 - hard to get quarterly data to make sample longer. Or calculating credit risk by bad debt ratio is very difficult because many banks don’t release this sensitive information. 3. Main implications From the model of factors determining the NIM of Vietnamese commercial banks, the relationship between NIM and determinants can help SBV some recommendations in the banking system management, and commercial banks in realizing which variables to be important to their NIM. For example, when banks attract customers by many forms of implied payment, such as presents, promotions, they need add more fees or interest rate into lending rate in order to cover expenses. So, the competition between banks didn’t come from development of banking operations. According to that reason, develop many kind of banks in types and size from urban to rural, it is avoided the unfair competition between banks in small market, in some big cities. Also, forbid strictly unsound competition by implied interest payment, banks will improve their services and provide suitable lending interest rates .Actually, SBV pay attention much in these problems recently. Many Decrees were issued, for example the decree No. SBV 02/2011/TT-NHNN, prohibiting promotions to raise interest rate and also requiring that deposit interest rate must published clearly. However, the SBV policy is not strong enough and often run after commercial banks to correct their faults. The insignificant relationship of interest rate risk and NIM also draws the attention about some tools which has not used effectively by SBV. Thus, SBV should monitor interest rates to lead the market by using the tools of open market, not administrative orders. For commercial banks, it is required to improve the financial capability to reduce credit risk and interest rate risk, also diversify many kinds of services. 4. Suggestion for future research From the limitations of this study, future researches will build a model more completely than this model, with full of necessary data as international papers. For instance, the proxy interest rate risk will be measured for each bank, following the method of Flannery and James (1984). Or measuring this proxy by the ratio net short term as Angbazo (1997) model. For further future, these methods can be applied if the bond market develops and provides the daily interest rate of any short term, medium term and long term treasury bond; or commercial banks pay more attention to the release of detailed financial statements. - 26 - - 27 - REFERENCES Aboagye, Anthony Q.Q. , Akoena, S.K. , Antwi-Asare, T.O and Gockel, A.F. (2008). Explaining interest rate spreads in Ghana. African Development Review, 20(3), 378–399. Aczel, Amir D. (2009). Complete business statistics. The McGraw Hill Company Angbazo, Lazarus (1997). Commercial bank net interest margins, default risk, interest rate risk and off–balance sheet banking, Journal of Banking and Finance, 21, 55–87. Brock, Philip and Rojas-Suárez, Liliana (2000). Understanding the behavior of bank spreads in Latin America, Journal of Development Economics, 63, 113–134. Flannery, Mark J. and James, Christopher M. (1984). The effect of interest rate changes on the common stock returns of financial institutions. The Journal of Finance, 39(4), 1141-1153. Garza-García, Jesús Gustavo (2010). What influences net interest rate margins? Developed versus developing countries. Banks and Bank Systems, 5(4), 32-41 Gujarati, Damodar N. (2004). Panel data regression models. basic econometrics. The Fourth Edition (636-655). The McGraw Hill Company Hausman, Jerry A. (1978). Specification tests in econometrics, Econometrics, 46, 1251-1272. Ho, Thomas S.Y. and Saunders, Anthony (1981). The determinants of bank interest margins: theory and practice, Journal of Financial and Quantitative Analysis, 16(4), 581–600 Hoang Dung (2012, Aug.30). Ngân hàng khó có lãi lớn. Bao Moi Newspaper. Retrieved from http://www.baomoi.com/Ngan-hang-kho-co-lai- lon/126/9219097.epi Horváth, Roman (2009). Interest margins determinants of Czech banks. Charles University Prague, Faculty of Social Sciences Working Papers IES 2009/11 Maudos, Joaquín and Fernández de Guevara, R. Juan (2004). Factors explaining the interest margin in banking sectors of the European Union, Journal of Banking and Finance, 28, 2259–2281. Mc Shane, R.W. and Sharpe, I.G. (1985). A time series/cross section analysis of the determinants of Australian trading bank loan/deposit interest margins: 1962– 1981. Journal of Banking and Finance, 9, 115–136. - 28 - Ngo, Dang Thanh (2010): Evaluating the efficiency of Vietnamese banking system: an application using data envelopment analysis. Available from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1626009 Nguyen, Thuy Duong and Tran, Hai Yen (2011). Factors affecting bank credit growth in Vietnam 2011: Quantitative evidence. Available from http://www.sbv.gov.vn/wps/wcm/connect/da8c7c0049d91c04b12cf7a9dc1392e4 /nguyen+thuy+duong.pdf?MOD=AJPERES&CACHEID=da8c7c0049d91c04b1 2cf7a9dc1392e4 Quach, Thuy Linh (2012). Bank sector update report Q1.2012. Available from http://www.vcbs.com.vn/Research/Report.aspx?report_type=2 Saunders, Anthony and Schumacher, Liliana (2000). The determinants of bank interest rate margins: an international study. Journal of International Money and Finance, 19, 813–832. SBV annual report 2008, 2009, 2010, 2011. Available from http://www.sbv.gov.vn/wps/portal/!ut/p/c4/04_SB8K8xLLM9MSSzPy8xBz9CP 0os3gDFxNLczdTEwODQG8TA09_E2PPINcwQwtDU_2CbEdFAL88O8k!/ Ugur, Ahmet and Hakan Erkus, Hakan (2010). Determinants of the net interest margins of banks in Turkey. Journal of Economic and Social Research, 12 (2), 101-118 Williams, Barry (2007). Factors Determining Net Interest Margins in Australia: Domestic and Foreign Banks, Financial Markets, Institutions and Instruments, 16(3), 145-165. - 29 - Appendix A : Joint-stock commercial banks An binh Commercial Joint Stock Bank Asia Commercial Joint Stock Bank BAC A Commercial Joint Stock Bank DONG A Commercial Joint Stock Bank Great Asia Commercial Joint Stock Bank Great Trust Joint Stock Commercial Bank Housing development Commercial Joint Stock Bank Kien Long Commercial Joint Stock Bank Mekong Development Joint Stoct Commercial Bank Military Commercial Joint Stock Bank Nam A Commercial Joint Stock Bank Nam Viet Commercial Joint Stock Bank OCEAN Commercial Joint Stock Bank Orient Commercial Joint Stock Bank Petrolimex Group Commercial Joint Stock Bank Saigon Thuong Tin Commercial Joint Stock Bank (Sacombank) Saigon bank for Industry & Trade Saigon-Hanoi Commercial Joint Stock Bank Sotheast Asia Commercial Joint Stock Bank Southern Commercial Joint Stock Bank The Maritime Commercial Joint Stock Bank Viet A Commercial Joint Stock Bank Viet Capital Commercial Joint Stock Bank Viet nam Commercial Joint Stock of Private Enterprise Viet Nam Technologicar and Commercial Joint Stock Bank Vietnam Commercial Joint Stock Bank of Private Enterprise Vietnam International Commercial Joint Stock Bank Wetern Rural Commercial Joint Stock Bank - 30 - Appendix B Descriptive statistics of all variables of entire sample Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis NIM 0.034796 0.033094 0.091717 0.003364 0.014562 1.07044 5.230922 MPO 0.023464 0.009872 0.180134 0.000832 0.033245 2.381959 8.746265 MRV 0.127016 0.101146 0.413903 0.029051 0.079794 1.550782 5.307784 IRR 1.59957 1.044289 3.866855 0.442844 1.337226 1.032082 2.255426 CR 0.008512 0.006057 0.050683 0.000000 0.008229 2.856519 13.90285 MQU 0.46493 0.434885 0.88775 0.121708 0.1442 0.741603 3.502662 IP 0.009436 0.009657 0.065762 -0.03203 0.01205 -0.16701 7.583011 Jarque-Bera 52.58207 306.4296 82.20062 26.4834 833.3096 13.48913 116.1356 0 0 0 0.000002 0 0.001177 0 Probability Sum Sum Sq. Dev. Observations 4.593015 3.097216 16.76609 211.1432 1.123615 61.37076 1.245503 0.027779 0.144783 0.834093 234.2506 132 132 132 132 0.00887 2.723981 0.019021 132 132 - 31 - 132 Appendix C Descriptive statistics of all variables of SOCBs Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis NIM 0.033721 0.031451 0.05143 0.018983 0.008618 0.535175 2.655955 MPO 0.084756 0.087524 0.180134 0.009654 0.045629 -0.05375 2.884502 MRV 0.056135 0.059808 0.078094 0.029051 0.012439 -0.67769 2.933611 IRR 1.59957 1.044289 3.866855 0.442844 1.366758 1.032082 2.255426 CR 0.01119 0.010693 0.023174 0.000167 0.006851 0.111846 1.741124 MQU 0.534027 0.480391 0.88775 0.299686 0.165646 0.833677 2.640307 IP 0.013012 0.013604 0.024096 0.001618 0.006764 -0.06895 1.903976 Jarque-Bera 1.053346 0.020747 1.534539 4.012637 1.362338 2.424538 1.016906 Probability 0.590567 0.98968 0.464279 0.134483 0.506025 0.297521 0.601425 Sum Sum Sq. Dev. Observations 0.674417 1.695129 1.122707 31.99139 0.223805 10.68053 0.260234 0.001411 0.039558 20 20 0.00294 35.49252 0.000892 0.521334 0.000869 20 20 20 20 - 32 - 20 Appendix D Descriptive statistics of all variables of JSCBs Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis NIM 0.034987 0.033305 0.091717 0.003364 0.015405 1.025944 4.844117 MPO 0.012519 0.008456 0.057377 0.000832 0.012255 1.627907 5.395075 MRV 0.139673 0.112289 0.413903 0.042556 0.080131 1.465144 4.953702 IRR 1.59957 1.044289 3.866855 0.442844 1.338138 1.032082 2.255426 CR 0.008034 0.005895 0.050683 0.000000 0.008388 3.231214 15.72679 MQU 0.452591 0.425467 0.881677 0.121708 0.137222 0.640706 3.418703 IP 0.008797 0.009088 0.065762 -0.03203 0.012681 -0.06103 7.230079 Jarque-Bera 35.51805 76.23798 57.88319 22.47077 950.7598 8.480864 83.57285 0 0 0 0.000013 0 0.014401 0 Probability Sum Sum Sq. Dev. Observations 3.918598 1.402087 15.64338 179.1518 0.899811 50.69022 0.985269 0.026341 0.016671 0.712729 198.7581 112 112 112 112 0.00781 2.090109 112 112 - 33 - 0.01785 112 Appendix E Hausman Test result Correlated Random Effects - Hausman Test Equation: Untitled Test cross-section random effects Chi-Sq. Statistic Chi-Sq. d.f. Test Summary Cross-section random 15.392837 Prob. 6 0.0174 Random Var(Diff.) Prob. Cross-section random effects test comparisons: Variable MPO MRV IRR CR MQU IP Fixed 0.053076 0.082282 0.083040 0.080355 -0.000441 -0.000523 0.353095 0.203545 -0.055701 -0.054571 0.981254 0.871824 0.008245 0.000079 0.000000 0.005403 0.000010 0.001402 0.7477 0.7629 0.4820 0.0419 0.7157 0.0035 t-Statistic Prob. Cross-section random effects test equation: Dependent Variable: NIM Method: Panel Least Squares Date: 04/08/13 Time: 10:32 Sample: 1 132 Periods included: 4 Cross-sections included: 33 Total panel (balanced) observations: 132 Variable Coefficient C MPO MRV IRR CR MQU IP 0.037340 0.053076 0.083040 -0.000441 0.353095 -0.055701 0.981254 Std. Error 0.004726 7.900660 0.096778 0.548431 0.014543 5.709755 0.000455 -0.968619 0.122738 2.876816 0.006367 -8.748141 0.075782 12.94840 0.0000 0.5847 0.0000 0.3352 0.0050 0.0000 0.0000 Effects Specification Cross-section fixed (dummy variables) R-squared 0.873346 Mean dependent var 0.034796 - 34 - Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.821595 0.006151 0.003518 507.8496 16.87587 0.000000 S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat 0.014562 -7.103782 -6.252045 -6.757675 2.098570 - 35 - Appendix F Fixed effect model Dependent Variable: NIM Method: Panel Least Squares Date: 04/08/13 Time: 10:30 Sample: 1 132 Periods included: 4 Cross-sections included: 33 Total panel (balanced) observations: 132 Variable Coefficient C MPO MRV IRR CR MQU IP 0.037340 0.053076 0.083040 -0.000441 0.353095 -0.055701 0.981254 Std. Error t-Statistic 0.004726 7.900660 0.096778 0.548431 0.014543 5.709755 0.000455 -0.968619 0.122738 2.876816 0.006367 -8.748141 0.075782 12.94840 Prob. 0.0000 0.5847 0.0000 0.3352 0.0050 0.0000 0.0000 Effects Specification Cross-section fixed (dummy variables) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.873346 0.821595 0.006151 0.003518 507.8496 16.87587 0.000000 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat 0.034796 0.014562 -7.103782 -6.252045 -6.757675 2.098570 - 36 - [...]... of a bank’s net interest income and leads to the ratio net interest margins (NIM), which measures the return on bank’s earning assets, is high Accordingly, commercial banks have been criticized to have maintained high net interest margin and no difficulty sharing with companies Despite net interest margins being one of the major determinants of bank profits, little is known of the determinants of Vietnamese... high lending interest rates Although the State Bank of Vietnam (SBV) imposed a ceiling deposit interest rate in the hope of dragging down lending interest rates, the access to banking loans remains harsh for companies In the mean time, the interest rate spreads (i.e., the difference between lending interest rates and deposit interest rates) brings huge profits to commercial banks This is the largest... non -interest expenses must be accounted for in the net interest margins Besides, the high credit risk also partly contributes to the high net interest margin, as credit risk premium increases in the current economic downturn Consequently, the principal objective of this research is to empirically test the model of bank interest margin determination in the context of Vietnamese banking system Based on the. .. the findings of the research, the SBV would be able to have effective solutions (instead of the administrative measures) to reduce the lending rates 2 Research objectives Based on the above-mentioned problems, this research is formulated towards the following objectives:  To identify the factors determining the net interest margins of 33 Vietnamese commercial banks  To examine the impact of determinants... herein These characteristics of Vietnamese banking system help in understanding and explaining for later analysis in the next chapters 1 Growth of Vietnamese banking system Though the Vietnamese banking system is quite young compared with others in the world, it has been growing very fast The number of commercial banks increases dramatically, rising from 8 banks in 1991 to 85 banks in 2007 and 98 banks in. .. rate, or within a cap of 18% per annum, banks add more extra fees into VND lending interest rate to cover the high mobilizing interest rate Therefore, both real mobilizing interest rate and lending interest rate increased very high From 2009 until now, SBV continuously enacts many decrees to stop the interest rate race and keep it stable Base interest rate Refinancing interest rate Discount interest rate... Vietinbank, MHB were equitized, SBV still sort them into group State-owned commercial banks (SOCBs) Therefore, of those 98 banks, there are 50 branches of foreign banks, 5 foreign banks, 4 joint-venture banks, 34 jointstock commercial banks (JSCBs), and 5 SOCBs In the period of 2006-2012, the Vietnamese banking system has increasingly attracted foreign attention The number of foreign banks in Vietnam. .. trading bank interest margins Why Vietnamese commercial banks need to maintain high NIM or which variables have strong impact on NIM become the interesting questions for all those who care about bank sector in Vietnam Therefore, in this context, the study will help to explain the queries above It is likely that the NIM reflects the costs such as the implied interest payments that the banks have to offer... while the outputs were total asset, interest and similar income and other incomes The result showed the way for Vietnamese banking system to increase its efficiency is managing their lending activities Nguyen Thuy Duong and Tran Hai Yen (2011) analyzed the determinants of credit growth of commercial banks in Vietnam in 2011 The dataset was collected from some banks Vietnam in quarter 1, 2, 3 of 2011 They... the interest rate risk In the researches of Ho and Saunders (1981) or Saunders and Cornett (2009), they analysis that there are two kinds of interest rate risk which banks must face to With shortterm borrowing but long-term lending, banks cannot avoid the refinancing risk if interest rates increase in the future In this case, banks must pay more for new liabilities or higher re-borrowing cost for the ...THESIS FACTORS DETERMINING NET INTEREST MARGINS IN THE COMMERCIAL BANKS IN VIETNAM In Partial Fulfillment of the Requirements of the Degree of MASTER OF BUSINESS ADMINISTRATION In Finance... Accordingly, commercial banks have been criticized to have maintained high net interest margin and no difficulty sharing with companies Despite net interest margins being one of the major determinants of. .. bank interest margin determination in the context of Vietnamese banking system Based on the findings of the research, the SBV would be able to have effective solutions (instead of the administrative