Assessing the determinants of bank performance in vietnam empirical evidence from domestic commercial banks in the years 2005 2011

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(OUM OPEN UNIVERSITY MALAYSIA

RESEARCH PROJECT JOLSOS

(BMBR5103)

ASSESSING THE DETERMINANTS OF BANK PERFORMANCE IN VIETNAM: EMPIRICAL EVIDENCE FROM DOMESTIC

COMMERCIAL BANKS IN THE YEARS 2005-2011 HUTECH LIBRARY 4- €565

STUDENT’S FULL NAME :LuongHaiChau

STUDENT ID : 2449213

INTAKE : May 2012

ADVISOR’S NAME & TITLE :PhanThiGiac Tam, Ph.D

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Advisor’s assessment

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ACKNOWLEGEMENT

I would like to express my sincere gratitude to my thesis and academic Advisor, Phan Thi Giac Tam, Ph.D, for her encouragement, guidance and patience, time and invaluable input throughout the preparation of this work Thank for her dedication and expertise, I am able to complete the research so effectively in a short period of time Grateful thanks are therefore due

to my Advisor, Phan Thi Giac Tam, Ph.D

Furthermore, I would like to thank my family and friends for their love, encouragement and

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ABSTRACT

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TABLE OF CONTENTS Page Title Page Acknowledg€rT€TIE << 5< s25 x4 0 T1 Hàn Hán 141801441411014.1 7811118 i ASTTACE cà HH HH HH TH ng g4 1 14841111101001101110020.1000010 1n ii Tabl€ OŸ COTIE€TIẨS Hà nh nh TH nàn HH0 18.1011414102101011721172000700 iii List of Abbreviations 2.0.0.0 seesssssscscssssnenssssessusesenscsevensessnseteasenesersenssensatoneneneseesessnsaeneneees iv List of Tables and EÏEUT€S - sa cà HH HH H091 0101 14003131340118120100070141204e V Chapter One: INTRODUCTION sssssssssesssseseccessersrserenceserssserserernsnssonsnsnensssceneneasennens 01 Chapter Two: LITERATURE REVIEW ccccssssscscssesssssscesenserersnsneserensaees 03 2.1 The studies outside of Vietnam .ccceccsssssssssssscsstsnserenesceseesesensereassnsenseeesseeetes 03 2.2 The studies of Vietnamese banking s€CfOT che 16 2.3 Summary and remarks ccesssecssscecseescssessssssensesesscsssenseneseesssssnsessnsassansessssneeeeeeres 19 Chapter Three: METHODOLOGY AND DA TA « eeessseseseeseesesessesersresee 23

3.1 Methodology and econometric mOd€ÏL .- «- se s3 t S1 1411111 rirexxe 23

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CAU DEA GMM IMF JSCB OLS PNS ROAA ROAE SBV SFA SOCB SOEs UNDP VCBS LIST OF ABBREVIATIONS

Country Analysis Unit Data envelopment analysis Generalised Method of Moments International Monetary Fund Joint Stock Commercial Bank Ordinary Least Squares Phuong Nam Securities Return on Average Assets

Return on Average Equity

State Bank of Vietnam Stochastic frontier approach State-owned Commercial Bank

State-owned Enterprises

United Nations Development Programme Vietcombank Securities

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LIST OF TABLES AND FIGURES

Table 1: Summary of cross-country studies on bank performance Table 2: The comparison between GMM and OLS estimators Table 3: The details of sample banks studied

Table 4: List of the variables studied in the study Table 5: Descriptive statistics

Table 6: Correlation matrix for the explanatory variables Table 7: Multiple regression results

Figure 1: The number of banking institutions in Vietnam (2005-2011)

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Chapter One INTRODUCTION

Banking industry has been widely considered as the most performing and contributing service sector in an economy as it encourages people to save and invest in productive streams and become contributing individuals of a nation and it also provides security and ensure safety of investments and savings (Raza et al., 2013) Similar to the economy in Vietnam, although banking sector does not directly create goods or products, the growth in this industry is an incentive for developments in other sectors (PNS, 2013) In other words, the strong and stable position of banking system has become more and more important, especially in a world of global financial system as recent Hoffmann (2011) states that in the liberalization markets, the foreign bank involvement in domestic banking markets has increased, it tends to result in intensifying competition and reduction in margins So as to operate effectively in such severe

environment, it is essential to answer the following questions: what are the factors that can

help banks achieve better performance? To what extent do these control variables have impact on bank profitability? The findings of these questions are widely believed not only important to bank managers but also crucial banking regulators in enhancing the policy (Berger and Humphrey, 1997; Vu and Nahm, 2013) As far as ] am aware, the relationship between bank profitability and its indicators has been conducted in the developed countries for a while but it has just been concerned in the developing and emerging markets from last two decades In spite of the recent appearance, this topic has been an exciting phenomenon studied by a great number of analysts such as Guru et al (2002), Sufian and Chong (2008), Flamini et al (2009), Gardener et al (2011), Reddy and Nirmala (2013) However, to the best of my knowledge, there are only few researches in the Vietnamese banking system and almost all of them are focused on bank efficiency’ instead of profitability As being acutely conscious of the importance of the objective but insufficiency of existing resources, I decide to choose “Assessing the determinants of bank performance in Vietnam: Empirical evidence from domestic commercial banks in the years 2005-2011.” as my thesis topic

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As the first study which examines the determinants affecting bank performance measured by profitability ratios in Vietnam, the paper is expected to achieve two following aims The principal target is to identify the correlation between explanatory indicators and bank profitability on the basis of a fixed effects model The second target is to compare and contrast the effect of these factors on two groups of commercial banks: state-owned banks and joint-stock banks Employing the ordinary least squares (OLS) and data set for the period '

2005-2011, the results indicate that bank specific characteristics, macroeconomic conditions

and market structure altogether have affected to bank performance but in disparate magnitude and sign It is found that the factors which have significant impact on joint-stock banks’ performance, for instance bank size, the growth rate of economy, and annual inflation rate are insignificantly related to profitability of state-owned banks Conversely, the significant

indicators in state-owned banks, such as credit risk and the effect of fund source are :

insignificant in joint-stock banks Moreover, while the performance of the latter group seems reliant on the macroeconomic conditions, the regression results reveal that there is no significant correlation among macroeconomic risk factors and state-owned banks’ performance The further details are exhibited in chapter four

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Chapter Two LITERATURE REVIEW

This chapter is going to review the existing papers that are related to determinants of bank profitability For a clear structure, this section is mainly divided into three parts: the first part is the general review of studies in different countries, except Vietnam The second part is going to focus on the analyses in Vietnam banking system Finally, based on the experience from various literature and cross-reference studies, the direction for the current analysis is

exhibited

2.1 The studies outside of Vietnam

For the origin of the objective, it seems that the researches on bank performance and its determinants have been the interest of a large number of authors for many decades ago (Alhadeff, 1954; Schweiger and McGee, 1961; Grebler and Brigham, 1963; Benston, 1972) In these early studies, the examination of production efficiency and contro] factors are generally for the purpose of testing the presence of the economies or diseconomies of scale”

At first, the observation of scale economies is mainly based on the relation among size,

organizational form and cost efficiency Then, it is proved that beyond the influence of size factor, the cost efficiency is also explained by other explanatory indicators (Smith, 1955) Investigating for the different control variables in scale economies is the way how these early studies closely related to the current papers on assessing influencing factors of bank performance (Spathis et al., 2002) Schweiger and McGee (1961), Gramley (1962), Grebler and Brigham (1963) are regarded as the pioneering work on diversifying explanatory indicators in economies of scale Apart from bank size variable captured by deposits, Schweiger and McGee (1961) select the ratio of time to total deposits; the level of earning asset structure captured by the ratios of business loans to assets, consumer loan to assets, and farm loans to assets; the rate of asset growth; and other structure variables as independent variables affecting cost efficiency In line with the statement that there are many of the

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determinants which influence bank costs other than total assets, Gramley (1962) take more independent variables into account, such as percentage growth of assets, the ratios proxy for earning asset structure (total loans to total assets, non-government securities to total assets, and consumer to total loans) Employing multiple regression technique, Schweiger and McGee (1961), Gramley (1962) testing for the all member banks in Chicago and Tenth District respectively, both studies conclude that the model design highly presents economies of scale In other words, it indicates that the additional variables also affect cost efficiency Taking Grebler and Brigham (1963) as another example, more independent variables are also added to assess the cost efficiency in California, such as average deposit size, the rate of assets growth, the ratio of buildings to total assets However, the findings of Grebler and Brigham show no evidence for the presence of economies of scale So a raising question here is that applying similar functional forms why the results are completely conflicting among studies With regard to these above studies, Hester and Zoellner (1966) state that the findings obtained from model employed are unreliable Explaining for the weaknesses, Hester and Zoellner assert that as the model was not formulated to derive estimates of net rates of return so in actually, the regressions did not take into account all earning assets and deposit liabilities as explanatory variables Another limitation of the regression models is the multicollinearity problem among variables, for example the close correlation between dependent variable (measured by the ratio of total operating costs to total assets) and size variable (total assets) may lead to unexpected misstatement In support of the remaining issues in Schweiger and McGee (1961), Gramley

(1962), Grebler and Brigham (1963), it is criticized that:

The assumed functional relationship among the output variables is not based on any production theory or other rationale .[and] single point stock data were used to

estimate service flow, which introduces an unknown bias into the coefficients

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Based on the experience from Gramley (1962) as mentioned above, Hester and Zoellner (1966) do a research on the relation between bank portfolios and earnings in Kansas City and Connecticut over the period 1957-1963 This study is marked as one of the earliest papers in the U.S with the aim of examining the determinants of bank profits instead of economies of scale Analyzing the influence of balance sheet items on profits, Hester and Zoellner (1966) find that almost all of items on the debit side have significant positive impact on bank profits, whereas the items on credit side is negatively related to bank earnings To some extent, although the structure of bank is found to have importance relation to its earnings, the explanatory power of the model design is quite low In other words, it is quite probable that bank performance is likely affected by other factors beyond the internal resources

In support this statement, Haslem (1968, 1969) expands the scope of determinants with management quality, size, and location of commercial banks in California Using 64 operating ratios to assess the management of banks, classifying size and location features into different groups, it is found that all these explanatory factors (management, size, location) have significant relationship with bank profitability (Haslem, 1968& 1969) Whereas, in another study conducted in Texas, Fraser and Rose (1971) point out the opposite results With the data from 1966 to 1967, it is found that the bank profitability in Texas is not influenced by loan

composition, bank size, and bank costs

Due to the considerably contradictory findings, Rhoades (1977) makes an observation to compare the relative studies in the U.S In the total 39 papers reviewed, it is found there were 9 cases without evidence of the concerned relation and 30 cases presenting the significant correlation To explain for the difference, the geographic scope of the studies is believed the main cause (Rhoades, 1977; Wheelock and Neely, 1997) In the 9 cases without bank structure-performance relation, they observe the individual banks instead of whole market as a unit As a result of smal! sample, it just concerns “little room for variation in the degree of concentration” and that cause conflicting results among studies (Rhoades, 1977)

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Heggestad (1977) as an example, if in the preceding studies, the independent variables are generally restricted to bank compositions, the industry-specific and market structure variables are additional concerns Using the ratio of net income after taxes to total assets (a flexible form of return on assets) as proxy for bank profitability, it is found that the relation between market structure variables and bank profitability is more statistically significant than between bank concentration and bank behaviour (Heggestad, 1977) In line with this statement, Smirlock (1985) also highlights the considerable influence of market structure on return on assets, return on equity, and return on total capital For further researches, Smirlock (1985) also posits that beyond bank concentration and market structure, there may be other potential

Tesources

Beside the familiar profitability ratios, such as return on assets, and return on equity,

bank performance is also measured by net interest margins (Ho and Saunders, 1981) Four principal explanatory factors are believed to have strong effect on banker’s mark-up are the implicit interest rate, the opportunity cost of required reserve, default premiums on loans and market structure Lerner (1981) comments on Ho and Saunders (1981) that even though the model design is highly effective in assessing the determinants of net interest margins, the difficulty of the analysis is the variability of independent variables In spite of the limitation,

there are many studies (Alle, 1988; Angbazo, 1997; Naceur, 2003; Ongore and Kusa, 2013) is

developed based on the relationship of net interest margins and its determinants

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bank efficiency, the analyses usually consist of two stages The estimation of efficiency score is going to be conducted first and then the evaluation of relationship between bank efficiency and its determinants is concerned According to Akhigbe and McNulty, the techniques employed are stochastic frontier analysis’ and Tobit regression model respectively The empirical results indicate that bank size, bank age, fee-based services, credit risk, and location have significant and positive effect on bank efficiency By contrast, bank management ability and state ownership are found to highly affect profit efficiency in negative trend (Akhigbe and McNulty, 2011)

Together with the level of bank efficiency, the researches on determinants of bank performance measured by financial ratios continue to be concerned One of the most recent studies of the US market is Hoffmann (2011) examining the determinants of bank profitability over the period 1995-2007 The analysis adopts three different types of method: fixed effect estimations, Generalised Method of Moments (GMM) with system estimator, and OLS estimations Consequently, the findings in fixed effect approach and ordinary least squares technique are fairly similar to each other but quite different from GMM For example, the business capacity ratio is found to be significantly and negatively related to return on equity in the former approaches, conversely, in the latter approach, the coefficient indicates significantly positive relation Taking the ratio of interest expense to total equity as another example, while the correlation between this ratio and bank profitability is significant and positive in fixed effects and ordinary least squares models but considerably negative in GMM

estimation

In the European region, the examinations of determinants of bank profitability are also the interest to study Molyneux and Thorton (1992) analysed this subject on a sample of 18 countries for the period from 1986 to 1989 It is found that there are positive associations between bank profitability and bank concentration, government ownership Arguably, Goddard et al (2004a) focus on six developed markets in the EU (Denmark, France, Germany, Italy, Spain, the UK) and find that ownership is insignificantly related to return on equity The leverage of banks significantly and positively correlates with profitability, this finding implies

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that the higher the ratio of capital to total assets, the more profitable the banks In the terms of bank size, Goddard and his colleagues state that the relationship between bank size and profitability is still unconvincing In accordance with off-balance sheet activities, only the UK indicates positive relation, by contrast in other markets, the non-traditional — profitability relationship is neutral or negative (Goddard et al., 2004) Another typical study also analysing the determinants of bank profitability in European countries as a group is introduced by Athanasoglou et al (2006) Using least squares methods of fixed effect and random effect models, Athanasoglou and his partners reveal that bank specific characteristics, industry specific determinants and macroeconomic conditions altogether have strong effect on bank profitability Collecting the data in South Eastern European bank during the period 1998 to 2002, capital adequacy ratio, bank size, foreign ownership and inflation rate are found to have positive and significant influence on return on assets and return on equity The factors which may reduce considerably the profitability are credit risk, the overheads efficiency ratio One of the key points of the study is that it not only identifies the correlations between bank performance and explanatory indicators but also provides the solutions for negative ones to the purpose of maximizing profits For example, the increase in credit risk is declared to be one of main causes of earnings decrease Hence, it is postulated that South Eastern European banks should pay more attentions to bad-debt management Otherwise, the increasing proportion of non-performing loans might become a serious problem (Athanasoglou et al., 2006)

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found that when bank size, credit risk, capitalization level, and the proportion of off-balance

sheet items to total assets increase, bank efficiency is also rising, especially with cost efficiency Conversely, the levels of cost and profit efficiency significantly decrease when non-performing provision ratio increases (Yildirim and Philippatos, 2007)

Apart from the cross-countries analyses, there are a great number of specific country

studies in the EU and other developed countries, such as Ho and Tripe (2002), Williams

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domestic banks gain benefits from the increases in inflation rate and growth rate of real GDP per capita increase Conversely, foreign banks seem to suffer from statistical reduction in earnings From this point of view, Pasiouras and Kosmidou (2008) point out that state ownership also has particular impact on bank profitability and it can be regarded as a separate explanatory factor in further researches

In the Switzerland banking system, Dietrich and Wanzenried (2011) evaluate the determinants of profitability from 1999 to 2009 The testing period is divided into before and after the crisis (1999-2006, 2007-2009) Employing the GMM estimation, the empirical results depict that bank performance is mainly explained by operational efficiency, the growth of total loans, funding costs and business model (Dietrich and Wanzenried, 2011) From the general view of findings, the Switzerland banking system presents inefficiency in productivity even for whole period tested The relationship of return on average assets with a majority of explanatory variables, such as operating efficiency, the growth of deposits, bank size, the level

of capitalization, and asset quality, almost all of them tend to reduce the profits For instance,

the significant and negative coefficient of operating efficiency implies that the increases in administration costs as well as wages and salary expenses are going to reduce the profits rather than improve bank management ability (Dietrich and Wanzenried, 2011) Furthermore, the analysis is one of the scant studies presenting the changes in correlations before and after the peak of global crisis, especially with bank’s capital, credit quality, funding costs and Herfindahl index Taking the capital adequacy ratio as example, before the crisis 2007, the correlation between this control variable and bank profitability was insignificant When the crisis occurs, the relationship turns to significant and negative trend In line with this

statement, the relation of credit risk with earnings is also found to transform from insignificant

to significantly negative It implies the fact that the increases in provision for bad-debts are in parallel with the rises in monitoring expenditures

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return on assets, return on equity and net interest margins The determinants of these ratios are also divided into two categories: internal factors (bank specific characteristics) and external factors (market structure and macroeconomic conditions) Although the relative studies in Asian countries appear quite late comparing to the U.S and EU countries, it has been an inspiration to study with a great number of papers, for instance Guru et el (2002), Chantapong (2005), Vong and Chan (2009), Garcia-Herrero et al (2009), Sufian and Habibullal (2009), Alper and Anbar (2011), Raza et al (2013), Reddy and Nirmala (2013)

One of the remarkable papers on the Asian market is the examination of Guru and his colleagues (2002) Assessing the determinants of commercial bank profitability in Malaysia, Guru et al (2002) find that bank profitability is strongly affected not only by bank specific

characteristics, such as size, capital adequacy, credit risk, bank management ability but also by

market factors, for example inflation rate and interest rate For more details, bank size,

capitalization level, credit risk, inflation and interest rate have positive effect on bank

profitability while the correlation of management efficiency with profits is considerably negative From the coefficients of explanatory indicators, bankers might adjust their financial structure better in order to maximize the profitability For example, both loans and investments in securities and subsidiaries encourage increasing returns However, Guru et al (2002) suggest that the statistical significance of the former is higher than the latter so bankers

should focus on loans rather than investments

Chantapong (2005) analyses the relation between return on assets and its indicators in Thailand during the period 1995 to 2000 Using the generalized least squares technique, it is found that the most effective factors are credit risk, quality of assets, and the level of bank

diversification into non-traditional services On the one hand, the significant and positive

correlation between the ratio of loans to total assets and bank profitability encourages the Thai banks to increase loans to their customers On the other hand, the results also indicate that bank managers should focus on controlling non-performing loans as the influence of bad-debts is significantly negative on bank revenues The significant and positive relation between the earnings from non-traditional activities and bank profitability highlights the important contribution of these activities in improving bank performance In addition, due to the increasing participation of foreign banks in Thai banking market, Chantapong (2005) makes a

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comparison between foreign banks and domestic banks according to the determinants studied The results point out that the foreign banks obtain the higher profitability than domestic ones From this point of view, it is quite probable that state ownership should be considered as the separate indicator in the analysis, especially with the countries that have a great number of foreign participations Also examining Thai banking sector, Sufian and Habibullah (2010) analyse technical efficiency score and its determinants in the years 1999-2008 The analysis consists of two stages: firstly, the data envelopment analysis’ is employed to assess the level of efficiency Then, to evaluate the correlation between technical efficiency score and its determinants, both Tobit regression model and OLS are adopted Surprisingly, the empirical results exhibit that there is no significant difference between two regression models.° It is

pointed that bank size, credit risk, and bank diversification towards non-interest income are

significant and negative factors which lower the efficiency of banks By contrast, the significant and positive correlation is found between bank capitalization and technical efficiency score Based on the findings, Sufian and Habibullah (2010) conclude that the inefficiency is mainly dependent on scale of banks rather than pure technical efficiencies

From this point of view, small banks are the most efficient, whereas medium-sized banks are

the least efficient group However, the limitation of this statement is that the analysis seems to have no concern with the largest banks which may be benefits from economies of scale (Akhigbe and McNulty, 2003; Yildirim and Philippatos, 2007) Furthermore, Sufian and Habibullah (2010) remark that domestic banks seem to be more efficient than foreign counterparts This conclusion is quite conflicting to the study of Chantapong (2005) above It is fairly certain that difference results from two main reasons: the method employed and the objective studied as Tregenna (2009) state that high profitability may not be parallel to high efficiency Therefore, the correlation of determinants with bank efficiency may be dissimilar to that with bank profitability

? Data envelopment analysis (DEA) is one of the non-parametric techniques generally used to rank bank efficiency score For further details, please refer to Yue (1992)

® According to Hoff (2007) and Andries (2011), the ordinary least squares may be unbiased in assessing the

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Sufian and Chong (2008) analyse the determinants of bank profitability in the Philippines during the period from 1990 to 2005 Adopting the linear regression model for 24 commercial banks, it is found that the variations in bank performance are not only explained by endogenous factors but also exogenous indicators In terms of bank specific characteristics, bank profitability on the one hand suffers negative effects from bank size, credit risk, and the overhead expense ratio, on the other hand is supported significantly by the increase in capital adequacy ratio and fee-based services In terms of macroeconomic conditions, only annual inflation rate has significant and negative impact on bank profitability The other external indicators, such as economic growth, the growth of money supply, and the developments of stock-market though encourage bank profitability but their impact is insignificant

The determinants of Macao commercial banks have been examined by Vong and Chan (2009) With the combination of fixed effects model and panei data over the period from 1993 to 2007, the linear regression analysis indicates that bank specific attributes have significant influence on earnings The findings in Macao are quite similar to the Philippines case above Particularly, it is also found that bank size, credit risk, and quality of assets are negative determinants of bank profitability By contrast, Macao banks seem to achieve a higher level of profitability when they increase the level of capitalization and improve the bank management

ability In accordance with macroeconomic conditions, annual inflation rate is the most

important factor comparing to the real interest rate, the growth of economy, the size of the banking sector in the whole economy, and the Lerner Monopoly Index, that have positive impact on bank returns

In the China banking system, the determinants of commercial banks have just been considered for last few years (Garcia-Herrero et al., 2009; Sufian and Habibullah, 2009a) Although the analyses are conducted by different estimations (Generalized Method of Moments and linear regression model respectively), the conclusions seem similar to each other Both of studies conclude that almost all of bank specific characteristics, industry specific factors and macroeconomic conditions have statistically significant influence on bank profitability Garcia-Herrero et al (2009), Sufian and Habibullah (2009a) both conclude that bank profitability is significantly and positively related to capitalization level and quality of assets However, Sufian and Habibullah (2009a) declare that the influence of explanatory

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indicators and bank performance is inconsistent among different type of banks To support this statement, the authors run multivariate regressions four times (all banks, state-owned commercial banks, joint-stock commercial banks and City banks) Interestingly, the empirical

results illustrate dissimilar correlations For example, in the aspect of credit risk, the “all

banks” coefficient indicates positive but insignificant association, in line with that is joint- stock banks and City banks However, credit risk is a significant factor to optimize profits in state-owned banks In terms of quality of assets, the coefficients in “all banks” case and foreign banks indicate that increase in non-performing loan provision will reduce bank profits but insignificantly On the contrary, this connection is significant and positive in the other types of bank (Sufian and Habibullah, 2009a) Put another way, returns in state-owned banks and joint-stock banks are growing significantly when the ratio of assets quality rises As a result of disparate correlations, it is essential to divide all banks into smaller groups so as to provide the more accurate relations

Taking India as another typical example for the Asian region, Reddy and Nirmala (2013) point out statistically significant associations between profit inefficiency and explanatory indicators Employing the stochastic frontier approach and technical inefficiency effects model, it is revealed that profit efficiency of Indian commercial banks tends to increase when banks expand their size, the level of capitalization, the proportion of total loans over total assets, the ratio of demand deposit over total deposits, and off-balance sheet activities The other determinants relating to industry specific attributes, such as Herfindahl index, bank ownership (foreign banks or domestic banks) are found to have positive correlations with bank inefficiency (Reddy and Nirmala, 2013)

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Macroeconomic conditions though have impact on bank profitability, the influence of these variables are insignificant Based on the empirical analysis, it is noticed that in the correlation between bank profitability and its determinants, the opportunity of a fixed effects model has been rather than random effects model (Sufian and Habibullah, 2009b) This statement was also supported in the Philippines study Moreover, the results exhibit that the correlation of each independent variable with different profitability measures are dissimilar from each other For instance, when the level of capitalization increases, it will affect three profitability ratios in different ways as follow: return on average assets increases but insignificantly, net interest margins significant increases, and return on average equity slightly decreases It is in line with the suggestion in previous study that the choice of profitability ratio is dependent on the objective of measure (Guru et al., 2002)

One of other successful markets in Asia is Korea, Sufian (2011) analyses factors influencing bank profitability in this country over the period 1992 to 2003 In line with the existing studies, bank performance in Korea is highly affected by internal and external factors In the analysis, least squares method of the fixed effects model is adopted It is pointed that assets quality and bank management efficiency are the most significant factors in endogenous aspect that have negative effect on bank profitability On the contrary, fee-based services highly encourage bank performance In accordance with exogenous factors, annual inflation rate, the level of concentration measured by the ratio of three largest banks’ assets, and the development of stock market are found to have significant and positive influence on improving efficiency of assets management (Sufian, 2011)

In short, the studies relating to the determinants of performance in commercial banks

have been conducted worldwide The topic has attracted a large number of analysts not only in the developed countries but also in the developing nations A review of literature and cross- reference studies, excluding Vietnamese banking system shows that either financial ratios, such as return on assets, return on equity, net interest margins or the level of efficiency can be proxies for this response variable Based on the objective studied, the techniques employed are different from each other Particularly, in the cases of bank efficiency, the procedure consists of two stages: the non-parametric approach (DEA) or parametric approach (SFA) is applied to estimate efficiency score and the Tobit model is used to assess the determinants of bank

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efficiency In the cases of financial ratios as proxy bank profitability, the GMM estimator or OLS method is preferable technique to evaluate the influencing factors From the findings of each literature, it is likely that the impact of explanatory indicators on bank performance is inconsistent among countries or even at the same country The inconsistency highly results from different macroeconomic environments, the unequal size and operation of banking sector across countries (Demirguc-Kunt and Huizinga, 1998) As a result of that, it is worthwhile to focus on the target market to review how the determinants of bank performance have been

concerned

2.2 The studies of Vietnamese banking sector

In this section, the studies of evaluating bank performance in Vietnamese market are mainly regarded To my knowledge, the investigation of the efficiency in banks has conducted from the 2000s, such as Hung (2007), Ngo (2010), Vu and Turnell (2010) However, these studies just focus on measuring the value of cost and profit efficiency but not introducing their specific control factors yet The findings from these studies just provide the general statements such as the total factor productivity is mainly achieved from the advancement in allocative efficiency, meaning regulatory procedures; and technical efficiency, meaning managerial capacity (Hung, 2007) Ngo (2010) employs DEA approach to analyse the efficiency of commercial banks in the year 2008 Based on the inputs variables (wages, cost, expenses, etc.) and outputs variables (quantity, revenues, profit), the empirical result indicates that the efficiency score of Vietnamese banks is close to optimal score Vu and Turnell (2010) postulate that the mean of bank cost efficiency in the years 2000-2006 is 0.8721 and the average banks should reduce the costs by 12.8 percent to catch up the most efficient

associations Nonetheless, the study has not mentioned the solutions to obtain that target

Until 2011, the first study on the relation between bank efficiency and its determining variables is attempted by Gardener and his partners Nevertheless, this study is an analysis for a group of five countries in South East Asia’, not Vietnam individually Combining the non- parametric approach (DEA) with Tobit regression model, it is found that microeconomic conditions, such as the level of capitalization, and bank private credit are significantly and

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positively related to bank efficiency On the contrary, the increases in bank size tend to lower bank efficiency score considerably Additionally, the coefficient of ownership dummy variable reveals that foreign banks are superior to local counterparts in efficiency In terms of macroeconomic conditions, the influence of the rate of economic growth and annual inflation rate are selected to review but the empirical results exhibit no significant correlation between these external factors and bank efficiency Arguably, Ngo (2012) focuses whole attention on the influence of market structure on Vietnamese bank efficiency but comes to contradictory findings against Gardener et al (2011) In accordance with exogenous variables studied, it is pointed out that bank efficiency is significantly affected by external factors Particularly, the nominal interest rate, government expenditures, and the concentration level of the banking system (measured by the total assets of three largest banks to all banks) are found to be positively related to bank productivity The correlations of nominal exchange rate and inflation rate with bank efficiency are significant and negative The conflicts appear between two studies although both of them employ the same methodology The disagreement in sample size may be the possible explanation for the conflicts Gardener et al (2011) is a cross-country study, whereas, Ngo (2012) is a single country paper Consequently, the findings of Gardener et al (2011) are for the whole group As Berger and Mester (1997) claim that the study whose data set including many countries may have the difficulties in employing the best efficiency concepts and measurement techniques Furthermore, there are always the gaps in accounting standards, managerial policy, economic conditions and financial structures among different nations (Demirguc-Kunt and Huizinga, 1999) From these points of view, it implies the notion that the accuracy of the results for each member country is just at moderate level

The more details of explanatory factors in Vietnamese banking system have recently been introduced by Minh et al (2013), Vu and Nahm (2013) Once again, the level of efficiency is the main interest in both studies and they both employ DEA approach with Tobit regression model However, the control variables studied in these papers are disparate On the former paper, Minh and his collaborators hypothesize that the potential determinants of efficiency score are bank size, state ownership, capital intensity (captured by the ratio of net capital to number of labours), quality of labour (estimated by the average salary expense of each bank), and market share (measured by the ratio of each bank’s loans to the total loans of

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all banks in the market) According to Minh et al (2013), it is found that bank size and market share have significant and positive effect on profit efficiency, whereas ownership structure is negatively and significantly related to productivity The latter paper, Vu and Nahm (2013) is seen as the improvement of the previous one There are more explanatory indicators to be concerned in the model and they can be divided into four following groups: (1) bank specific characteristics, (2) ownership, (3) transitional indicators, and (4) macroeconomic conditions Assessing balance panel data during the period from 2000 to 2006, Vu and Nahm (2013) find

that bank size, management quality, ownership, the development of the stock market, growth

rate of real GDP per capita, interest margin have strong positive impact on bank profit efficiency By contrast, the empirical results also indicate that the other indicators such as capital adequacy, asset quality, and inflation rate greatly affect profit efficiency in negative way The advantage of Vu and Nahm (2013) is that the analysis covers not only a large number of familiar factors from previous studies but also takes into account the effect of some typical factors, such as transitional variables including reform effect and international

commitments effect In which, the international-commitments variable is found to have

significant and negative impact on productivity Furthermore, Vu and Nahm (2013) divide capital adequacy ratio and ownership indicator into lesser extent so that they can gain more efforts for these variables The demand for more attention on both determinants results from the assumptions that the relationship between the level of capitalization and profit efficiency is significant but it may be non-linear, the different origins of foreign banks can lead to dissimilarity in profits (Vu and Nahm, 2013) Interestingly, it is pointed that capitalization does not always negatively affect bank productivity If bank managers maintain the capital adequacy ratio between 4% and 14%, they even gain more profit efficiency In terms of ownership structure, not all of foreign banks have higher profit efficiency than their counterparts but only foreign banks from the developed countries, for example the U.S, Europe, Australia and Japan banks

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the efficiency As far as I am concerned, the most relevant study assessing the determinants of bank performance in Vietnam is merely Vu and Nahm (2013) On this observation, the response variable is also profit efficiency In other words, there has not been any literature on the determinants of bank performance through financial accounting ratios yet As the

experience from other countries mentioned above, it is worthwhile and feasible to examine the

correlation of bank productivity and its explanatory factors in Vietnam Table 1 below will recap the main points of studies which have been reviewed so far

2.3 Summary and Remarks

To sum up the whole section, the relationship between bank performance and its potential determinants have been conducted for a few decades (Spathis et al., 2002) Starting from the observations of scale economies in the U.S banks to assess the correlation of bank efficiency with inputs, outputs data, the earliest studies illustrate the correlation between bank size and bank performance Recently, this relation has been observed in more details with a wider range of explanatory variables and more appropriate methodologies A review of literature has shown that bank productivity is usually measured by either bank efficiency or profitability ratios Based on the response variable studied, the methodologies employed differ

from each other For example, to assess the relation of bank efficiency and its control factors,

parametric (SFA) or non-parametric (DEA) and Tobit regression model are generally applied, whilst the GMM estimation or OLS approach is preferable technique to examine the impact of various factors on bank profitability However, the control variables seem similar no matter which dependent variable explored From various studies, it is widely accepted that both internal factors, captured by bank-specific characteristics and external factors, identified by industry-specific features together with macroeconomic conditions have effects on bank profitability or efficiency In particular, the prevalent endogenous indicators are bank size (measured by total assets or deposits), the level of bank capitalization, assets quality, credit risk, fund source, management capacity, and bank ownership The exogenous indicators usually perceived are the real rate of gross domestic product per capital, annual inflation rate,

real interest rate, and stock-market capitalization level For further details, the next chapter

will introduce the methodology employed and the explanatory indicators studied

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Chapter Three

METHODOLOGY AND DATA 3.1 Methodology and Econometric Model

This chapter is going to present three main points: first, briefly reviewing various methodologies employed in recent studies to choose the most appropriate technique Second, establishing the econometric model applied in the analysis In the final part, the data set and the complete model included selected variables are performed

This study is employing the balanced panel data to assess the determinants of bank profitability A large number of papers, such as Goddard et al (2004b), Alper and Anbar (2011), and Hoffmann (2011) have considered panel data as the most suitable tool According to Vong and Chan (2009), this technique is preferable to other estimations because of two main reasons Firstly, panel data provide more informative facts as the sample comprises of both cross-sectional and time-series data Put another way, it is believed that panel data model is effective to identify a common group of characteristics and to take into account the heterogeneity The second advantage of this technique is that it allows analysts to evaluate separately the influence of microeconomic factors on bank productivity Then, industry- specific factors and macroeconomic conditions can be examined in combination with less collinearity among variables and greater efficiency (Vong and Chan, 2009)

As mentioned in the preceding chapter, the reasonable methodologies employed to examine the influencing factors of bank profitability are the GMM estimation and the OLS technique A review of literature also shows that the results from both approaches can be similar to each other (Raza et al., 2013) or contrasting to each other (Hoffmann, 2011) Following that, the comparison between these techniques is essential before proceeding to the details of the empirical analysis It is quite probable that there is no dominant model to approach the topic Each method consists of both advantages and disadvantages (Hansen,

1982; Stock and Wright, 2000; Baum et al., 2003; Roodman, 2006)

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In terms of GMM technique, it is defined as:

[a] part of a broader historical trend in econometric practice toward estimators that fewer assumptions about the underlying data-generating process and use more complex techniques to isolate useful information

(Roodman, p.16, 2006) On the one hand, the advantage of this estimator has been implied in a great number of studies is the possibility to deal with potential endogeneity of regressors (Goddard et al.,

2004a; Athanasoglou et al., 2008; Dietrich and Wanzenried, 2011; Hoffman, 2011; Raza et al.,

2013) It is widely believed that there is likely endogeneity issue in the nature of the economic relationships being tested So as to avoid the problem, GMM method is introduced as a highly reliable tool (Tregena, 2009).To support for the usage of this methodology, it is declared that the GMM estimator is particularly optimal in the following cases: (1) “small T, large N” panels, in which there are a large number of observations but only a few time periods covered; (2) a linear functional relationship; (3) dynamic single left-hand-side variable depending on its own past realizations; (4) non strictly endogenous independent variables, meaning correlated with past and possibly current realizations of the error; (5) fixed effects model; and (6)

heteroskedasticity and autocorrelation within individuals but not across them (Roodman,

2006) On the other hand, the GMM system consists of disadvantages, such as it may require a large amount of samples to obtain reasonable estimates of fourth moments (Newey, 1985; Stock and Wright, 2000) or it is too sophisticated and easily generates invalid estimates

(Roodman, 2006)

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estimation is that it may exclude the variables which are correlated with one of the regressors Although OLS is regarded as a flexible form of GMM, Baum et al (2003) find that it is efficient in homoscedastic conditions rather than heteroskedastic estimations The main points of GMM and OLS techniques are summarized in the table as follows:

Table 2: The comparison between GMM and OLS estimators

the parameter of interest (Levine, 2004) — a superior estimation in the presence of

heteroskedasticity cases (Baum et al.,

Harker, 1999) Advantage

but less complex (Baum et al., 2003

‘Disadvantage’

As mentioned above, each measure has both advantages and disadvantages In favour of simplicity, a great number of studies, for example Hester and Zoellner (1966), Molyneux and Thornton (1992), Vong and Chan (2009), Alper and Anbar (2011) have postulated that OLS estimation is optimal option In line with that, I decide to choose simple linear regression technique combining with fixed effects model as the methodology of the current analysis It is selected because of three main reasons: this technique is much more accessible than the GMM estimator (1), the sample size may be inadequacy to conduct a reasonable GMM test (2), and the last but not least there have not been any Vietnamese studies which adopt linear regression model yet (3) so the application of this technique is highly meaningful for the purpose of diversifying approaches

~25~

the relationship among variables (Xue and

— OLS is a flexible form of GMM estimator

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In addition to panel data technique and OLS estimation, it is widely known that panel data technique can be estimated by either fixed effects model or random effects model.® According to Javaid et al (2011), Ongore and Kusa (2013), in the correlation of bank profitability with its explanatory indicators it is unnecessary to cover all dimensions of the profitability and control variables From this point of view, the fixed effects model comprised of relevant factors is combined with balanced panel data and OLS approach

Following to Guru et al (2002), Pasiouras and Kosmidou (2007), Sufian and Chong (2008), Vong and Chan (2009), the baseline regression model is established as the following

form:

lự = C+ Xa Bụy + XP Xụy + vie (@)

In which, Jj, is dependent variable represents bank performance for bank i at time ¢ C reflects the intercept of the whole model B;j;, is proxy for internal composition of the bank i at time ¢ X,; is proxy for external aspects at time ¢ a and f are coefficients of independent variables Finally, u;, illustrates the error term

3.2 Data set

3.2.1 Selection of Samples

According to the statistics of SBV (2011), Vietnam has 101 banks and foreign bank branches On the basis of ownership, they can be classified into three groups: 5 SOCBs, 38 JSCBs and 58 foreign financial institutions In which, the data of two first groups can be easily

obtained, whereas it is quite difficult to collect the data of foreign banks Due to the nature of

this group, they are the joint-venture banks that are 50% owned by banks from abroad and 50% owned by a state-owned bank, or the branches of overseas banks The financial information of these institutions is only presented in the conglomeration reports The separated data of unit banks or branches in specific country are not disclosed, hence, there is restriction

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in access to the foreign association data As a result of that, this paper merely focuses on examining SOCBs and JSCBs Adopting the method to select bank samples of Vong and Chan (2009), it is unnecessary to investigate all of banks From the total population of 44 domestic

commercial banks, 15 financial institutions, including 4 SOCBs and 11 JSCBs are chosen as

the final samples for the analysis This selection assures that the total assets of samples altogether account for at least 60% the total assets The details of samples are exhibited in

table 3 below

Table 3: The details of sample banks studied

No Name Ownership Total Assets

(bil VND) 183,567 138,831 6 eA w ROW Noe 10 OrientCommercialJSB JSCB 25,424

11 Westerm Rural commercial JSB

12 Bank for Foreign Trade (Vietcom an )

13 Industrial and Commercial Bz t

14 _ Bank for Investment and Development (BIDV) SOCB 405,755

15 Bank for Agriculture and Rural Dev

Source: total assets are obtained from the annual reports 2011 of each bank

With 15 samples selected, the relevant information will be gathered for the period from 2005 to 2011 The current paper intently examines this particular period because of two following reasons: (1) updating the assessment of determinants of bank performance in the earlier studies whose observations were stopped at 2006 (Minh et al., 2013; Vu and Nahm, 2013), and (2) the years 2005-2011 have been marked as eventful period in Vietnam

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Specifically, Vietnamese banking system has to release the restrictions on foreign banks as a member of the WTO in 2006 The structure of banking market has changed dramatically as a result of an increasing number of foreign banks (Figure 1) Following that, severe competitions among banks have affected their profitability The other significant changes are the adjustments in bank regulations to increase the minimum of capital adequacy ratio to 8% and 9% in 2005, 2010 respectively

Figure 1:

The number of banking institutions in Vietnam

(2005-2011) 70 60 3° mSOCBs 3 40 ề mJSCBs 3 30 «Foreign banks = 20 10 0 2005 2006 2007 2008 2009 2010 2011 Year Source: SBV (2011)

As the total of samples and the period studied have been defined, the next section will introduce specific variables which are observed in the model

3.2.2 Selection of Variables 3.2.2.1 Dependent Variable

In terms of profitability ratios, return on assets (ROA), return on equity (ROE), and net interest margins (NIM) are the most attention measurements in a great number of studies

(Smirlock, 1985; Kaushik and Lopez, 1996; Dietrich and Wanzenried, 2011) In which, NIM

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postulate that this profitability ratio is likely appropriate measure when interest rate revenues and expenses are closed to bank performance and not government’s decisions Whereas, in Vietnam, it is almost certain that the borrowing and lending interest rates are dominated by SBV As a result of that, NIM is excluded from the analysis For the usefulness of ROA and ROE, the former is captured by the net income to total assets ratio, evaluates the profitability and efficiency of management how much profit can be generated from one unit of total assets While ROE is the ratio of net income to total equity, it indicates how well a bank’s management is in using capital of shareholders Although both ratios indicate the profitability of an entity, the former, ROA, is fairly preferred to be used as the following notions Firstly, Guru et al (2002) assume that total equity encompasses shareholders capital and reserves whose nature is actually undistributed net profits Secondly, Sufian and Chong (2008) explain the reason why ROA is considered as the better ratio for bank performance that it is not distorted by high equity multipliers Lastly, it is claimed that an analysis of ROE tends to ignore the relevant exposures associated with high leverage and financial leverage is likely decided by regulation (Athanasoglou et al., 2008; Flamini et al., 2009) Thus, ROA is more appropriate to proxy bank performance than ROE In accordance with time of financial recording, Golin (2001) suggests to employ return on average assets (ROAA) instead of ROA It is clarified that net income is measured for a whole fiscal period, whereas total assets is just estimated at a point of time, usually at the end of year The gap in reported period can lead to the misstatement if there is any material change in total assets during the year In order to minimize the unusual differences in calculation, it is postulated that the average of total assets in two successive years should replace total assets (Kosmidou, 2008) From the advantages of

ROAA as mentioned above, it is selected to represent bank performance in the model

3.2.2.2 Independent Variables

In terms of explanatory variables, they are divided into internal and external determinants According to Guru et al (2002), the former are defined as controllable factors by bank strategies and policies On the contrary, the external factors are beyond the control of management but they are believed to have strong effects on bank activities From these points

of view, bank specific attributes have been used to express the interior determinants and

macroeconomic conditions together with financial structure have been applied to assess the

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exterior determinants of commercial banks (Pasiouras and Kosmidou, 2007; Reddy and Nirmala, 2013) In the following section, the details of bank specific characteristics, macroeconomic conditions and market structure will be introduced Each variable is presented according to two main points: (1) the definition and the ratio employed to measure the variable, (2) the prediction of the correlation between every control variable and ROAA Bank specific characteristics

From the review of literatures and cross-reference studies, there are six variables to be selected as internal indicators as follows:

SIZE: from the existing papers, bank size is one of indispensable factors when

assessing the determinants of bank performance (Benston, 1972; Yildirim and

Philippatos, 2007; Pasiouras and Kosmidou, 2007) A majority of studies measure bank size by the value of total assets (ASS) or natural logarithm of total assets (inTA), such as Guru et al (2002), Kosmidou (2008), Sufian and Habibullah (2009a,b) In the other papers, such as Heggestad (1977), Vong and Chan (2008), the value of total deposits (DEP) or natural logarithm of total deposits (INDEP) is frequently applied as proxies of size As the importance of ASS and DEP, the study of Sufian (2009) can be seen as the first and unique paper that employs both

InTA and InDEP to assess bank size Ironically, it seems unreasonable when

applying simultaneously assets and deposits for the same explanatory variable Especially in the current case, if ASS and DEP or their natural logarithm values are in combination, there will be the presence of multicollinearity’ Through testing correlation coefficients among independent variables, it is proved that ASS and DEP have significantly close relationship Therefore, for the best regression

model, only one in two parameters should be chosen to represent bank size

Although ASS is proxied for bank size rather than DEP, Guru et al (2002) postulate that ASS deflated the dependent variable, especially ROA Therefore, in

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this context, bank size is measured in favour of DEP?? Another concern about

bank size is the consideration between DEP and InDEP, which variable is more appropriate It is highly likely that the translog form of bank size has just appeared in recent decades (Sufian, 2009; Flamini et al., 2009; Reddy and Nirmala, 2013) There are a handful of explanations for the reasons why the natural logarithm values should be chosen instead of the real values Firstly, it is proposed that the real values tend to deflate dependent variable and the translog form can mitigate the deflation (Guru et al., 2002) Secondly, according to Reddy and Nirmala (2013), translog form is preferred for specification of parameters so that it might reduce the unnormal distribution of the real values Put another way, the gap in size among banks can be narrowed down to normal distribution In spite of two advantages, there is no clear evidence that the logarithm form is more effective than the original form By running linear regression analysis, DEP illustrates the higher reliability for a whole model than InDEP'' As a consequence, bank size in my analysis is measured by DEP For a prediction about the relationship between

DEP and ROAA, it seems inclusive In some studies, for examples Bell and

Murphy (1968), Benston (1972), it is found that DEP has positive effect on bank performance, usually namely as economies of scale According to other authors (Vong and Chan, 2009; Stimpert and Laux, 2011) banks with higher level of deposits achieve lower profits than the smaller banks From these conflicting findings, the sign of bank size and bank performance is unpredictable yet

= EQTA: is the representative capital adequacy and measured by the ratio of equity capital to total assets The ratio is defined as a key composition to improve the competition among banks The impact of EQTA on ROAA can be either positive or negative Put another way, too much or too little capital can cause the less

'° Tn addition, to ensure the superior of DEP to ASS, each of them is applied in the regression as bank size parameter respectively In other words, two separate regression models are tested: ASS is applied in the first model and then DEP used in the latter The latter experiment indicates the higher level of reliability and more significance so I decide to choose DEP

" Similar to the choice between ASS and DEP, InDEP and DEP is replaced to each other in regression models to consider which indicator provides better result Comparing the reliability and significance level between two experiments, it is shown that the model with DEP as proxy for bank size is well-performed than InDEP’s one

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profitability On the one hand, the high level of equity capital assures the safety for depositors, especially during unstable period (Guru et al., 2002) The higher equity proportion, the lower bankruptcy risks as well as demand to go for external funding On the other hand, remaining capital at too high level may have negative effects on bank performance, for example high costs and low return (DeYoung and

Hasan, 1998; Goddard et al., 2004) However, from the experience of the majority

of developing-country studies in the same region (Sufian and Chong, 2008; Vong and Chan, 2009; Sufian and Habibullah, 2009a), it is expected that the level of capitalization will have significantly positive relation to bank performance as it has been defined as the core of strengthening in Vietnamese commercial banks (VCBS, 2011)

LOTA: there is little doubt that loans are the main component of earning assets LOTA is captured by the ratio of total loans to total assets This ratio is widely known as the credit risk measure in banks because total loans seems to be the highest but also the most sensitive proportion of bank assets The relationship

between LOTA and ROAA is inconclusive The higher ratio of LOTA, the more

profits are expected to be earned as the increasing in interest income However, it is also claimed that the growth of LOTA follows with the rise in default risk and provision for non-performing loans Furthermore, Naceur and Omran (2011) state that banks tend to suffer from higher cost for their funding requirements when increasing credits Hence, the correlation between LOTA and ROAA can be either positive or negative

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meaning redundant deposits may have negative impact on bank profitability As a

result of that, the correlation between DETA and ROAA is inconclusive

PRTO: is captured by the ratio of non-performing loans to total loans This ratio is known as the proxy for asset quality in banks On the one hand, it is found that PRTO has significant and negative impact on profits as it increases expenses

(Bernstein, 1996; Athanasoglou et al., 2006; Kosmidou, 2008; Alper and Anbar,

2011) On the other hand, a minority of analysts (Akhigbe and McNulty, 2003) assume that the increase in loan loss provision implies the rising proportion of loans as well as earning assets over total assets Based on the experience of Vu and Nahm (2013), it is supposed that the relation of quality of assets and bank profitability is negative

NIETA: is measured by the ratio of non-interest expense to total assets This ratio is regarded as the best proxy for the average cost of non-financial inputs to banks (Athanasoglou et al., 2006) Non-interest expenses are comprised of wages and salary for employees and administration expenses As the nature of this ratio is the costs so it is expected to have negative effect on ROAA In common sense, the income rises when the expenditures decrease or at least the increase in profits has to be higher than raise in expenses (Naceur, 2003)

Ownership: as mentioned in the preceding section, this context focusing on two types of banks is SOCBs and JSCBs The impact of ownership has been exhibited in a great number of studies On the one hand, Himmelberg et al (1999) propose that managerial ownership does not have impact on bank performance On the other hand, the significant effect of proprietary right has been declared in an increasing number of studies, for example Akhigbe and McNulty (2011), Dietrich and Wanzenried (2011), Ongore and Kusa (2013), Vu and Nahm (2013), Minh et al (2013) Dissimilar to these studies, ownership will not be treated as an independent variable but running the separate regression test for SOCBs and JSCBs The purpose of that is to compare and contrast the effect of determinants on bank profitability in each type of banks (Sufian and Habibullah, 2009a)