TÓM tắt TIẾNG ANH tác động của rủi ro thanh khoản đến hiệu quả hoạt động

MINISTRY OF EDUCATION THE STATE BANK OF VIETNAM BANKING UNIVERSITY OF HO CHI MINH CITY TRAN THI THANH NGA THE IMPACT OF LIQUIDITY RISK ON BANK PERFORMANCE EFFICIENCY: EMPIRICAL EVIDENCE FROM SOUTH EAST ASIA COUNTRIES DOCTOR OF PHILOSOPHY IN ECONOMICS THESIS SUMMARY Major: Finance – Banking Code: 62 34 02 01 Academic advisor: Assoc Prof Dr Tram Thi Xuan Huong Dr Le Thi Anh Dao HO CHI MINH CITY - 2018 CHAPTER 1: INTRODUCTION 1.1 Research Problem The relationship between liquidity risk and performance efficiency has made interested through the approach to hypotheses such as Market Power Hypothesis, Efficient Structure Hypothesis (Diamond and Dybvig, 1983) showed that the impact of liquidity risk on bank performance efficiency is unclear Some studies in Africa (Sayedi, 2014; Aburime, 2009; Athanasoglou et al.,2008; Ajibike & Aremu, 2015; Alshatti, 2015); in Asia (Wasiuzzaman & Tarmizi, 2010; Arif & Nauman Anees, 2012; Shen et al.,2009), in Europe (Bourke,1989; Poposka & Trpkoski, 2013; Goddard, Molyneux & Wilson, 2004; Kosmidou, Tanna & Pasiouras, 2005) found out the positive effect Others in Asia (Lee & Kim, 2013); in Africa (Bassey & Moses, 2015) found out the negative effect A number of studies was also found that there was a very weak relationship or no relationship between liquidity risk and bank performance (Sufian & Chong, 2008; Roman & Sargu, 2015; Alper & Anbar, 2011; Almumani, 2013; Ayaydin & Karakaya, 2014) or depends on economic characteristics and research models (Naceur & Kandil, 2009; Ferrouhi, 2014) After reviewing studies, the authors found that the majority of the previous studies approaching the impact of liquidity risk on bank performance efficiency (Sufian & Chong, 2008; Sayedi, 2014; Oluwasegun & Samuel, 2015; Lartey, Antwi, & Boadi, 2013; Bourke,1989; Tabari, Ahmadi & Emami, 2013; Arif & Nauman Anees, 2012; Bassey & Moses, 2015; Ferrouhi, 2014; Alshatti, 2015; Aburime,2009; Athanasoglou et al., 2008; Naceur & Kandil, 2009) Some studies have approached the impact of bank performance efficiency on liquidity risk in different countries (Vodova, 2011; Abdullah & Khan, 2012; Roman & Sargu, 2015) This shows that the trend of the impact of the liquidity risk on bank performance efficiency has been attented by the researchers and managers, especially the impact of the financial crisis on bank performance efficiency (Lee Kim, 2013) Most empirial researchs approaching to factors affecting to liquidity risk and the impact of liquidity risk on bank performance efficiency have taken in the region of one country only, except study of (Roman & Sargu, 2015) based on European data or (Bordeleau & Graham, 2010) in America, (Shen et al.,2009) both in Europe and America Cross-countries studies on aspect to examine the interlinkage between liquidity risk and bank performance efficiency On the other hand, some recent study claimed that liquidity is endogenously determined and the question on the impact of liquidity risk on bank performance efficiency cannot be studied without controlling for endogeneity The empirial researchs showed that Vietnamese is one of the countries with lower average income population of Southeast Asia and there are too many banks but lacked a main banking to competitive with other regional economies (Nguyen Cong Tam & Nguyen Minh Ha, 2012) Thus, this study used Bankscope data of 171 banks during the period 2004–2016 and the system Generalized Method of Moments (SGMM) method to analyze the impact of liquidity risk on bank efficiency performance in South-East Asia countries to estimate the impact of liquidity risk on banks financial performance in South-East Asia countries case Studies in different spaces and periods will give unequal results To fill the gap research, thesis combining research approach to factors influencing liquidity risk and the impact of liquidity risk on bank efficiency performance in SouthEast Asia countries are extremely important and valuable So, the author selected the topic "The Impact of Liquidity Risk on Bank Fficiency Performance : A Case Study in South-East Asia Countries" as a thesis In addition, the study combines a case study of South - East Asia and Vietnamese to propose policy suggestions for Vietnam This study will contribute to empirical evidence and provide some useful information on the factors affecting liquidity risk and the impact of liquidity risk on bank efficiency performance 1.2 Research objectives 1.2.1 Research objectives The main objective of the thesis is to identify factors influencing liquidity risk and analyze the impact of liquidity risk on bank efficiency performance, study the case of South-East Asia countries in the period of 2004 - 2016 1.2.2 Specific objectives: Based on that, the specific objectives of the project are defined as: The firstly, to analyze factors influencing liquidity risk, study the case of South-East Asia countries and Vietnamese The secondly, to analyze the impact of liquidity risk on bank efficiency performance, study the case of South-East Asia countries and Vietnam Thirdly, to suggested policies on liquidity risk management and Effective management of banking in Vietnam 1.2.3 Research questions 1) What factors influence the liquidity risk and the level impact of factors on liquidity risk the case of South-East Asia countries? 2) Are there any differences in the results study the factors affecting the liquidity risk in the case of South-East Asia countries and Vietnam? 3) What is the impact of liquidity risk on bank efficiency performance, in the case of of South-East Asia countries? 4) Are there any differences in the results study impact of liquidity risk on bank efficiency performance, in the case of South-East Asia countries and Vietnam? 5) What are the policy implications of liquidity risk management and Effective management of banking in Vietnam 1.3 Research Object and research scope: Research Object: The study Object of the thesis are liquidity risk and bank efficiency performance, in the case of South-East Asia countries Research scope: The scope of the study was extended to for 11 countries in South-East Asia (Brunei, Cambodia, EasiTimor, Indonesia, Laos, Myanmar, Malaysia, Philippines, Singapore, Thai Land, Viet Nam) from 2004 to 2016 The database was collected from sources: (i) bank-level data from Bankscope, (ii) macroeconomic information data from Asian Development Bank (ADB) 1.4 Recearch methodology: The research has combined the approach of (Ferrouhi & Lahadiri, 2014; Trenca, Petria & Corovei, 2015) to analyze impact of the factors on liquidity risk and its approach (Growe cộng sự, 2014; Ferrouhi, 2014) to analyze the impact of liquidity risk on bank efficiency performance, in the case of South-East Asia countries On the other hand, comparative study of the result in the case of South-East Asia countries and Viet Nam to proposed policy implication for Vietnam 1.5 Thesis structure: The structure of the thesis consists of chapters: Chapter 1: Introduction Chapter 2: Theory basis and literature review Chapter 3: Research Methodology Chapter 4: Research results Chapter 5: Conclusions and Policy Implications CHAPTER THEORY AND LITERATURE REVIEW 2.1 Liquidity Risk in Commercial Banks 2.1.1 Theoretical Framework Underlying the Study 2.1.1.1 Commercial Loan Theory and Liquidity 2.1.1.2 The Shiftability Theory of Liquidity 2.1.1.3 Anticipated Income Theory of Liquidity 2.1.2 The concept of liquidity risk The Basel Committee on Banking Supervision (2003) contends that Liquidity Risk is a risk that a bank's inability to accommodate decreases in liabilities or to fund increases in assets Rudolf Duttweiler1, contends that Liquidity represents the ability to payment all payment obligations upon maturity The inability of banks to raise liquidity can be attributed to a funding liquidity risk that is caused either by the maturity mismatch between inflows and outflows and/or the sudden and unexpected liquidity needs arising from contingency conditions Lack of liquidity will lead to liquidity risk According Bonfim and Kim (2014), the complexity of the functions of banks gives rise to an intrinsic risk that lies deep in their core function; their unique intermediation role Banks use a limited amount of their own resources in granting loans to entrepreneurs and consumers and thus provide them with the liquidity to finance their investment and consumption demands Much of these resources used by these banks are normally associated with liabilities to third parties traditionally in the form of deposits For profit purposes, this transformation of liquid liabilities (deposits) into risky liquid (illiquid) assets in the form of loans capitalizing on their maturity mismatch expose them to liquidity risk (Diamond and Dybvig, 1983; Jekinson, 2008) In order to lessen the maturity gap between assets and liabilities or the inherent illiquidity, banks can adequately manage the liquidity risk underlying their balance sheet structure by holding a buffer of liquid assets However, aside the high opportunity cost of holding a buffer of liquid assets as compared to the higher returns associated with illiquid assets, it manifests a degree of inefficiency on the part of management as it limits banks' ability to provide liquidity to entrepreneurs and Rudolf Duttweiler: "Liquidity Management in Banking: Top-down Approaches", Ho Chi Minh City General Publisher, p.23 consumers Hence, even though banks have some incentives to hold a fraction of liquid assets (in the form of cash, short term assets or government bonds), these buffers will hardly ever be sufficient to fully insure against a bank run or liquidity risk (Bonfim and Kim, 2014) 2.1.3 Liquidity risk measurement methods 2.1.3.1 Approaches to the guaranteed ratio is regulated by the the Basel Monitoring Committee 2.1.3.2 Approaches to liquidity indicators 2.1.4 Empirical literatures on determinants of liquidity risk Though liquidity risk has always been considered in literature as a major determinant of bank performance, only a few of studies have gone further to take into consideration the various determinants of liquidity risk in the daily operations of a bank Work done by some few researchers show varied determinants in different banking environments basically categorized under bank specific and macro-economic factors The determinants affected liquidity risk are focused on the following factors: The bank size: previous studies found that a negative relationship between bank size and liquidity risk (Lucchetta, 2007; Munteanu, 2012; Abdullah & Khan, 2012; Delécha et al., 2012; Bonfim & Kim, 2014) While other studies suggested that the relationship between bank size and liquidity risk may be nonlinear or ambiguous (Vodova, 2011; Shen et al., 2009; Aspachs & cộng sự, 2005; Truong Quang Thong, 2013) Asset quality: A key liquidity ratio is the liquid assets ratio (Liquid assets/Total assets) Previous studies (Bonfim Kim, 2014; Bunda Desquilbet, 2008; Delécha cộng sự, 2012; Lucchetta, 2007; Munteanu, 2012; Vodova, 2011) was found that lower liquidity means higher risk The portfolio theory suggests higher risk leads to higher profitability In addition, some studies (Lucchetta, 2007; Bunda & Desquilbet, 2008; Vodova, 2011; Delécha et al., 2012) is used be the ratio of liquid assets/total deposits Liquidity has also been measured by liquid assets to total deposits (Liquid assets/deposits) and some studies measured by liquid assets to shortterm deposits (Bunda & Desquilbet, 2008;Vodova, 2011; Cucinelli, 2013; Delécha et al., 2012) Hence, higher values of this ratio denote less liquidity The higher the liquidity structure, the lower the liquidity risk Capital: indicators measure the strength of the bank’s capital position, including its ability to withstand and recover from economic shocks Theoretical expectations, as well as empirical results (Lucchetta, 2007; Bunda & Desquilbet, 2008; Cucinelli, 2013; Munteanu, 2012; Bonfim & Kim, 2014; Trương Quang Thông, 2013), for the equity to assets ratio (Total equity/Total assets) suggest that the ratio will be positively related to liquidity risk In addition, studies across industries have found that the actual relationship between Capital and liquidity risk was negative (Delécha et al., 2012; Berger & Bouwman, 2013) The implication is that the level of equity in a bank’s capital structure should be negatively related to liquidity risk Credit risk: is the risk that a portion of interest or both interest and principal of a loan will not be repaid as committed The competitiveness of the bank depends largely on its ability to manage credit risk (Bonfim & Kim, 2014) Previous studies (Delécha et al., 2012; Cucinelli, 2013; Bonfim & Kim, 2014; Trenca, Petria & Corovei, 2015) using Loan Loss Provision/Total Loans is assessed by credit risk While other studies (Bonfim & Kim, 2014; Cucinelli, 2013; Delécha et al., 2012) suggested that higher lending ratios, lower liquidity It means, bank have more vulnerable capital structures, liquidity risk is higher Interest income: a key ratio is the efficiency or cost to income ratio (interest expense/Total income) Several studies have found that high values on it higher liquidity risk (Abdullah & Khan, 2012; Bonfim & Kim, 2014; Delécha et al., 2012; Munteanu, 2012) The macro factors include: The GDP growth variable is assessed by the year’s real change in gross domestic product (GDP) for the nation the bank is located in, sometimes on a per capita basis GDP growth was related to liquidity risk in a number of studies (Aspachs et al., 2005; Bonfim & Kim, 2014; Bunda & Desquilbet, 2008; Cucinelli, 2013; Delécha et al., 2012; Munteanu, 2012; Growe et al., 2014; Trương Quang Thông, 2013; Vodova, 2011) The inflation rate is assessed by entering in the CPI change rate for the nation and the year GDP as a measure of total economic activity in an economy, higher economic growth encourages banks to lend more and permits them to charge higher margins, and improve the quality of their assets as suggested by previous studies (Aspachs et al., 2005; Bonfim & Kim, 2014; Bunda & Desquilbet, 2008; Cucinelli, 2013; Delécha et al., 2012; Munteanu, 2012; Growe et al., 2014; Trương Quang Thông, 2013; Vodova, 2011) Financial crisis (Bunda & Desquilbet, 2008; Delécha et al., 2012; Lucchetta, 2007; Munteanu, 2012; Growe et al., 2014; Shen et al., 2009; Skully & Perera, 2012; Vodova, 2011) was found that is one of the factors affecting the liquidity risk As, the findings of previous studies are quite consistent with the realities in the financial markets The empirical studies continue to assess the determinants of the impact on the liquidity risk in banks through the specific factors (bank size, asset quality, capital, credit risk, interest income,…) and macro factors (Real GDP growth rate, fluctuations of inflation, financial crisis, ) 2.2 Bank Performance Efficiency 2.2.1 Theories on Bank Performance Efficiency Bank Performance Efficiency is often measured by profitability Studies of Bank Performance Efficiency or profitability is used basing on two theories: market power theory (MP – market power) and structural efficiency theory (ES - efficient structure) 2.2.1.1 The theory of market power 2.2.1.2 The theory of efficient structure 2.2.2 The concept of Performance Efficiency in bank When evaluating the Performance Efficiency in business, it can be based on two indicators that are absolute efficiency and relative efficiency Absolute efficiency: Measured by business results minus cost to achieve results This ratio reflects the scale, volume and profits gained in specific conditions, time and place Relative efficiency: based on comparative ratio between inputs and outputs Relative efficiency is defined as: Efficiency = output / input or Efficiency = input / output This assessment is very convenient on comparing different organizations from sizes, space and time 2.2.3 Methods of measuring bank’s Performance Efficiency In methodology, previous studies used three different approaches to measuring business performance: measure Performance Efficiency by the ratio method, measure the Performance Efficiency from market data and measure Performance Efficiency from profit margin The results of applying different measurements can lead to different results No research has shown which measurement method is the best For the selection methods, the authors combine the assessment, analysis of advantages and disadvantages of each method, then approach methods of measuring bank’s Performance Efficiency in accordance with the scope and objects of research 2.3 The impact of liquidity risk on bank’s Performance Efficiency 2.3.1 Theory of the relationship between liquidity risk and bank’s Performance Efficiency 2.3.1.1 The theory of risk for profit 2.3.1.2 The theory of Banking Specificities Theory 2.3.2 Empirical reseachs on the relationship between liquidity risk and bank’s Performance Efficiency Previous studies have shown that research gaps exist in relation to the relationship between liquidity risk and bank’s Performance Efficiency Firstly, Gap on the approach There are many studies on liquidity risk, but studies focus on the causes of liquidity risk (Ahmed, Ahmed & Naqvi, 2011; Angora & Roulet, 2011; Bonfim & Kim, 2014; Bunda & Desquilbet, 2008; Gibilaro, Giannotti & Mattarocci, 2010; Horváth et al., 2012; Rauch et al., 2010; Skully & Perera, 2012; Vodova, 2013) and studies on liquidity risk management to stabilize banks such as (Acharya & Naqvi, 2012; Scannella, 2016; Wagner, 2007) Researchs on liquidity risk is also considered one of the types of bank risk such as credit risk (BissoondoyalBheenick & Treepongkaruna, 2011) or one of the factors affecting bank’s Performance Efficiency (Athanasoglou et al., 2008; Shen et al., 2009) However, only a few studies that combine an analysis of factors affecting liquidity risk and the impact of liquidity risk on bank’s Performance Efficiency across multiple countries Secondly, Gap on the spaces and periods researchs Most empirial researchs have taken in the region of one country only, except study of (Roman & Sargu, 2015) based on European data or (Bordeleau & Graham, 2010) in America, (Shen et al., 2009) both in Europe and America Cross-countries studies on aspect to examine the interlinkage between liquidity risk and bank’s performance efficiency According to the author, these are only three well-known empirical studies of liquidity risk and bank’s performance efficiency across multiple countries and are published in highly reliable journals In the case of South East Asia countries, there is no separate study on the impact of liquidity risk on bank’s performance efficiency across multiple countries Different spaces and periods researchs, will be result in dissimilar results on the relationship between liquidity risk and bank’s performance efficiency Thirdly, Gap on the measurement elements Other empirical studies also showed that there are many factors affecting the bank’s performance efficiency such as: lending chanel through the ratio of loan of total assets (Nguyen Viet Hung, 2008; Gul et al., 2011; Trinh Quoc Trung & Nguyen Minh Sang, 2013…); Banking capital mobilization and operation using bank capita used be the ratio of total mobilized capital of total loan (Nguyen Viet Hung, 2008; Nguyen Thi Loan & Tran Thi Ngoc Hanh, 2013 ); the size of equity (Nguyen Viet Hung, 2008; Gul et al., 2011; Nguyen Thi Loan & Tran Thi Ngoc Hanh, 2013; Ongore & Kusa, 2013 …); the size of asset (Nguyen Viet Hu, 2008; Gul et al., 2011; Ongore & Kusa, 2013; Ayadi, 2014 …), the economic growth rate (Gul et al., 2011; Ongore & Kusa, 2013;…), the inflation rate (Gul et al., 2011; Ongore & Kusa, 2013;…) Particularly, the factors influencing liquidity risk have used but rarely Some studies had just used liquidity ratios to measure liquidity risk but Poorman and Blake (2005)2 indicated that it was not enough to measure liquidity just using liquidity ratios and it was not the best solution In this study, the combination of financial gap method and liquidity ratios to measure liquidity risk in banking business Fourthly, Gaps in character: Liteliture review showed that the trend of studying the impact of liquidity risk on bank’s performance efficiency is mainly (Sufian & Chong, 2008; Sayedi, 2014; Oluwasegun & Samuel, 2015; Lartey, Antwi & Boadi, 2013; Bourke,1989; Tabari, Ahmadi & Emami, 2013; Arif & Nauman Anees, 2012; Bassey & Moses, 2015; Ferrouhi, 2014; Alshatti, 2015; Aburime, 2009; Athanasoglou et al., 2008; Naceur & Kandil, 2009; Wasiuzzaman & Tarmizi, 2010; Lee & Kim, 2013) Other studies have approached the impact of bank’s performance efficiency on liquidity risk across multiple countries (Abdullah & Khan, 2012; Roman & Sargu, 2015) So, the recent trend, scientists and managers are very interested in the impact of liquidity risk on bank’s performance efficiency Especially, the impact of the financial crisis on bank’s performance efficiency (Lee & Kim, 2013) However, it have rarely researched combined analysis of factors affecting liquidity risk with the impact of liquidity risk on bank’s performance efficiency across multiple countries In general, previous studies identified that it is necessary to study the impact of liquidity risk on bank’s performance efficiency In this study, we use each bank’s financing gap ratio (FGAP) as the independent variable and financial crisis variable to compare the case studies of South East Asia and Vietnamese countries From which to propose policy suggestions for Vietnam Besides, research thesis the case of Vietnam to to compare the different impact of liquidity risk on bank’s performance efficiency with the case of South East Asia countries Diamond and Dybvig (1983) developed a model to explain why banks choose to issue deposits that are more liquid than their assets infl [0.73] [1.72] [0.72] [5.04] [0.83] [1.72] [0.84] [1.64] [0.87] [1.12] [0.87] [1.61] -0.000118 -0.000101 -0.000187 0.000494 0.00353 -0.00465 -0.00214 -0.0366 0.105 0.0858 0.105 0.14 [-0.15] [-0.16] [-0.26] [0.05] [-0.07] [-0.03] [0.91] [1.04] -0.00000222 -0.0000026 -0.00027 -0.00022 -0.00025 [-0.70] 0.000727** [1.04] -2.92E-06 [1.23] 0.00000332*** -0.00058 -0.000503 -0.00058 [1.58] 0.000744*** [-0.93] [-0.84] [-0.88] [-2.80] [-0.88] [-0.86] [-0.86] [-2.40] [-1.38] [-1.31] [-1.38] [-2.87] -0.00924 -0.00714 -0.00928 -0.00716 -0.158 -0.451 -0.243 0.0788 1.423 1.74 1.423 2.769 [-0.72] [-0.64] [-0.76] [-0.77] [-0.13] [-0.42] [-0.20] [0.05] [0.83] [1.07] [0.83] [1.09] -0.217*** -0.320*** -0.220*** -0.552*** 35.38*** 50.21*** 38.68*** 43.47*** 28.13*** 46.12*** 28.13*** 21.33* [-3.35] [-4.52] [-3.41] [-9.40] [5.46] [7.49] [6.01] [3.98] [3.17] [4.56] [3.17] [1.68] 157 157 157 130 157 157 157 130 157 157 157 130 0.913 0.804 0.908 0.795 0.899 0.682 m2 d_cris _cons N R-sq Mean VIF White's test F-test Hausman test BreshPagan test Sargan test 4.15 4.6 Ho: homoskedasticity chi2(102) = 134.04 Prob > chi2 = 0.0183 Ho: homoskedasticity chi2(102) = 134.52 Prob > chi2 = 0.0171 F test that all u_i=0: F(24, 119) = 4.47 Prob > F = 0.0000 Ho: difference in coefficients not systematic chi2(12) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 80.75 Prob > chi2 = 0.0000 Test: Var(u) = chibar2(01) = 6.57 Prob > chibar2 = 0.0052 H0: overidentifying restrictions are valid chi2(57) = 12.071 Prob > chi2 = 1.0000 F test that all u_i=0: F(24, 119) = 4.27 Prob > F = 0.0000 Ho: difference in coefficients not systematic chi2(12) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 22.03 Prob > chi2 = 0.0372 Test: Var(u) = chibar2(01) = 7.5 Prob > chibar2 = 0.0031 H0: overidentifying restrictions are valid chi2(57) = 15.141 Prob > chi2 = 1.0000 5.8 Ho: homoskedasticity chi2(102) = 121.85 Prob > chi2 = 0.0877 F test that all u_i=0: F(24, 119) = 2.86 Prob > F = 0.0001 Ho: difference in coefficients not systematic chi2(11) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 211 Prob > chi2 = 0.0000 Test: Var(u) = chibar2(01) = 0.00 Prob > chibar2 = 1.000 H0: overidentifying restrictions are valid chi2(57) = 11.682 Prob > chi2 = 1.0000 Note: The symbols (***), (**), (*) indicate statistically significant levels of 1%, 5%, 10% 21 Considering both models of factors affecting the liquidity risk, the case study of South East Asia countries and Vietnam is shown in Table 4.8: Table 4.8: Factors influencing the liquidity risk, case study of South East Asia and Vietnam Result Variable Expected South East Asia Vietnam FGAP NLTA NLST FGAP (+) (+) (+) (+) (-) (-) lag (liquidityt-1 ) (+) SIZE (-) SIZE ^2 (+/-) (+) (+) (+) LIA (-) (-) (-) (-) LLR (-) LADS (-) ETA (+) (+) (+) (+) (+) LLP (+) (-) (-) (-) (-) NIM (+) (+) (+) (+) (+) GDP (+) M2 (+) (-) INF (+) (-) (-) D_CRIS (+/-) (+) (+) NLTA NLST (-) (+) (-) (-) (-) (-) (-) (+) (+) (+) (+) (-) (-) (-) (-) (-) (-) Source: summary from research results of the author In summary, considering both models of factors affecting the liquidity risk, the case study of South East Asian countries and Vietnam, the regression results in the Southeast Asian case study model show that the majority of explanatory variables in the regression models is significantly higher than in the case of Vietnam In particular, the main explanatory variables related to lag liquidity risk, bank size, asset quality, equity to total assets, credit risk, net interest income, GDP growth, money supply, inflation are statistically significant in many models This is a remarkable result in empirical studies By using different methods of measurement and estimation, the study gives some clear results on the expected correlation In the case of Vietnam, the study found 22 no statistically significant evidence of the impact of bank size, GDP growth and financial crisis on liquidity risk 4.2 Impact of liquidity risk on Performance Efficiency 4.2.1 Descriptive statistics Table 4.9: Descriptive statistics, case studies of South East Asia countries in the impact model of liquidity risk on bank Performance Efficiency Table 4.10: Descriptive statistics, case studies of Vietnam in the impact model of liquidity risk on bank Performance Efficiency 4.2.2 Analysis of correlation coefficient Table 4.11: Correlation between the independent variables in the impact model of liquidity risk on bank Performance Efficiency, case studies of South East Asia countries Table 4.12: Correlation between the independent variables in the impact model of liquidity risk on bank Performance Efficiency, case studies of Vietnam 4.2.3 Analyze and discuss, case studies of South East Asia countries The study has used 12 different estimation models with three ratios (ROA, ROE,NIM), which each model was determined by OLS, REM, FEM, SGMM to assess the impact of liquidity risk to performance of banks Table 4.13 reports the empirical results of bank liquidity risk and performance model using FGAP (Bank’s loans – customer deposits/ total assets), NLTAit (Loans/total Assets), NLSTit (Loans/deposits+Short term liabilities) to measure liquidity risk Table 4.13 Results in the impact model of liquidity risk on bank Performance Efficiency, case studies of South East Asia countries 23 Bảng 4.13 Results in the impact of liquidity risk on Performance Efficiency, case of South East Asia countries (Appendix) The model: Pt = f(α, Pt-1, LIQUIDITY RISK it, CONTROLit, u) Dependent variables: P (NIM, ROA, ROE) measure to bank’s Performance efficiency Independent variables : Pt-1 – lag of bank’s Performance; LIQUIDITYRISK - (FGAP- financing gap to total assets, NLTA- Loans /total Assets, NLST- Loans/deposits+Short term liabilities), CONTROL VARIABLÉ (SIZE- natural logarithm of total assets; SIZE^2- natural logarithm of total assets squared; LIA- the ratio liquid assets to total assets; LLR- the ratio liquid assets / total Loans, LADS- the ratio liquid assets / short term liabilities; ETA- the ratio of equity to total assets.; LLP- the ratio of loan loss provision to loans, GDP- Annual percent change of GDP, INF- Annual percent change of inflation, d_cris - Dummy variable ) Database from 2004 to 2016 Estimation technique: OSL, FEM, REM SGMM Model OLS Variable L.roa FEM REM SGMM OLS FEM ROA REM SGMM 0.433*** 0.101*** 0.433*** 0.114*** [20.45] [4.26] [20.40] [15.13] nlta lia llr lads size size2 eta llp REM SGMM NIM 0.115*** 0.0169 0.115*** 0.0394*** [5.14] [0.72] [5.14] [49.70] L.nim nlst FEM ROE L.roe fgap OLS 0.836*** 0.546*** 0.806*** 0.668*** [80.34] [27.93] [69.44] [32.38] 3.391*** 1.187 3.394*** 1.392*** 20.75 -6.217 20.75 -4.768 0.108 0.438 0.27 0.185 [4.27] [1.46] [4.28] [5.48] [1.37] [-0.35] [1.37] [-1.10] [0.21] [0.80] [0.52] [0.68] 0.00129 -0.00161 0.00129 0.000841*** -0.00797 -0.000242 -0.00797 0.0230*** -0.000863* -0.00196** -0.00113** 0.00106*** [1.61] [-1.19] [1.61] [-3.39] [-0.52] [-0.01] [-0.52] [-4.08] [-1.65] [-2.14] [-1.97] [-4.21] -0.0299*** -0.0027 -0.0299*** -0.000409*** -0.0891 0.0836 -0.0891 0.0860* 0.0101* 0.0281*** 0.0127** 0.0299*** [-3.45] [-0.27] [-3.46] [-0.11] [-0.54] [0.38] [-0.54] [-1.72] [1.79] [4.15] [2.17] [8.54] 0.0484*** 0.0931*** 0.0485*** 0.0977*** 0.508* -0.135 0.508* 0.510*** 0.00864 0.0129 0.00796 0.0114*** [3.38] [4.78] [3.39] [8.47] [1.84] [-0.32] [1.84] [-7.35] [0.92] [0.99] [0.80] [-1.01] -0.0000909*** -0.000147*** -0.0000909*** -0.000138*** -0.00140** -0.001 -0.00140** -0.000291** 0.00000555 -1.62E-05 6.26E-07 -0.0000102** [-2.83] [-4.18] [-2.83] [-9.27] [-2.25] [-1.29] [-2.25] [-2.43] [0.26] [-0.69] [0.03] [1.29] 0.00351* 0.00557** 0.00352* -0.00657*** 0.0742* 0.0505 0.0742* 0.00984 0.00148 0.00145 0.00151 -0.00112 [1.74] [2.29] [1.74] [11.92] [1.90] [0.94] [1.90] [0.77] [1.11] [0.89] [1.07] [-1.34] 0.308*** -0.619*** 0.309*** 0.0696*** 4.620*** 2.874 4.620*** 5.214*** -0.0487 -0.0281 -0.0292 0.312*** [5.11] [-2.76] [5.11] [-0.73] [4.00] [0.58] [4.00] [8.27] [-1.24] [-0.19] [-0.67] [2.92] -0.0304*** 0.0391** -0.0304*** -0.0177*** -0.409*** -0.285 -0.409*** -0.471*** 0.0130*** 0.00804 0.0110*** -0.0167** [-4.96] [2.24] [-4.97] [-0.24] [-3.50] [-0.74] [-3.50] [-9.55] [3.28] [0.69] [2.61] [-2.02] 0.0254*** 0.0229* 0.0254*** 0.00425*** -0.137 0.443* -0.137 1.041*** 0.0111* 0.0253*** 0.0148** 0.0589*** [2.78] [1.96] [2.78] [-0.65] [-0.79] [1.72] [-0.79] [22.19] [1.87] [3.23] [2.39] [9.68] 0.00305*** 0.00526*** 0.00306*** 0.00648*** 0.0452** 0.0356 0.0452** 0.00423*** -0.000352 0.00122 3.49E-06 0.0000953 24 gdp m2 infl d_cris _cons N R-sq [2.62] [3.74] [2.62] [9.62] [2.01] [1.15] [2.01] [-0.66] [-0.46] [1.29] [0.00] [0.16] 0.00166* 0.00161* 0.00166* 0.00191*** 0.00903 0.00485 0.00903 0.00238*** 0.00219*** 0.00202*** 0.00211*** 0.00209*** [1.67] 0.000241*** [1.84] 0.000237** [1.67] 0.000241*** [12.94] 0.000601*** [0.47] 0.00139** [0.25] 0.00164 [0.47] 0.00139** [3.22] 0.00251*** [7.07] 0.000005 [-2.24] [-4.32] [-5.97] [-2.32] [-1.33] [-2.32] [-6.81] [3.43] 0.000184* ** [4.30] [3.29] 0.000122*** [-4.32] [3.35] 0.000118* ** [-4.57] [-4.28] [0.17] 0.0221** 0.0184 0.0222** 0.0103** 0.427** 0.254 0.427** 0.146*** 0.0248*** 0.0175** 0.0242*** 0.0170*** [2.14] [1.58] [2.14] [2.44] [2.14] [0.98] [2.14] [5.43] [3.60] [2.23] [3.39] [4.60] -0.0194 0.113 -0.0192 -0.0650* 2.17 3.493* 2.17 -0.0626 -0.0156 -0.292*** -0.0616 -0.284*** [-0.21] [1.32] [-0.21] [-1.71] [1.24] [1.83] [1.24] [-0.18] [-0.26] [-5.01] [-1.05] [-7.20] 2.600*** 1.995** 2.602*** 1.157*** 10.24 -8.866 10.24 1.007 0.0146 0.169 0.115 -1.269*** [2.94] [2.10] [2.94] [3.03] [0.60] [-0.42] [0.60] [0.22] [0.03] [0.26] [0.19] [-4.14] 1372 1372 1372 1194 1372 1372 1372 1194 1372 1372 1372 1194 0.52 0.116 0.056 0.011 0.883 0.478 Mean VIF White's test 5.99 Ho: homoskedasticity chi2(149) = 751.71 Prob > chi2 = 0.0000 F-test Hausman test Bresh-Pagan test Sargan test Arellano-Bond test Turning Point Size (%) Ho: homoskedasticity chi2(149) = 529.53 Prob > chi2 = 0.0000 F test that all u_i=0: F(151, 1205) = 4.26 Prob > F = 0.0000 Ho: difference in coefficients not systematic chi2(14) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 1065.67 Prob > chi2 = 0.0000 Test: Var(u) = chibar2(01) = 77.87 Prob > chibar2 = 0.0000 H0: overidentifying restrictions are valid chi2(65) = 78.998 Prob > chi2 = 0.1138 H0: no autocorrelation Prob > z = 0.6255 7.1427774 5.83 5.89 Ho: homoskedasticity chi2(149) = 519.12 Prob > chi2 = 0.0000 F test that all u_i=0: F(151, 1205) = 1.31 Prob > F = 0.0098 Ho: difference in coefficients not systematic chi2(13) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 182.49 Prob > chi2 = 0.0000 Test: Var(u) = chibar2(01) = 0.00 Prob > chibar2 = 0.0000 H0: overidentifying restrictions are valid chi2(65) = 105.975 Prob > chi2 = 0.001 H0: no autocorrelation Prob > z = 0.2656 253.4159 F test that all u_i=0: F(165, 1458) = 3.71 Prob > F = 0.0000 Ho: difference in coefficients not systematic chi2(13) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 358.54 Prob > chi2 = 0.0000 Test: Var(u) = chibar2(01) = 4.72 Prob > chibar2 = 0.0149 H0: overidentifying restrictions are valid chi2(54) = 92.234 Prob > chi2 = 0.0148 H0: no autocorrelation Prob > z = 0.8494 11640.84658 Note: The symbols (***), (**), (*) indicate statistically significant levels of 1%, 5%, 10% Turning points are calculated according to the formula 25 the same Ouyang Rajan (2010) used to find Turning points Results in the impact of liquidity risk on Performance Efficiency, case of South East Asia countries can be summarized through the following table: Table 4.14: Impact of liquidity risk on Performance Efficiency, case of South East Asia countries Results Variable Expected OLS FEM SGMM ROA ROE NIM ROA ROE NIM ROA ROE NIM ROA ROE NIM (+) (+) (+) - (+) (+) (+) (+) (+) (+) (+) (+) (+) (+) (+) (-) (+) (+) lag (Pt-1 ) (+) FGAP (+) NLTA (+) (-) (+) NLST (+) (+) (+) LIA (+) (+) LLR (-) (-) (-) LADS (-) (-) (-) SIZE (+) (+) SIZE ^2 +/- ETA (+) (+) (+) (+) (+) LLP (-) (-) (-) (+) (-) GDP (+) M2 (-) INF (-) D_CRIS REM (+) (+) (+) (+) (-) (+) (+) (+) (+) (+) (+) (-) (+) (-) (+) (+) (+) (+) (+) (-) (-) (+) (-) (-) (+) (+) (+) (-) (+) (+) (+) (+) (+) (+) (+) (+) (+) (+) (+) (+) (+) (-) (-) (+) (+) (+) (+) (+) (-) (-) (-) (+) (-) (+) (+) (+) (+) (-) (-) (+) (+) (+) Source: summary from research results of the author In summary, the research results of the model of Impact of liquidity risk on Performance Efficiency, case of South East Asia countries has many interesting contents: Firstly: Liquidity risk is measured by the difference ratio, using FGAP (Bank’s loans – customer deposits/ total assets), NLTAit (Loans/total Assets), NLSTit (Loans/deposits+Short term liabilities) to measure liquidity risk We find that liquidity risk (FGAP, NLTA, NLST) is positively and significantly related to bank's performance (ROE,NIM) The result is statistically significant at 1% level This shows that liquidity risk is a strong factor bank's performance and the result is quite consistent with the "risk-return trade" theory Banks tend to engage in high-risk activities to seek profits 26 (+) (+) (-) (+) (+) Secondly: impact of bank size on performance Efficiency is nonlinear or U shape backwards The increase scale impact on Performance Efficiency, but increasing the scale to a certain point can lead to inefficiency Larger banks size can diversify their investments venture capitalists Or rely on government intervention in cases of liquidity shortages Which increases costs and affects bank performance Efficiency Thirdly: credit risk (LLP) has the positive effect on bank’s performance efficiency (ROA, ROE, NIM), showing that banks should focus on credit risk management The increase in lending, the higher bank’s performance efficiency that the bank faces a higher credit risk Thus, we further investigate the effect of the realization of the financial crisis on bank’s performance fficiency in South-East Asia countries case different depend on the model used Banks’ performance efficiency depends positively on the ratio of equity to total assets, change in GDP, change in inflation (INF) and negatively on the ratio of loan loss provision to loans, while the dependence between Banks’ performance efficiency (ROA, ROE) 4.2.4 Analysis and discussion of results in the impact model of liquidity risk on bank Performance Efficiency, case studies of Vietnam To evaluate impact of liquidity risk on bank Performance Efficiency, case studies of Vietnam, the study has used 12 different estimation models with three ratios (ROA, ROE,NIM), which each model was determined by OLS, REM, FEM, SGMM to assess the impact of liquidity risk to performance of banks Table 4.15 reports the empirical results of bank liquidity risk and performance model using FGAP (Bank’s loans – customer deposits/ total assets), NLTAit (Loans/total Assets), NLSTit (Loans/deposits+Short term liabilities) to measure liquidity risk Based on the data of 26 commercial banks in Vietnam for the period 2004-2016, the thesis will analyze the impact of liquidity risk on bank Performance Efficiency, case studies of Vietnam Table 4.15 Results of impact of liquidity risk on bank Performance Efficiency, case studies of Vietnam 27 Table 4.15 Results of impact of liquidity risk on bank Performance Efficiency, case studies of Vietnam (Appendix) The model: Pt = f(α, Pt-1, LIQUIDITY RISK it, CONTROLit, u) Dependent variables: P (NIM, ROA, ROE) measure to bank’s Performance efficiency Independent variables : Pt-1 – lag of bank’s Performance; LIQUIDITYRISK - (FGAP- financing gap to total assets, NLTALoans /total Assets, NLST- Loans/deposits+Short term liabilities), CONTROL VARIABLÉ (SIZE- natural logarithm of total assets; SIZE^2- natural logarithm of total assets squared; LIA- the ratio liquid assets to total assets; LLR- the ratio liquid assets / total Loans, LADS- the ratio liquid assets / short term liabilities; ETA- the ratio of equity to total assets.; LLP- the ratio of loan loss provision to loans, GDPAnnual percent change of GDP, INF- Annual percent change of inflation, d_cris - Dummy variable ) Database from 2004 to 2016 Estimation technique: OSL, FEM, REM SGMM Model OLS FEM REM SGMM OLS FEM REM SGMM OLS FEM REM SGMM Variable L.roa roa roa roa roa 0.456*** 0.385*** 0.456*** 0.307** [6.80] [4.87] [6.80] [2.26] L.roe roe roe Roe roe 0.562*** 0.461*** 0.562*** 0.362** [9.72] [6.33] [9.72] [2.36] L.nim fgap nlst nlta lia llr lads size size2 eta nim nim nim nim 0.715*** 0.474*** 0.715*** 0.317*** [10.56] [5.41] [10.56] [2.75] 3.136*** 26.98*** 3.268** [3.40] [-2.68] [1.98] 0.00367 0.000794 0.00367 0.0161** 0.0851 0.0622 0.0851 0.181** 0.00551 0.00909 0.00551 0.012** [0.58] [0.09] [0.58] [2.23] [1.19] [0.65] [1.19] [2.18] [0.47] [0.61] [0.47] [-0.72] 0.0245** 0.0246* -0.00686 0.0180* 0.148 0.192 0.418*** 0.0741 0.0023 0.0267 -0.0304 0.0197** [2.51] [1.92] [-0.60] [1.94] [1.36] [1.33] [3.19] [1.37] [0.13] [1.15] [-1.47] [1.29] 0.0314*** 0.0371** 0.00268** -0.270*** -0.308* 0.446*** 0.0327** 0.0484* 0.0908 [3.40] [2.60] [-0.14] [-2.68] [-1.94] [-2.96] [1.98] [1.89] [1.39] -0.0129*** -0.0110*** -0.0129*** -0.0164*** -0.121*** -0.113*** -0.121*** -0.135*** 0.0012 0.00268 0.0012 0.0169 [-11.77] [-6.70] [-11.77] [-3.55] [-9.66] [-6.12] [-9.66] [-4.70] [0.60] [0.97] [0.60] [2.00] 0.0540* 0.0182 0.0540* -0.011 0.599* 0.121 0.599* 0.353 0.0925* 0.0135 0.0925* 0.254 [1.89] [0.38] [1.89] [-0.33] [1.88] [0.22] [1.88] [0.43] [1.77] [0.16] [1.77] [1.51] -8.102*** -7.676*** -8.102*** -13.94** -74.71*** -84.62*** -74.71*** -84.07** 1.995 3.452 1.995 -31.06** [-6.00] [-4.34] [-6.00] [-2.24] [-4.99] [-4.27] [-4.99] [-1.85] [0.81] [1.12] [0.81] [2.29] -6.011** -3.12 -6.011*** -5.985 -48.84* -15.81 -48.84* -54.91 0.0176 -0.0207 0.0176 -2.631 [-2.60] [-0.89] [-2.60] [-0.98] [-1.88] [-0.40] [-1.88] [-1.55] [0.00] [-0.00] [0.00] [-0.29] -0.0956* -0.125 -0.0956* 0.190** -0.731 -1.359 -0.731 2.936* 0.135 0.203 0.135 0.541** 28 llp gdp m2 infl d_cris _cons N R-sq Mean VIF White's test F-test Hausman test BreshPagan test Sargan test ArellanoBond test [-1.78] [-1.64] [-1.78] [-2.21] [-1.25] [-1.60] [-1.25] [-1.81] [1.45] [1.55] [1.45] [1.30] 0.0855*** 0.0709*** 0.0855*** -0.115*** 0.787*** 0.729*** 0.787*** -0.876*** -0.0125 -0.018 -0.0125 -0.146** [-1.98] [9.16] [5.09] [9.16] [2.77] [7.42] [4.63] [7.42] [3.42] [-0.74] [-0.77] [-0.74] 0.0719 0.00972 0.0719 0.0269 1.185 0.295 1.185* 0.644** -0.157 -0.198 -0.157 -0.135 [1.12] [0.14] [1.12] [0.53] [1.65] [0.37] [1.65] [0.88] [-1.37] [-1.63] [-1.37] [-0.70] 0.000179*** 0.000289*** 0.000179*** 0.000313*** 0.00181*** 0.00279*** 0.00181*** 0.00264*** 0.00000562 -0.0000674 0.00000562 -0.0000998 [-3.15] [-4.31] [-3.15] [-4.63] [-3.43] [-4.43] [-3.43] [-3.81] [0.09] [-0.96] [0.09] [-0.99] 0.0152** 0.0204*** 0.0152** 0.0187*** 0.154** 0.207*** 0.154** 0.122** -0.00979 -0.00169 -0.00979 0.0119* [2.49] [3.18] [2.49] [4.04] [2.26] [2.89] [2.26] [2.02] [-0.88] [-0.15] [-0.88] [-1.70] -0.0000139 0.00000241 -0.0000139 0.000013 -0.00023 -0.00023 -0.00014 -0.00102** [0.01] [-0.56] [1.04] [-0.84] [-0.84] [-0.52] [-2.23] 0.000554 [-1.21] 0.00102** [-2.23] -0.000132 [-0.56] 0.0000909 [-0.31] -0.211** -0.224** -0.211** -0.385*** -1.637 -2.206* -1.637 -2.567* 0.0188 -0.0435 0.0188 0.266 [-2.08] [-2.03] [-2.08] [-2.63] [-1.44] [-1.78] [-1.44] [-1.95] [0.10] [-0.23] [0.10] [0.65] -2.252*** -1.365* 0.883 -1.286** -18.83*** -8.515 -45.82*** -9.46 0.83 -0.151 4.098** -1.444 [-3.83] [-1.67] [0.87] [-2.03] [-2.83] [-0.93] [-4.32] [-1.64] [0.78] [-0.11] [2.28] [-0.94] 9.42 9.38 [-3.05] 9.37 Ho: homoskedasticity chi2(115) = 137.47 Prob > chi2 = 0.0752 Ho: homoskedasticity chi2(114) = 130.87 Prob > chi2 = 0.1335 Ho: homoskedasticity chi2(115) = 125.04 Prob > chi2 = 0.2459 F test that all u_i=0: F(24, 118) = 1.15 Prob > F = 0.0307 Ho: difference in coefficients not systematic chi2(11) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 21.30 Prob > chi2 = 0.0304 F test that all u_i=0: F(24, 118) = 1.12 Prob > F = 0.3366 Ho: difference in coefficients not systematic chi2(11) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 13.92 Prob > chi2 = 0.2374 F test that all u_i=0: F(24, 118) = 1.82 Prob > F = 0.0187 Ho: difference in coefficients not systematic chi2(11) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 54.82 Prob > chi2 = 0.0000 Test: Var(u) = chibar2(01) = 0.00 Prob > chibar2 = 1.0000 H0: overidentifying restrictions are valid chi2(57) = 12.392 Prob > chi2 = 1.0000 Test: Var(u) = chibar2(01) = 0.00 Prob > chibar2 = 1.0000 H0: overidentifying restrictions are valid chi2(57) = 11.093 Prob > chi2 = 1.0000 Test: Var(u) = chibar2(01) = 0.00 Prob > chibar2 = 1.0000 H0: overidentifying restrictions are valid chi2(57) = 15.988 Prob > chi2 = 1.0000 H0: no autocorrelation Prob > z = 0.3863 H0: no autocorrelation Prob > z = 0.1163 H0: no autocorrelation Prob > z = 0.1395 Note: The symbols (***), (**), (*) indicate statistically significant levels of 1%, 5%, 10% 29 Table 4.16: Results of impact of liquidity risk on bank Performance Efficiency, case of South East Asia countries and Vietnam Results Variable Expected South East Asia countries ROA ROE NIM ROA ROE NIM (+) (+) (+) (+) (+) lag (Pt-1 ) (+) (+) L1= FGAP (+) (+) L3 = NLTA L4 = NLST LIA LLR LADS SIZE SIZE ^2 ETA (+) (-) (+) (+) (-) (-) (+) (-) (+) (+) (+) (-) (+) (-) (+) (-) (+) (+) (-) (+) (-/+) (+) (+) (+) (+) (-) (+) (-) (-/+) (+) (-) LLP GDP M2 INF D_CRIS Vietnam (+) (+) (+) (+) (+) (+) (-) (+) (+) (+) (+) (+) (+) (-) (+) (-) (-) (-) (-) (-) (+) (-) (+) (+) (+) (+) (+) (+) (+) (+) (-) Source: summary from research results of the author In summary, the results show that liquidity risk is a strong factor on bank Performance Efficiency, case of South East Asia countries We found that, The results of the case study in Vietnam is consistent with in the case study of Southeast Asia and are quite consistent with the authors' expectations and previous studies In general, the majority of variables in case of South East Asia countries are significantly higher than in case of Vietnam Bank size is negatively correlated with Performance Efficiency, case in Viet Nam while there is a nonlinear impact in the case of South East Asia countries This shows that the banks in the case study of Vietnam increase the scale does not work effectively The results of this study show that banking activities in Vietnam are different from other countries This also has implications for policymakers in Vietnam 30 The credit risk has a negative correlation with Performance Efficiency in the case of Vietnam, while the positive correlation in the case of South East Asia countries That means that credit risk will increase the bank's Performance Efficiency, but it may be increase in short term When bank management notice an increase in credit risk, they tend to increase the cost of monitoring the quality of loans and controlling bad debt Bad debt increases the instability in banking operations, the bank administrators must allocate more resources to monitor loans, which reduced the bank's operating profitability The fact that the control of credit activities in commercial banks in Vietnam is not effective, this can be explained by “the specific theory” of the bank Bank's Performance Efficiency is not affected by the financial crisis, while have the negative relationship in the case of South East Asia countries The reason is that Vietnam's financial market has not been deeply integrated so it is very sensitive to market fluctuations 31 CHAPTER CONCLUSION AND SUGGESTIONS 5.1 Conclusion The thesis has used the OLS, REM, FEM, SGMM estimation methods for the balance sheet data of 171 banks in South East Asia countries and 26 Vietnamese banks in the period 2004 - 2016 to analyze the Factors affecting liquidity risk and the impact of liquidity risk on Bank's Performance Efficiency, in the case of South East Asia countries and Viet Nam The results of the study have passed the tests and the following important results: Factors influencing liquidity risk in banking: Firstly, in the case of South East Asia countries The results show that the bank's specific characteristics (lag liquidity, bank size, quality assets, bank capital, credit risk, net interest income) and Macroeconomics factors (economic growth, money supply, inflation and financial crisis) have an impact on liquidity risk and are statistically significant in different liquidity risk models The factor such as: credit risk, money supply and inflation are contrary to theoretical expectations The effect of bank-size variables on liquidity risk is nonlinear and U-shaped, suggesting that bank size is a buffer to limit the risk of bank failures Increasing the scale will at some time affect the liquidity risk In addition, research shows that banks face higher liquidity risk in the financial crisis Secondly, in the case of Vietnam Research has found no statistically significant evidence of the impact of bank size, GDP growth, and financial crisis on liquidity risk The results show that Banking operations in Vietnam are not affected by the financial crisis, perhaps due to the fact that the Vietnamese capital market is not deeply integrated The impact of liquidity risk on Bank's Performance Efficiency, The research results have achieved some important contents as follows: + The thesis had supplemented empirical evidence of impact of liquidity risk on Bank's Performance Efficiency, in the case of South East Asia countries Research results show that most of the banks have higher business efficiency, the higher the liquidity risk This, it is consistent with the theory of trade offs between risk and return + The results of the study had found a nonlinear relationship between bank size and Bank's Performance Efficiency This is consistent with the economic theory of scale, the efficiency of the bank will increase by scale to sometime scale increase will reduce the bank operating income + The study found that the increase in equity would help improve bank business performance his is consistent with the theory of structure - behavior - efficiency This 32 model assumes that changes in market structure or bank concentration affect bank efficiency, which suggests that larger firms are more efficient (Akhavein et al., 1997) Or, the increased bank capital will allow banks to diversify their products and the banking market + We found that, The results of the case study in Vietnam is consistent with in the case study of Southeast Asia and are quite consistent with the authors' expectations and previous studies In general, the majority of variables in case of South East Asia countries are significantly higher than in case of Vietnam Bank size is negatively correlated with Performance Efficiency, case in Viet Nam while there is a nonlinear impact in the case of South East Asia countries 5.2 Policy suggestions Policy recommendations to control liquidity risk and improve Performance Efficiency in Vietnam case 5.3 Contributions of the thesis 5.3.1 Theoretical contributions The thesis will theoretically provide reliable empirical evidence on the direction of the impact of the liquidity risk on the Performance Efficiency, in the case of South East Asia countries and Vietnam, with theoretical contributions such as: Firstly, the thesis supplemented and improved the theoretical basis on the factors affecting the liquidity risk and the impact of the liquidity risk on the Performance Efficiency This is the basis for the argumentation and development of empirical studies by previous researchers Thus, the study contributed to the completion of the theoretical framework for liquidity risk and bank’s Performance Efficiency in South East Asia countries and Vietnam Secondly, approach to commercial Loan Theory and Liquidity and the Shiftability Theory of Liquidity, this study complements the experimental results for the factors influencing the liquidity risk in South east Asia countries and the impact of these factors on liquidity risk In addition, the study compared the results of the study to the case in Viet Nam The results show that macro-economic factors (GDP growth, inflation, money supply and financial crisis) and micro factors (bank size, capital, asset quality, liquidity risk, credit risk, interest income) had affected to liquidity risk In addition, the financial crisis has impacted on bank’s Performance Efficiency in South East Asia countries, while the financial crisis no affect bank’s Performance Efficiency in Vietnam Thirdly, approach to market power theory, Efficient Structure theory and risk-return theory, this study complements the experimental results for the impact of the liquidity risk 33 on the Performance Efficiency in the case of South East Asia countries and Vietnam From that, propose policies for control liquidity risk and improve Performance Efficiency in Vietnam case 5.3.2 Practical contributions In addition to its theoretical contributions, the thesis has contributions to the managers and investors: Firstly, this thesis has identified the factors that affect the liquidity risk in the case of South East Asia countries and Vietnam At the same time, the thesis examines the impact of liquidity risk on bank’s Performance Efficiency in South East Asia and Vietnam Secondly, from the results of the empirical analysis, the thesis provides some policy implications to limit banking risk in general and liquidity risk in particular Thereby promoting safe and sustainable development of the commercial banking system in Vietnam Thirdly, this is the first study to separately examine the impact of liquidity risk on bank’s Performance Efficiency, in the case of South East Asia countries and in comparison with the case of Vietnam Based on this, the research results showed factors affecting the liquidity risk and the impact of the liquidity risk on bank’s Performance Efficiency in South East Asia countries and Vietnam Fourthly, research scope for all South east Asia countries, thus provides comprehensive analytical results on factors influencing liquidity risk and the impact of liquidity risk on bank’s Performance Efficiency in South East Asia countries and compares with case of Vietnam Thereby ensuring science for policy suggestions Thus, the study compared the impact of liquidity risk on bank’s Performance Efficiency in South East Asia countries and Vietnamese This is an empirical basis for bank managers to add the regulatory framework in both macroeconomic level (regulatory agencies) and micro level (bank management) to effectively control the liquidity risk and improve the competitiveness of the banking system This research is a reference for whom are interested in liquidity, the relationship between liquidity risk and bank’s Performance Efficiency refer to methodology, scale and research model 5.4 Limitations of the thesis and future research 5.4.1 Limitations of the thesis The thesis has the following research limitations: Firstly, when analyzed bank’s Performance Efficiency, The research used the ROA, ROE, and NIM ratio derived from the financial statements is the time information it is assumed that the Performance Efficiency information from the financial statements has been adjusted to the business strategy of the bank 34 Secondly, the limitation of the study, the sample collected over the period 2004-2016 is relatively short compared with developed countries with long-established financial systems and databases that have not been fully updated Thirdly, this study only measures liquidity risk and analyzes the impact of liquidity risk on bank’s Performance Efficiency Research has not yet found tools and means to control or change in the liquidity risk, not set the liquidity risk management process Thus, no practical basis to propose solutions to control and manage liquidity risk in banking Fourthly, the study has not considered the impact of liquidity risk on bank’s Performance Efficiency among large, medium and small banking groups and has not yet assessed the similarity of results with developed countries in the region and other developed countries outside the region to increase the reliability of research results Fifthly, the thesis has not yet evaluated the two-way relationship between the liquidity risk and bank’s Performance Efficiency, mainly considered the one-way effect to the impact of liquidity risk on bank’s Performance Efficiency Sixthly, this study does not measure the liquidity risk on the interbank market and fluctuates at what level is suitable for each bank Because a bank will be willing to take risks to achieve higher bank profits From here, a question as to how much a bank will take liquidity risk to achieve its Performance Efficiency and competitive goals? 5.4.2 Future research The causal relationship between the liquidity risk and bank’s Performance Efficiency has not been verified, mainly considered the one-way effect to the impact of liquidity risk on bank’s Performance Efficiency Research has not yet found tools and methods of control for change in the liquidity risk, not set the liquidity risk management process To this, a future study should incorporate qualitative research to provide a practical basis for proposing solutions for controlling banking risk and improving the efficiency of banking business This study has not yet been the impact of liquidity risk on bank’s Performance Efficiency among large, medium and small banking groups and has not yet assessed the similarity of results with developed countries in the region and other developed countries outside the region to increase the reliability of research results The author expects further studies to supplement and overcome the objective limitations of this study 35 ... OLS FEM REM SGMM OLS FEM REM SGMM Variable L.roa roa roa roa roa 0.456*** 0.385*** 0.456*** 0.307** [6.80] [4.87] [6.80] [2.26] L.roe roe roe Roe roe 0.562*** 0.461*** 0.562*** 0.362** [9.72]... through the following table: Table 4.14: Impact of liquidity risk on Performance Efficiency, case of South East Asia countries Results Variable Expected OLS FEM SGMM ROA ROE NIM ROA ROE NIM ROA... combined the approach of (Ferrouhi & Lahadiri, 2014; Trenca, Petria & Corovei, 2015) to analyze impact of the factors on liquidity risk and its approach (Growe cộng sự, 2014; Ferrouhi, 2014) to