The Determinants of Capital Adequacy Ratio: The Case of Vietnamese Banking System in the 2011-2015 period

Ali found out that profitability indicators such as return on assets (ROA), return on equity (ROE) do not have any influence on CAR while nonperforming loans (NPL), loan to deposit ratio[r]

The Determinants of Capital Adequacy Ratio:

The Case of Vietnamese Banking System in the 2011-2015 period

Pham Thi Xuan Thoa 1 , Ngoc Anh Nguyen 1,1

1 University of Economics and Business, Vietnam National University Hanoi, Hanoi, Vietnam

144 Xuan Thuy, Cau Giay, Hanoi, Vietnam

Abstract

The analysis of a data set of observation for Vietnamese banks in period from 2011 - 2015 shows how Capital Adequacy Ratio (CAR) is influenced by selected factors: asset of the bank SIZE, loans in total asset LOA, leverage LEV, net interest margin NIM, loans lost reserve LLR, Cash and Precious Metals in total asset LIQ Results indicate based on data that NIM, LIQ have significant effect on CAR On the other hand, SIZE and LEV do not appear to have significant effect on CAR Variables NIM, LIQ have positive effect on CAR, while variables LLR and LOA are negatively related with CAR

Received …., revised…, accepted…

Key Words: Capital adequacy ratio (CAR), Vietnamese banks, Basel, NIM, LIQ

1 Introduction

Commercial banks (CBs) operates business in finance monetary sector that is very sensitive to changes in economic cycle, fiscal and corporation policy Therefore, risk management, capital adequacy in banking system are always in the top concern by managers, State Bank as well as government In the world today, the regulations for safety operations in general and capital adequacy in particular have been standardized by CAMEL, PEARL model These models codify operational areas in commercial banks: capital, asset, management and profitability through qualitative and quantitative indicators In the earlier periods, capital adequacy was assessed through how capital meets banks’ size and business activities by assets classification and CAR (Capital Adequacy Ratio) in Basel records It is said that the study about CAR ratio

in commercial banks is very necessary

1 Corresponding author Tel: +84-942139699

Email: anhnguyenngm@yahoo.se

In recent years, Vietnam has witnessed the development and completion of banking system However, the increase in terms size and diversity leads to high risk directly affecting on safety

of the system To prevent the collapse of banks and protect depositors, Vietnamese banking executives are interested in the importance of capital adequacy ratio CAR based on Basel standards This is one of important indicators for remaining the safety in commercial banks If a bank could guarantee CAR, that means it has a concrete cushion against financial shocks, protect both for themselves and depositors Therefore, a rising question for bank executives is how to improve CAR? To answer this question, first of all, we need to determine the factors that influence on CAR in banking system

2 Literature Review

The relationship between the capital adequacy ratio and macroeconomic and banking factors is very important taking into account that the bank capital serves as cushion in case the value of the bank’s assets declines or its liabilities rise There are a lot of academics and other financial institutions that have tried to investigate the main factors that determine the capital adequacy ratio Below we give a short literature review of some authors who have studied the relationship between capital adequacy ratio and some internal and external factors

Ahmad A.A (2013) gathered from the financial statements of nine Saudi banks listed in the stock exchange market over the period of 2007-2011 [1] Profitability represented the return on assets and return on equity has a negative relationship with capital adequacy The cost-income ratio is negatively related to bank profitability

Ahmet B and Hasan A (2011) investigated the determinants of the capital adequacy ratio (CAR) in the Turkish banks using data from annual reports during 2006-2011 [2] From the regression results Ahmet found that loans, loans loss reserves, leverage, ROA and ROE have a significant relationship with CAR while bank size, deposits, liquidity and net interest margin NIM do not have effect on the CAR in the Turkish banks Similarly, Nada D (2013) also used regression for selected Bosnian banks and indicated that SIZE, DEP, LOA, ROA, ROE AND LEV have significant effect on CAR, and LLR and NIM do not appear to have significant effect on CAR [3]

Abdullah A.M, Kamal N (2015) identified determinants of capital structure in a sample of commercial banks listed on the Gulf Cooperation Council (GCC) stock markets [4] They

found that profitability and liquidity affect bank’s capital structure decision and emphasizes the importance of long-term debts in commercial bank’s financing in GCC

Ali S (2015) used a regression model like the ordinary least squares analysis [5] Ali found out that profitability indicators such as return on assets (ROA), return on equity (ROE) do not have any influence on CAR while nonperforming loans (NPL), loan to deposit ratio (LTD) and equity multiplier (EM) have negative and significant impact on CAR in the Albanian banking system while the bank size has a positive impact

Abou E.S.H (2015) [6], examined a sample of 560 US bank holding companies for the period 2003-2009, results reveal that the association between the core (Tier 1) capital ratio and bank failure becomes significant only if the bank holding company has a Tier 1capital ratio of less than 6 percent This is the level below which US bank regulators do not regard banks as being well capitalized

Chernykh L.C (2015) tested the predictive power of several alternative measures of bank capital adequacy U.S bank failures during the recent crisis period [7] The paper found that an unconventional ratio-the non-performing asset coverage ratio –significant outperforms Basel-based ratios including the Tier 1 ratio, the total Capital ratio, and the leverage ratio- throughout the crisis period

Cummings J.R (2016) found evidence that: (i) banks increase provisions in anticipation of future lending growth, (ii) banks allocate part of surplus capital above regulatory requirements top re-fund future credit losses through provisions, and (iii) banks allocate part of higher earnings for the same purpose [8]

Dakito A.K (2015) examined the determinants and level of capital adequacy of the banking industry using empirical model [9] The finding shows that, lagged value capital adequacy, portfolio risk, average capital adequacy of the industry and asset size are significant factor that affects the capital adequacy ratio of banking sector in Ethiopia

Harley T.W (2011) studied the relationship between capital base and some macroeconomic, financial structure and banking variables using an error correction model during

1980-2008 in Nigeria [10] The author concludes that the money supply is a very important determinant of the capital adequacy The real exchange rate is significant determinant but its

coefficient is not as expected while the deposit liabilities and liquidity risk are not statistically significant

Hassan M.K (2016) [11] investigated changes in bank’s capital adequacy ratio (CAR) under different stress scenarios and examine the results by comparing conventional banks to participation banks in Turkey The results report that the capital adequacy ratio of the banks declines substantially given the stress scenarios

Leila B, Hamidreza V & Farshid A (2014) focused on influential factors (precisely seven financial factors) over capital adequacy in Iranian private banks for the period 2006–2012 [12] The results obtained indicate negative relationship between bank size and capital adequacy ratio of banks and positive relationship between Loan Asset Ratio (LAR), Return on Equity (ROE), and Return on Asset (ROA), Equity Ratio (EQR), and capital adequacy ratio RAR and DAR do not have any impact on capital adequacy ratio

Mehdi M.J (2016) used data from 310 subsidiaries and 265 branches to test the impact of parent banks’ fundamentals on subsidiaries’ and branches’ capital ratios [13] The results provide strong evidence that the CAR of subsidiaries and branches operating in developing and developed countries do not depend on the same set of explanatory factors

Mastura A.K, Kabir H, Taufiq H, et al (2013) analyzes and compares Islamic and conventional banks in 14 Organization of Islamic Conference (OIC) countries from 1999 to 2009 [14] The empirical evidence suggests that capital requirements have significant impact on the deposit and lending behaviors of the 52 Islamic Banks (IBs) and 186 conventional banks (CBs) in the sample There is a strong positive relationship between capital requirement and deposit and loan growth for both IBs and CBs

Md A.A.M (2013) evaluated performance of prime bank [15] Data of the bank is analyzed using capital adequacy ratio, debt equity ratio and advance to asset ratio for the period from

2008 to

2012 The study finds, though high debt equity ratio bank maintains capital above regulatory requirement This will help the researcher and bank to further improvement in capital adequacy

to meet regulatory requirement and enhance bank performance

Mohammad B, Wadad S, Mohammed B (2016) [16] suggested that compliance with the Basel capital requirements enhances bank protection against risk, and improves efficiency and

profitability The impact of capital requirements on bank performance is more pronounced for too-big-to-fail banks, banks in periods of crisis and banks in countries with good governance Inwon S (1998) [17] also indicates that while some cosmetic adjustments might have been made by partial recognition of unrealized stock losses and expected loan losses, efforts to increase capital in ways that effectively reduced risk exposure seemed to dominate the response

to strengthened capital requirements

Ioana R.S (2014) [18] reflected that Basel II comes mainly with an enlargement of the areas covered by the risks to be taken in the calculation of the capital adequacy indicator but also with a diminishing of risks share related to the retail exposures Therefore comparing the Basel III Accord to the previous ones, the paper showed the capital increases which the international banks must undertake in order to comply with the requirements imposed

Parvesh K.A, Afroze N (2014) investigates the determinants of capital adequacy ratio in Indian Private Sector Banks [19] The regression results have revealed that Loans, Management Efficiency, Liquidity and Sensitivity have statistically significant influence on the capital adequacy of private sector banks

Pamuji G.R and colleagues (2014) [20] used panel data regression model, the result shows that the capital ratio of these state-owned banks is affected by the size of the bank, the bank’s leverage, the quality of management, and the interest rate risk Contrary to the existing literatures, this paper does not support the effect of management capability to generate income

on the bank’s capital ratio

Salma L, Ilhem G.A, Younes B (2015) [21] used data from 12 Mena and South East Asian countries characterized by the coexistence of Islamic and conventional banks They concluded that the funding ratio has a significant impact on the behavior of 70 conventional banks and 47 Islamic banks However, competitive conditions have no significant effect on the relationship between the weighted assets ratio and Islamic bank behavior

Shirley J H, Su C.H (2010) examined the relation between firms’ financial structures and their risky investment strategies in Taiwan’s banking industry [22] Regressions cover two superiors: before the first financial reform (1996-2000) and after the first financial reform (2001–2006) Second, the firm performance is significantly and positively related to firm size, leverage and financial cost Finally, the regression results show that financial structures for banking firms are

positively related to the states of business cycle The positive signs coincide with Proposition 4

in analytical model

Raoudha D (2016) analyzed the impact of bank transparency on capital adequacy ratio in a developing country [23] He suggest that bank transparency, lagged capital and foreign ownership are positively correlated with capital adequacy ratio and managerial efficiency is negatively correlated with capital adequacy ratio

Rubi A, M Ariff, Michael J.S (2009) had results from an unbalanced panel data set spanning eight years around the period of the 1997–1998 Asian financial crisis [24] Test results suggest

a strong positive link between regulatory capital and bank management’s risk-taking behaviour The risk-based capital standards of the regulators did not have an influence Finally, bank capital decisions seem not to be driven by bank profitability

Yakup A, Serkan Ö (2007), used a panel data set that employs bank-level data from the Turkish banking sector covering the period 2002–2006 and estimated the model with generalized method of moments (GMM) [25] The findings of this study suggest that lagged capital, portfolio risk, economic growth, average capital level of the sector and return on equity are positively correlated with capital adequacy ratio and share of deposits are negatively correlated with capital adequacy ratio

3 Analytical framework and research variables

The objectives of this study is to empirically investigate the determinants of the capital adequacy ratio (CAR) in 29 Vietnamese banks using data from annual reports during

2011-2015 by using OLS ordinary least squares method

The effects of determinants on CAR as described in Figure 1

Figure 1 Research framework

NIM

LIQ LOA

Where:

CAR -dependent variable, capital adequacy ratio

SIZE- natural logarithm of the total assets

LEV- leverage, ratio of equity to total liabilities

LLR -loan loss reserves, ratio of loan loss provision to total loans

NIM- ratio of net interest margin

LOA- return on assets, ratio of loans to assets

LIQ- return on assets, ratio of cash and precious metals

The linkage of CAR and 6 determinants are hypothesized as follows:

H1: Bank SIZE has significant impact on banks’ capital adequacy ratio.

The natural logarithms of total assets used as a proxy of bank’s size Banks’s size is important because of its relationship to bank ownership characteristics and access to equity capital Bank access to equity capital may reflect a relative importance of bankruptcy cost avoidance or managerial risk aversion Ahmet Büyükşalvarcı and Hasan Abdioğlu (2011) [2] found that a banking organization’s asset-size is an important determinant of its capital ratio in an inverse direction, which means that larger banks have lower capital adequacy ratios

H2: LEV ratio has positive impact on banks’ capital adequacy ratio.

The final bank specific variable is the bank leverage factors which proxy by the total equity to total liabilities ratio Shareholder will find high leveraged banks are more risky compared to other banks

H3: Loan loss reserve LLR has positive impact on banks’ capital adequacy ratio.

Loan loss reserve defined as a valuation reserve against a bank's total loans on the balance sheet, representing the amount thought to be adequate to cover estimated losses in the loan portfolio We consider loan loss reserves to gross loans ratio as a proxy of bank risk as this ratio may indicate the banks’ financial health A negative impact of loan loss reserve in capital could mean that banks in financial distress have more difficulties in increasing their capital ratio In contrast, a positive effect could signal that banks voluntarily increase their capital to a greater extent in order to overcome their bad financial situation Ali Shingjergji (2015) [5] found that reserve of loan losses caused a decline in capital adequacy

ratio Hassan (1992) [11] argued a negative relationship between capital adequacy ratio and loan loss reserve

H4: Net interest margin NIM has statistically significant impact on banks’ capital adequacy ratio.

Net interest margin is defined as the ratio of net interest income to average earning assets It is

a summary measure of banks' net interest rate of return While it is well known that the net interest margin is a significant element of bank profitability, however the effects

of market interest rate volatility and default risk on the margins are not well recognized The net interest margins are set by banks to cover the costs of intermediation besides reflect both the volume and mix of assets and liabilities More specifically, adequate net interest margins should generate adequate income to increase the capital base as risk exposure increases The charter value which discussed in introduction predicts a positive relationship between bank management quality and bank capital However, bank management may reduce the capital cushioning if the default risk is very low As a result, a negative relationship is expected between net interest margin and capital adequacy ratio

H5: Share of loan LOA has negative impact on banks’ capital adequacy ratio.

Share of loans is a ratio of total loans to total assets This ratio is important because of it’s relationship with diversification and the nature of investment opportunity set It measures the impact of loans in assets portfolio on capital When risk increases, depositors should be compensated for loss so capital adequacy ratio should increase Therefore, a positive relationship is expected between share of loan and capital adequacy ratio

H6: Liquidity LIQ has positive impact on banks’ capital adequacy ratio.

A liquid asset to customer and short term funding are included to proxy bank liquidity Angbazo (1997) [26] states that as the proportion of funds invested in cash or cash equivalents increases, a bank's liquidity risk declines, leading to lower liquidity premium in the net interest margins Therefore, an increase in bank liquidity (high LIQ) may have a positive impact to capital ratio

From those hypotheses, an econometric model is mentioned as followed:

CAR it = α + β1 SIZE it + β2 LEV it + β3 LLR it + β4 NIM it + β5 LOA it + + β5 LIQ it +ε it

4 Data collection

The aim of this research is to identify factors influencing CAR in 29 commercial banks in Vietnam included: An Binh Bank (ABB), Asia Commercial Bank (ACB), Bank for investment and development of Vietnam (BIDV), Bao Viet Bank (BVB), Vietnam Joint Stock Commercial Bank for Industry and Trade (CTG), Eximbank (EIB), Military Commercial Bank (MBB), Viet Capital Bank (GDB), HDBank (HDB), Kien Long Bank (KLB), LienViet Post Bank (LVB), MBBank (MBB), MaritimeBank (MSB), Nam A Bank (NAB), North Asia Bank (NASB), National Citizen Bank (NVB), Oricombank (OCB), PGBank (PGB), PVcomBank (PVF), Saigon Commercial Bank (SCB), SeaBank (SEAB), SaigonBank (SGB), SH Bank (SHB), Sacombank (STB), Techcombank (TCB), Viet A Bank (VAB), Vietcombank (VCB), VIBBank (VIB), VPBank (VPB)

This study used secondary data and the data get from annual reports of the sample banks Data directly took from the commercial bank balance sheet statement, profit and loss statement and from notes to account It is edited as cross-sectional data Time study period is five years from 2011-2015 The methodology used is Ordinary least square (OLS) The paper analyzed the relationship bank size (amount of total assets), leverage, loan loss reserves, loans, liquidity, interest margin ratio as independent variables and dependent variable CAR The selection of those variables is based on their influence on CAR which has been proven in before researches and are suitable for real conditions in Vietnam

5 Model results

5.1 Variable statistics

Various descriptive statistics are calculated of the variables under study in order to describe the basic characteristics of these variables Table 1 shows the descriptive statistics of the data containing sample means, standard deviations, minimum and maximum value

Table 1: Descriptive Statistics of Variables

(Source: Author’s Calculation)

5.2 Regression model test failure

Table 2: Correlation Matrix

CAR 1.000000 0.243218 -0.106017 -0.072746 0.143080 -0.159208 -0.156970 NIM 0.243218 1.000000 -0.145636 0.133341 0.193400 -0.061293 0.275155 SIZE -0.106017 -0.145636 1.000000 0.109809 -0.137181 -0.106711 0.202629 LIQ -0.072746 0.133341 0.109809 1.000000 -0.005453 0.001918 -0.047783 LEV 0.143080 0.193400 -0.137181 -0.005453 1.000000 0.027128 -0.039521 LLR -0.159208 -0.061293 -0.106711 0.001918 0.027128 1.000000 -0.019306 LOA -0.156970 0.275155 0.202629 -0.047783 -0.039521 -0.019306 1.000000

(Source: Author’s Calculation)

The dependent and independent variables are tested for multicollinearity based on a simple correlation and covariance matrix As depicted in Table 1 and Table 2, all of them have no collinearity problem

Table 3: Estimated equation

Variable Coefficient Std Error t-Statistic Prob.

SIZE -0.004332 0.006698 -0.646857 0.5188

LLR -0.244930 0.133504 -1.834629 0.0687 LOA -0.109049 0.051641 -2.111686 0.0365 LIQ(-1) 1.565142 0.573893 2.727235 0.0072 R-squared 0.184885 Mean dependent var 0.112028 Adjusted R-squared 0.149186 S.D dependent var 0.088973 S.E of regression 0.082068 Akaike info criterion -2.115144 Sum squared resid 0.922720 Schwarz criterion -1.970778 Log likelihood 159.2903 Hannan-Quinn criter -2.056481 F-statistic 5.179058 Durbin-Watson stat 2.128461 Prob(F-statistic) 0.000079

(Source: Author’s Calculation)

Before analyzing the coefficient, one should look at diagnostics of regression in the table 3 There are 4 in total 6 variables have statistic meaning and be consistent with economic theory

So the model has no multicollinearity problem With computed F-value of 17.3348 (p<0.000) for the cross-sectional data regression, we reject the null that all coefficients are simultaneously zero and accept that the regression is significant overall

Table 4 Breusch-Godfrey Serial Correlation LM Test

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 1.519464 Prob F(2,135) 0.2225

Obs*R-squared 3.170160 Prob Chi-Square(2) 0.2049

Test Equation:

Dependent Variable: RESID Method: Least Squares Sample: 2 145 Included observations: 144 Presample missing value lagged residuals set to zero.

(Source: Author’s Calculation)