Introduction
Banking is one of the most sentitive industries not only in Vietnam but alsothroughouttheworldanditplaysanextremelyimportantroleineconomicdevelopme nt.Banksdonotonlyaffectbutalsofacilitatetheintegrationo f economicactivitiessuchas mobilizingresources,productionactivities,publicfinancedistributionandevendistribut ionofsocialwelfare.Therefore,bankingmanagement is always a matter of special concern by government carrying outmanagementandsupervisionactivities.
A typical example of the banks’ heavy influence on economy is the globalfinancial crisis that happened in 2007 which led to a series of bankruptcies, bringingtheeconomicstagnationtoitspeak.AccordingtoBankforInternationalSettlemen ts, during global financial crisis, many banks struggled to sustain adequateliquidity, anumberofbanks still failed, being forced intomergerseven whenreceiving extraordinary support from the central banks Several years before thecrisis, liquidity and its management was not really a priority, funding was availableat low cost However, this crisis has totally changed market conditions that capturedtheimportanceofrelatedliquidityissuesmeasurementthusits management.
It is evident that liquidity risk measurement is up-to-date and is playing animportant role, which is why Basel III has been officially introduced since 2013,putting a considerable effort into the design of banking regulation as a way ofreducing the damage to the economy by banks Many financial market participantsincludingVietnamarestillstrugglingtodeployBaselIIforhopingag r e a t e r stability and decrease the likelihood of a repeat of the events in 2007 In addition tothis,afterjoiningtheASEANEconomicCommunityin2015,Vietnamhascommitted to ease restrictions in the banking industry, giving this sector manyopportunities such as increasing the level of economic integration, increasing theopportunitiest o a c c e s s a n d a t t r a c t c a p i t a l , e t c b u t a l s o m a n y c h a l l e n g e s s u c h a s competitive pressure from regional banks and international banks, especially in thecontext of our country's limited financial potential compared to other banks in othercountries.
Therefore,thestudyofliquidity issuesinthebankingsystemisextremelynecessary,ifthebankshavegoodliquidity,itdoes notonlyhelps t a b i l i z i n g financialmarketbutalsodevelopingthecountry'seconom y.Especially,inthecurrent conditions of Vietnam, liquidity issues are of one of the most concern andare often discussed from the beginning of every year Those are the reasons for theauthor to chose to study on the topic "Factors affecting the liquidity risk of jointstockcommercialbankslistedonstockexchanges inVietnam".
Previousstudies
Aspachs et al (2005)provided a comprehensive analysis of factors that affectliquidity policy of banks in United Kingdom In particular, they investigated howcentral bank’s policy affected liquidity buffers and how the economic cycle changedthe liquidity buffers The result was thatmonetary policy rates affected negativelyon UK banks’ amount of liquid assets which meant when central banks attempted toreduce the interest rate and increase the monetary base, banks seemed to keep theadditional liquidity on their balance sheets Secondly, banks appeared to increasetheir liquidity buffers while economic downturn and drop them down in economicupturn This study used unconsolidated financial reports on a quarterly basis from1985to2003.
Praet and Herzberg (2008)indicated the complex relationship between banksand financial markets, in which banks are dependent on and exposed to financialmarkets as regards liquidity The authors investigated the mechanics of liquiditycrisis in the market and its impact on bank’s liquidity, as well as spillovers to otherbanks.Theresultwasthatassetliquidityconsiderablyrelyonfunctioningoffinancial markets,especially forsecuredlendingtransactionsandsecuritizationmarket.T h e y a l s o f o u n d t h a t l o w i n t e r e s t r a t e s h a v e a c c e l e r a t e d l i q u i d i t y i n t h e market beyond sustainable level Together with liquidity management, they alsorealized that a greater transparency could reduce asymmetric information whichreduces market vulnerability However, information appears to be limited that acomprehensivea n d c o m p a r a b l e i n f o r m a t i o n g a p s w e r e l a r g e i n
Vodova(2011)focusedonthecausesofliquidityriskthatsheidentifieddeterminantso fliquidityinCzechcommericalbanksfrom2001to2009withliquidity measured by different balance sheet indices The result revealed that thereis a positive connection between liquidity and capital adequacy as well as ratio ofnon-performing loans and interest rate on loans. However, the connection betweensize of banks and liquidity is unclear Vodova also found that larger banks presentlower liquidity according to the “too big to fall” theory, that larger banks are lessmotivated to hold liquidity as they rely more on government supports when inshortages.
Vodova (2013)used 3 formulas to evaluate liquidity positions of
Hungariancommericalbanksfrom2001to2010:(1)liquidassets-to- totalassetsratiogivesusa general look on liquidity shock absorption capacity; (2) liquid assets-to-depositsand short- term borrowing ratio focuses on sensitivity to selected funding types; (3)liquid assets-to-deposits ratio captures bank’s liquidity when bank cannot borrow ininterbank market when they need to The result was liquidity of banks was relatedpositively to capital adequacy, interest rate of loans, profitability whereas it wasrelatednegatively tothebank’ssize,interestmargin,interestrateofmonetarypolicy, interest rate on interbank market transactions The impact of the growth rateofGDPonliquiditywasunclear.
Trương Quang Thông (2013)used Financing Gap formula to evaluate liquidityrisk in 27 Vietnamese commercial banks from 2002 to 2011 Plus, he divided twodeterminants into two groups: internal and external variables He found out thatincreasingthesizeofbankswouldeventuallyincreaseliquidityrisk,theincreaseof the ratio of liquid reserve to total assets will reduce liquidity risk, whereas thedecrease of the ratio of bank loans and other loans to total capital also helps banksreduce liquidity risk Thus, he found a negative correlation between liquidity riskandtheratioofequitytototalassets,ashigherthebank’sequity,theh i g h e r liquidity risk exposure In addition, the result also showed the impact of the growthrateofGDPandinflationrateonliquidityrisk.
Vũ Thị Hong (2015)researched on determinants of liquidity of 37 commercialbanks in Vietnam from 2006 to 2011 The study was based on Vodova (2011)’sliquidity measurements as having two liquid indices and two illiquid indices; andwas also based on Basel’s principles on liquidity management to build a set offactors as independent variables The result highlights that the liquidity of banks ishigherwhenequity ratio,non- performingloanratioandnetincomeishigher.Meanwhile, liquidity is negatively liked with loans-to-deposits ratio Furthermore,the relationship between liquidity and size of the bank, provision credit losses ratioisunknown.
Researchobjectives
- Determinethelevelofimpact(strongorweak,negativeorpositive)ofthesefactor sonliquidityriskofbanks;
Researchquestions
Researchsubjectsandscope
Researchsubjects
Research object is the liquidity risk of JSCBs listed on Vietnam stock exchanges(HOSE,HNX,UPCoM).
Researchscope
- Scope of time: Data were surveyed during the period 2010-2018 because inthis period: (i) The economy recovered after being affected by theGreatDepression in 2007; (ii) Vietnam joined the ASEAN EconomicCommunity(2015), that in which Vietnam has committed to ease banking policies; (iii)Banks need to mobilize medium and long-term capital to meetCircular No.06/2016/TT-NHNN dated May 27 th , 2016; (iii) The influence of objectivefactors such as the US-China war, Fed rate changes or Basel II piloted on 10banksfrom Q4/2017,etc.
Methodology
The study is based on researches and panel data regression models of Vodova(2011) and Trương Quang Thông (2013) to build a model of liquidity risk and itsimpactfactors asfollow:
: Non-performingloanratio ROE : Return on
Frome m p i r i c a l r e s u l t andt h e c o l l e c t e d d a t a , a u t h o r w i l l p e r f o r m s t a t i s t i c a l analysis to describe the basic characteristics of sample data on the average of themeasurement variables; perform correlation analysis to determine the relationshipbetween the independent variables to evaluate the predictable and forecast level ofthe model;perform panel data regression analysis to measure the level of influenceandindicatethedirectionoftheimpactofeachindependentvariableonthedepen dentvariabletoanswerthedissertation'squestionaboutfactorsaffectingliquidity risk of JSCBs viaPooled Ordinary Least Square (Pooled-OLS), FixedEffectModel(FEM); RandomEffectModel(REM)withStata15software.
Contributionofthestudy
This paper is based on reliable quantitative methods of processing reliable datafrom the audited financial statements to provide empirical evidence on the impact ofdeterminantsonthe liquidityriskofJSCBsin theperiod2010-2018
Proposing solutions to complete the policy framework in the management andadministration of Vietnam's commercial banking system in order to improve abilityto face liquidity shocks and improve the competitiveness of the current commercialbankingsysteminVietnam
Dissertationstructure
Thepaper is dividedinto5chapters,asfollows:
Describe reasons for selecting the topic; review some previous studies; stateresearch objectives, research questions, subjects and scope of research as well asresearchmethodsandcontributionsinpracticeandinscientificstudies
State some concepts used in the study, present the theoretical basis related toliquidityriskatthebankaswellasintroduceanoverviewofdeterminantvariables basedo n r e f e r e n c e m o d e l s o f p r e v i o u s s t u d i e s f o r t h e p u r p o s e o f e s t a b l i s h i n g a n impact model.
Presenting the research model, research methods, methods of data sampling andprocessing, building and testing scales to measure the impact factors on liquidityrisk.
Summarize the main findings of the study, the significance and contribution ofthe research to the banking sector in particulara n d t o t h e e c o n o m y i n g e n e r a l A t the same time, giving some suggestions to improve the liquidity of the bank Thisresearch should create a basis for others to continue to explore and develop, whileshowingsomelimitationsof researchandproposingfurtherresearchdirections.
Theoryofliquidityriskofjoin-stockcommercialbanks
JointstockCommercialbanks
AccordingtoArticle4oftheLawonCreditInstitutions(LawNo.47/2010/QH12), commerical bank is a type of bank where it is allowed to conductall banking activities and other business activities in accordance with this Law forprofit purposes.In which, Joint stock commerical bank (JSCB) isa c o m m e r c i a l bank organized in the form of a joint stock company Under this Law, bankingactivity is the business of monetary and banking services with the maina c t i v i t i e s are receiving deposits, using this money to provide credit and payment services viaaccounts.
Commercial banks are one of the financial intermediaries that play an importantrole in establishing the financial environment With core activity is transferringmoney from capital surplus to capital shortage, banks make the idle money to befully utilized and make money available to consumers and businesses that theymight not be able to earn, or at least not for a very long time Besides, banks alsocreate creditworthiness of customers by safeguarding money so that good money isonly for goodloansandnot lost on badloans.Inother words,banks connectindividuals,businessesandotherinstitutionstogetherthathelpskeepingtheecon omy going Therefore, if banks fall, it will cause a collapse for a whole systemoft h e e c o n o m y , a n d b e c a u s e b a n k s a n d m o n e y are t h a t e s s e n t i a l t o m a i n t a i n n o t only economies but entire societies, they are extremely regulated and must operatebystrictproceduresandprinciples.
Bankliquidityrisk
Bank for InternationalSettlement defines liquidity as theability ofbank tofinanceincreasedassetsandmeetobligationswhendue,withoutincurringunacceptable losses.Therefore,liquidityriskariseswhenthebankisunabletomeet capital needs at some point of time; or must raise capital from other sources withhigh costs to meet its obligation; or due to other subjective reasons that affects thesolvency of the bank, accordingly it will lead to undesireable consequences In otherwords, this is the type of risk that occurs in cases when the bank is insolvent due tofalling to promptly liquidate assets in a short period of time and at less than marketprices.
AccordingtoVodova(2011),therearetwotypesofliquidityrisk:marketliquidityriska ndfundingliquidityrisk.Inwhich,marketliquidityriskariseswhena bank can not sell their assets in the market in the shortest possible time and at thelowestcost,whereas,fundingliquidityriskariseswhenabankcannotmeetefficiently current and future cash flow needs without affecting their operations ortheir financial conditions These two types of liquidity risk often interact with eachotheramongfinancialmarketswhichwillaffectmanyfinancialinstitutions,includin gcommercialbanks.
Liquidity gap is first mentioned inRisk Management in Bankingby J. Bessis(2009) which is estimated by the difference between assets and liabilities at bothpresent andfuture dates However, the drawback ofthismethod isthat it isv e r y hard to collect data since a minority banks publish their annual reports with liquiditygapswhichleadingtoastronglyunbalanceddatasetthatisunableto estimate.
Another measurement is liquidity ratios - various balance sheet ratios that can beused to identify the main liquidity trends A number of studies has indicated variousliquidityratiosasfollows:
The first three ratios (1), (2), (3) are used to measure the liquid assets inbanks, the higher these ratio are, the higher liquid assets that the bank isholding meaning less liquidity risk Meanwhile, (4) and (5) formulas can beused to measured the illiquid feature, the higher these ratios are, the lessliquid assets that the bank is holding meaning suffering from higher liquidityrisk.
Liquidityriskimpacts
- To customers and the bank itself: regard the main function of the banks isintermediaries, banks act as a bridge between borrowers and lenders; and theinterest difference is the largest sourceof revenue.When bank liquiditydecreases, the bank is forced to race a capital raise, thus, leading to highdeposit rates; high deposit rates force lending rates to increase and making itdifficult to lend; the bank is forced to pay high deposit interest but hard tomakealoan,thenclearly,thebankwillsufferlosses.F u r t h e r m o r e , t h e fail ure to meet demands of withdrawals will lead to the loss of trusts ofdepositors (including interbank transactions) and fail to meet disbursementneedsforcreditextensions.
- To the economies: low liquidity will affect investment activities as capitalmobilization reduction because money are concentrated in banks due to highdepositrates.Besides, italsoaffectsbusinessactivitiesofmanyco mpanies due to high credit interest rates, then product and service prices will escalatewhichasaresult,inflationratewashigher thanexpected.
In summary, if the bank does not have enough money to meet the market’sdemands,thesolvencywillbelostaswellascreditworthiness,finallyisthebreakd own of the whole system In contrast, banks will have good liquidity or theydo not face liquidity risk when they have available captal at reasonable costs at thevery right time Therefore, it is very necessary to study liquidity issues and itsdeterminantsinthe bankingsystemforthesakeofstablemarketsand economies.
Previouss t u d i e s o n f a c t o r s a f f e c t i n g l i q u i d i t y r i s k o f j o i n t -
Externalfactors
Group of external factors are exogenous factors in macroeconomic level such asGross Domestic Product rates (GDP), inflation rates, unemployment rates or eveninterest lending rates, interbank rates, etc Bunda (2003) found a positive correlationbetween bank liquidity and lending rates, GDP, inflation rate Meanwhile, Vodova(2011) found a positive correlation between liquidity in Czech Commercial Banksand interest rate on loans, whereas, he also found a negative correlation betweenilliquidity and interest rate on interbank transactions, inflation rate and a positivecorrelation between illiquidity and GDP However, in 2013, Vodova continued hiswork on Hungarian banks and found that there was a positive correlation betweenliquidity and GDP which was in contrast to his result in 2011 Besides, Vodovafound no relationship of unemployment rate with liquidity Furthermore, liquidityrisk might be also affected by policies of government such as monetary policies, aswellasmarkettrendon technologiesorcompetition,etc.
However,theyareexternalfactorsthattheircharacteristicsaretobeuncontrollableo r u n e x p e c t e d f r o m t h e b a n k s ’ v i e w s W h i l e c h a n g e i s i n e v i t a b l e , having the flexibility to deal with unexpected market variation is different frombanks to banks; and one of the most effective way to be flexible and adaptive is todevelopaframeworkoranenvironmentalscanwhichisPESTLEa n a l y s i s : Politica l,Economic,Social,Technological,LegalandEnvironmental.Listing,selectinga n d a n a l y z i n g e a c h e l e m e n t c a n t a k e q u i t e a l o n g t i m e a n d e f f o r t t o c omplete a full evaluation of external factors on liquidity risk Therefore, in thispaper, theauthoronly focuses on subjectivefactors without considering thea f f e c t of factors of “market” level and government policies on bank liquidity As a result,theseobjectivefactorswillnotbeelaboratedandanalyzedanyfurther.
Internalfactors
According to Aspachs (2005) and Nikolaou (2009), liquidity does not simplydepend on exogenous factors but more importantly, it is influenced by endogenousfactorssuchasnetincome,equity, sizeofthebank,non-performingloan,etc.
Equity-to- total assetsrat io
Equity-to-total assets ratioshows how much of the assets are funded by equityshares Equity is the resource that the bank own itself, eventhough it accounts foronly a small proportion in the total capital of the banks, equity is one of the basicfactor determining the existence and development of a bank Besides, equity is alsoconsidered as a collateral to build trusts towards customers, maintains solvency andliquidity to the bank Many studies have found a positive correlation between thisratio and bank liquidity meaning the higher this ratio is, the higher liquidity of thebank is such as Bunda (2003), Vodova (2011), Tran Hoàng Ngân (2016); whereasTrươngQuangThông(2013)founditwasanegativecorrelationbetween theratioofequitytoassetsandbankliquidity.
Non-performing loansare those of group 3, 4 and 5 that are more than 90 daysoverdue,accordingtoArticle3ofCircularNo.02/2013/TT-
NHNNo n classificationofassets,levelsandmethodofsettingupofriskprovisi ons,anduseofprovisionsagainstcreditrisksinthebankingactivityofcrediti n s t i t u t i o n s , foreignb a n k s ’ b r a n c h e s M o r e o v e r , b a n k s alson e e d t o b a s e o n t h e r e p a y m e n t capacity of customers to account loans into the appropriate groups Non-performingloan ratio (NPL) is the ratio of non-performing loan to total loans which are fromgroup 1 to 5 Thus,NPL has a significanti n f l u e n c e o n c r e d i t o r s a s w e l l a s t h e banks, leaving both at risk of capital losses Therefore, many previous studies fromLuchetta
(2007), Vong et al (2009) showed a negative correlation between NPL andbank liquidity. However, studies of Vodova (2011), Vũ Thị Hong (2015) and MaiThị Phương Thùy (2018) showed a positive correlation between these two variablesandtheirexplanationisthatwhenNPLarises,thebankwillhavemorem otivationtoneutralizeitwithliquidassetsleadingtothebankliquidityincrease.
Return on equityis calculated by dividing net income by shareholder’s equity,therefore, it reflects the level of effectiveness in the use of equity as a measure offinancialperformance.Vodova(2011)expectedtoseeanegativecorrelationbetweenth isratioandbankliquiditybutthenwitnessednocorrelationintheregression result Meanwhile, in the research of Vũ Thị Hong (2015), there is apositive correlation between this two variables; which is similar to the result of TranHoàngNgân(2016)andMaiThịPhương Thùy(2018).
Size of the bankin many previous studies will be calculated by taking thenaturallogarithmoftotalassets.Vodova(2011)foundapositivec o r r e l a t i o n bet ween size of banks and liquidity in Czech Commercial Banks, but then in 2013,Vodova continued her research in Hungarian Commercial Banks and received anegative correlation between these two variables.DorianaCucinelli (2013)a l s o gave different results for the dependent variables calculated in two different waysthat represented bank liquidity There are two points of view can be explained tothesedifferentresults:
(2) the hypothesis “too big to fail” meaningb a n k s w h o h a v e l a r g e a s s e t s w o u l d have less motivation to hold liquid assets because big banks can rely on otherabundant resouces such as interbank market, or the Lender of Last Resort (Vodova,2013), this hypothesis explains the negative correlation between size of banks andtheirliquidity.
Provision for credit lossesis an estimation of potential losses due to customersnot fulfilling their committed obligations The level of provision for credit losses inbankingactivitiesisprescribedinArticle12,CircularNo.02/2013/TT- NHNNprovidingonclassificationofassets,levelsandmethodsofsettingupr i s k provisions and use of provisions against credit risks in the banking activity of creditinstitutions, foreign banks’ branches Whereby, specific provisioning rates are asfollows:
The majority of previous studies by Luchetta (2007); Sufian and Chong (2008);Vong and Chan (2009) showed a negative correlation between the provision forcredit losses and theliquidity;meaning thehigher costs ofr i s k p r o v i s i o n s , t h e higher liquidity risk But recent studies have not shown any correlation betweenthese two variables such as Trương Quang Thông (2013), VũThị Hong (2015), MaiThịPhương Thùy(2018).
In this chapter, the author states some concepts used in the study: theory ofliquidity and liquidity risk of commercial bank, as well as its determinants. Theauthoralsopresentsanoverviewofdeterminantvariablesbasedonr e f e r e n c e mo delsofpreviousstudies.
Startingfromthebasictheoriesandpreviousempiricalresearchmodelsonliquidity risk of commercial banks, also based on the practical situation of Vietnambanking market, the author has a basis to apply and establish a panel regressionmodel of factors affecting the liquidity risk in
Vietnam join-stock commercial banksthatlistedonHOSE,HNXandUPCoM,whichwillbepresentedinthen e x t chapter.
Setting research questionsBuilding theoretical caseSetting research strategyData collectionData analysisWrite upChoosing the main article
Dataset
This paper use panel data to analyse determinants of liquidity risk of JSCBs inVietnam Data is obtained and calculated from consolidated reports, because mostbanksarenowdevelopinginthedirectionofmulti-industryandmulti- sectorcorporations,theirunconsolidatedreportscannotreflecttheactualfinancialperfom ance as well as the actual business activities of the whole system The authorchoose only JSCBs that are listed on stock exchanges (HOSE, HNX, UPCoM) toensurethetransparencyandcorrectnessofthereports.Besides,dataisalsoreconciledwit hmanagementreportstoensuretheconsistency(AppendixB).
The dataset includes 152 observations of 17 JSCBs in Vietnam (cross- sectionalunits) over the period of 9 years from 2010 to 2018 (time series), and it is anunbalanceddatasetduetothelackofBacÁBank’sreportin2010(AppendixA).
Analysisprocess
H0: Pooled-OLS is more appropriate Accept H0: Pooled-OLS Reject H0: FEM/REM
Difference in coefficient is systematic?
Regress FEM/REM Regress Pooled-OLS
The author collected data of 17 Vietnam join-stock commercial banks fromfinancial statements in the period of 2010-2018 and then calculated the independentand dependent variables After that, the author used Stata software todescribestatistically the dataseta n d t e s t t h e c o r r e l a t i o n m a t r i x b e t w e e n v a r i a b l e s , w h e t h e r the obtained results are consistent with conditions for authors to compose economicmodelsa n d a r e s a t i s f i e d t h e c o n d i t i o n s t o u s e r e g r e s s i o n m o d e l s i n c l u d i n g regression OLS, FEM, REM Next, the author used F-test and Hausman test tochoose which model is more appropriate Afterwards, testing the defects of thatmodel will be deployed and fixed incomplete model with FGLS method From theabove results, the author gave her own conclusion and recommendations to banks toreduceliquidityrisk.
Research modelandhypothesis
Dependentvariable
Liquidity risk can be measured by two basic methods: liquidity gap and liquidityratios Liquidity gap method is first announced by J Bessis in Risk management inBanking that it calculates the difference between liabilities and assets at present andfuturedates,inwhichanypositivegapbetweenliabilitiesandassetsisconsi deredas deficit However, for this method, thereis an uncertainty on the volume ofdeposits and also the volume of new requests for loans in the future. Meanwhile,liquidityratiosarethebalancesheetratiosthatidentifymainliquiditytrends. Vodova(2011)usedfourformulastoindicatethistrendsasfollow:
In which, L3 and L4 described illiquidity and the rest were used to describeliquidity For the purpose of this research, the author will use formulas that candemonstrate illiquidity which are L3 and L4 However, within the collected datasources, it is difficult to separate short-term loans and long-term loans in the totalcapitalofJSCBs.Therefore,theauthorwilluseonlyL3todemonstrateillquidityin order to indicate how much percentage of the assets is tied up in illiquid loans. Inother words, the higher this ratio is, the less liquid the bank is meaning the higherliquidityriskthe bankhastoface.
Independentvariables
Table3.2 Estimatedeffects Hypothesis Variable Definition Estimatedeffect
Equity-to-total assets ratiois able to used as an replacement for Basel’s capitaladequacy ratio The lower this ratio is, the more debt that the bank has used to payfor its assets Furthermore, equity is also considered as a shield, a defense to combatdifferent risks of a bank Therefore, the author indicates that the higher ratio ofequity to assets is, the less liquidity risk that the bank has to face meaning lower LRratioin thisresearchis.
ManypreviousstudiesfromLuchetta(2007),Vongetal(2009)showedanegative correlation between NPL and bank liquidity However, studies of Vodova(2011), Vũ Thị Hong (2015) and Mai Thị Phương Thùy (2018) showed a positivecorrelation between these two variables and their explanation is that when NPLarises,thebankwilltendtostrictly controlonprovidingloanslateron,then,liquidity risk will tend to decrease Therefore, in this paper, the author expects thehigherNPLis,thelowerloansofbanks,meaninglowerliquidityrisk.
HypothesisH2:Non- performingloanratio(NPL)hasanegativeeffectonliquidityrisk(LR).
ROE is calculated by dividing net income by shareholder’s equity, therefore, itreflects the level of effectiveness in the use of equity as a measure of financialperformance Banks profit by earning more money than what they pay in expenseswhich mostly comes from the interest paid on its liabilities such as its deposits andthemoneytheyborrows.The majorportion ofprofitabilityofabankcome sfromthef e e s t h a t i t c h a r g e s o n i t s s e r v i c e s a n d t h e i n t e r e s t t h a t i t e a r n s o n i t s a s s e t s whichareloansforindividuals,businessesandotherorganizations.Theref ore,higher profitability means higher revenue from mostly loans or lower interest onliabilities, either way, it both means banks providing loans greater than the previousperiod, meaning bank liquidity risk increases Consequently, the author expects towitnessapositive correlationbetweenROEandliquidityrisk.
According to “too big to fail” theory, banks who have large assets would be lesslikelytohavemotivationtoholdliquidassetsbecausebigbankscanrelyoninterbank market, or the assistance from the Lender of Last Resort whereas mediumand small banks would instead hold a buffer of liquid assets in their hands (Vodova,2013) In this case, the author indicates that the bigger the bank, the bigger the loansdue to their reliance on interbank market or on the Lender of Last Resort, and as theresult,thehigher LR ratiois.
Provision credit losses represents the level of credit risk in the bank (Chung- HuaShen et al, 2009) and is therefore also used to measure the impact on liquidity Theauthor indicates that the higher bank’s expenses for provision credit losses, thegreater the likelihood of banks becoming subjective and careless in loans approval,meaningthehigherLRratiois.
𝑇𝑜𝑡𝑎𝑙𝑙𝑜𝑎𝑛𝑠HypothesisH5:Provisioncreditlossesratio(PCL)hasapositiveeffectonliquidityrisk(LR).
From theories of liquidity risk and empirical evidence that are mentioned inchapter 2, the author has built a dataset as well as a analysis process to establish apanelregressionmodel.Particularly,thedependentvariable isliquidityra tio(LR)as illiquidity and independent variables are ratio of equity to assets (ETA), non-performing loan ratio (NPL), return on equity (ROE), natural logarithm of totalassets as size of the bank (LnSIZE), and provision for credit losses ratio (PCL).Meanwhile, estimated effects have also been presented with a positive correlationbetween LR and ROE, LnSIZE, PCL and a negative correlation between LR andETA,NPL.
Descriptivestatistics
Descriptive statistics are used to describe a summary of the relationship betweeninternalfactorsandliquidityratioofVietnamJoint- stockcommercialbanks.Descriptive statistics of variables are presented according to the following criteria:number of observations, average, standard deviation, minimum value, maximumvalue of 8 objects from 2010 to 2018 The panel is unbalanced because Bac Á Bankdoesnotreport overthewholetimeofresearch(AppendixC.1).
Table4.1 Variablestatistics Variable Obsrvation Mean StandardDeviation Min Max
Table 4.1 shows that the dataset has 152 observations of 17 banks in the periodof 2010 to 2018 Research object of this paper is Liquidity Risk (LR) which has anaverage value of 55.08%, a minimum value of 14.73% belonged to TPBank in 2011and a maximum value of 75.3% belonged to BIDV in 2018 Based on the LR valuesof these two banks over the years of
2010 - 2018, it can be seen that both the largestand smallest values of LR are the abnormal fluctuation and do not maintain aroundtheseareasinsubsequentyears.Therefore,thedescriptivestatisticstablecanrefl ect part of the liquidity risk ratio of banks, which can fluctuate significantly over theyearsdependingontheirbusinessactivitiesineachyear.
Ratio of equity to assets (ETA): The average value is 8.43% with the minimumvalue is 4.06% belonged to BIDV in 2017 and maximum value is 25.54% belongedto Kien Long Bank in 2010 Although having the highest value, Kien Long Bank isnotthebankwith thehighestequityintheresearchbanks.
Non-performing loan ratio (NPL): This variable has lowest standard deviation atonly 0.01 The average value is 1.99% with the minimum value is 0.02% belongedto TPBank in 2010 The NPL ratio of SHB was the highest at 8.81% in 2012, butlater, the NPL ratio of this bank dropped to only 2.40% in 2018, showing that
Return on Equity (ROE): The average value is 11.15% in which TPBank has thelowest value at -56.33% in 2011 and Techcombank has the highest value at 28.79%alsoin2011.
Size of the bank (Logarithm of SIZE - LnSIZE): The average value is 18.78 withthem i n i m u m v a l u e i s 1 6 3 5 o f K i e n L o n g B a n k i n 2 0 1 0 a n d m a x i m u m v a l u e i s
20.99 of BIDV in 2018 This variable has the highest deviation with a standarddeviation of 1.04, proving that commercial banks in this paper have different sizesovertheyears.
Provisioncreditlossesratio(PCL):Theaveragevalueis-1.59%withtheminimum value is -32.27% belonged to NCB in 2015 and maximum value is3.15%ofVietcombankin2010.
Correlationanalysisofvariables
Thecorrelationmatrixbetweenvariablesdescribesthecorrelationbetweenvariables in the research model, including the correlation between each independentvariable(ETA, NPL, ROE, LnSIZE, PCL) and the dependent variable (LR); and thecorrelationbetweeneachindependentvariableandotherindependentvariables.
LR ETA NPL ROE LnSIZE PCL
Thei n d e p e n d e n t v a r i a b l e E T A h a s a c o r r e l a t i o n c o e f f i c i e n t o f -0.1777< 0 meaning that ETA is negatively correlated with dependent variable LR with level ofsignificnace of 5% This indicates there is a negative correlation between this ratioandliquidityrisk,whichisconsistentwiththe HypothesisH1
TheindependentvariableROEhasacorrelationcoefficientof0.1510>0meaning that ROE is positively correlated with dependent variable LR with level ofsignificance of 10% This indicates there is a positive correlation between return onequityandliquidityrisk,whichisconsistentwiththeHypothesisH3
> 0 meaning that LnSIZE is positively correlated with dependent variable LR withlevel of significnace of 1% This indicates there is a positive correlation betweensizeof thebankandliquidityrisk,whichisconsistentwiththe HypothesisH4
To conclude, independent variables LnSIZE, ETA and ROE are correlated withliquidity risk (LR) with level of significance of 1%, 5%, 10% correspondingly.Particularly, LnSIZE has the strongest correlation (nearly 50%) and ROE has thelowest correlation (only 15.1%) Besides, ROE and LnSIZE has positive correlationwithLiquidityRiskwhereasETAhasnegativecorrelation.
However,independentvariablesthathavecorrelationwithLRalsohavecorrelation with other independent variables such as ROE has negative correlationwith NPL with the level of significance at 5%; and LnSIZE has strong negativecorrelation with ETA and strong positive correlation with ROE both with level ofsignificanceof1%.Thehigherthelevelofcorrelationbetweenanyt w o independentva riables,themorelikelythemodelwillhavemulti- collinearity.Therefore,itisnecessarytotestmulti- collinearityafterchoosingappropriatemodel.
Regression analysis
In this section,the author usesthreemethods includingP o o l e d - O L S ,
R E M , FEM regression to estimate the impact of the model, in which thereby, choosing themost appropriate model to continue the defect tests Then, the author will estimatetheimpactlevel,significancelevelofeachcoefficientandthelevelofexpl anationofthemodeltotheliquidityriskof17joint-stockcommercialbanksinVietnam.
Table4.3 Regression resultsof Pooled-OLS,FEM,REM
Table 4.3 describes regression results of estimating models using Pooled- OLS,FEMandREM.Theestimationresultsshowthattheregressioncoefficientisstatistically significant with ETA (P>|t| |t| F=0.00( F = 0.0000 chi2=0.0812>𝛼=5%,thereisnobasistorejectnullh y p o t h e s i s meaning there exists difference in coefficient not systematic, as a result, REM ismoreappropriatewithlevelofsignificanceof5%.
Defecttests
Multi-collinearitytest
Multi-collinearityisthephenomenonofanexplanatoryvariableisstronglycorrelated to other explanatory variables Regression of a model with high multi-collinearity willhavethefollowingconsequences:varianceandcovariancea r e large, wider confidence interval, meaningless t-statistic, high but meaningless
R 2 ,estimatesandtheirstandarderrorsbecomeverysensitivetosmallchangesind ata, sign of the estimates of the regression coefficientm a y b e w r o n g
In this research, multi-collinearity test will use VIF index, if VIF is less than 10,there will be no multi-collinearity; otherwise, variables with VIF > 10 should beremoved.
Table4.6 Multi-collinearitytest Variable VIF
The result of the VIF test of model shows that all coefficients are less than10,which means that multi-collinear phenomenon does not affect significantly on thismodel.
Homoskedasticitytest
One of the important hypotheses of the classical linear regression model is thatthe variance of error term is a constant number (homoscedasticity). Unconstantvariance (heteroscedasticity) is often encountered when collecting cross-sectionaldata.When heteroscedasticity occurs, the estimates are as follows:it is not aneffective estimate despite of unbiased characteristic, t-distribution and F- distributionis not an reliable level of significance and confidence interval Therefore, usingmodel with heteroscedasticity will lead to wrong conclusions which is why we needtodetectandfixit.
In this paper, Breusch-Pagan Lagrangian multiplier test will be conducted forRandomEffects Modelasfollows:
Prob > chibar2 = 0.00 F = 0.00