Credit risk management tools in vietnamese joint stock commercial banks in vietnam

FOREIGN TRADE UNIVERSITY FACULTY OF BUSINESS ADMINISTRATION -*** GRADUATION THESIS Major: International Business Management CREDIT RISK MANAGEMENT TOOLS IN VIETNAMESE JOINT STOCK COMMERCIAL BANKS Intake : Supervisor : HANOI, 6/2012 K47 TABLE OF CONTENTS LIST OF TABLES AND GRAPHS .iii LIST OF EQUATION .iv LIST OF ABRREVIATIONS v ABSTRACT CHAPTER 1: INTRODUCTION 1.4 Layout of study CHAPTER 2: LITERATURE REVIEW .3 2.2.3 Portfolio management 11 CHAPTER 3: HYPOTHESIS TESTING 22 3.1 Research design and methodology 22 3.2.1 Financial criteria 23 3.2.1.2 Bad debt –NPL 24 3.2.1.3 Credit profitability 26 3.2.1.4 Capital efficiency .28 3.2.1.5 Provisions 30 3.2.2.3 Testing and evaluating the hypothesis .35 CHAPTER 4: RECOMMENDATIONS AND SUGGESSTIONS 44 4.1 Suggestions for Banks .44 4.1.1 Credit risk management strategies and policies .44 4.1.1.1 Strategy .44 4.1.1.2 Policy 45 4.1.2 Credit procedures and delegation of Authority 46 4.1.2.1 Credit procedures .46 4.1.2.2 Delegation of Authority 46 ii 4.1.3 Credit risk management process 47 4.1.3.1 Credit grating 47 4.1.3.2 Risk mitigation 48 4.1.3.3 Credit monitoring .49 4.1.3.4 Credit review .50 4.1.3.5 Classification and Provision 51 4.1.3.6 Problem credit 52 4.1.5 Credit portfolio risk management 53 4.1.5.1 Portfolio Management Approach 54 4.1.5.2 Value at Risk 55 4.2 Conclusions .55 LIST OF REFERENCES 56 Sumant Palwankar (29/10/2009) The presentation slides about “An overview of credit risk management practice - A banker’s perspective” available at http://www.slideshare.net/sumant3063/ accessed on 21/03/2012 56 10 Xinzheng Huang and Cornelis W Oosterlee (2011) An article on “Improving banks credit- isk management” available at http://ercim-news.ercim.eu/en78/special/ accessed on 17/03/2012 56 ANNEXES .59 LIST OF TABLES AND GRAPHS Number of table Content Page (graph) Table Table Table Table Credit risk management model in banks The structure of Risk Management system in VJCBs The functional model of risk management in banks Strategies for reducing and coping with portfolio credit 14+15 risk iii Table Table Table Table Table Table 10 Table 11 Table 12 Table 13 Table 14 Table 15 Graph Graph A typical example of complete risk map Some of the frequently used ratios in credit analysis Overdue loans ratios in VJCBs 2007-2011 NPL ratios in VJCBs 2007-2011 Credit profitability ratios in VJCBs 2007-2011 Capital efficiency VJCBs 2007-2011 CRP ratios in VJCBs 2007-2011 ROE of VJCBs 2007-2011 ROA of VCBs 2007-2011 The table of testing results Regression models for VJCBs The relationship between ROE and NPL/TL The relationship between ROE and NPL/TL 19 23 25 28 29+30 31+32 33+34 35 35+36 39 40 36 37 LIST OF EQUATION Number of equation 10 11 12 13 14 Content Value at Risk models Altman’s credit scoring model RAROC model Change in loan class model Overdue loans ratio formulation Non - performance Loan ratio formulation Rate of interest income from credit formulation Profitability rate of credit activities formulation Mobilizing capital efficiency formulation Assets efficiency for loans formulation Credit risk provisions ratio formulation General regression model Sample regression model Research hypothesis model iv Page 12 20 22 22 25 28 29 29 31 31 33 37 37 38 15 Research hypothesis model 38 LIST OF ABRREVIATIONS Abbreviations VJCBs ACB CTG VCB EIB MBB TCB SCB MSB VIB EAB CRM CR BCBS VAR KCSA KRI RAROC EBIT P/E ROE ROA NPL Full text Vietnamese Joint – stock Commercial Banks Asia Commercial Bank Vietinbank Vietcombank Eximbank Military Bank Techcombank Sacombank Maritime Bank Vietnam International Bank Earth Asia Bank Credit risk management Credit risk Basel Committee on Banking Supervision Value at Risk Key Control Self-Assessment Key Risk Indicator Risk-Adjusted Return on Capital Earnings before tax Price to Earnings Return on Equity Return on Assets Non- performance Loan v TL CRP Total Loans Credit Risk Provisions vi ABSTRACT With the longstanding history and development, the banks are always the trusty partners of enterprises in the economic promotion process of nations and grow together in a close relationship, which is represented by the relation between lender and borrower (credit transaction) and credit operation is the most profitable of the bank In other words, all activities of banks are concerned about one word “credit” However, the banks would produce negative results because of the heavy dependence on credit work, which is able to create the risks from lending and raising funds In accordance with managerial point of view, if the bank wants to improve the quality of credit activity, it should focus on and consider the credit risk management as the most necessary and serious issue The main purpose of this study is to have a clearer picture of how banks manage their credit risk In this light, the study in its first section gives a background to the study and the second part is a detailed literature review on banking and credit risk management tools and assessment models The third part of this study is on hypothesis testing and use is made of a simple regression model This leads us to conclude in the last section that banks with good credit risk management policies have a lower loan default rate and relatively higher profitability CHAPTER 1: INTRODUCTION 1.1 Research background In terms of management, an adequate system for assessing credit risks in financial institutions is essential for the survival and growth of them In the case of banks, the issue of credit risk is even a greater concern because the higher levels of perceived risks resulting from some of the characteristics of clients and business conditions that can help banks control their performance more accurately and more effectively In fact, credit creation is the main income generating activity for the banks, but this activity involves huge risks to both the lender and the borrower The risk of a trading partner, who does not fulfill his or her obligation as per the contract on due date or anytime thereafter, can greatly threaten the smooth functioning of a bank’s business On the other hand, a bank with high credit risk has high bankruptcy risk that puts the depositors in jeopardy Additionally, credit risk is also one of great concern to most bank authorities and banking regulators because credit risk is the risk that can easily and most likely prompts bank’s failure In recent years, the world economic situation has a lot of unexpected changes and the probability of a credit crisis booming rise increasingly Vietnam which has an open economy cannot avoid the effects of world economy To face with that condition, it is certain to require that VJCBs must improve the process of managing credit risk to limit the potential possibilities which cause the risk at the lowest level As a result, the subject “Credit risk management tools in Vietnamese joint stock commercial banks in Vietnam” is implemented to research the credit business situation in reality and the effectiveness of credit assessment methods in Vietnamese joint - stock commercial banks from which identifying the signs, finding the causes and proposing useful solutions for managing credit risk in commercial banking system 1.2 Research question The following questions will illustrate for us the problems of credit risk management in Vietnamese joint - stock commercial banks of which this article will look for the answers - What is a credit risk? How banks control it? Which tools banks should - apply to deal the problems of credit risk? Why most VJCBs suffer weak risk management? It is shown clearly from the Mergers and Acquisitions tendency in Vietnamese banking system in - recent years A few banks have the adequate and effective system to manage credit risk? Why? How did they use the credit risk management tools? What lessons - were learned? How to improve this situation and what new solutions are available in the world that Vietnam has adopted it yet? Research objective 1.3 The main objective of the study is to have a whole scene of how banks manage the credit risks in their performance, thus focusing on some following aspects: - Ascertaining why and how credit risk exposure in VJCBs is evolving - recently Ascertaining and evaluating how VJCBs use the credit risk management and - assessment tools to mitigate their credit risk exposure Approaching the process of credit risk management, including: identifying and developing a control system, analyzing financial information and how to - mitigate the risks Determine the relationship between the theories, concepts and models of - credit risk management and what needs going into a reality Determine how and what should apply or improve effectively for managing credit risk in VJCBs at the moment and in near future However, this research will only give about information about top 10 biggest banks in terms of total assets from 2007 to 2011 because these can cover and represent for 37 VJCBs, namely: Asia Commercial Bank (ACB), Vietinbank (CTG), Vietcombank (VCB), Eximbank (EIB), Sacombank (SCB), Military Bank (MBB), Techcombank (TCB), Maritime Bank (MSB), Vietnam International Bank (VIB) and Earth Asia Bank (EAB) (Sourced by: Vnr500.com.vn) 1.4 Layout of study This study is divided into four sections: - The first section is on background to the study and cuts across a general - introduction, research question, research objective, and layout of the study Section two is on literature review on commercial banking and credit risk - management Section three is on hypothesis testing using a simple linear model on E-view - Here we also interpret the findings of the tests Section four concludes the study with a summary and some useful suggestions CHAPTER 2: LITERATURE REVIEW 2.1 Credit risk management (CRM) 2.1.1 Overview about CRM in banking performance 2.1.1.1 Banking credit It is the agreement of the bank to allow the customers can use an asset (cash, real property or reputation) with the principle of reimbursement by individual evaluated for impact on the customer’s creditworthiness Credit reviews should also be conducted on a consolidated group basis to factor in the business connections among related entities in a borrowing group Credit reviews should be performed at least once a year More frequent reviews should be conducted for new accounts and for classified accounts Procedures should also be instituted to ensure that reviews are conducted at the appropriate times A process to approve delay of credit review should also be put in place For consumer loans, the banks may dispense with the need to perform credit reviews of individual customers for certain types of products Nonetheless, they should monitor and report credit exceptions and deterioration 4.1.3.5 Classification and Provision The banks should make adequate provisions for classified credits In the case of banks, classified credits are loans graded substandard, doubtful and at risk of loss as defined by Decision No 493/2005/QD-NHNN The provisions should meet regulatory guidelines and internal policy The banks should also consider the general economic conditions of the countries they have exposure to when determining provision levels Loan classification and provisions should be subject to independent review and approval The banks should ensure that loans are properly and promptly graded to reflect their assessment of the borrower’s credit strength In addition, the criteria for loan grading should be sound and consistent with regulatory Since provisions are dependent on the recoverable value of collateral it holds, banks should obtain appropriate valuations of collateral The banks should have a reliable and timely collateral valuation system The valuation system should include factors such as the legal enforceability of claims on collateral, ease of realization of collateral and current market conditions Where appropriate, the banks should apply a standard to the estimated net realizable value of collateral or use the forced sale value of the collateral to provide more realistic estimates 51 4.1.3.6 Problem credit The banks should have processes, based on diligent credit monitoring and loan grading, to identify and manage problem credits at an early stage Classified accounts should be managed under a dedicated remedial process This process should comprise the following elements: - Review of Collateral and Security Documents The banks should ascertain the loan recoverable amount by updating the values of available collateral with formal valuation Security documents should also be reviewed to ensure the completeness and enforceability of contracts, collateral and guarantee - Formulation of Remedial Strategies Depending on the size and age of a problem credit, appropriate remedial strategies should be established to revive and recover the credit These strategies may include restructuring of facility and rescheduling of payments The strategies should take into account the specific condition of the customer and the bank’s interest, and should be approved by the relevant authority - Negotiation and Follow-up As it implements remedial plans, a bank should monitor their effectiveness through maintaining regular contact with customers and tracking follow-up actions - Status Report and Review Problem credits should be subject to more frequent review and monitoring The reviews should update the status of the loan accounts and the progress of the remedial plan These reports should be submitted to senior management on a timely basis The banks should consider establishing a separate unit to focus on problem credit management This workout function should preferably be separate from the loan origination function to ensure independence and objectivity in managing problem credits 4.1.4 Credit staff and internal supervisors 52 The banks should have a team of officers with the experience, knowledge and background to assess credit risks It should also allocate adequate resources to ensure that the credit decision process is rigorous, timely and efficient The banks should improve the staff’s ability and ethics This can help to advance the quality of human resource and the skepticism of staff in activity and thinking since the compliance is the most crucial point of credit operation Credit staff should perform accurately, seriously and sufficiently the process of credit evaluation for loan customers Any credit institutions have to set up clearly their own assessment process in accordance with appropriate standards and specific steps However, they often not care seriously about all steps and only focus on a part of process based on the forecast or experience of credit staff in the assessment about finance of customers or clients For example,…This issue seems to be the flexibility in their work but it can take the inherent risks Therefore, it is needed that in order to select accurately customers or reduce the risks, the bank should require the staff to perform correctly and strictly all steps in the evaluated process, no skip any steps 4.1.5 Credit portfolio risk management The banks should classify, manage the customers followed by groups, sectors and business scale Although, some banks have classified and managed the quality of loan sections very carefully and professionally, the banks should make more details in each group, each individual and organization, that can beneficially support for its credit quality The banks should develop the system of financial standard for each sector and business line The comparison of financial criteria among organizations in one industry was analyzed by an average index which can be more satisfied than compared with each year In reality, this system may be difficult and costly to build up but it is really essential to evaluate the financial situation of enterprise effectively The two technical methods as below would help banks approach easily and effectively the credit portfolio risk management 53 4.1.5.1 Portfolio Management Approach The banks should monitor credit risk on a portfolio basis to manage concentration risk Concentration of credit risk could arise when credit granted to the following customers accounts for a substantial proportion of the banks’ total credit portfolio or capital funds: - A single borrower or group of connected borrowers Entities in a particular industry or economic sector Entities in an individual country or a group of countries with inter-related economies The banks should identify and measure the concentration risk in its credit portfolio It should monitor areas of significant concentration such as the exposure to an industry in a particular stage of the business cycle The impact of such developments on the customer and therefore on the credit quality of the portfolio should then be assessed The banks should establish appropriate limits to cap concentration risk at an acceptable level Significant concentration risk should be reported to the Board and senior management for review and deliberation Stress tests could be conducted to assess the risk in a particular market segment under adverse conditions Appropriate measures should be taken to mitigate undue concentration risk such as pricing for additional risk, unwinding of positions, increasing capital or reserves, securitization and credit risk hedging Branches that serve a particular client segment or region as part of a bank’s global strategy are likely to have high credit exposure to certain customers or countries While such concentration risk may have been captured under the respective limits and control mechanism at the head office, branches should continue to monitor and manage their concentration risk Besides analyzing concentration risk, a bank should also monitor trends in loan growth, collateral values and asset quality to detect potential weakness in its portfolio For consumer loan portfolios, trends in deviation, delinquency and loan volume should be tracked and analyzed Such analysis should be reported to senior management for their review and deliberation 54 4.1.5.2 Value at Risk The credit risk of a portfolio may be quantitatively measured using credit Value at Risk (Credit VAR) models These models generate a single VAR number that estimates the credit loss that is likely to occur for a portfolio, at a certain confidence level, over a given period of time As with any statistical obligor rating models, management should ensure that appropriate validation and assurance testing are performed before using a credit VAR model Following its introduction, the credit VAR model should be regularly back-tested to ensure its continued validity External expert organizations may be contracted to conduct the validation exercise 4.2 Conclusions This study shows that there is a significant relationship between bank performance (in terms of profitability) and credit risk management (in terms of loan performance) Better credit risk management results in better bank performance Thus, it is of crucial importance that banks practice prudent credit risk management and safeguarding the assets of the banks and protect the investors’ interests The study summarizes that banks used different credit risk management tools, techniques and assessment models to manage their credit risk, and that they all have one main objective, i.e to reduce the amount of loan default which is a principal cause of bank failure The study also reveals that banks with good or sound credit risk management policies have lower loan default ratios (bad loans) and higher interest income (profitability) The study also reveals banks with higher profit potentials can better absorb credit losses whenever they crop up and therefore record better performances 55 LIST OF REFERENCES Annual reports of banks 2007, 2008, 2009 and 2010 Anthony M Santomero Scholarly article on “Commercial Bank Risk Management: an Analysis of the Process” available at fic.wharton.upenn.edu/fic/papers/95/9511b accessed on 13/03/2012 Auditing reports 2011 of banks updated on 13/4/2012 Clause 7, decision 493/2005/QĐ-NHNN H Brett Humphreys and David C Shimko (2002) Scholarly article on “Credit risk exposure” available at http://www.pur.com/pubs/3870.cfm accessed on 17/03/2012 Jaroslaw Knapik Scholarly article on: “Solution guide to credit risk management in banking” available at www.sas.com/ accessed on 13/03/2012 Law on enterprises 2005- Phillips Fox translation R.S.Raghavan Scholarly article on “Risk Management In Banks” available at www.thunderbird.edu/ /bank management accessed on 13/03/2012 Sumant Palwankar (29/10/2009) The presentation slides about “An overview of credit risk management practice - A banker’s perspective” available at http://www.slideshare.net/sumant3063/ accessed on 21/03/2012 10 Xinzheng Huang and Cornelis W Oosterlee (2011) An article on “Improving banks credit- isk management” available at http://ercimnews.ercim.eu/en78/special/ accessed on 17/03/2012 56 11 Takang Felix Achou and Ntui Claudine Tenguh (2008) Scholarly research on “Bank performance and credit risk management” available at his.diva-portal.org accessed on 13/03/2012 12 Khánh Linh - Cao Sơn (14/9/2011) An article on “Chất lượng tín dụng ngân hàng suy giảm rui ro” available at http://cafef.vn/ accessed on 14/03/2012 13 Lê Công Hội - PVFC (9/7/2011) The article on” Tại phải quản trị rui ro hoạt động tổ chức tín dụng” http://www.petrotimes.vn/thuong-truong/dien-dan-kinh-te/2011/07/ available at accessed on 14/03/2012 14 Nguyễn ĐàoTố (25/11/2008) Schoalrly article on “Xây dựng mơ hình quản trị rủi ro tín dụng từ ứng dụng nguyên tắc Basel quản lý nợ xấu” available at http://luattaichinh.wordpress.com/ accessed on 21/4/2012 15 Nguyễn Minh Kiều (2008) Giáo trình “Quản trị tài chính”-NXB Thống Kê 17 Nguyễn Tiến Thành - Vietinbank Scholarly article on: “Quản lí rủi ro góc độ ngân hàng” available at www.vnpt.com.vn/Upload/CMS/Quantri_Ruiro_gocdoNN accessed on 13/03/2012 18 Nguyễn Văn Tiến (2009) Giáo trình “Ngân hàng thương mại”-NXB Thống Kê 19 Quách Thùy Linh (2011) - Vietcombank An analysis “Báo cáo ngân hàng” available at http://investor.vietinbank.vn/Handlers/ accessed on 17/04/2012 20 Trịnh Việt Hương (2010) The article on” Quản trị rui ro ngân hàng cần siết chặt” available at http://www.baomoi.com/ accessed on 14/03/2012 Websites (online) http://www.credit-risk-measurement.com/accessed on 13/03/2012 http://www.scribd.com/doc/11760698/Risk-Management-for-Banking-Sector accessed on 13/03/2012 57 http://www.sbv.gov.vn/wps/portal/ accessed on 13/03/2012 http://www.bis.org/publ/bcbs54.html accessed on 13/03/2012 http://www.doanhnhan.net/quan-tri-rui-ro accessed on 17/03/2012 http://www.frsglobal.com/solutions/solutions-credit-risk accessed on 13/04/2012 http://www.investopedia.com/terms/c/credit-exposure accessed on 17/03/2012 http://www.bionicturtle.com/forum/threads/question-31-raroc-model.3513/ accessed on 22/03/2012 Wikipedia (www.wikipedia.org) (categories: Value at risk, RAROC, Basel Accord II, Credit risk) 58 ANNEXES Appendix: The E-view results for regression models of 10 VJCBs Dependent Variable: ROE (CTG) Method: Least Squares Date: 04/25/12 Time: 15:47 Sample: 2007 2011 Included observations: ROE=β1 x NPL/TL+β2 Coefficient β2 25.89844 β1 -6.759214 R-squared 0.285888 Adjusted R-squared 0.047851 S.E of regression 6.112521 Sum squared resid 112.0887 Log likelihood -14.86932 Std Error t-Statistic 6.577571 3.937386 6.167660 -1.095912 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Durbin-Watson stat Prob 0.0292 0.3532 19.34200 6.264233 6.747730 6.591505 1.296796 Dependent Variable: ROA (CTG) Method: Least Squares Date: 04/25/12 Time: 15:53 Sample: 2007 2011 Included observations: ROA=β1 x NPL/TL+β2 Coefficient β2 1.526837 β1 -0.365811 R-squared 0.255573 Adjusted R-squared 0.007431 S.E of regression 0.357232 Sum squared resid 0.382844 Log likelihood -0.670778 Std Error t-Statistic 0.384411 3.971890 0.360454 -1.014862 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Durbin-Watson stat Prob 0.0285 0.3849 1.172000 0.358567 1.068311 0.912087 1.852533 Dependent Variable: ROE (VCB) Method: Least Squares Date: 04/25/12 Time: 16:00 Sample: 2007 2011 Included observations: ROE=β1 x NPL/TL+β2 Coefficient β2 22.49064 β1 -0.522656 R-squared 0.028219 Adjusted R-squared -0.295708 S.E of regression 3.743244 Sum squared resid 42.03563 Std Error t-Statistic 5.844143 3.848407 1.770795 -0.295154 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Prob 0.0310 0.7871 20.83800 3.288475 5.766957 5.610732 59 Log likelihood -12.41739 Durbin-Watson stat 1.563562 Dependent Variable: ROA (VCB) Method: Least Squares Date: 04/25/12 Time: 16:05 Sample: 2007 2011 Included observations: ROA=β1 x NPL/TL+β2 Coefficient β2 1.566650 β1 -0.053969 R-squared 0.114853 Adjusted R-squared -0.180195 S.E of regression 0.182852 Sum squared resid 0.100305 Log likelihood 2.677756 Std Error t-Statistic Prob 0.285478 5.487811 0.0119 0.086501 -0.623915 0.5769 Mean dependent var 1.396000 S.D dependent var 0.168315 Akaike info criterion -0.271103 Schwarz criterion -0.427327 Durbin-Watson stat 1.613514 Dependent Variable: ROE (ACB) Method: Least Squares Date: 04/25/12 Time: 16:11 Sample: 2007 2011 Included observations: ROE=β1 x NPL/TL+β2 Coefficient β2 34.87652 β1 -9.296007 R-squared 0.137233 Adjusted R-squared -0.150356 S.E of regression 9.628022 Sum squared resid 278.0964 Log likelihood -17.14102 Std Error t-Statistic 8.239247 4.232973 13.45715 -0.690786 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Durbin-Watson stat Prob 0.0241 0.5394 30.02400 8.976788 7.656407 7.500182 1.049599 Dependent Variable: ROA (ACB) Method: Least Squares Date: 04/25/12 Time: 16:15 Sample: 2007 2011 Included observations: ROA=β1 x NPL/TL+β2 Coefficient β2 2.106874 β1 -0.511253 R-squared 0.081513 Adjusted R-squared -0.224650 S.E of regression 0.708897 Sum squared resid 1.507605 Log likelihood -4.097404 Std Error t-Statistic 0.606644 3.473001 0.990830 -0.515984 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Durbin-Watson stat Prob 0.0403 0.6415 1.840000 0.640586 2.438962 2.282737 0.791943 60 Dependent Variable: ROE (TCB) Method: Least Squares Date: 04/25/12 Time: 16:22 Sample: 2007 2011 Included observations: ROE=β1 x NPL/TL+β2 Coefficient β2 17.51890 β1 3.579975 R-squared 0.834832 Adjusted R-squared 0.779776 S.E of regression 1.012074 Sum squared resid 3.072879 Log likelihood -5.877635 Std Error t-Statistic 2.164208 8.094829 0.919353 3.894016 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Durbin-Watson stat Prob 0.0039 0.0300 25.76000 2.156652 3.151054 2.994829 2.224711 Dependent Variable: ROA (TCB) Method: Least Squares Date: 04/25/12 Time: 16:27 Sample: 2007 2011 Included observations: ROA=β1 x NPL/TL+β2 Coefficient β2 1.988143 β1 0.022527 R-squared 0.003482 Adjusted R-squared -0.328690 S.E of regression 0.242202 Sum squared resid 0.175985 Log likelihood 1.272293 Std Error t-Statistic 0.517922 3.838693 0.220013 0.102390 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Durbin-Watson stat Prob 0.0312 0.9249 2.040000 0.210119 0.291083 0.134858 1.217064 Dependent Variable: ROE (SCB) Method: Least Squares Date: 04/25/12 Time: 16:43 Sample: 2007 2011 Included observations: ROE=β1 x NPL/TL+β2 Coefficient β2 30.30233 β1 -25.02326 R-squared 0.766714 Adjusted R-squared 0.688952 S.E of regression 2.765184 Sum squared resid 22.93873 Log likelihood -10.90317 Std Error t-Statistic 4.400951 6.885404 7.969129 -3.140024 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Durbin-Watson stat Prob 0.0063 0.0517 17.04000 4.958044 5.161266 5.005041 3.080959 Dependent Variable: ROA (SCB) Method: Least Squares Date: 04/25/12 Time: 16:52 61 Sample: 2007 2011 Included observations: ROA=β1 x NPL/TL+β2 Coefficient 3.439493 -3.055648 0.716764 0.622352 0.384806 0.444227 -1.042550 Std Error t-Statistic 0.612441 5.616039 1.108993 -2.755336 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Durbin-Watson stat Prob 0.0112 0.0704 1.820000 0.626179 1.217020 1.060795 2.239916 Dependent Variable: ROE (MBB) Method: Least Squares Date: 04/25/12 Time: 17:01 Sample: 2007 2011 Included observations: ROE=β1 x NPL/TL+β2 Coefficient β2 25.78532 β1 0.126602 R-squared 0.000261 Adjusted R-squared -0.332985 S.E of regression 2.187840 Sum squared resid 14.35993 Log likelihood -9.732202 Std Error t-Statistic 6.244649 4.129186 4.521643 0.027999 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Durbin-Watson stat Prob 0.0258 0.9794 25.95800 1.894972 4.692881 4.536656 1.862422 Dependent Variable: ROA (MBB) Method: Least Squares Date: 04/25/12 Time: 17:09 Sample: 2007 2011 Included observations: ROA=β1 x NPL/TL+β2 Coefficient β2 3.959441 β1 -1.122758 R-squared 0.401055 Adjusted R-squared 0.201406 S.E of regression 0.383298 Sum squared resid 0.440752 Log likelihood -1.022915 Std Error t-Statistic 1.094029 3.619136 0.792168 -1.417323 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Durbin-Watson stat Prob 0.0363 0.2514 2.428000 0.428917 1.209166 1.052941 1.511216 β2 β1 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood Dependent Variable: ROE (MSB) Method: Least Squares Date: 04/25/12 Time: 17:14 Sample: 2007 2011 Included observations: 62 ROE=β1 x NPL/TL+β2 Coefficient 43.33097 -10.53000 0.482550 0.310067 8.215375 202.4771 -16.34767 Std Error t-Statistic 11.11319 3.899058 6.295500 -1.672623 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Durbin-Watson stat Prob 0.0299 0.1930 25.78800 9.890630 7.339066 7.182841 2.454919 Dependent Variable: ROA (MSB) Method: Least Squares Date: 04/25/12 Time: 17:17 Sample: 2007 2011 Included observations: ROA=β1 x NPL/TL+β2 Coefficient β2 2.133090 β1 -0.485648 R-squared 0.576639 Adjusted R-squared 0.435519 S.E of regression 0.313517 Sum squared resid 0.294879 Log likelihood -0.018124 Std Error t-Statistic 0.424104 5.029633 0.240251 -2.021424 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Durbin-Watson stat Prob 0.0151 0.1365 1.324000 0.417289 0.807250 0.651025 2.458755 Dependent Variable: ROE (EIB) Method: Least Squares Date: 04/25/12 Time: 17:23 Sample: 2007 2011 Included observations: ROE=β1 x NPL/TL+β2 Coefficient β2 15.72734 β1 -1.668909 R-squared 0.241849 Adjusted R-squared -0.010868 S.E of regression 5.147730 Sum squared resid 79.49739 Log likelihood -14.01041 Std Error t-Statistic 4.238421 3.710661 1.705995 -0.978261 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Durbin-Watson stat Prob 0.0340 0.4001 12.24600 5.119983 6.404163 6.247938 1.117807 β2 β1 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood Dependent Variable: ROA (EIB) Method: Least Squares Date: 04/25/12 Time: 17:26 Sample: 2007 2011 Included observations: ROA=β1 x NPL/TL+β2 63 β2 β1 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood Coefficient 1.921884 -0.030625 0.200083 -0.066556 0.106678 0.034140 5.372082 Std Error t-Statistic Prob 0.087834 21.88088 0.0002 0.035354 -0.866249 0.4501 Mean dependent var 1.858000 S.D dependent var 0.103296 Akaike info criterion -1.348833 Schwarz criterion -1.505058 Durbin-Watson stat 1.834974 Dependent Variable: ROE (VIB) Method: Least Squares Date: 04/25/12 Time: 17:30 Sample: 2007 2011 Included observations: ROE=β1 x NPL/TL+β2 Coefficient β2 27.70015 β1 -4.232646 R-squared 0.648654 Adjusted R-squared 0.531539 S.E of regression 2.154309 Sum squared resid 13.92314 Log likelihood -9.654979 Std Error t-Statistic 3.239991 8.549454 1.798508 -2.353421 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Durbin-Watson stat Prob 0.0034 0.1000 20.42000 3.147539 4.661992 4.505767 2.005348 Dependent Variable: ROA (VIB) Method: Least Squares Date: 04/25/12 Time: 17:34 Sample: 2007 2011 Included observations: ROA=β1 x NPL/TL+β2 Coefficient β2 1.317923 β1 0.024463 R-squared 0.004315 Adjusted R-squared -0.327580 S.E of regression 0.256996 Sum squared resid 0.198141 Log likelihood 0.975839 Std Error t-Statistic 0.386512 3.409788 0.214551 0.114021 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Durbin-Watson stat Prob 0.0422 0.9164 1.360000 0.223047 0.409664 0.253440 1.065589 Dependent Variable: ROE (EAB) Method: Least Squares Date: 04/25/12 Time: 17:38 Sample: 2007 2011 Included observations: ROE=β1 x NPL/TL+β2 Coefficient β2 20.63804 β1 -1.421637 Std Error 1.295241 0.779003 64 t-Statistic 15.93375 -1.824944 Prob 0.0005 0.1655 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood 0.526098 0.368131 1.181694 4.189200 -6.652372 Dependent Variable: ROA (EAB) Method: Least Squares Date: 04/25/12 Time: 17:41 Sample: 2007 2011 Included observations: ROA=β1 x NPL/TL+β2 Coefficient β2 1.896414 β1 -0.170233 R-squared 0.259389 Adjusted R-squared 0.012519 S.E of regression 0.251923 Sum squared resid 0.190396 Log likelihood 1.075521 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Durbin-Watson stat 18.48000 1.486590 3.460949 3.304724 1.481453 Std Error t-Statistic 0.276130 6.867820 0.166074 -1.025041 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Durbin-Watson stat Prob 0.0063 0.3808 1.638000 0.253515 0.369791 0.213567 1.055035 65 ... Provisioning Grooming (Source: Report of risk management department of Vietinbank) Vietnamese Join - stock Commercial Banks and Basel Accord II 2.1.2.1 Vietnamese Joint - stock Commercial Banks In. .. problems of credit risk management in Vietnamese joint - stock commercial banks of which this article will look for the answers - What is a credit risk? How banks control it? Which tools banks should... managing credit risk to limit the potential possibilities which cause the risk at the lowest level As a result, the subject Credit risk management tools in Vietnamese joint stock commercial banks