UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS SMEs CREDIT RATING MODEL IN VIETNAM: PROBABILITY OF DEFAULT ASSESSMENT By NGUYEN VIET DUC MASTER OF ARTS IN DEVELOPMENT ECONOMICS Ho Chi Minh City, December, 2016 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS SMEs CREDIT RATING MODEL IN VIETNAM: PROBABILITY OF DEFAULT ASSESSMENT by Nguyen Viet Duc A Thesis Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Art in Development Economics Academic Supervisor: Dr. Nguyen Thi Thuy Linh Class 21 Ho Chi Minh City, December, 2016 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com CERTIFICATION “I certify the content of this dissertation has not already been submitted for any degree and is not being currently submitted for any other degrees. I certify that, to be the best of my knowledge, any help received in preparing this dissertation and all source used, have been acknowledged in this dissertation.” Signature NGUYEN VIET DUC Date: December 2016 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com ACKNOWLEDGEMENT I would like to extend my grateful thanks to all people who always encouraged me, supported me, and helped me, by their very own ways to the completion of this thesis. First and foremost, in special to my parents, my Chip (NGUYEN Duc Ha Anh), who inspired me to participate this MDE class, firmly supported me, always love me while supported me during hard studying time.
To my supervisor, Ms. NGUYEN Thi Thuy Linh and Mr. TRUONG Dang Thuy, who were always willing to, patiently, comprehensively guide me to build up my ideas and structure them in an academic added value paper. Last but not the least, I want to extend my thanks to my friends, to all the Risk Management Division members and colleagues from An Binh bank that in some way contributed to my study experiences and made my days happier and more pleasant.
LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com TABLES AND FIGURES TABLE 1-SAMPLE OF CREDIT RISK ASSESSMENT RANKING BY MOODY IN US 2015 16 TABLE 2 – VARIABLES 21 TABLE 3 – A SAMPLE OF VARIABLE SELECTION BY ALTMAN 28 TABLE 4-VARIABLES USED BY ALTMAN2 29 TABLE 5- VARIABLES USED BY MOODY 29 TABLE 6 – DROPPED VARIABLES DUE TO UNFULL FILLED OR MEANINGLESS 30 TABLE 7-APPROPRIATED INDICATORS WITH VALUE TYPE AND TRANSFORMATION 33 TABLE 8 – INDEPENDENT VARIABLES EXPECTED SIGNS IN THE RELATIONSHIP 37 TABLE 9-DEPENDENT VARIABLES DESCRIPTIVE STATISTIC 39 TABLE 10-INDEPENDENT VARIABLES DESCRIPTIVE STATISTIC 40 TABLE 11-DEFAULT STATISTIC FREQUENCY 43 TABLE 12-EXPLANATION OF INDEPENDENT, STATISTICAL SAMPLE 44 TABLE 13-SUMMARY OF FULL MODEL FOR BINOMINAL DEFAULT01 LOGISTIC FUNCTIONS 45 TABLE 14-FINAL MODEL FOR BINOMINAL DEFAULT01 LOGISTIC FUNCTIONS 46 TABLE 15-EXAMPLE OF CHECKING THE POWER OF QUALITATIVE VARIABLES’ CLASSIFYING 49 TABLE 16-EXAMPLE OF QUALITATIVE MARGINAL EFFECTS ON CREDIT RATING 51 TABLE 17-EXAMPLE OF AN AUTOMATIC DEFAULT PREDICTING TOOL 52 TABLE 18-LOAN GROUP DISTRIBUTION FREQUENCY 53 TABLE 19-SUMMARY OF FULL MODEL FOR OLOGIT LOAN GROUP LOGISTIC FUNCTIONS 54 TABLE 20-FINAL MODEL FOR OLOGIT LOAN GROUP LOGISTIC FUNCTIONS 55 TABLE 21-MARGIN TESTING 60 TABLE 22-PREDICTING LOAN GROUP SAMPLE 60 TABLE 23-CLIENT’ PROBABILITY OF FUTURE LOAN GROUP 61 TABLE 24-NUMBER OF DAY IN LATE PAYMENT DISTRIBUTION FREQUENCY 62 TABLE 25-SUMMARY OF FULL MODEL FOR LINEAR DAY OF LATE 63 TABLE 26-FINAL MODEL FOR OLOGIT LOAN GROUP LOGISTIC FUNCTIONS 64 TABLE 27-CLIENT WITH EXPECTED NUMBER OF LATE DAYS IN PAYMENT 68 TABLE 28- CORRELATION OF DUMMY VARIABLES. 70 TABLE 29- TEST FOR MULTICOLLINEARITY. 71 TABLE 30- TEST FOR HOMOSCEDASTICITY 72 TABLE 31-COMPARE MODEL 1.3-4 (BOTH IN PREDICTING DEFAULT RISK), BETWEEN RISK LOVER, RISK NEUTRAL AND RISK ADVERSE 72 FIGURE 1 – LOAN’S SIZE 6 FIGURE 2 - CREDIT RISK MANAGEMENT TECHNIQUES IN BANKING MANAGEMENT 10 FIGURE 3- SAMPLE OF A LOAN’S LIFE 14 FIGURE 4 – CONCEPTUAL FRAMEWORK 20 FIGURE 5: DEPENDENT VARIABLES DISTRIBUTION 28 FIGURE 6: DISTRIBUTION COMPARING (AT 10% CUT VALUE) 74 FIGURE 7: OLOGIT BACK TEST 75 FIGURE 8: LINEAR BACK TEST 75 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com ABSTRACT The largest part in Asset of any bank in its balance sheet is loans, which accounted over 70% of total bank’s asset. Therefore loan becomes the biggest factor affecting bank’s profit/loss (PnL) and managing loan becomes the main point in banking management.
For the reason that credit portfolio plays important role in bank’s PnL, it is required that banks need to issue and implement policies and techniques to manage risk in every stage of granting loans process. Figure 1 – Loan’s Size Audited Public Data End of 2014 End of 2015 Bank Loans Total Loans Total assets (bill % assets (bill (bill % (bill dong) dong) dong) dong) Vietinbank 661,242 542,674 82. It seems to be not an efficient way for banks to do so. Together with the growth of banking industry, many statistical methods for credit rating were developed and introduced as an important tool in finance and banking area.
Credit models are become an effective way to evaluate the credit risk of clients. Many applications of the statistical techniques with more precisions and powers in predicting credit risk create benefits for financial institutions by helping them establish an appropriated strategy for risk mitigation. This thesis presents the approach and results of an attempt at using logistic regression to develop a probability of default (PD) predicting model, a linear regression which is also supported by literatures of relevant factors by an ologit model for predicting the future loan group of any applicant. Both logistic and linear regression are applied to find out the fit models for commercial banks.
By choosing suitable models and deeply data analysis from Vietnamese commercial banks, the paper address almost big concerns in credit risk LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com management and client credit worthiness assessment: determine suitable models for Vietnamese SMEs market for both predicting probability of default (PD), number of late payment days (ELG); specify factors that could cause a loan’s potential downgrade (PDL), important information that contributes in creditworthiness of an individual SMEs, the role of cut-off points in implementing banks’ risk appetite and suitable data treatment approaches. Key words: credit rating, logistic regression, binominal, multinomial, linear regression, prediction, risk assessment. LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com NGUYEN Viet Duc-MDE21 TABLE OF CONTENTS CHAPTER 1 - INTRODUCTION. Research objectives and research questions.
Scope of the study. Contributions and Implications. Organization of the thesis. 11 CHAPTER 2 – LITTERATURE REVIEW.
Concepts of credit rating. Worldwide approaches for credit rating. Empirical studies on credit rating. 17 CHAPTER 3 – RESEARCH METHODOLOGY.
Analytical framework and hypotheses. Binominal logistic regression. Multinomial logistic regressions. Linear regression quick reviews.
Data sources and data treatment. The Data Set. Independent variables selecion .2-Independent variables transformation. 33 CHAPTER 4 – EMPERICAL RESULTS.
The first model with Dependent variable is Default01. The author’s observation after run a “full model”:. Final binominal regression models. Interesting results for Model 1.1-Qualitative marginal effects .2-Initiative for automatic tool.
52 VIETNAM – NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS Page 1 of 102 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com NGUYEN Viet Duc-MDE21 4. The second model with Dependent variable is F2-loan group. The author’s observation after run a “full model”:. Final ologit regression models.
Some more interesting results. The third model with Dependent variable is F2n-day of late payment. The author’s observation after run a “full model”:. Final linear regression models.
Some more interesting results. Test for any other limitation. Checking for Multicollinearity. Checking for Homoscedasticity.
Comparing results of some models. Logit functions back test. Ologit functions back test. Linear functions back test.
75 CHAPTER 5 – CONCLUSION AND IMPLICATIONS. Limitations of the study. Suggestion for further studies. 84 Appendix 1-Variable Descriptive Statistic.
87 Appendix 2-Full model for Logit functions. 90 Appendix 3-Full model for Ologit functions. 94 Appendix 4-Full model for Linear functions. 96 Appendix 5-Testing of Multicollinearity.
98 Appendix 6-Testing of Homoscedasticity. 101 VIETNAM – NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS Page 2 of 102 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com NGUYEN Viet Duc-MDE21 CHAPTER 1 - INTRODUCTION 1. Problem statement Credit is a large industry in the worldwide economy and plays an important role in the growth of firms and countries. In one hand, many enterprises have utilized credit lines to make profit or spur their sales.
In another hand, credit injects capital to the economy, allow production and expansion to bring development to firms in particular and country in general. Bank credit can be categorized into four primary types which are loans, discounts, finance leasing and warranties. The granting of credit plays a crucial role in the economic development because the fact that not every businesses have enough money or use all their money to finance their projects. However, credit institutions do not grant credits on its own to all applicants but it should come through a procedure in which they decide whether or not to provide credit to a particular applicant.
The reason for that is they need to avoid the risk of accepting bad loan (high probability of default) or rejecting good loans (profitable loans). Therefore credit risk management holds an important role in banking industry. The banks manage credit risk exposure through a credit risk policies system in which evaluate the credit risk of applicant, so to minimize the default risk together with maximize profit. The process of identify credit risk includes collecting previous borrowers’ information, classifying, analyzing multi elements and variables to assess the ability of clients’ repayment.
The increasing demand of credit and industrial intense competition force banks to implement new schemes to refined statistical methods to facilitate the procedure of making decisions that is also the standard required by Basel Committee on Banking Supervision (BCBS) through their sophisticated documents and papers leading the banking global to a new credit rating/grading generation. The credit models make process systematically and also shorten time spent in the loan granting process, thus reduce the cost of banks in approving, minimizing objectiveness and inaccurate decisions by using statistical techniques and VIETNAM – NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS Page 3 of 102 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com NGUYEN Viet Duc-MDE21 predicting the performance of customers as well as help banks in measuring the risks and profits. Using models, the banks can manage the pre-issuing, approving procedure and situation after granting loan of the borrowers and easily improve the credit risk management from individual perspective to an entire portfolio one as well. Information technologies help banks in compiling huge mass of data - details of bank customers at various stages of their loan life cycles.
All the records tracked for customers and/or accounts, with their own “characteristics” at points in time. In credit rating and default predicting, these records are important for final outcomes. Did the customers pay or not? Did they pay on time or nor? What could be their problems and whether their problems relate to their ability of paying back? If we assume that future will follow the trend that created in the past, then customer’s historical data can be used to predict an estimated future. We need to keep in mind that, these are only heuristic models (as opposed to deterministic), that can only provide guesses, not absolute answers (and if all things are correctly predictable, life is so boring).
For many countries over the world, Small and Medium-sized Enterprises (SMEs) are considered as backbone of the economy. In OECD members, the number of SMEs is accounted for over 97% of all the enterprises.