DETERMINING TECHNICAL DEFAULT FACTORS FOR CREDIT RATING MODELS

MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM BANKING UNIVERSITY OF HO CHI MINH CITY NGUYEN TRAN MAI VY DETERMINING TECHNICAL DEFAULT FACTORS FOR CREDIT RATING MODELS BACHELOR THESIS MAJOR: BANKING AND FINANCE CODE: 7340201 Ho Chi Minh City, 2021 MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM BANKING UNIVERSITY OF HO CHI MINH CITY NGUYEN TRAN MAI VY DETERMINING TECHNICAL DEFAULT FACTORS FOR CREDIT RATING MODELS BACHELOR THESIS MAJOR: BANKING AND FINANCE CODE: 7340201 INSTRUCTOR: Ph.D NGUYEN MINH NHAT Ho Chi Minh City, 2021 i ABSTRACT In the process of integration to the world economy, every bank in every country has to face with new opportunities, as well as new challenges The fierce survival competition between commercial banks becomes not only a major problem, but also regular one To be survived and develop, every administrator needs to have directions, and specific strategies to compete with others, and gain profit for the bank in this current context Credit activities are traditional activities and bring the highest profit for banks But of course, with high returns comes great risks This risk not only affects credit lending banks but also can adversely affect the entire economy, especially the developing economy in Vietnam The credit rating system always plays an important role at commercial banks in assessing customers' credit risk and assisting the bank in making credit decisions as well as in management activities and risk treatment at the bank In fact, it is a prerequisite for advanced credit risk management and each credit institution wishes to establish an internal credit rating system for its own Moreover, a legal framework for credit rating has been establishing by the Government to improve information transparency and support for banks to control credit risk from the very beginning as well as support the bond market and the stock market to not only promote capital mobilization through the stock market, but also protect the rights and interests of investors Because of that, the research and selection of appropriate rating models will significantly contribute to the development of credit rating activities in Vietnam Besides, at present, the world economy in general and Vietnam's economy in particular are facing a lot of fluctuations, so the role of the bank becomes particularly important in reviving and bringing the economy to development ii However, through the research process, the current models revealed some limitations and inconsistency about its reliability that leads to difficulty in choosing the suitable models Determining which factors affect the ranking result is an inevitable and strategic issue to more complete the credit rating system Up to now, there are still not many studies published in Vietnam on finding out technical default factors that affect credit rating models Because of those reasons above, the researcher choose the topic "Determining technical default factors for credit rating models” to research on In this research, the author will conduct to find out the technical default factors that affect credit rating models to systematically provide commercial banks with theoretical basis and empirical evidence related to the selection of an appropriate corporate bankruptcy prediction model to contribute to efficiency improvement of the bank‟s credit risk management in the future By consulting with people who are knowledgeable about this field, have long-term work experience and have a good grasp of reality to be able to give more accurate technical default factors that can affect credit rating models Beside that, there are 04 stages that need to be implemented as below: First stage: Collecting and processing data; Second stage: Selecting the input variables of the model; Third stage: Running the regression on selected credit rating models, which are: the Logit model, the Probit model, and the Complementary Log-Log model; Last stage: Using the Confusion matrix and F1 - Score for evaluating each model's regression results From that, selecting the appropriate credit rating model that has the ability to accurately predict the default probability of customers This research was conducted based on the data taken from the annual financial statements of about 400 enterprises from 09 business fields from 2017 to 2019 In order to ensure the quality of the information source, these financial statements have been audited iii COMMITMENT AND THANKS This thesis is the researcher's own work, the research result is honest, in which no previously published content or content of other researchers are presented, except for citations are fully cited in the thesis After a period of time studying, the researcher was helped by all the teachers and friends in support of implementing the knowledge more and more abundant With deepest gratitude, the researcher would like to sincerely thank all the teachers of the Banking and Finance Department who use their knowledge and enthusiasm to convey the precious knowledge to students during the study period at Banking University HCMC In particular, the researcher would like to give a special thank to Ph.D Nguyen Minh Nhat for spending time in guidance and support for the researcher‟s bachelor thesis Due to the researcher‟s limited knowledge and many more, the researcher will not be able to avoid the shortcomings Consequently, the researcher would like to receive valuable comments from teachers and classmates so that the knowledge in this field will be enhanced and improved The researcher NGUYEN TRAN MAI VY iv TABLE OF CONTENTS CHAPTER INTRODUCTION 1.1 The urgency of the research 1.2 Objectives of the research 1.3 Questions of the research 1.4 Object and scope of the research 1.4.1 Research object 1.4.2 Research scope 1.5 Research methods 1.6 Determination of the study sample 1.7 Expected Contributions 1.8 Structure of the research CHAPTER 2.1 LITERATURE REVIEW .11 Overview of Technical Default 11 2.1.1 Definition of Technical Default 11 2.1.2 Regulations for Technical Default 12 2.1.3 Types of Technical Default 12 2.2 Probability of Default (PD) 13 2.2.1 Definition of Probability of Default 13 2.2.2 Characteristics Probability of Default 14 2.2.3 Application of Probability of Default 14 2.3 Overview of credit rating models 15 2.3.1 Statistical models commonly used in credit rating 15 2.3.2 The difference between Logit model, Probit model and Complementary Log-Log model 25 2.4 Related studies 27 2.4.1 Related studies in the world 27 2.4.2 Related studies in Vietnam 30 v CHAPTER DATA AND METHODOLOGY OF RESEARCH 33 3.1 Theoretical framework 33 3.2 Data collection and processing 34 3.3 Selection of input variables in the default prediction model 37 3.4 Models for predicting the probability of default 49 3.4.1 Logit model 49 3.4.2 Probit Model 50 3.4.3 Complementary Log-Log Model 51 3.5 The evaluation criteria of default prediction models 51 3.5.1 Confusion Matrix 51 3.5.2 F1 - Score 54 CHAPTER EMPIRICAL RESULTS 55 4.1 Descriptive statistics results 55 4.2 Regression results of parametric models 58 4.2.1 The Logit model 58 4.2.2 The Probit model 60 4.2.3 The Complementary Log – Log model 62 4.2.4 Overall conclusion about the regression results of parametric models 64 CHAPTER CONCLUSION AND RECOMMENDATION OF THE RESEARCH 72 5.1 Conclusion for the research results 72 5.1.1 Achieved results of the study 72 5.1.2 Limitations of the study: 74 5.2 Recommendations drawn from the results of the study 75 5.2.1 Recommendations for optimizing the most effective technical default factors for credit rating models 75 5.2.2 Recommend using the results of the model to predict default probability of customers at commercial banks in Vietnam 77 vi 5.2.3 Recommend using the results of the model to predict default probability of customers at credit rating agencies in Vietnam 80 5.3 Future research direction 86 vii LIST OF ACRONYMS ACB A Chau Commercial Bank ANN Artificial Neural Network BIDV Bank for Investment and Development of Vietnam CIC Credit Information Center DA Discriminant Analysis EBIT Earnings Before Interest and Taxes E&Y Ernst and Young Corporation Etc Et Cetera FICO Fair Isaac Corporation FN False Negative FP False Positive HNX Hanoi Stock Exchange HOSE Ho Chi Minh City Stock Exchange IATA International Air Transport Association KNN K - Nearest Neighbor PBT Profit Before Tax PD Probability of Default ROS Return on Sales ratio ROA Return on Assets ROE Return on Equity SME Small and Medium Enterprise TA Total Assets TD Total Debts TN True Negative TP True Positive UK United Kingdom US United States viii LIST OF FIGURES AND TABLES  FIGURES: Figure 1.1 Forecast Bankruptcy Rate in 2021 compared to 2019 Page 03 Figure 1.2 Euler Hermes‟s global insolvency index and regional indices (yearly change in %) Page 03 Figure 5.1 General process of credit rating Page 83  TABLES: Table 2.1 Statistical models commonly used in credit rating Page 15 Table 2.2 The difference between Logit model, Probit model and Complementary Log-Log model Page 25 Table 3.1 Synthesize about Business fields and Number of businesses Page 35 Table 3.2 Statistics of Bankruptcy and Non-bankrupt companies Page 36 Table 3.3 Some financial analysis criteria in corporate credit rating Page 40 Table 3.4 Independent variables in probability default prediction model Page 42 Table 3.5 The structure of variables data in Logit model Page 50 Table 3.6 Confusion Matrix Page 52 Table 4.1 Descriptive statistics of the independent variables Page 55 Table 4.2 Correlation Matrix Page 57 Table 4.3 Regression results of the Logit model Page 58 Table 4.4 Confusion matrix of the Logit model Page 59 Table 4.5 Regression results of the Probit model Page 60 Table 4.6 Confusion matrix of the Probit model Page 61 Table 4.7 Regression results of the Complementary Log – Log model Page 62 Table 4.8 Confusion matrix of the Complementary Log - Log model Page 63 84 b) Overall about Credit Rating Agency Credit rating agency is a company specializing in providing services to assess the issuer's ability to pay principal and interest according to the committed term of the issuer for a specific issuance in the form of a credit rating system Investors can rely on credit ratings provided by credit rating companies to consider making their investment decisions The credit rating agency has the following rights: (i) Providing services in accordance with the law; (ii) Receive service charges from the provision of services in accordance with the law; (iii) Request the credit rating organization to provide necessary documents and information related to the credit rating contract The credit rating agency has the following obligations: (i) Credit rating services may only be provided when the Certificate of eligibility for business is granted and information is disclosed according to the regulations; (ii) The organization providing credit rating services must comply with operating principles as prescribed; (iii) Ensure the payment of salaries, remunerations and bonuses to analysts and members of the Credit Rating Council regardless of service costs and credit rating results of the credit rating contract duties in which the person is engaged; (iv) To be responsible for maintaining all necessary conditions of capital, personnel and operation as prescribed; (v) To take responsibility before the law and to the credit rating organization towards the credit rating results according to the signed credit rating contract 85 The credit rating agency collects credit information of borrowers on a monthly basis at all credit institutions across the country After that, they use a customer default probability prediction model to classify groups of debt, summarize information credit information of each customer The credit institutions then acquire the borrower's aggregated credit information from the credit rating agency From the results of this study, the researcher hopes that this will be a reference for credit rating agencies in making more accurate credit rating reports through statistically significant financial index variables that have a great influence on the SME's insolvency as described in chapter 4, including: (i) Inventory Turnover (ii) Asset Efficiency (iii) Liabilities / Total Assets (iv) Profit after tax / Total assets From there, make appropriate comments and suggestions for SMEs Moreover, this result can also help the banks to manage credit risk more easily Selecting the accurate customer data and information is critical for the credit rating agencies to be able to predict the probability of default The data used in this study may also play a part in the data sourcing process for predicting the default probability of customers for credit rating agencies Based on the regression results in Chapter 4, the researcher recommends that credit rating agencies can apply the Probit model, which gives the best results with predictive accuracy up to 83%, for predicting the probability of default in the coming time The researcher believes this is the necessary foundation for credit rating agencies to choose the suitable credit rating model 86 5.3 Future research direction The researcher has some recommendations for further research in the future as follows: (i) It is necessary to expand the data set as well as the period of time for getting more accurate and reliable results The minimum number of financial statements obtained by the businesses must be 1,000 or even higher Furthermore, it is possible to collect quarterly corporate financial statements to achieve a higher level of accuracy instead of collecting annual financial statements (ii) In addition, if the collected data is large enough and can be segregated for each group of customers in different business fields, it will also bring accurate and relevant results to the specific business activities, and increase the applicability of the model in practice Specifically, it is important to focus on 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Investopedia Retrieved January 08, 2021, from https://www.investopedia.com/terms/t/technical-default.asp Kleimeier, D T (n.d.) A credit scoring model for Vietnam's retail banking market Martin, D (1977) Early Warning of Bank Failure: A Logit Regression Approach Journal of Banking and Finance, 249 - 276 Mester, L J (2004) What Is the Point of Credit Scoring? Michel Crouhy, D G (2001) Risk Management The Journal of Risk and Insurance Oslo Hosmer, D W (1989) Best subsets logistic regression Platt, H D (1991) A note on the use of industry-relative ratios Journal of Banking & Finance, 1183 - 1194 Thanh, L T (2009) The application of Logit function to build a model to predict the credit rating of Vietnamese enterprises Vuong Quan Hoang, D G (2006) Statistical Methods to build a model of credit rating of alternate customers Zhou‟, L T (1992) A Multinomial Logit Approach to Estimating Regional inventories by Product Class INSTRUCTOR’S CONFIRMATION Ph D NGUYEN MINH NHAT THESIS RESEARCHER NGUYEN TRAN MAI VY ... "Determining technical default factors for credit rating models? ?? to research on In this research, the author will conduct to find out the technical default factors that affect credit rating models. .. .11 Overview of Technical Default 11 2.1.1 Definition of Technical Default 11 2.1.2 Regulations for Technical Default 12 2.1.3 Types of Technical Default 12... 5.2.1 Recommendations for optimizing the most effective technical default factors for credit rating models 75 5.2.2 Recommend using the results of the model to predict default probability