1434 determining technical default factors for credit rating models 2023

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 BANKING UNIVERSITY OF HO CHI MINH CITY Ho Chi Minh City, 2021 MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM 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 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 ii 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 ii i 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 .6 1.4.1 Research object 1.4.2 Research scope .7 1.5 Research methods 1.6 Determination of the studysample 1.7 Expected Contributions 1.8 Structure of the research .9 CHAPTER LITERATURE REVIEW .11 2.1 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 theworld 27 2.4.2 Related studies in Vietnam 30 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 v 3.4.1 Logit model 49 3.4.2 Probit Model 50 3.4.3 Complementary Log-LogModel 51 3.5 The evaluation criteria of defaultprediction 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 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 vi LIST OF ACRONYMS ACB ANN BIDV CIC A Chau Commercial Bank Artificial Neural Network Bank for Investment and Development of Vietnam 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 LIST OF FIGURES AND TABLES vii • 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 CHAPTER INTRODUCTION In this chapter, the researcher generalizes the urgency of the research, the reasons for choosing the topic, the research objectives, the object and scope of the research, the research methods, and the determination of the study sample as the basis for the entire research process of the topic In addition, the author also states the expected contributions, as well as the structure of the whole research, which helps readers, who are interested in the article, have a better understanding and clearer vision about this topic 1.1 The urgency of the research Today, banking activities are contributing significantly to the development of the country, in which banks play a very big role for the economy Through banking operations, all capital sources are accumulated, concentrated and redistributed to objects in need of capital, thereby promoting the growing economy Commercial banks' activities are constantly being expanded and developed Among those activities, credit activities can be said to be the most traditional and important activity of commercial banks The bank credit market in Vietnam is growing and has great potential in the future when bank capital is still in the process of reaching more people With income gradually increasing, many business opportunities open up and the consumption is increasing; the demand for loans in particular and the use of banking services in general of people in Vietnam is also increasing The banks all want to dominate to this market, maximize profits, but also share the same concerns about risks in lending Banks encountered a difficult problem to solve when considering loan documents to make lending decisions In addition to the goal of market expansion and revenue growth, the bank must be careful with its decisions and can not easily accept funding for every individual in need If good customers bring in regular sales