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MINISTRY OF EDUCATION AND TRAINING THE STATE BANK OF VIET NAM BANKING UNIVERSITY OF HO CHI MINH CITY TRINH THANH DAT APPLYING LOGISTIC MODEL TO PREDICT THE PROBABILITY OF DEFAULT FOR CONSTRUCTION ENTE[.]
MINISTRY OF EDUCATION AND TRAINING THE STATE BANK OF VIET NAM BANKING UNIVERSITY OF HO CHI MINH CITY TRINH THANH DAT APPLYING LOGISTIC MODEL TO PREDICT THE PROBABILITY OF DEFAULT FOR CONSTRUCTION ENTERPRISES IN VIETNAM FROM 2014 TO 2016 GRADUATION THESIS MAJOR: FINANCE – BANKING CODE: 7340201 HO CHI MINH CITY - 2018 - Tai ngay!!! Ban co the xoa dong chu nay!!! MINISTRY OF EDUCATION AND TRAINING THE STATE BANK OF VIET NAM BANKING UNIVERSITY OF HO CHI MINH CITY TRINH THANH DAT APPLYING LOGISTIC MODEL TO PREDICT THE PROBABILITY OF DEFAULT FOR CONSTRUCTION ENTERPRISES IN VIETNAM FROM 2014 TO 2016 GRADUATION THESIS MAJOR: FINANCE – BANKING CODE: 7340201 INSTRUCTOR M.S TRAN KIM LONG HO CHI MINH CITY - 2018 i THE AUTHOR'S DECLARATION Full name: Trinh Thanh Dat Student class: HQ02-GE01, faculty of Banking and Finance, Banking University of Ho Chi Minh city Student code: 030630141126 I declare that this thesis has been composed solely by myself and that it has not been submitted, in whole or in part, in any previous application for a degree Except where states otherwise by reference or acknowledgment, the work presented is entirely my own Ho Chi Minh City, May 18, 2018 Author Trinh Thanh Dat ii THE AUTHOR'S ACKNOWLEDGEMENT First of all, I would like to thank all lecturers at Banking University of HCMC Your enthusiastic and devoted instruction helped me to improve my logical thinking ability and knowledge In addition, I would like to thank Mr Tran Kim Long who enthusiastically instructed and encouraged me to complete this graduation thesis However, due to limited knowledge and practical experience and limited research time, the study cannot avoid certain shortcomings The author wishes to receive the comments of members in the committee to complete the thesis Ho Chi Minh City, May 18, 2018 Author Trinh Thanh Dat iii BANKING UNIVERSITY OF HO CHI MINH CITY VIETNAM High-Quality Program of Banking and Finance ABSTRACT Author DAT, Thanh TRINH Title Applying logistic model to predict the probability of default for construction enterprises in Vietnam from 2014 to 2016 Year 2018 Language English Instructor M.S LONG, Kim TRAN In the current overall development of the economy, banking credit plays a very important role in the economy of every country in the world and is especially important for countries with underdeveloped financial markets like Vietnam because it is a main source of funding for businesses However, recently, excessive credit growth, resulting in uncontrolled credit quality, has caused some consequences for the banking system such as: high credit risk, declining profit, liquidity reduced The paper focuses on building a model estimating credit risk for construction firms in Vietnam from 2014 to 2016 Based on the results of the study, the paper provides not only an effective tool to predict the probability of default of construction companies but also comments and policy implications for commercial banks to improve the quality of credit and reduce credit risk in the future Key words: Credit risk, Logistic model, Basel II, Probability of default, Construction companies, Vietnam iv INDEX LIST OF ACRONYMS LIST OF TABLES AND FIGURES CHAPTER 1: INTRODUCTION 1.1 Research background 1.2 Significance of research 1.3 Object and scope of the study 1.4 Research questions 1.5 Research methods 1.6 Structure of the themes SUMMARY OF CHAPTER CHAPTER 2: LITERATURE REVIEW AND THEORETICAL FOUNDATIONS 2.1 Credit risk (Default risk) 2.1.1 Definition 2.1.2 Measuring credit risk 2.2 Probability of default (PD) 10 2.2.1 Definition 10 2.2.2 Measuring PD 10 2.3 Some previous research on measuring PD 12 2.4 Model evaluation methods 22 2.4.1 Confusion matrix 22 2.4.2 Accuracy 23 2.4.3 Sensitivity 23 v 2.4.4 Specificity 23 2.4.5 Precision 23 2.4.6 F1 score 24 2.5 ROC Curve 24 2.5.1 Definition and some overviews 24 2.5.2 ROC‟s construction 25 SUMMARY OF CHAPTER 26 CHAPTER 3: MODEL ESTABLISHMENT 27 3.1 General concept 27 3.2 Building model 27 3.2.1 Logistic model and Model selection 27 3.2.2 Collection and cleaning data 27 3.2.3 Building models 32 3.2.4 Apply the models into estimating the PD in 2016 35 3.2.5 Choosing cutoff values 35 SUMMARY OF CHAPTER 36 CHAPTER 4: VALIDATING MODEL‟S PERFORMANCE AND EVALUATING RESULTS 37 4.3 Evaluation indicators 42 4.4 ROC Curve (Receiver operating characteristic curve) 43 4.5 The area under curve (AUC) 44 SUMMARY OF CHAPTER 45 CHAPTER 5: CONCLUSIONS 46 5.1 Final result 46 vi 5.2 Limitations 46 5.3 Recommendations 47 5.4 Future research direction 47 PREFERENCES 48 APPENDIX 1: LOGISTIC MODEL EXPLANATION 54 APPENDIX 2: CODES IN R 56 APPENDIX 3: PROBABILITY OF DEFAULT OF LOGIT MODEL 59 APPENDIX 4: PROBABILITY OF DEFAULT OF PROBIT MODEL 60 APPENDIX 5: PROBABILITY OF DEFAULT OF C LOG-LOG MODEL 61 LIST OF ACRONYMS Abbreviations AUC BS CAGR CRAs EAD EL IRB Approach IS LEF LGD PD ROA ROE ROC Curve ROS SMEs UL Full meaning Area Under Curve Balance Sheet Compound Annual Growth Rate Credit Rating Agencies Exposure at Default Expected Loss Internal Ratings Based Approach Income Statement Loan Equivalency Factor Loss Given Default Probability of Default Return on Asset Return on Equity Receiver Operating Characteristic Curve Return on Sales Small and Medium-sized Enterprises Unexpected Loss LIST OF TABLES AND FIGURES Page Figure 2.1 Relationship between Expected and Unexpected Loss Figure 2.2 The Standardized Probit, Logit and C-Log-Log Links 12 Table 2.1 List of Ratios Tested 13 Table 3.1 Number of observations and defaults per year 27 Figure 3.1 Distribution of financial ratios before handling outliers 29 Figure 3.2 Distribution of financial ratios after handling outliers 30 Table 3.2 Financial ratios in six categories 33 Table 4.1 Descriptive statistical table 35 Table 4.2 Correlation matrix 36 Table 4.3 Regression table of Logit model 37 Table 4.4 Regression table of Probit model 38 Table 4.5 Regression table of C log-log model 39 Table 4.6 Matrix confusion for logit model at cutoff of 0.01 40 Table 4.7 Matrix confusion for probit model at cutoff of 0.01 40 Table 4.8 Matrix confusion for C log-log model at cutoff of 0.01 40 Figure 4.1 Receiver Operating Characteristic Curves of three models 42 48 PREFERENCES Altman, E I (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy The Journal of Finance, 23(4), 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