Applying logistic model to predict the probability of default for construction enterprises in vietnam from 2014 to 2016

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 - 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), 589-609 doi: 10.2307/2978933 Basel Committee on Banking Supervision (BCBS) (2004), Basel II: International Convergence of Capital Measurement and Capital Standards: A Revised Framework Retrieved from http://www.bis.org/publ/bcbs107.htm (15 April 2018) Beaven, W H (1966) Financial Ratios as Predictors of Failure Journal of Accounting Research, 4(1), 71-111 doi: 10.2307/2490171 Bessis, J (2015) Risk Management in Banking (4th ed.) Chichester: John Wiley & Sons Ltd CXL Institute (2017) How to Deal with Outliers in Your Data Retrieved from https://conversionxl.com/blog/outliers/ (18 May 2018) Deakin, E B (1972) A Discriminant Analysis of Predictors of Business Failure Journal of Accounting Research, 10(1), 167-179 doi: 10.2307/2490225 Engelmann, B., Rauhmeier, R (2011), The Basel II Risk Parameter Estimation, Validation, Stress – Testing with Applications Loan Risk Management (2nd ed.) New York: Springer Engelmann, B., Hayden, Evelyn., & Tasche, D (2003) Measuring the Discriminative Power of Rating Systems Bundesbank Series Discussion Paper, No 2003(01), pp 32 Finlay, S (2012) Credit Scoring, Response Modeling and Insurance Rating: A practical guide to forecasting customer behavior (2nd ed.) Basingstoke: Palgrave Macmillan 49 Griner, P F., Mayewski, R J., Mushlin, A I., & Greenland P (1981) Selection and interpretation of diagnostic tests and procedures Annals of Internal Medicine, 94, 555-600 Hand, D J., Henley, W E (1997) Statistical Classification Methods in Consumer Credit Scoring: A Review Journal of The Royal Statistical Society: Series A, 160(3), 523-542 Hayden, E (2002), Modelling an Accounting-Based Rating Model for Austrian Firms, unpublished PhD dissertation, University of Vienna Investopedia (2017) (10 May 2018) John, W C (2014) Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (4th ed.) SAGE Publications, Inc Langohr, H M., Langohr, P T (2015) The Rating Agencies and their Credit Ratings: What They Are, How They Work and Why They Are Relevant (1st ed.) Chichester: John Wiley & Sons Ltd Lau, A H L (1987) A Five-State Financial Distress Prediction Model Journal of Accounting Research, 25(1), 1987, 127-138 Martin, D (1977) Early warning of bank failure: A logit regression approach Journal of Banking & Finance, 1(3), 249-276 doi: 10.1016/0378-4266(77)90022-X Memić, D (2015) Assessing credit default using Logistic regression and multiple discriminant analysis: Empirical evidence from Bosnia and Herzegovina Interdisciplinary Description of Complex Systems, 13(1), 128-153 doi: 10.7906/indecs.13.1.13 Metz, C E (1978) Basic principles of ROC analysis Seminars in Nuclear Medicine, 8(4), 283-298 50 Ming-Yu, L (2015) Choosing Logistics Regression’s Cutoff Value for Unbalanced Dataset Retrieved from (10 May 2018) Natasha, M (2017) Credit Scoring: Part – Credit Scorecard Modelling Methodology Retrieved from (18 May 2018) Nguyen, D T (2004) Phương pháp ước tính tổn thất tín dụng dựa hệ thống sở liệu đánh giá nội Ha Noi: Banking Institute Publisher Ohlson, J A (1980) Financial Ratios and the Probabilistic Prediction of Bankruptcy Journal of Accounting Research, 18(1), 109-131 doi: 10.2307/2490395 Pontius Jr, G P., Si, K (2014) The total operating characteristic to measure diagnostic ability for multiple thresholds International Journal of Geographical Information Science, 28(3), 570-583 doi: 10.1080/13658816.2013.862623 Powers, D M W (2011) Evaluation: From Precision, Recall and F – Measure to ROC, Informedness, Markedness & Correlation Journal of Machine Learning Technologies, 2(1), 37-63 Rating Credit Risk, Comptroller’s Handbook (2017) Retrieved from (15 May 2018) Rodríguez, G (2017) Generalized Linear Models Retrieved from (28 May 2018) Satchell, S., Xia, W (2007) Analytic Model of the ROC Curve: Applications to Credit Rating Model Validation A volume in Quantitative Finance, 113-133 doi: 10.1016/B978-075068158-2.50011-1 Schuermann, T (March 2003) What we know about Loss-Given-Default? Federal Reserve Bank of New York paper 51 Servigny, N D., & Renault, O (2004) Measuring and managing credit risk New York: McGraw-Hill Sobehart, J R., Sean, C K (2004) Performance Evaluation for Credit Spread and Default Risk Model (2nd ed.) London: Risk Books Song, Y (2015) Decision tree methods: applications for classification and prediction Shanghai Arch Psychiatry, 27(2), 130-135 Doi: 10.11919/j.issn.1002- 0829.215044 Standard & Poors (2006) Staples Inc Corporate Credit Rating Raised to 'BBB+' On Improved Credit Metrics Retrieved from (23 May 2018) Stehman, S V (1997) Selecting and interpreting measures of thematic classification accuracy Remote Sensing of Environment, 62(1), 77-89 doi: 10.1016/S00344257(97)00083-7 Stephanou, C., Mendoza J C (2005, April 22) Credit Risk Measurement Under Basel II: An Overview and Implementation Issues for Developing Countries World Bank Policy Research Working Paper, pp 33 Swets, J A (1988) Measuring the Accuracy of Diagnostic Systems Science, New Series, 240(4857), 1285-1293 Swets, J A (1996) Signal Detection Theory and Roc Analysis in Psychology and Diagnostics Collected Papers Retrieved from (08 May 2018) The State Bank of Vietnam (2014) Overall Basel II Retrieved form (12 May 2018) Thomas, L C., Edelman, D B., & Crook J N (2002) Credit Scoring and Its Applications (1st ed.) Philadelphia: SIAM Tram, T X H (2002) Phân tích tài xếp loại doanh nghiệp cơng tác thẩm định tín dụng ngân hàng Tạp chí phát triển kinh tế, 125, pp Tran, T K (2003) Hồn thiện phương pháp xếp hạng tín nhiệm doanh nghiệp phân tích tín dụng ngân hàng thương mại Việt Nam HCMC: University of Economics of HCMC Vu, N D T (2018) Applying Decision Tree Model to Predict The Probability Of Default Of Construction Enterprises In Vietnam HCMC: Banking University of HCMC Wang, S., & Kapfer, C (2017) Vietnamese banks are playing a risky game Retrieved from (12 March 2018) World bank (2014) Bank nonperforming loans to total gross loans Retrieved from (29 April 2018) Wiginton, J C (1980) A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior The Journal of Financial and Quantitative Analysis, 15(3), 757-770 doi: 10.2307/2330408 53 Zavgren, C V (1985) Assessing The Vulnerability To Failure Of American Industrial Firms: A Logistic Analysis Journal of Business Finance & Accounting, 12(1), 1945 doi: 10.1111/j.1468-5967.1985.tb00077.x Zweig, M H., Campbell, G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine Clinical Chemistry, 39(4), 561577 54 APPENDIX 1: LOGISTIC MODEL EXPLANATION Logistic regression solves such problems applying the logit transformation Logistic regression predicts the logit of Y to X Logit model could be written as: pi = p: PD x: financial factors To create the best model, we find the set of weights β to create the best fit between Pi and observations are default It means that we want Pi close to 100% for default customers and near to 0% for non-default customers This can be done by maximizing Likelihood function (this method is called Maximum Likelihood Estimation (MLE)) Likelihood function can be written as: Li = ( (b‟xi))yi * (1- (b‟xi))1-yi (1) If customers default (yi=1), Li =  (b‟xi) = pi If customers not default (yi=0), Li = 1- (b‟xi) = 1- pi This is the likelihood function for one observation  The likelihood function for the observed set N is of the form: L= ∏  =∏ ((b‟xi))yi(1-(b‟xi))1-yi ∏  Log(L) = log(L1) + log(L2) + log(L3) +…+log(LN)  Log(L) = ∑ ( ) 55 From (1), we have equation: Log(L) = ∑ (( (b‟xi))yi * (1- (b‟xi))1-yi] Maximizing Likelihood, we have: ∑ (yi - (b‟xi))xi Finally, we have this equation: pi = 56 APPENDIX 2: CODES IN R Creating train_set and test_set index_2014