Predicting the probability of default for small and medium enterprises based on financial indications bachelor thesis of banking and finance

MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM BANKING UNIVERSITY OF HO CHI MINH CITY -oOo - NGUYEN DIEU LINH PREDICTING THE PROBABILITY OF DEFAULT FOR SMALL AND MEDIUM ENTERPRISES BASED ON FINANCIAL INDICATORS GRADUATION THESIS MAJOR: FINANCE & BANKING CODE: 7340201 HO CHI MINH CITY, 2021 Tai ngay!!! Ban co the xoa dong chu nay!!! MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM BANKING UNIVERSITY OF HO CHI MINH CITY NGUYEN DIEU LINH PREDICTING THE PROBABILITY OF DEFAULT FOR SMALL AND MEDIUM ENTERPRISES BASED ON FINANCIAL INDICATORS GRADUATION THESIS MAJOR: FINANCE & BANKING CODE: 7340201 SCIENCE INSTRUCTOR Ph.D NGUYEN MINH NHAT HO CHI MINH CITY, 2021 i ABSTRACT The internal 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, risk treatment at the bank At the same time, the Government has been building a legal framework for the credit rating to improve information transparency and support for banks to control credit risk from the beginning as well as support the stock market, the bond market to promote capital mobilization through the stock market, protect the rights and interests of investors Researching and selecting suitable rating models will significantly contribute to the development of credit rating activities in Vietnam However, the current models for predicting default probability have certain limitations and are being debated, inconsistency about these models' reliability, which leads to difficulty in choosing the model is suitable to predict the probability of default of the business Besides, determining which financial ratios affect the ranking results is always the goal, which needs to be studied in default prediction research Up to now, there are still not many studies published in Vietnam on selecting models to forecast the probability of default of enterprises based on financial indicators Therefore, the thesis focuses on the issue of "Predicting the probability of default for Small and Medium Enterprise based on financial indicators" to provide commercial banks systematically a theoretical basis and empirical evidence related to the selection of an appropriate business bankruptcy prediction model to contribute to improving the efficiency in credit risk management of the bank in the future Based on the importance and necessity, the objective of this study is to: (i) determine the criteria of an appropriate forecasting model; (ii) how to choose a model capable of predicting the default probability of Small and Medium Enterprises (SMEs) at Vietnamese commercial banks based on financial indicators The results obtained from this study aim to provide additional quantitative scientific evidence to answer which predictive model gives the best results in predicting the probability of default of medium firms and small in Vietnamese commercial banks; (iii) The most important ii contribution of this study is to develop a basic idea in the use of financial indicators to forecast the default probability of SMEs, thereby contributing to improving efficiency results in the credit risk control of commercial banks in Vietnam in the coming time SMEs play a major role in most economies, particularly in developing countries SMEs account for the majority of businesses worldwide and are important contributors to job creation and global economic development Micro, small and medium enterprises, commonly known as small and medium enterprises, are smallsized enterprises in terms of capital, labor or turnover Small and medium enterprises can be divided into three categories based on their size: micro enterprises, small enterprises and medium enterprises According to the World Bank Group's criteria, a micro enterprise is an enterprise with a number of employees less than 10 people; a small enterprise with a number of employees from 10 to less than 200 people and a capital of 20 billion or less; medium enterprises have from 200 to 300 employees with capital of 20 to 100 billion Probability of default is an important component applied in many credit risk analysis and risk management activities According to Basel II, it is a key parameter used in calculating the level of economic capital capable of absorbing risks at credit institutions PD is one of the most useful ratios for classifying borrowers All banks, whether using standard or other advanced methods must provide supervisors with an internal estimate of the PD relative to the borrower to the extent of the score The ranking result based on PD is considered relatively accurate as it is calculated on the firm's actual financial ratios and can practically reflect the business's state PD can effectively reduce credit risk if fully considered Through a review of domestic and foreign studies shows that financial institutions can apply many different credit rating models to predict the default probability of enterprises These predictive models can be polynomial models, logit models, probit models, artificial neural network models Besides, these ranking models use inputs or different financial indicators to forecast the bankruptcy of a business Financial ratios are commonly used as short-term solvency, rate of return/total assets, total iii liabilities/total assets However, with data sets built in different periods, the conclusions about choosing the appropriate credit rating model and financial indicators affecting the probability of default in the researchers are different, as well as the application in Research to predict the possibility of default of SMEs customers in Vietnam according to the author which is a new point Through the analysis, comparison and synthesis of the above studies and related issues, the author has pointed out some research gaps, proposing the proposed research model and expected method for the topic To accomplish the research objectives, the author implemented through 04 stages according to the following steps: Stage one is collect and process data; The second phase select the input variables of the model; The third stage run the regression on selected credit rating models (the logit model, the probit model, the complementary log-log model); The last stage use the Confusion matrix and F1 - Score to evaluate each model's regression results On that basis, select an appropriate credit rating model and has the ability to predict well the probability of default of customers The study was conducted based on the data, which are taken from the annual financial statements of approximately 400 companies from 2017 to 2019 These financial statements have been audited to ensure the quality of the information source Out of 400 businesses, there are 31 businesses in the field of consumer goods trading; 35 enterprises in the petroleum business sector; 39 businesses in the automotive business; 40 enterprises in the construction and installation industry; 43 enterprises in the pharmaceutical industry and medical equipment; 45 enterprises in the textile and garment industry; 47 enterprises in the fisheries sector (fish, shrimp, clam, ); 54 businesses in the iron and steel industry and 66 businesses in the agricultural sector (rice, coffee, pepper, ) Based on the studies, the author selected 14 financial indicators as independent variables for the credit rating models in the research paper Through analyzing the regression results from parametric models, and based on criteria calculated from the confusion matrix (Accuracy, Sensitivity, Specificity, Precision, F1 - Score) to compare and evaluate the ability to predict default iv probability of each model Thereby finding a suitable model to predict the default probability of enterprises The final result of the research shows that there are 5/14 variables play an important role in predicting the default probability of customers, these are Income before tax/Total assets, Total liabilities/Total assets, Earnings before tax, interest and amortization/Long-term debt, Average cost of goods sold/Inventory and Total revenue/Total assets Through the research results, commercial banks can evaluate and select customers in practice to minimize the risk that customers cannot repay their loans From the research results, the author proposes some suggestions for commercial banks on the development of the internal credit rating system in the coming time The thesis has found a model to predict the solvency (default probability) of SMEs customers at commercial banks in Vietnam The model can help stabilize credit quality, minimize arising bad debts Customers with a qualified credit rating (rated A or higher) combined with the results of measuring the good repayment capacity according to the model will have a low probability of incurring bad debt, according to which credit risk for this group of customers is small The model can be seen as a supporting tool for commercial banks in credit granting, assuring credit quality, and facilitating an efficient, safe, and sustainable expansion and growth From there, it can help banks select and maintain a good customer structure, promote marketing strategies towards low-risk customers and develop a network of reputable customers, ensuring debt repayment The model results are the basis for commercial banks to orient credit shrinking to weak customers (high probability of default) and effective credit growth for wellperforming customers (low probability of bankruptcy) Simultaneously, building a credit policy suitable for each type of customer in terms of credit terms, interest rates, fees, requirements for security measures…to ensure safety in operation On the other hand, information to measure the solvency and the results of the model also reflects many problems related to the business performance of the business and v the field - production and business sector As a result, the model becomes a source of information for future credit policy analysis, assessment, forecast and administration vi DECLARATION This thesis is the author’s own research, the research results are truthful, in which there is no previously published content, or the content made by others except the full citations cited in the thesis The author Nguyen Dieu Linh vii ACKNOWLEDGEMENTS First of all, I would like to express my sincere thanks and express my deep ratitude to the teachers of Banking University of Ho Chi Minh City for their enthusiastic teaching, as well as consolidating the solid foundation knowledge, helping me successfully complete the university curriculum In particularly, I would like to express my sincere thanks to Mr Nguyen Minh Nhat for giving me the detailed guidance and wholehearted assistance in completing the graduation thesis Without his thoughtful support, it would be difficult for me to complete this thesis well Due to my limited practical experience, the content of the graduation thesis cannot avoid some shortcomings, I am looking forward to receiving further advice from teachers to learn more experiences I believe these experiences are extremely valuable so that I can develop myself well in the future I sincerely thank you! viii TABLE OF CONTENTS ABSTRACT i LIST OF ABBREVIATIONS x LIST OF FIGURES x LIST OF TABLES xi CHAPTER 1: INTRODUCTION 1.1 The urgency of the research 1.2 Research Objectives 1.3 Research Questions 1.4 Research Subjects 1.5 Research Methods 1.6 Expected Contributions 1.7 The Structure of Research .7 CHAPTER 2: LITERATURE REVIEW 2.1 Small And Medium Enterprises (SMEs) 2.2 Probability Of Default (PD) 11 2.3 Financial Indicators 13 2.4 Overview of probability of default models 14 2.4.1 probability of default models .14 2.4.2 The difference between Logit model, Probit model and Complementary log-log model 21 2.5 Related studies .22 2.5.1 Related studies in Vietnam .22 2.5.2 The other related studies 24 CHAPTER 3: DATA AND METHODOLOGY OF RESEARCH .27 49 Table 4.6: Confusion matrix of the complementary log-log model C log-log Model Actual class Predicted class Non-default Default Non-default 206 96 Default 14 85 Accuracy = 0.73 Sensitivity = 0.86 Specificity = 0.68 Precision = 0.47 F1 – Score = 0.61 Source: Statistics from the author Table 4.6 is complementary log-log model's confusion matrix table This table illustrate that the model correctly predicts that 206 businesses will not go bankrupt, and 85 companies goes bankrupt Accuracy ratio of this model is 0.73, lower than the two other model's, this shows that the probit model can accurately distinguish between bankrupt and non-bankrupt is just 73% But the model's sensitivity ratio of this model has the higher value than the two other model's, is up to 86% In contrast, the specificity ratio and precision ratio of the Complementary log-log model are 0.68 and 0.47, respectively, these are the lowest value of them in three model F1-score of the probit model is 0.61, lower than the two other model's, so the evaluation of this model gives less reliable results than the logit and probit model Through the confusion matrix table of the three parameter default prediction models mentioned above, we can give a few comments as follows: The Accuracy of the probit model is at its highest, which corresponds to an accuracy ratio of 0.82 This shows that the probit model is able to accurately distinguish between bankrupt and non-bankrupt firms up to 82% 50 Sensitivity of log-log complementary model reached 0.86 - the highest of the three models, shows that this model has the ability to detect bankruptcy cases accurately up to 86% The specificity of the probit model is the highest, which corresponds to an accuracy rate of 0.83, showing that this model has the ability to detect the cases of companies that are not bankrupt with accuracy up to 83% Although the models differ in Accuracy, Sensitivity, Specificity or Precision, the probit model gives the best results when F1 Score reaches the highest value, corresponding to the level of forecast accuracy is 69% 51 CHAPTER 5: CONCLUSION AND RECOMMENDATION In this chapter 5, the author gives some recommendations for organizations that can use the model to predict the probability of default in Vietnam, and at the same time raises the limitations in the process of implementing the topic as well as suggests further research directions All research results of the topic were mentioned in chapter of the thesis, whereby the author has found answers to two research questions mentioned in chapter To solve the first question, the author used 14 independent variables corresponding to 14 important financial indicators of the business to determine the default of customers The results found that in the parametric model, variables play an important role in predicting the default probability of customers, these are Income before tax/Total assets (X3), Total liabilities/Total assets (X5), Earnings before tax, interest and amortization/Long-term debt (X10), Average cost of goods sold/Inventory (X12) and Total revenue/Total assets (X14) With the second question, the author used specific criteria including confusion matrix and F1 - Score, thereby selecting the appropriate model to predict the probability of default for small and medium enterprises at commercial banks in Vietnam The regression results in Chapter show that the probit model gives the best results with a forecast accuracy of up to 82%, it helps banks know which independent variables or financial index affect the bankruptcy of a business 5.1 Applying the model to forecast probability of default of SMEs customers at commercial banks in Vietnam 5.1.1 Tools to assist in identifying groups of potential customers The thesis has found a model to predict the solvency (default probability) of SMEs customers at commercial banks in Vietnam The model can help stabilize credit quality, minimize arising bad debts Customers with a qualified credit rating (rated A or higher) combined with the results of measuring the good repayment capacity 52 according to the model will have a low probability of incurring bad debt, according to which credit risk for this group of customers is small The model can be seen as a supporting tool for commercial banks in credit granting, assuring credit quality, and facilitating an efficient, safe and sustainable expansion and growth From there, it can help banks select and maintain a good customer structure, promote marketing strategies towards low-risk customers and develop a network of reputable customers, ensuring debt repayment 5.1.2 The model results are the basis of credit policy orientation The model results are the basis for commercial banks to orient credit shrinking to weak customers (high probability of default) and effective credit growth for wellperforming customers (low probability of bankruptcy) Simultaneously, building a credit policy suitable for each type of customer in terms of credit terms, interest rates, fees, requirements for security measures to ensure safety in operation The group with a low probability of default: granting credit with many preferential conditions such as applying preferential interest rates, extending credit with no property security, or partly secured by assets, not regulated commitment conditions on financial indexes, The group with an average probability of default: granting credit in accordance with the general regulations of the bank, considering reducing interest rates when customers mortgage collaterals below the prescribed rate of customer groups and credit products The group with a high probability of default: no new credit, gradually narrowing the credit balance previously granted, during the period of credit, applying high-interest rates and strict financial index conditions or other stringent requirements to minimize the risk of customers not paying their debts Besides, it is necessary to focus on providing credit to customers in highly efficient business lines with low risk of default; on the contrary, it is necessary to have policies 53 and orientations to narrow and strengthen control for customers in the group of industries with low profitability and high bankruptcy risks In addition, the regression results of the parametric models in Chapter show that there are financial index variables that are statistically significant and have a great influence on the insolvency of SMEs, including Income before tax/Total assets, Total liabilities/Total assets, Income before tax, interest and amortization/Long-term debt, Cost of goods sold/Average inventory and Total sales/Total assets The empirical research results show that the indexes of Income before tax/Total assets, Earnings before tax, interest and depreciation/Long-term debt, and Total revenue/Total assets have opposite fluctuations with the possibility of bankruptcy of the business, that is the greater the ability to generate revenue as well as the profit of the business, the lower the possibility of business bankruptcy In contrast, the ratios of Total Liabilities/Total Assets and Cost of Goods Sold/Average Inventory fluctuate in the same direction as the firm's default; this shows that the more debt a business uses, the greater the financial pressure leads to the higher the likelihood of bankruptcy; at the same time, if the enterprise does not have appropriate inventory management and storage measures, it will easily lead to the possibility of default Through the above research results, commercial banks can evaluate and select customers in practice to minimize the risk that customers cannot repay their loans Specifically: giving credit priority to businesses with high revenue and profitability capabilities and carefully analyzing and evaluating before granting credit for businesses with heavy debt use, low self-reliance finance, as well as considering how inventory management for manufacturing enterprises, it ensure continuous production and minimizes damage caused by fluctuations in raw material prices change or not On the other hand, information to measure the solvency and the results of the model also reflects many problems related to the business performance of the business and the field - production and business sector As a result, the model becomes a source of information for future credit policy analysis, assessment, forecast and administration 54 5.1.3 Applying the model results to improve the efficiency of credit risk management in commercial banks Predicting customers' ability to repay debts is a tool to assist banks in identifying potential customers and helps banks improve quality in monitoring and re-rating customers after granting credit Based on the model results, commercial banks can promptly detect and take measures to solve credit quickly to customers with solvency problems (high probability of default), thereby contributing to limit the risk of loss of capital for the bank Currently, according to Decision No 493/2005/QD-NHNN dated 22/04/2005 on debt classification, setting up, and using provisions to deal with credit risks in banking operations of credit institutions; most commercial banks in Vietnam still apply the provisioning according to the results of customer debt classification, from which they set up the provision according to the appropriate ratio Therefore, if commercial banks can well forecast the repayment ability (default probability) of customers, it becomes much easier to make provisions, thereby building a credit risk reserve fund is also more effective 5.2 Suggest using the model to forecast the probability of default at Credit Rating Agencies in Vietnam A credit rating agency (CRA) is a for-profit company that collects information about individuals' and businesses' debts and assigns a numerical value called a credit score that indicates the borrower's creditworthiness CRAs play a central role in the debt (bond) markets of many countries Creditors and lenders, such as credit card companies and banks, report their customers' borrowing activity and history to credit agencies Individuals and businesses can obtain copies of the information reported about them by contacting the credit agency or a related third-party company and paying a nominal fee On September 26, 2014, the Ministry of Finance issued Decree 88/2014 / ND-CP regulating credit rating services At the same time, the Prime Minister has also 55 approved the Credit Rating Service Development Plan to 2020 and a vision to 2030 Accordingly, by 2030, it is expected to issue a business eligibility certificate for the maximum enterprises and a vision to 2020, all bond issues of enterprises must be rated with credit Accordingly, on July 21, 2017, the Ministry of Finance licensed Saigon Phat Thinh Rating Joint Stock Company as the first enterprise in Vietnam to provide credit rating service Followed by FiinRatings, a brand of FiinGroup, certified by the Ministry of Finance to perform a credit rating in Vietnam on March 20, 2020 CRAs can collect credit information of customers who borrow money every month at all credit institutions across the country, then use the model to forecast probability of default of customer to classify debt groups, synthesize credit information of each customer Finally, CRAs resell the borrower's aggregated credit information when credit institutions request it and pay CRAs a fee for such information According to the research results of this thesis, CRAs should focus on financial index variables that are statistically significant and have a great influence on the insolvency of SMEs, including Income before tax/Total assets, Total liabilities/Total assets, Income before tax, interest and amortization/Long-term debt, Cost of goods sold/Average inventory and Total sales/Total assets Attention and in-depth analysis of these variables can help CRA produce more accurate credit rating reports, thereby giving relevant comments and suggestions to SMEs, in addition it also helps banks manage the credit risk easier This thesis provides a fairly complete and comprehensive way of published research to see the gaps in previous studies related to the selection of the most suitable model to forecast the default probability of small and medium enterprises in Vietnamese based on financial indicators Basis on the regression results in Chapter 4, this thesis suggests CRAs to use the probit model to forecast probability of default because that result show that the probit model gives the best results with a forecast accuracy of up 56 to 82%, this is an important basis for CRAs to choose an appropriate credit rating model In theory, competition ensures innovation, and it represents a healthy check on product quality However, the rating business is entirely based on reputation A certain contradiction between competition and reputation exists The reputational capital the leading CRAs enjoy is enormous Investors trust CRAs’ judgement and they ask for a certain risk premium based on the issuer’s rating Even after the subprime debacle, rating decisions are widely discussed in the financial press, highlighting their continuous importance Therefore, choosing correct customer information and data is very important for CRAs to be able to predict the probability of default The data used in this research can also contribute in part to the process of sourcing the data to predict the default probabilities of CRAs customers 5.3 Limitation of the topic and future research direction 5.3.1 Limitation Besides the results of the thesis, there are also certain limitations and difficulties The most prominent limitation of this study is the small data set Due to time constraints, only 400 businesses were collected and limited to business areas The number of input variables is 14, matching the number of observations However, this sample was considered small, resulting in a lack of important results In addition, the quality of input information is not high Although to ensure the quality of information sources, the collected financial statements have been audited, but the quality of auditing financial statements in Vietnam is not really transparent, clear and effective as in developed countries In Vietnam, an enterprise can prepare three or four financial statements to serve many different purposes such as sending to tax authorities, sending to banks, auditing, and internal control Therefore, controlling and evaluating the model's input quality is very important to obtain the most accurate results 57 Also, the credit rating model mentioned in the thesis is built only on financial indicators, not considering non-financial factors such as the applied internal credit rating models at commercial banks in Vietnam today In fact, in many cases, customers' financial statements not accurately and thoroughly reflect the business results and the financial position of the units, so banks must rely on non-financial information to screen and classify customers 5.3.2 Future research direction This thesis recommends further research in the following areas/areas: Expanded data sets and time intervals: To get more reliable results, the number of businesses collected is expanded to 1,000 or even higher Besides, instead of collecting annual financial statements, collect quarterly corporate financial statements can be collected to achieve a higher degree of accuracy Besides, if the collected data is large enough and can be disaggregated for each group of customers in different business sectors, it will also bring accurate results and suit each business's specific business activities, increasing the applicability of the model practice REFERENCES Altman, E I (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy The journal of finance, 23(4), 189-209 Beaver, W H (1966) Financial ratios as predictors of failure Journal of accounting research, 71-111 Breiman (1980) Classification and regression trees Chapman and Hall Breiman, L., Friedman, J., Stone, C J., & Olshen, R A (1984) Classification and Regression Trees R.A Olshen Dombolena, I and S Khoury (1980) Ratio Stability and Corporate Failure The journal of Finance, 1017-1026 Fernández-Delgado, M., Cernadas, E., Barro, S., & Amorim, D (2014) Do we Need Hundreds of Classifiers to Solve Real Worls 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