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1 INTRODUCTION longer appropriate in many cases From the above reasons, the author chose the thesis titled "Developing a default warning model for commercial joint stock banks in Vietnam" to contribute to solving the issues presented by theories and reality Rationale Banking system plays an important role in the economy, it is considered as “vascular system” of the whole economy As an inseparable factor in the activities of commercial banks in the market, risk is always contained in any banking activity It can lead to bigger damages to the economy than other business and the cost of reparation is huge The early warning of default helps to avoid the bank default, to minimize the loss of depositors, the deposit insurance companies and the economy The default of an inefficient bank can create a chain failure in the banking system and badly affect the sustainable development of the system Therefore, it is important for to find out soon which banks are in financial trouble and at high default risk in order to prevent crisis in the banking system, and to maintain the stability of the financial market and macroeconomic The remarkable development of the system of joint stock commercial banks in the period of 1991-1996 and the following period of 2006-2010, has contributed significantly to the country's economic development Apart from various achievements, joint stock commercial banks have however exposed their shortcomings and weaknesses Many of them have become insolvent by the end of 2011 The Scheme of Credit Institutions Restructuring for the period of 2011-2015, thus, was issued The Resolution of the 3rd Plenum (XI Session) affirmed that one of three pilars of economic restructuring is the financial system restructuring with the banking system at the center To accomplish a successful result, it is important to identify and classify inefficient banks at the risk of default So far in the world, there have existed many theories and warning models of default crisis such as: Univariate Analysis, Multiple Discriminant Analysis (MDA), Logit Analysis (LA), Probit Analysis (PA), etc The recent models, such as Artificial Neural Networks (ANNs), Decision Tree (DT), Trait Recognition (TR), etc have been applied in default warning and have promised good results The studies also show that each method and model has its own advantages and disadvantages; each model applied in different countries or different regions provides different variants It depends on the economic conditions of each country and each region Many models have been created to explain the causes as well as to forecast and to prevent debt crisis However, the unpredictable default of banks and financial institutions at increasing scale and impact shows that default warning models should be paid attention to and improved Significant socio-economic changes, the unpredictability of natural and socio-economic events make traditional and current methods no Purpose The purposes of the thesis are as follows: - Develop and select the system of indicators applied in the evaluation of the probability of default of joint stock commercial banks - Develop the empirical model of risk warning in Vietnamese joint stock commercial banks - Propose some solutions to limit the default risk of Vietnamese joint stock commercial banks Research questions: - In Vietnam's context, what factors could characterize bank default; what factors, indicators may affect the default risk of Joint stock commercial banks and how? - Each bank has specific characteristics making their own probability of default How to point out the differences? - What method and model of default warning should be applied to Vietnamese joint stock commercial banks? - Implications for policy drawn from the model? Object and scope of the thesis - Research object: Object of this thesis is default risk, model of default warning in Vietnamese joint stock commercial banks - Research scope: The research has been carried out on Vietnamese joint stock commercial banks includes 35 Joint stock commercial banks, including joint stock banks in which the State holds controlling shares such as BIDV, MHB, Vietinbank and VCB The study has been done in the period from 2010 to 2015 Research method In order to be in line with the content, requirements and research objects, the thesis applies the quantitative analysis and qualitative analysis Some applied models are Logit Analysis with array data, neural network model and decision tree model to create a risk warning model for Vietnamese joint stock commercial banks 3 The data in this thesis is collected from reports of State Bank of Vietnam, the audited financial statements of Joint stock commercial banks in the period of 2010-2015 Non-parametric models: Artificial Neural Networks, Decision Trees, character analysis, genetic algorithms, etc The scientific and practical significance of the thesis - The thesis builds the theoretical basis of the default warning model for joint stock commercial banks - The thesis builds and selects the system of indicators of default warning to be applied in banking system Identification of factors and characters affecting the default risk in Joint stock commercial banks - Quantifying the specific characteristics of each bank which affects the probability of default - Develop a model of default warning for joint stock commercial banks - Propose some solutions to reduce the default risk of joint stock commercial banks based on the analysis of the thesis Composition of thesis Apart from the introduction, conclusions, appendices, tables and lists of references, the content of the thesis is divided into chapters as follows: Chapter 1: Overview of bank default Chapter 2: Theoretical background on default in commercial banks Chapter 3: Situation of operations, default risk of Vietnamese joint stock commercial banks in the period of 2009-2015 Chapter 4: Creation of a default warning model for commercial banks in Vietnam Chapter 5: Conclusions and policy recommendations CHAPTER 1: OVERVIEW OF BANK DEFAULT 1.1 Definition of default and default in commercial banks The definition of default, default in commercial banks, and the consequence of bank default 1.2 Overview of international studies on default and bank default 1.2.1 Overview of representative models and studies on default Most of the current default warning studies in the world focus on two branches: Parametric models and methods of analysis: Univariate Analysis, Multivariate Analysis, Logit Analysis, Probit Analysis, Survival Analysis, etc; Univariate Analysis: The main content of the univariate analysis in default warning studies is: the examination of individual factors and comparison the factors between two groups of insolvent companies and non-insolvent ones In case that the financial factors show signs of difference between these two groups, they are used as predictors The studies applying univariate analysis are: studies by FitzPatrick (1932), Smith and Winakor (1935), Merwin (1942), etc A widelyapplied study was the one by Beaver published in 1966 The advantages of the univariate analysis method are: simplicity, quick and convenient application and high rate of prediction However, this method exposes three shortcomings To avoid the disadvantages of the univariate analysis, many researchers applied the Multiple Discriminant Analysis (MDA) with Edward Atlman (1968) as the representative author Based on the data of bankrupt enterprises in the United States, he identified the discriminant function which was widely used later Altman's MDA in 1968 became, for many years, an influential model to the studies of default warning, especially the studies prior to 1980 such as the work of Deakin (1972), Blum (1974), Altman and Edward, Haldeman, Narayanan (1977), Norton and Smith (1979), Karel and Prakash (1987), etc Howver when the time and location of study changed, the observations in Altman’s sample didn’t represent the market anymore The estimated values were no longer appropriate Currently, many authors have developed their own discriminant function for each country and each sector The Logit model and the Probit model appeared in the late 1970s and until the late 1980s it became more common than the MDA method in the study of default warning The Logit and Probit model focus on the probability of default/default of enterprises Logit model and Probit model can be used to evaluate the level of interpretation of independent variables Ohson (1980) used the Logit model to replace the MDA model to predict default in enterprises The LA model was also used by Platt (1991), Smith and Lawrence (1995), Koundinya (2004), Prasad and associates (2005), etc Odom and Sharda (1990) were the first ones using neural network (ANN) in their default warning studies The other authors are Hawley, Johnson and Raina (1990); Boritz and Kennedy (1995); Alam and associates (2000); Celik and Karatepe (2007) West (1985) used Logit model in combination with factor analysis to measure and to describe the financial and operational characteristics of banks The data was taken out from income reports, as well as reports of 1900 commercial banks in various US states The important factors identified by the Logit model in this study are similar to the factors used in the CAMELS model The study also showed that the combination of factor analysis and Logit Analysis was useful for evaluate the bank performance Author/ Year of publication Approach/ Factors divided in groups Main result sample and at 85.2% for test sample Nowadays witness the recent trend of application of smart technics and the computer technologies in default warning such as neural networks, decision trees, character analysis, etc Odom and Sharda ANN/5; MDA/5 Data Rate of 100% for training (1993) collected from 38 insolvent sample and 77% for test sample enterprises and 36 noninsolvent ones The most international outstanding studies on default warning are summarized in table 1.4 Kolari and TR and LA associates (1996) Table 1.4: Summary of international studies on default, default warning Author/ Year of publication Beaver (1966) Atlman (1968) Martin (1977) Hanweck (1977) Ohson (1980) Approach/ Factors Main result UDA/30 Data collected from Identification of factors, 79 insolvent enterprises and 79 accuracy rate from 50% to 92% non-insolvent ones in 38 sectors MDA/22 Data collected from Identification of factors, the 33 enterprises for each group accuracy rate of 95% per each sample enterprise MDA; LA/25 Data collected Creation of models, from American banks in the identification of factors, the period of 1970-1976 best efficiency rate at 92.3% PA/6 Data collected from 177 Out of factors, factors are non-insolvent enterprises and statistically significant The 32 insolvent ones accuracy rate of 83.8%, the test pattern of 91.1% LA/9 Data collected from 1025 The accuracy rate of 96.3% insolvent enterprises and 2000 non-insolvent ones Tam and Kiang ANN/19, MDA/19, LA/19 NN network with the accuracy (1992) Data collected from 118 banks rate of 96.2% for training Lanine Vander (2006) Rate of 98.6% for original sample and of 95.6% for test sample and TR model and Logit model in Accuracy rate of 91.6% for Vennet major banks of Russia original data and and of 85.1% for test data Ravi and Pramodh ANN/9;12 Data from Turk Rate of 96.6% for Turk banks (2008) banks and Spanish banks and 100% for Spanish banks In which: UDA- Univariate Discriminant Analysis; MDA- Multiple Discriminant Analysis; ANN- Artificial Neural Network; TR- Trait Recognition; DT- Decision Tree; LA- Logit Analysis; PA- Probit Analysis Source: Synthesis from references 1.2.2 Overview of criteria to determine default or high default risk in existing studies 1.2.3 Factors and variables in studies on default 1.3 The studies on default warning and bank default in Vietnam The studies on default, bank default in Vietnam are summarized in Table 1.8 Table 1.8: Various studies on default, bank default in Vietnam Author/ Year publication of Approach/ Factors Main content/Main results Nguyen Trong Hoa MDA/37; LA/37 Data Estimating the discriminant (2009) collected from 268 function, the Logit function to enterprises in 2007 calculate the probability of default and ranking the observations of the five selected samples 7 Nguyen Quang MDA/40 Data collected Ranking the banks’ credit Dong (2009) from 37 banks in 2008 Identifying two factors, the accuracy rate of 90.6% for the original data and 84.4% for the test data the research gap, the author chose the following topic: "Developing a default warning model for commercial joint stock banks in Vietnam" Phan Hong (2012) Mai The model of Vickers company The Measure the risk of default of construction companies Identifying the cause of the increased risk of default is the poor asset management Nguyen Viet Hung Models of currency crisis Currency crisis forcast and Ha Quynh Hoa forecast Identifying indicators reflecting (2012) the probability of economic instability Nguyen Thi Luong Merton-KMV model Data (2014) collected from 380 listed companies in the period of 2011-2013 Nguyen (2015) Phi Measuring the default risk of enterprises Providing evidences for the reasonable measurement capacity of the method Lan Application of structure Evaluating the risk of system failure model of financial institutions in Vietnam, estimating the credit loss and the risk of failure of the banking system Nguyen Thi Hong Array data model, GMM Determining micro and macro Vinh (2015) (Gaussian mixture factors affecting bad debts in model) banks Source: Synthesis from references Research gap: Default in joint stock commercial banks has not been fully evaluated; the default in banks has not been monitored since the study was carried out in only a year The existing studies identify the factors leading to the default risk in each case, but whether are those factors leading to the default risk in Joint stock commercial banks in a certain period? In addition, each bank owns their individual characteristics which affect the probability of default At present, there is no study to identify the measurement criteria The macroeconomic factors affecting the default risk in Vietnamese banks have not been verified yet From Chapter conclusion In chapter 1, the author presents the concept of default, bank default and the consequences of bank default The author summarizes the studies of default, bank default in the world as well as in Vietnam Specifically, the author provides a full overview of the models and studies on default, from univariate discriminant analysis to non-parametric smart techniques The author presents the criteria for default or high default risk in existing studies and systematically summarizes the factors applied in those studies By analyzing the main research methods in representative studies, the advantages and disadvantages of each method, there is still no model that is superior to others Each one has its own pros and cons The number of predictors of default in those studies is diverse The more or less factors in a model not affect the prediction rate The summary helps the author to find the research gap and to fill that gap, the author has set the object of the thesis and carried out the study in the following chapters CHAPTER 2: THEORETICAL BACKGROUND ON DEFAULT IN COMMERCIAL BANKS 2.1 Criteria to determine the default risk In banking activities, credit risk is the biggest fear for managers The quality of credit reflects in the ratio of bad debt Being a permanent problem in the operation of every bank, bad debt causes some negative effects as follows: Bad debt reduces the bank's profitability, liquidity, credibility and lead to default The increase of bad debt in the banking system is the most urgent problem in the period of 2011-2015 Banks with a non-performing loan (NPL) ratio of 3% or higher are placed under strict supervision by the State Bank Many studies in the world have shown the negative impact of high ratio of non-performing loan on many aspects of banking operations; many studies have demonstrated the relation between high ratio of non-performing loan and bank default In addition, the commercial bank is the monetary organization whose biggest goal is profit and profit is the important indicator to evaluate the success or failure of the management and operation of the bank Profits are also needed to make up for lost loans and to set up the full provision 9 10 Therefore based on the performance of banks, more grounds will be added into categorizing bank risks In particular, the author argues that banks of poor credit (represented by high ratio of non-performing loans) are at high risk of asset loss In case of medium or poor performance, banks will not be able to make up for the loss; the high level of vulnerability consequently lead to default credit institutions The factors in CAMELS (Capital adequacy, Assets, Management Capability, Earnings, Liquidity - asset liability management, Sensitivity to market risk, especially interest rate risk) To evaluate the performance of banks, the thesis applies the DEA method to estimate the technical efficiency of the banks, and then assess the operational efficiency (through profitability) The banks are classified in groups: (group A good performance, B - average performance, group C – weak performance) On the basis of the above arguments, the author determines the default risk as follows: the variable of default risk (Y) is assigned a value of (high default risk) if the bank has a bad debt ratio of 3% or more then it belongs to group C when using DEA for grouping The Y is assigned a value of (low default risk) in other cases The criteria selection to classify the defaults in this thesis is different from other studies because of the following reasons: Many default studies in the world use information about defaulted banks while in Vietnam, no default case as in international practice is recorded Few studies applied the coefficient of safety as the criteria of categorization while in Vietnam, the State Bank made a mandatory requirement on capital adequacy ratio so all commercial banks met this criteria a) Capital adequacy: factors b) Asset quality: Non-performance loan ratio/total outstanding loan; Bad Debts / (Equity and Provisions for bad debts); Provision for bad debts / Bad debts, provision for bad debts /outstanding loans In addition, the following indicators may be considered: loan rate/earning asset; interbank deposit and loan/earning asset c) Management: factors d) Earnings: 13 factors e) Liquidity: factors By analyzing the criteria in the CAMEL system, the author summarizes and expects the sign of criteria affecting the default risk in Table 2.1 2.3 Theoretical backgrounds for models applied in studies on default warning 2.3.1 Logit Analysis, Logit Analysis with array data 2.2 Factors affecting the default risk in commercial banks Upon the advantages of array data, Logit Analysis and the purpose of the thesis (The default risk in Vietnamese Joint stock commercial banks in the period of 2010-2015), the author chooses the Logit Analysis with array data The study also experiments the Artificial Neural Network, Decision Tree to classify, forecast the default risk in Joint stock commercial banks in Vietnam 2.2.1 The macro factors affecting banking activities 2.3.2 Neural network • Economic development 2.3.3 Decision tree • Legal, economic, financial and monetary policies of the State 2.4 Data envelopment analysis (DEA) to evaluate the efficiency in Joint stock commercial banks’ activities • Competitive level 2.2.2 Micro factors affecting the default risk in JCBs The factors in Atlman’s model (1968): factors Choudhy (2007), Pavlos Almanidis and Robin C Sickles (2012) as well as other authors has pointed out the financial ratios in CAMELS model are the important criteria to evaluate the performance of financial institutions, particularly the banks; these criteria is also important to forecast the bank default The CAMEL rating system by National Credit Union Administration (NCUA) was created and has been applied from October 1987 as a tool to supervise the 2.5 The frame of the research thesis Conclusion of Chapter In chapter 2, the author presents the criteria to determine the default risk, clarify the theoretical basis for the models of default risk warning in joint stock commercial banks, analyze macro and micro factors affecting the risk of bank default Based on theoretical basis, the advantages and disadvantages of some risk warning models matching the object of thesis as well as the existing data, the thesis chose to apply the Logit Analysis with array data, Neural Network, and Decision Tree to build risk warning model for Vietnamese joint stock commercial banks 11 12 dollars CHAPTER 3: CURRENT OPERATION, DEFAULT RISK OF JOINT STOCK COMMERCIAL BANKS IN VIETNAM DURING 2009 - 2015 In Chapter 3, the thesis analyzed the macroeconomic context and monetary policies during the period of 2009 – 2015 Clear analysis of current operation condition in the joint stock commercial bank system of Vietnam during 2009 – 2015 is also provided for the following aspects: structure, scale, capital adequacy level, profitability, management efficiency, and asset quality 3.1 Macroeconomic context during 2009 - 2015 Inflation rate sharply increased with its peak in 2011, and then gradually decreased after that thanks to the intervention and persistent inflation control of the Government The rate reached its lowest point in 2015 6.42 5.98 5.25 • Open market operation • Deposit rate • Lending rate 3.3 Operation of banking industry 3.3.1 Structure, scale, and operational scope of banks • Ownership structure, distribution of operations in commercial bank system 3.3.2 Capital adequacy level and total asset scale of joint stock commercial banks 6.68 6.24 5.4 • Interest policy • Scale, operational scope GDP 3.2 Several monetary policies during 2009-2015 • Exchange rate policy GDP growth remained quite stable with an average of 5.7% However, this is a low number in correlation with the economy potential From 2010 to 2012, GDP growth gradually decreased reflecting the turbulence in macroeconomic condition From 2012 to 2015, the economy started to recover resulting in the improving GDP growth year after year Budget income and spending: During 2009 – 2015, total income and spending of the budget has been increasing over the year and showed constant status of overspending The period 2010 – 2015 witnessed the decrease of credit growth within banking system The credit growth constantly decreased during 2010 – 2013 reflected the difficult situation for businesses 5.42 GD P a) Equity capital and capital adequacy level of joint stock commercial banks: Period 2005-2011 2009 2010 2011 2012 2013 2014 2015 Period 2011 to 2015 Graphic 2.2: GDP growth 2009-2015 (%) Source: Statistic bureau Import and export: Export has become one of the most important factors pushing for economic breakthrough during the period Both import and export growth showed strong increasing trend with approximately 25% increase annually Today, the volume for export and import has accounted for 80% GDP of the whole economy, reflecting its importance toward general economic growth In years from 2010 to 2014, export volume has been constantly higher than import volume, leading to excess of export in the economy since 2012 For the year 2014, the excess volume of export has reached approximately billion Graph 3.4: Capital adequacy ratio groups 13 14 Source: Author’s calculation Capital adequacy level of banks is guaranteed according to regulation from the State Bank However, the ratio is still quite low compared to other countries in the region and should be increased to counter latent risks in the coming time Capital adequacy ratio of banks has been constantly decreasing since 2012 leading to instability within the commercial bank system • The growth of credit and deposit • Liquidity 3.3.5 Asset quality, deficit level Graph 3.9 showed the bad debt ratio of the joint stock commercial banks in the research samples b) Total asset scale of joint stock commercial banks From 2008 to 2011, total assets of banks have shown increasing trend with real breakthrough within the joint stock commercial bank section Joint stock commercial banks have actively been actively opening their branch networks resulting in dramatic growth in raising capital and effectively exploit the capital of citizens In 2012, the total asset scale of joint stock commercial bank section showed the declining trend Till September 30, 2013, total assets from banking sector reached 5,637 thousands in billion dongs Till end of July, 2015, the total asset of the whole financial credit system reached over 6.6 million in billion dongs, increasing 150,097 billion dongs compared to end of 2014 Despite the dramatic growth, banks in Vietnam are still of small asset scale comparing to other countries in the region Total assets need to be increased to sustain the growing need of a developing economy 3.3.3 Profitability, asset management efficiency Graph 3.9: Bad debt ratio of joint stock commercial banks in Vietnam during 2010 - 2015 Source: Author’s calculation Bad debt ratio in Vietnam is currently at a high level comparing to other countries in the region such as Thailand (2.7%), Indonesia (2.4%) By end of 2015, bad debt ratio has been constraint at 2.9%; however, there remains a lot of concerning issues ROE ratio of banks Several weak points of the commercial bank system in Vietnam Income distribution of banks • Extremely high risk in banking operations • Administrative ability and competitiveness are low 3.4 Risk of default of several typical joint stock commercial banks during the period of 2009 - 2015 Weak banks with high probability of default include SCB Bank, Tin Nghia Bank, Ficombank, Habubank, Dai Tin bank, Ocean Bank, Westernbank, Dong A Bank, and GPBank The author analyzed these weak banks to present highlight characteristics of these banks Conclusion for Chapter In Chapter 3, the thesis presented three key points as followed: Graph 3.5: Indicators in profitability category Source: Author’s calculation 3.3.4 Credit and deposit growth, liquidity 1) Analysis of macroeconomic context as well as major monetary policies during 2009 – 2015 The global and regional economic conditions with great instability ranging from global financial crisis to sovereign debt crisis have 15 16 negatively affected Vietnam’s economy During the period of 2010 – 2012, macroeconomic environment showed many turbulences leading to the economic growth decline to its lowest point within 10 years Moving to the 2013 – 2015 period, the economy started to recover, however, sustainable growth has yet to be achieved The monetary policies during this period had many major movements strongly affecting the banking operations Based on the assumption that all banks try to maximize their profits, input indicators and outcome results are selected to run DEA model evaluating the business performance of joint stock commercial banks 2) Analysis of operations within banking system through following indicators: structure, scale and scope of operation, capital adequacy ratio, total asset scale, credit and deposit growth, financial efficiency and liquidity risk The author focused on analyzing several indicators in CAMEL model, bad debt indicator, causes and negative effects of bad debts At the same time, the research pointed out the several weakness of commercial bank system in Vietnam including: extremely high risk in banking operation of commercial bank system, low capability in administration and competitiveness 3) Analysis of default risk for several typical joint stock commercial banks and pointing out highlight characteristics of weak banks These weak banks are those with low capability in risk management, low asset quality, high bad debt and overdue debt rate, low profitability CHAPTER 4: DEVELOPING DEFAULT WARNING MODEL FOR JOINT STOCK COMMERCIAL BANKS IN VIETNAM 4.1 Research design 4.1.1 Data The banks included in the research consist of 35 joint stock commercial banks In detail, the number of banks over the year is indicated in table 4.1 Table 4.1: Number of banks in the research Year 2010 2011 2012 2013 2014 2015 Number of Banks 33 35 35 33 27 25 Financial indicators used to predict default probability of banks are calculated from index and criteria within the audited financial statements by year end of joint stock commercial banks in Vietnam from 2010 to 2014 with the total of 163 observations In 2015, there were 25 banks used to test out-of-sample forecasting of the model 4.1.2 Categorization of default probability level for the banks a) Calculation of business performance of joint stock commercial banks: Table 4.2: Selected Income / Outcome Variables DEA Model (Profitability) Income • Total assets Outcome Pretax Profit • Owner’s Equity • Operation cost Source: Synthesis from reference materials and model design by the author After estimation results of business performance of joint stock commercial banks from the DEA model, the thesis categorized the performance of joint stock commercial banks into categories c) Identification of default probability for joint stock commercial banks For this indicator, the thesis calculated bad debt ratio of banks over the years from their published financial statements and combined these with the categories defined in section a) as well as the analysis of non-financial information in order to identify the default probability of banks Of the observations in category C, there are 39 observations with bad debt ratio from 3% and up The rest 70 observations of category C have bad debt ratio lower than 3% Analysis of these observations showed that, despite low business performance, these banks all have credit quality indicators satisfying the requirements from the State Bank and capital adequacy ratio CAR higher than 10% Thus, banks are considered having high default probability if they belong to category C and have bad debt ratio from 3% and up – equivalent to status Y = as opposed to Y = in other status Results showed 39 out of 163 observations belonging to the high default probability group (Y = 1), accounting for 23.92% of all observations The rest of observations fallen under low default probability group (Y = 0) equal 124 observations, accounting for 76.08% of total 4.1.3 System of indicators affecting default probability • Three indicators of macro element group: Gross domestic product; inflation rate; credit growth • Indicators of micro element group: The author selected and built a total 17 18 of 39 indicators These were initially categorized into groups: Profitability (11 indicators); Deficit index (3 indicators); Asset management (4 indicators); Asset quality (7 indicators); Adequacy index (4 indicators); Sustainable growth index (4 indicators); and Liquidity (6 indicators) The data used in neural network model consists of 163 observations These are randomly categorized into sub-sets including: i) Training set of 115 observations; ii) Validation set of 24 observations; iii) Test set of 24 observations 4.1.4 Statistical analysis To identify the optimal number of neurons in hidden layer, the author used the iterative process to review the number of neurons until finding the smallest mean squared error (MSE) The author’s neural network structure consists of 21 input nodes corresponding to variables in Table 4.9 and Table 4.8, 10 nodes in hidden layer, and output nodes The classification performance of neural network on the samples is presented in Table 4.19 The author conducted correlation analysis to identify indicators within groups having ability to recognize level of risk This resulted in 18 variables Then, the thesis applied correlation analysis between any two variables within the groups 4.2 The Logistic model with array data From the set of 18 variables in Table 4.9 and macro variables in Table 4.8, the research used the entry method in which predictors are entered one at a time into the Logistic regression model with array data Hausman test resulted in the adequacy of fixed effect model ^ After estimation results were out for β of β , the next step was to estimate αi Each bank has their characteristics represented by αi With a data set expanding years from 2010 to 2015, the calculation of αi led to solving quintic, quartic or cubic equations depending on the number of years we have data for each bank Matlab software is used to handle this work Model result: ln( p ) = α i -1.2955*RGDP + 1.0346 * d3 − 2.014 * e11 + 3.0769 * l3 1− p Source: The author’s calculation in which p is the probability of observation being in the high default risk group + Variable RGDP has negative effect on p at significance level of 6%, while variable e11 has similar effect at significance level of 1% + Variable d3 has positive effect on p at significance level of 1% while variable l3 has similar effect at significance level of 6% Calculation of default probability and testing of the model efficiency shoed the ratio of correct categorization for the Logistic model is at 87.71% Other intercept values showed characteristics of each bank affecting its default probability indicate that four banks coded 22, 14, 19, and in the research have high intercept values or high latent default risk compared to others 4.3 Neural network model Table 4.19: Neural Network Performance Samples Training set Validation set Test set Accuracy 95% 91.6% 91.6% Source: The author’s calculation Applied on the data set of 114 observations used to run Logistic regression model with array data and fixed effect, the accuracy of classification for ANN model is 92.98% compared to 87.71% of Logistic model Moreover, class I error of ANN model is also lower than Logistic model 4.4 Decision tree model The author experimentally constructed decision tree model to forecast default probability for joint stock commercial banks using the data set of 163 observations Independent variables for decision tree consist of 21 variables in Table 4.9 and 4.8 The author used J48 algorithm on Weka software version 3.6.9 to create decision tree The algorithm in decision tree returned positive indicators for the classification The accuracy level of classification using decision tree model on the 163observation data set is 96.93%, which is a considerably high number With the data set of 114 observations (previously used in Logistic regression model), the decision tree model returned 95.61% accuracy rate The author has summarized and compared classification results returned by the three models (including Logistic model, neural network model, and decision tree model) on different data sets and concluded that using neural network or decision tree model will raise the classification accuracy 19 20 default probability Conclusions for Chapter In Chapter 4, the author has experimentally constructed default warning model for joint stock commercial banks in Vietnam with following details: calculation of business performance for banks, classification into performance groups, and identification of default probability of banks The thesis also constructed 39 financial indicators categorized into groups to use in default prediction + Variable e11 = (Receivable from interest – Payable from loan interest) / Earning assets, has β$ = − Net interest margin shows the difference between The Logistic model with array data and fixed effect has shown variables with direct effect on default probability of banks including overdue debt/ debt payable, net interest margin, net loan / total deposit Variable GDP reflects the growth of the economy and represent macro elements with negative effect on default probability of banks represents the liquidity of banks and its higher value means the banks’ lower liquidity Bankrupt probability is under positive effect of this indicator The author also experimented with ANN and DT models in default prediction for joint stock commercial banks Results showed these two models had higher classification accuracy comparing to the Logistic model Decision tree model showed indicators used to alert of default risk in banks 5.1 Obtained results a) The Logistic model results with array data and fixed impact β$ = This ratio + The intercept α i of banks are calculated in the model These indicate the differences and characteristics of banks which affecting their default probability According to calculation, there are four banks coded 22, 14, 19, and with high intercept values, meaning high default probability The three banks coded 10, 21, and have lowest intercept values, meaning lower default probability under the same condition of variables c) Summary and comparison of results of classification models: From the initial 42 variables, then 18 variables, the final Logistic model has only variables left During estimation, the author has tested the selection of fixed impact versus random impact, and the fixed impact model has been selected Thus, the impact of variables RGDP, d3, e11, l3 toward default probability is fixed impact This also means that the above-mentioned variables affecting the default probability of banks are of similar trend, fixed within the studied period, and sharing similarity between banks + Estimation result from variable RGDP model has + Variable l3 = Net loan / Total deposit with b) Marginal effect of variables toward default probability p: Based on the value of the coefficients on Logistic model, the author calculated the marginal effect of these variables on default probability CHAPTER 5: CONCLUSIONS AND POLICY RECOMMENDATIONS β$1 = − 1.29 revenue from earning interest and cost from paying interest The author expected that variable e11 had negative effect on default probability Regression test result showed a negative value as expected This means the RGDP variable has negative effect on default probability and Logistic model result proves the impact of macroeconomic conditions, specifically the growth of gross domestic product, on default probability of joint stock commercial banks in Vietnam + Variable d3 = Overdue debt / Debt payable in the model has β$ = , which is higher than This variable indicates the deficit level of banks, and the higher its value, the less safe the banks are This variable has positive effect on The author compares the effectiveness of models with the data set of 114 observations in 2015 In different data sets, neural network model and decision tree model both have higher classification effectiveness compared to Logit model Especially, the number of observations wrongly classified by all models are minimal at observation, thus, the combination of three models will bring higher classification effectiveness 5.2 Classification of joint stock commercial banks From the results of Logistic model with array data, fixed effect in section 4.2 and the regulation of bank classification standard according to Decision 06/2008 of the State Bank; the author has classified banks into categories The author then compares the research classification results with the actual classification results from the State Bank 5.3 Several proposals and implied policies After establishing and testing several default prediction models for the joint stock commercial banks in Vietnam, the author proposes several actions as 21 followed a) Proposals for joint stock commercial bank + According to results from the variable e11 model – Net interest margin has negative effect on default probability Thus, commercial banks should have certain methods to raise their net interest margin such as: pushing for promotion of brand, expanding market share, attracting low-cost deposits from individuals and economic sectors, expanding credits, and finding potential clients The Overdue debt / Debt payable index has positive effect on default probability of banks Firstly, there should be correct evaluation and classification of loans to identify the exact scope and severity of overdue debts Then, the banks should focus on lowering these numbers, especially bad debts, as soon as possible through support of financial resource to establish allowance for bad debt and doubtful receivable and make sure the amount could cover the worst case scenario Next step will be for banks to consider selling these bad debts to businesses, organizations, or individuals with competency and authority in handling said debts On the other hand, the banks need to take measures to limit the possibility of overdue debts right from the lending approval phase Overdue debt includes debt group 2, therefore, the banks should closely monitor these debts to prevent them turning into bad debts The variable of net loan/ total deposit has positive effect on default probability and there should be thorough review of possible causes for a high ratio between net loans against total deposits from clients From the review, necessary actions should be taken to lower the ratio + The model results showed variable RGDP having negative effect on default probability of banks Thus, as the macroeconomic overview, especially the growth of GDP has shown negative signals, the banks must focus on the safety of banking operation because these are when the default probability increases due to macroeconomic element + From the data collection and model establishment, the author realized that a regression model result with high creditability and significance requires correct and sufficient input data Therefore, the joint stock commercial banks should further complete their internal information system to ensure data is updated in a correct and timely manner to support the analysis and management of risks + Four banks coded 22, 14, 19, and 7, as per the author’s calculation; have 22 high intercept values implying the high latent default probability Thus, there must be thorough examination of all operational aspects of these banks to figure out specific solutions to lower their probability of default b) Proposals for authorized management organization: The State Bank is the governmental managing organization of banking industry with the objective to monitor banking operation and ensure a stable and healthy banking system Results from the default prediction model for joint stock commercial banks prompted the author to propose several actions for the State Bank and other authorized organizations as followed: + Results from Logistic model of variable RGDP showed the growth of gross domestic product having negative effect on default probability of banks As economic growth remains stable, banks will have favorable conditions to operate, raise their revenue, and lower default probability Thus, the Government should maintain annual economic growth and pay closer attention to the safety of the whole banking system as the economy declines because this is when the banks’ default probability increases due to economic regression In its supervising role, the State Bank need to build a script of annual economic growth and based on the script to identify at-risk banks to timely alert and monitor these banks + The Vietnamese Government should, along with creating favorable environment for domestic joint stock commercial banks to operate, support banks with legal matters and administration reform Monetary policies issued should take in consideration their impacts on the joint stock commercial banks, especially those with lower capacity At the moment, handling bad debts is a matter of urgency and importance to lower default probability of joint stock commercial banks At the same time, efforts from the joint stock commercial banks in quickly collecting their own bad debts and helping credit institutions to minimize transaction cost and time should be met by the Government’s effort in timely completing the secured asset handling procedure, shortening the time to process secured assets Additionally, the Government should consider some policies to increase resources for participation in the handling of bad debts and speeding up this process + To avoid the risk of the whole banking system breakdown, the State Bank should now encourage and later make it an obligation for all banks to apply regulations according to international practices in operation, statistical data information system, and prediction task The supervision and monitoring of the 23 24 State Bank must be conducted regularly and effectively There should be a compulsory mechanism to pressure banks into reporting their business operation results in an honest and transparent manner +Fourthly: The thesis has proposed the default prediction model for joint stock commercial banks in Vietnam being the Logistic regression model with array data This model helps to identify indicators and criteria affecting the default probability and calculate the probability of at-risk categories for the banks included in the sample data Results provided by the model are economically compatible and meet the required standard of a good model Experimental results from the thesis indicate that the neural network model and decision tree model – being to branch model of the intelligence modeling technique used to improve the effectiveness in classification c) Proposal of default prediction model and procedure to build the model for joint stock commercial banks: The author proposes to use Logistic model with array data of fixed effect to predict default probability for joint stock commercial banks in Vietnam based on the collected experimental results The author also proposes a procedure to alert banks of the default risk CONCLUSIONS & APPROACH FOR FURTHER RESEARCH Based on the necessity of default prediction task in joint stock commercial banks along with the research gap among existing researches in Vietnam and the world, this thesis has applied Logistic regression model with array data and several nonparametric models (neural network, decision tree) to construct a default prediction model for joint stock commercial banks in Vietnam In order to apply this model, the author has selected bad debt indicator in combination with business performance analysis of banks as the key indicators measuring default probability The predictor variables are mainly built from indicators in CAMELS model and calculated from financial statements of joint stock commercial banks during the period of 2010 – 2015 The results are as followed: + Firstly: The thesis provides a systematic overview of the methodologies and default prediction models applied for business, especially banks, ranging from single variable analysis models to modern models using intelligent techniques popularly used these days in default prediction The pros and cons of each method and model are analyzed to identify research room for selecting Logistic model with array data to construct default prediction model for joint stock commercial banks in Vietnam + Fifthly: The thesis has quantified differences and distinctive characteristics that affect default probability of each bank It has also identified four banks with latent high risk of default which require thorough examination + Sixthly: From the experimental construction of default prediction model in this thesis, the author has proposed a default alert procedure for joint stock commercial banks in Vietnam + Seventhly: From the collected results, the author has proposed several solutions and actions for the banks and authorized managing organizations to help minimize the default probability Approach proposal for further study To further improve the default prediction model for joint stock commercial banks in Vietnam, the author proposes several approaches for future study: + Firstly, due to limited accessibility to data sources for research model, the author has used bad debt indicator and ranking of business performance as the base to identify default probability Other researches could look for classification criteria, run tests and compare research results of these criteria + Secondly: The thesis has built the theoretical framework to explain the default probability of joint stock commercial banks in Vietnam Current operation capacity and default probability of joint stock commercial banks in Vietnam during the period 2010 – 2015 were analyzed The author provided analysis and proposed criteria evaluating the default probability of the banks based on their bad debts and business performance + Secondly, other researches could experiment with models such as survival analysis, characteristic analysis, or genetic algorithm… and compare to select the compatible model Combination of more than one methodology or model could also be studied to improve the effectiveness of classification + Thirdly: The thesis has built and selected a system of 39 micro indicators and macro indicators to use in the default prediction model Variables with direct positive effect on default probability of banks are: overdue debt/ debt payable; net interest margin; net loan / total deposit Research results have proven the impact and quantified the impact level of RGDP variable upon default probability of joint stock commercial banks + Fourthly, after calculating the default probability and ranking the banks, other researches could calculate the migration matrix of all banks or construct a model to identify variables affecting the migration of banks + Thirdly, further study of model construction and testing for the out-ofsample forecasting of the model should be carried out