Classification Of Debts And Methods Of Non-Performing Loans Assessment

CHAPTER 1

INTRODUCTION

1.1. Non performing loans of Vietnamese commercial banks

In the period from 2005 to 2015, the rising of NPL does not only increase the vulnerability of the banking sectors but also is a limiting factors to lending activities of commercial banks. The ratio of NPLs in Vietnam sharply increased in the year of 2012. SBV reported that the ratio of NPLs to total loans was 4.3% by the third quarter of 2012. IMF and World Bank (2014) estimate the ratio of NPLs for Vietnam banking sector was 12 % by the end of 2012. Meanwhile, Moody (2014) shows the ratio of NPLs to total assets in Vietnam was 15% by February of 2014. Researchers have shown that NPLs is caused by many determinants, but most studies suggest efficiency, credit growth, scale, capital adequacy, macroeconomic factors such as economic growth, inflation, interest rates, exchange rates, and house prices are the main factors affecting NPLs

1.2. Relevant research status and research issues

Recently, the topic of determinants of NPLs attracts a considerable attention. There have been plenty of studies and researches explain the causes, consequences and solutions of the NPLs. These studies focus on finding out the causes of bad debt and then finding solutions to manage unexpectedly high NPLs ratios. The studies highlight the role of specific factors such as bank efficiency, operational safety, financial capacity and credit growth related to NPLs (Louzis and Altmann 2012, Klein 2013). Studies of macroeconomic factors such as macroeconomic conditions in general, based on financial accelerator theory and bank lending channel theory, while studying the determinants of banks focus on the change in NPLs of banks due to the change of particular factors of the banks. One of the gaps of current researches is that the causes of NPLs have not been fully verified as well as the impact of NPLs on the behavior of commercial banks in Vietnam. Therefore, the thesis seeks evidence to fill to the gaps of previous studies.

1.3. Research objectives and questions

Firstly, the objective of this study is to identify the determinants of non-performing loans in the Vietnamese banking system. Secondly, this study also investigates the impact of NPLs on cost efficiency, profitability, equity and loan growth of Vietnamese commercial banks. In order to achieve these research objectives, this study goes to answer these questions.

1. What cause the remarkable rising of non-performing loans in Vietnam? Are they bank- specific or macro economic determinants of NPL in Vietnam? How is the trend and magnitude of the determinants?

2. How non-performing loans mutually affects banks’ cost efficiency, profitability, equity and loan growth of Vietnamese commercial banks?

1.4. The scope of this study

This study examines the determinants and impact of NPLs of Vietnamese commercial banks in the period from 2005 to 2015.

1.5. Research methodologies and data

1.5.1. Methodologies

In the first step, the study uses DEA model to measure cost efficiency of commercial banks. The next step, we apply the two-step dynamic panel data approach suggested by Arellano and Bover (1995) and Blundell and Bond (2000) and also uses dynamic panel Generalized Method of Moments technique to address potential endogeneity, heteroskedasticity, and autocorrelation problems in the data (Doytch and Uctum, 2011).

1.5.2. Data

This study analyzes a panel dataset comprising 34 Vietnamese commercial banks over the period 2005–2015. The panel data set is extracted from non-consolidated income statements and balance sheets of these banks, and it consists of 357 observations. The macroeconomic data come from IMF – IFS website.

1.6. The structure of this study

Chapter 1. Introduction

Chapter 2. Theoretical framework and Literature review Chapter 3. Methodologies

Chapter 4. Empirical evidences from Vietnam Chapter 5. Conclusions and policy implications

CHAPTER 2

THEORETICAL FRAMEWORK AND LITERATURE REVIEW

2.1. Theoretical framework

2.1.1. Non-performing loans

The definition of NPLs from international institutions related to three factors: (i) Full repayment is in doubt due to inadequate protection (e.g., obligor net worth or collateral) and/or interest or principal or both are more than 90 days overdue. (IMF, 2004); (ii) decline in borrowers' ability to repay; And (iii) Debts classified into three Group such as: substandard, doubtful and loss.

In this study, NPLs are those which are principal or interest or both are overdue more than 90 days overdue and in doubt borrower’s ability to repay. NPLs are debts, which have been classified as those in Groups 3, 4 and 5 stipulated in Article 6 or Article 7 of Decision 493/2005 / QD-NHNN. The ratio of bad debts to the total outstanding debt is used to assess the credit quality of credit institutions.

2.1.2. Classification of debts and methods of non-performing loans assessment

Bholat (2016) states that classification of debts has not uniform international accounting standards. Approaching debts classification is considered as a manager's responsibility or a monitoring report. In Vietnam, according to Decision 493/2005 / QD-NHNN, SBV allows credit institutions to carry out debts classification by the quantitative and qualitative method. However, most banks carry out their debts classification by quantitative method and qualitative factors have not yet been considered, except for three large banks such as Agribank, BIDV and VCB. Based on the above Decision, credit institutions shall carry out the debts classification as follows: Group 1 standard debts, Group 2 debts, which need special attention; Group 3 sub-standard debts; Group 4 doubtful debts; and Group 5 potentially irrecoverable debts. Bad debts (NPLs) are debts, which have been classified as those in Groups 3, 4 and 5.

2.1.3. Literature review on determinants of non-performing loans

2.1.3.1. Macroeconomic determinants of non-performing loans

The financial accelerator theory- discussed in Bernanke and Gertler (1989), Bernanke and Gilchrist (1999), and Kiyotaki and Moore (1997)- suggests that a small change in the financial market can make a difference in the economy and create a feedback cycle. Theory explains explaining bank lending behaviour and its relationship with the cyclical fluctuations in the economy. When asset values are hit by a temporary shock, a direct effect occurs because the changes in collateral values cause changes in obtained credit. On the bank side, when the central bank raises interest rates, the value of the bank's reserves is affected by the decline of stock prices.

The bank lending channel theory refers that bank debt is effected by monetary policy through an imperfect market. Monetary policy affects on the supply of intermediated credit, particularly bank loans. A restrictive monetary policy leads to a drop of banks’ reserves and typically insured

deposits. Only banks that have a larger share of liquid assets or that are bigger are able to shield their lending relationships. Bernanke and Gertler suggests two channels through borrower balance sheet channel and lending channel theory.

Monetary transmission mechanism theory mentions the process by which asset prices and general economic conditions are affected as a result of monetary policy decisions. Such decisions are intended to influence the aggregate demand, interest rates, and amounts of money and credit in order to affect overall economic performance. The traditional monetary transmission mechanism occurs through interest rate channels, which affect interest rates, costs of borrowing, levels of investment, and aggregate demand. Additionally, aggregate demand can be effected through friction in the credit markets, known as the credit view. In short, the monetary transmission mechanism can be defined as the link between monetary policy and aggregate demand (Blinder Maccini, 1991; Chirinko, 1993; Boldin, 1994).

In summary, these above theories indicate that macroeconomic policy affects the banking lending channel in the economy, thereby affecting the quality of bank loans or NPLs.

2.1.3.2. Bank-specific determinants of non-performing loans

Loan growth. Keenton (1009) explains the impact of loan growth through the shift of factors in the relationship between loan growth and NPLs. First, supply shifts implies that an increase in banks’ willlingness to lend by reducing credit standards requirements. The reduction in the credit standard increases the chances that some borrowers will eventually default on their loans. Thus, the outward supply shift not only raises total lending but also increases the likelihood of future loan losses. Second, demand shift notes that an increased demand for credit unrelated to borrowers. underlying creditworthiness will tend to boost loan growth and raise credit standards, reducing the likelihood of future loan losses. This is explained by the need to change the capital structure of the business or investment project. This change in capital structure will help to improve cash flow, thus, the borrower's ability to repay loans will be better, ensuring future loan quality. Third, Productivity shift shows an overall increase in the productivity of borrowers’ investment projects will also tend to boost loan growth and reduce the likelihood of future loan losses, although credit standards may decline in this case. The increase in labor productivity also indicate the good sign of the borrower.

Cost efficiency and profitability. Bad management hypothesis proposed by Berger and DeYoung (1997) suggests that the efficiency banks are better at managing their credit risk. This hypothesis also argues that low cost efficiency is a signal of poor management practices, thus implying that as a result of poor loan underwriting, monitoring and control, NPLs are likely to increase.

Capital adequacy. According to moral hazard hypothesis, Keeton and Morris (1987) find that low capitalization of banks leads to an increase in non-performing loans. In essence, low- capitalized banks are more risky, then they invest more in risky assets, which causes NPLs to increase because if the risk occurs, the lender is the one who suffers the most.

Bank size. Size effect hypothesis mentions the effect of bank size on asset quality. Bank size is negatively related to NPLs.

2.1.3.3. Industry-specific determinants of non-performing loans

Industry competition. The risk-shifting Paradigm refers that when the level of competition among banks is higher, the banking sector will be less risky or more stable. Accordingly, large commercial banks will receive greater assistance from regulators and lead to business operations risk. On the contrary, the other view that the moral hazard hypothesis is that the banking system will become more volatile and fragile if competition levels increase. For the large commercial banks select customers may be more careful, credit portfolio will more secure. In addition, these commercial banks have enough capacity to diversify their asset portfolio to minimize risk. Besides, a banking system with a few large banks will be easier to manage than a system with more small banks.

Owership. The Berle Mean theory implies that concentration ownership will increase the operational efficiency of the business, including financial institutions (Shehzad và ctg, 2010). The more centralized the ownership will be, the more prudent banks will be through tight control over loans.

2.1.4. Literature review on the impact of non-performing loans on bank behavior

2.1.4.1. Impact of non-performing loans on bank performance efficiency

The rising of non-performing loans can lead to efficiency problem for banking sector. The bad luck hypothesis suggests that loans become overdue, banks will increase operating cost to deal with NPLs. These costs increase as bad debts rise. Bad management hypothesis mentions that the high efficiency banks will be able to manage credit risk better than low efficiency banks. This is considered as a part of the bank's core competencies.

2.1.4.2. Impact of non-performing loans on bank capital adequacy

The moral hazard hypothesis referred the relationship between NPLs and bank captialization. High NPLs increase the uncertainty of the bank capital status and thus limit their access to capital mobilization. This in turn raises banks' lending rates and thus contributes to reducing loan growth. Increases in credit risk during recession cause a deterioration in the bank capital ratio and hence banks face much higher capital needs to fulfil regulatory requirements.

2.1.4.3. Impact of non-performing loans on bank loan growth

The financial accelerator effect also refers to the effects of NPLs on banks lending behavior. This theory relates to borrowers’ equity position (or net worth) which influences their access to credit. This also explains bank lending behavior and its relationship with the cyclical fluctuations in the economy. A net worth of a firm is improved and the greater it is, the lower the external finance premium as lenders assume less risk when lending to high net worth agents during business upturn. An adverse shock that lowers borrowers’ current cash flows leads to a decline in their net worth and raises external finance premium. The increase in borrowers’ cost of financing will discourage their desires to undertake more investment projects and consequently affect the demand for credit, and amplifying the effect of the initial shocks.

2.2. Previous empirical studies

2.2.1. Previous empirical studies on determinants of non-performing loans

- Loan growth (Clair 1992; Keeton 1999; Louzis 2012; Le 2016 and Jimenez 2006).

- Bank size (Louzis và ctg, 2010; Salad và Saurina, 2002; Jimenez, Salad, and Saurina, 2006).

- Cost efficiency and profitability (Berger và Humphrey, 1992; Wheelock and Wilson, 1995; Karim

et al., 2010).

- Capital adequacy and operational safety (Salas 2002; Le 2016).

- Economic growth (Salad and Saurina 2002; Klein 2013; Park and Zhang 2012…).

- Inflation and interest rates (Salad and Saurina 2002; Klein 2013; Pestova 2011)

- Exchange rate (Castro 2012; Beck 2013; Pestova 2011 and Washington 2014).

- Real estate market (Nkusu 2011; Fainstein, 2011).

- Industry competition (Lee và Hsieh 2013 and Jimenez 2007)

- Ownership (Iannotta et al., 2007; Shehzad et al., 2010).

2.2.2. Previous empirical studies on the impact of non-performing loans on bank behavior

Impact of NPLs on bank performance efficiency (Alkhar, 2011; Ponce, 2011; Le 2016; and Phạm Hữu Hồng Thái 2013,...).

Impact of NPLs on bank capital (Le 2016; Lee and Hsieh 2013)

Impact of NPls on bank loan growth (Le, 2016; Cucinelli, 2015; Stolz and Wedow 2009; Wangai et al., 2012).

CHAPTER 3

3.1. Research models

METHODOLOGIES

3.1.1. Research models of determinants of non-performing loans

The first model is applied to address the first objective of this study which is to analyze the causes of NPLs of commercial banks in Vietnam. Following the earlier literature discussion (Louzis et al. 2012, Salas and Sarina (2002), Klein 2013 and Le 2016 on banking and macroeconomic related studies), a dynamic approach is adopted in order to account for the time persistence in the NPLs structure. The relationships between determinants and NPLs can be specified as follows:

NPLit = αNPLit−1 + βMt + λ1Ht + π1 Fit + ηt + ε1,it, |α| ≤ 1 (3.1) where t and i denote time period and banks, respectively, 𝜂𝑖𝑡 is an unobserved bank-specific

effect, ε1,it is the idiosyncratic error term. To test for the persistence of NPLs, we use lagged NPLs (i.e., NPL t -1) as an explanatory variable and we expect a positive and significant sign. The vector of explanatory variables includes bank-specific variables (Fit), industry-specific variables (Hit) and macroeconomic factor (Mt).

3.1.2. Research models of impact of non-performing loans on bank behavior

The second model is used to answer the second objective which is to investigate impact of NPLs on bank behavior. Following the earlier literature discussion (Le 2016, Goddard et al. 2011 và Girardone et al. 2004), the three equations are set up as follows:

𝐸𝐹𝑖𝑡 = 𝛾2𝐸𝐹𝑖𝑡−1 + 𝜑2𝑀𝑡 + 𝜆2𝑁𝑃𝐿𝑖𝑡 + 𝜋2𝐹𝑖𝑡 + 𝜀2,𝑖𝑡 (3.13)

𝐸𝑇𝐴𝑖𝑡 = 𝛾3𝐸𝑇𝐴𝑖𝑡−1 + 𝜑3𝑀𝑡 + 𝜆3𝑁𝑃𝐿𝑖𝑡 + 𝜋3 𝐹𝑖𝑡 + 𝜀3,𝑖𝑡 (3.14)

𝐿𝐺𝑅𝑖𝑡 = 𝛾4𝐿𝑂𝐴𝑁𝑖𝑡−1 + 𝜑4𝑀𝑡 + 𝜆4𝑁𝑃𝐿𝑖𝑡 + 𝜋4 𝐹𝑖𝑡 + 𝜀4,𝑖𝑡 (3.15)

where t and i denote time period and banks, respectively, 𝜂𝑖𝑡 is an unobserved bank-specific effect, ε1,it is the idiosyncratic error term. 𝐸𝐹𝑖𝑡 is efficiency of banks, 𝐸𝑇𝐴𝑖𝑡 presented bank’s capital and 𝐿𝐺𝑅𝑖𝑡 is proxied by bank’s loan growth.

Table 3.1. Summary of explanatory variables

Classification

Variable	Expected sign
Non-performing loans	Ratio of non-performing loan to total loans
Efficiency	The lagged of NPL	L.NPL	(+)
	Profitability	ROA	(-)
	Cost efficiency	CE	(-)
Loan growth	Percentage change in gross loan	LGR	(+)
Bank size	Logarithms total asset	TA	(+)
Capitalization	Ratio of equity on total assets	ETA	(-)
Liquidity	Ratio of loan to customer deposit	LDR	(+)
Ability to offset risk	Loan loss Provision	LLR	(+)
Industry competition	Concentration of 4 largest banks	CR4	(-)
Industry competition	HHI Index	HHI	(-)
Ownership	ownership ratio	OWN	(-)
Macroeconomic Variables	Real GDP annual growth rate	GDP	(-)
	Inflation, average consumer price	INF	(+)
	Lending interest rates	INT	(+)
	Exchange rate	EXI	(+)
	Housing price growth index	ESI	(+)