The study explores the impact of macroeconomic factors and commercial bank factors on NPLs in the US over two separate periods from 2002 to 2006 (the financial crisis period) and 2007 to 2010 (during the financial crisis period). The variables included are both macroeconomic factors such as GDP growth rate, unemployment rate, lending interest rate and bank variables such as return on equity, liquidity ratio, inefficiency, bank size and non-interest income. During the financial crisis period, the study found that liquidity ratio, return on equity, GDP growth rate and unemployment rate affect NPLs. In addition, liquidity ratio has a negative and significant impact on NPLs. However, bank size has no impact on NPLs. During the financial crisis, liquidity ratio, GDP growth rate, unemployment rate and return on equity had negative impact on NPLs while the impact of lending rate on NPLs was found to be statistically insignificant. Large banks have more opportunities with diverse borrowers such as different industries, different geographical locations, capital size and different customer segments.
On the other hand, Ahmed and Bashir (2013) conducted a study on the macroeconomic determinants of bad debts of banks in Pakistan. The study was conducted on 30 commercial banks out of 34 commercial banks for the period 1990 – 2011. The main objective of the study was to explore the impact of inflation, credit growth rate, GDP growth rate, unemployment rate, consumer price index and lending interest rate on bad debts. The study found that GDP growth rate and lending interest rate have a negative impact on bad debts. The negative impact of lending interest rate on bad debts implies that when lending interest rate increases individuals start depositing savings in banks to earn interest but investors are reluctant to borrow to invest in profitable projects. In addition, borrowers have to repay the loans to maintain good credit standing to borrow at discounted interest rates in the future. Similarly, in their study on the determinants of bad debts in Pakistan from 2006 to 2011 and 2013, they found that ROA has a positive and significant impact on bad debts but ROE has an insignificant impact. Saba et al.’s (2012) study on the determinants of bad debts in US banks also found that macroeconomic factors and banking variables affect bad debts from 1985 to 2010 using OLS regression model. They considered total loans, lending rates and real GDP per capital as independent variables. The results showed that total loans have a positive and significant impact on the determinants of bad debts.
bad debts, whereas interest rate and GDP per capital ratio have negative and significant impact on bad debts. “Louzis et al. (2011) conducted a study to determine the determinants of bad debts in the Egyptian financial sector using a Fixed Effect model for the period 2003 – 2009, the variables included ROA, ROE, liquidity ratio, deposit and loan ratio, inefficiency, credit growth, lending interest rate and bank size, credit growth rate, unemployment rate, GDP growth rate. The results showed that deposit and loan ratio, liquidity ratio, credit growth have insignificant impact on bad debts. However, ROA and ROE have negative and significant impact on bad debts, whereas inflation and lending ratio have positive and significant impact on bad debts. This implies that performance and non-performance measures can be used as proxies for management quality.
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Ali and Iva (2013) conducted a study on the impact of bank factors on NPLs in the Albanian banking system considering interest rate on total loans, credit growth rate, inflation rate, real exchange rate and GDP growth rate as determinants. They used OLS regression model with panel data from 2002 to 2012. The results found a positive relationship between loan growth and exchange rate. A negative relationship was found between GDP growth rate and NPLs. However, the relationship between interest rate and NPLs was found to be negative. Inflation rate had no significant effect on NPLs. Similarly, Shingjergji (2013) conducted a study on the impact of bank factors in the Albanian banking system. In this study, capital adequacy ratio, asset-to-debt ratio, net interest margin, and return on equity were examined as determinants of NPLs. The study used a simple regression model with panel data for the period 2002–2012 and found that capital adequacy ratio has a negative but statistically insignificant impact, whereas ROE and asset-to-debt ratio have a negative and significant impact on NPLs. On the other hand, total loans and net interest margin positively affect NPLs. The study found that an increase in ROE will reduce NPLs. On the other hand, Mileris (2012) studied the macroeconomic determinants of credit risk of loan portfolios by using a multinomial regression model with cluster analysis, logistic regression analysis, and factor analysis for estimation. The study found that NPLs are highly dependent on macroeconomic factors. However, Swamy (2012) conducted a study to identify the macroeconomic and banking factors that determine bad loans in Indian banks using panel data for the period 1997 – 2009. The variables included GDP growth, inflation rate, return on capital, savings growth rate, bank size,
deposit-loan ratio, lending rate, operating expenses to total assets, sectoral loans to total loans ROA. The study found that real GDP growth rate, inflation, capital adequacy ratio, lending rate and savings ratio had insignificant impact on NPLs. On the contrary, deposit-loan ratio and ROA had positive impact but bank size had negative impact on NPLs. Similarly, Farhan et al. (2012) studied the economic determinants of NPLs; perceptions of Pakistani banks using both primary and secondary data for the year 2006. The data were collected from 201 bank managers who were involved in lending decisions or managing NPLs. Correlation and regression analysis were used to analyze the impact of selected independent variables. The variables include interest rate, energy crisis, unemployment, inflation, GDP growth and exchange rate. The results of the study indicate that interest rate, energy crisis, unemployment, inflation and exchange rate have positive and significant impact on NPL, whereas GDP growth has negative and insignificant impact on NPL. Badar and Yasmin examined the relationship between NPL and macroeconomic variables for the period 2002 - 2011 of 36 commercial banks in Pakistan and the macroeconomic variables were collected from 2002 - 2011. In this study, inflation, exchange rate, interest rate, gross domestic product and money supply were identified as macroeconomic factors. The authors used Vector error correction model. The research results have shown that in the long run there is a negative correlation between inflation, exchange rate, interest rate, gross domestic product and money demand with bad debt.
Ranjan and Chandra (2003) analyzed the determinants of non-performing loans of commercial banks in India in 2002. The purpose of this study was to assess how non-performing loans are affected by financial and macroeconomic factors. In this study, the authors used panel data regression model and found that lending interest rate has a positive impact on non-performing loans due to the expectation of higher interest rate causing change in energy cost conditions and increasing non-performing loans. On the other hand, deposit and loan ratio has a negative and significant impact on non-performing loans due to the fact that regular customers of the bank are most likely to face bankruptcy as the borrowers will have an expectation of turning to the bank for their financial requirements. Besides, Daniel and Wandera (2013) conducted a study on the effects of credit information on non-performing loans of commercial banks in Kenya. The purpose of this study is to assess the impact of shared credit information on bad debts, to identify factors that explain bad debts, and to identify economic sectors with high bad debts and measures to reduce risks for the sector.
The data collected were primary and secondary for the period 2007 - 2012. The variables included asymmetric information, interest rate/lending rate, loan management and legal framework and credit criteria. The study found that lending rate has a positive and significant impact on non-performing loans. It was identified as the reason why many borrowers do not repay their loans, thus leading to non-performing loans. Similarly, Joseph (2011) conducted a study on the spread of interest rate on the level of non-performing loans of commercial banks in Kenya considering the spread of interest rate/cost of funds as the independent variable and the non-performing loan ratio as the dependent variables. Both primary and secondary data were considered from 43 commercial banks in 2010. The results showed that the cost of funds and lending rate have a positive and significant impact on the formation of non-performing loans. However, Konfi (2012) studied the determinants of non-performing loans in the operations of the Sinapi Aba Trust financial center in Ghana and found that high interest rates were not a factor leading to non-performing loans. This proves that high interest rates can only be applied to defaulting borrowers who have managed to repay the principal and only defaulted in interest payments. If the borrower defaults in both interest and principal payments, it cannot be asserted that high interest rates are the real cause of loan defaults. On the other hand, the study conducted by Wondimagegnehu (2012) in Ethiopia on the determinants of NPLs in commercial banks in Ethiopia also found as bad credit assessment, faulty loan management, underdeveloped credit culture, lenient credit terms and conditions, aggressive lending, weak institutional capacity, unfair competition among banks, and intentional errors by borrowers and their limited knowledge. Diversity of funds for unexpected purposes and overdue finance have significant impact on loans. On the other hand, the study by Wondimagegnehu (2012) also considered interest rate as bank factor and discovered that interest rate has an impact on NPLs of commercial banks in Ethiopia.
Business cycle phase: George (2004) has published a large body of literature that shows the connection between business cycle phases and bank stability. Macroeconomic stability and bank soundness are closely linked. Both economic theory and empirical evidence have shown that macroeconomic instability is associated with instability in banks and financial markets and vice versa. Studies have shown that expansionary phases of the economy are characterized by relatively low levels of debt as consumers and firms face a sufficient flow of income and revenue to repay their debts. However, in boom periods, credit is extended to low-quality debtors and the resulting
That is, when recessions occur, bad debt increases (Fisher 1933, Minsky 1986, Kiyotaki and Moore 1997, Geanakoplos 2010).
GDP Growth: There is empirical evidence of a relationship between GDP growth and NPLs (Louzis et al., 2011; Khemraj and Pasha, 2009; Salas and Suarina 2002; Rajan and Sarat, 2003; Fofack, 2005; Jimenez and Saurina, 2005). If we look at the explanation of the negative relationship provided by the literature, we find that GDP growth generally increases income which increases the borrower’s ability to repay thereby contributing to a reduction in NPLs and vice versa (Khemraj and Pasha, 2009).
Inflation: There is empirical evidence of a positive relationship between inflation in the economy and non-performing loans (Khemraj and Pasha 2009; Fofack, 2005). While Nkusu (2011) has interpreted this relationship as positive or negative according to the author inflation can affect the borrower’s ability to repay loans either positively or negatively. Higher inflation can increase the borrower’s ability to repay loans by reducing the real value of credit outstanding, higher inflation can also reduce the borrower’s ability to repay loans by reducing real income when wages are low, further by highlighting the role of inflation in the presence of interest rate variables, Nkusu further explains that in this scenario inflation reduces the borrower’s ability to repay loans as lenders adjust their lending rates to adjust for real income. Thus, according to this study, the relationship between inflation and bad debt can be positive or negative depending on how the economy operates.
Unemployment: There is evidence that shows a positive relationship between unemployment in an economy and bad debt (Nkusu, 2011; Vogiazas and Nikolaidou, 2011; Bofondi and Ropele, 2011; Berge and Boye, 2007; Rinaldi and Sanchis-Arellano, 2006; Gambera et al., 2000). As the theoretical explanation of this relationship is concerned an increase in unemployment in a country negatively affects the income of individuals increasing their debt burden, it is obvious that when someone loses their source of income how will they repay their debts. Similarly, increased unemployment in an economy also negatively affects the demand for goods and services affecting the sales of businesses leading to reduced income of businesses and conditions of debt that are more susceptible to default (Louzis et al., 2011).
Exchange Rate: According to Khemraj and Pasha (2009) there is a relationship between exchange rate and bad debt. A depreciation in exchange rate can have different impacts it can negatively affect the loan repayment ability of manufacturing enterprises (Fofack, 2005). On the other hand exchange rate can positively affect the repayment ability of borrowers of foreign loans, the relationship between exchange rate
nominal exchange rate and bad debt are determined. Macroeconomic stability and banking activities are closely linked, so that what happens in one will affect the other. Evidence from most other countries has shown that, except for state-owned or state-controlled banks, instability that starts in the macroeconomy spreads to the banking sector. The result is instability in the banking sector and its effects on the macroeconomy. Therefore, to enhance stability in the economy, it is necessary to pursue macroeconomic policies in each period and reduce the instability of the banking sector in relation to macroeconomic shocks that can be expected in a particular economy (Committee, 1998). Overall, the existence of quite convincing empirical evidence has shown that favorable macroeconomic conditions such as stable economic growth, low interest rates and unemployment tend to have better loan quality for banks; favorable macroeconomic context makes it easier for borrowers to receive adequate income streams and to meet their debt obligations. Moreover, this result is strongly indicated by different empirical methods of analysis and maintained across countries.
1.1.3 Measures to handle bad debt
According to Pham Thi Kim Anh (2014), to solve the problem of bad debt, "first of all, it is necessary to set aside a sufficient and correct credit risk reserve fund and for banks that are dishonest in reporting their bad debt situation, the State Bank must take strict measures. Commercial banks must improve their risk management capacity in banking activities, develop risk management principles to proactively handle bad debt, improve the quality of credit work, price appraisal, lending rates, customer classification, and carefully consider production and business plans. In addition, the Government must introduce policies to open up the real estate market and reduce inventory in enterprises such as; dividing large apartments, implementing social housing, and at the same time, must have measures to promote investment, increase consumption among people and economic growth. Classify bad debts according to the correct standards, thereby proposing appropriate solutions for each type of debt by determining the scale and nature of bad debts for classification and appropriate solutions. On the other hand, the State encourages banks to convert debts into capital contributions and shares of borrowing enterprises. At that time, banks will change from creditors to shareholders of enterprises. This will reduce debt payment pressure, interest expenses, and business results of enterprises will be significantly improved. On the other hand, it is necessary to encourage and create favorable conditions for small and weak commercial banks to merge and consolidate.
with large banks, allowing foreign banks with strong financial potential to buy weak banks. At the same time, the State needs to issue policies and have specific mechanisms to resolve debt extensions for businesses with banks. Banks have debt extension policies for businesses with good reputation in debt payment and businesses with unfinished construction projects, etc. And if possible, the State Bank can allow businesses to proactively request banks to extend debt for medium and long-term loans."
In the conditions of economic development and financial market in Vietnam, most of the bad debt handling measures recommended and applied above are direct bad debt handling methods. Meanwhile, research and proposals for bad debt handling through the market are still limited or not popular. Some scientific articles are written about bad debt handling through the market such as bad debt handling through selling debt to VAMC or through securitization. In the article on bad debt handling by VAMC, Le Thi Thuy Van (2014) stated that in addition to the achieved results, there are still many challenges to VAMC's efforts to handle bad debt purchased in the following years: first, VAMC's capital is still limited, so buying back all bad debts of credit institutions is difficult to do; the debt trading market is underdeveloped and lacks competitiveness; Third, there is a lack of an effective coordination mechanism between VAMC and credit institutions in the debt settlement process, causing the debts that VAMC has purchased to be slowly settled while credit institutions have not really actively sold debts to VAMC. Bad debts have been sold to VAMC but credit institutions still directly handle them, still setting aside 20% of the provision for 5 years; VAMC currently only performs the function of managing the bad debt portfolio and records, and has not clearly separated the debt buyer and the debt seller, so the debt settlement efficiency of VAMC is still low. According to author Dang Ngoc Duc (2014), the State budget should not be used to handle bad debts, instead, bad debts need to be thoroughly resolved through securitization of bad debts by issuing government bonds. Bad debt settlement requires the coordination of all three main entities: the Government, commercial banks and enterprises on the basis of applying a synthesis or combination of both direct and indirect bad debt settlement methods through the market. The direct nature is reflected in the aspect of building a cooperation mechanism between enterprises with bad debts and commercial banks and the support of commercial banks for enterprises with bad debts. The indirect nature is under the support of the Government to securitize - "Government bonds" bad debts and put them into market transactions. The author believes that only securitizing bad debts and through Government bonds can bad debts be sold to investors and widely traded in the market. The author also points out that the difficulty
The problem with this solution is: first, according to regulations, an enterprise that wants to issue securities must satisfy certain conditions, including having a certain level of equity and having effective production and business for 3 consecutive years, while enterprises with bad debts are those that do not meet the standards of production and business efficiency; the second difficulty is that when issuing convertible bonds, the scale and ceiling of public debt increase, while Vietnam's public debt in recent years is at an alarming level; third, there is concern about the risk that enterprises with bad debts that issue convertible debt notes to handle bad debts will continue to incur losses and will not be able to redeem the convertible debt notes or go bankrupt before the convertible debt notes mature.
Up to now, the way of dealing with bad debts through the market of buying and selling bad debts has become more popular in many countries, especially in the UK and the US. This comes from the inability of creditors to collect debts or they want to transfer the risks in their financial business to debt buyers. Stifler and Parrish (2014), Rubin (2008), Terp and Bowne (2006) have pointed out that the basic motivation of debt buyers is a huge profit from buying and selling bad debts. When the profit from buying bad debts increases, the demand for buying bad debts will develop, resulting in the formation of a bad debt market. However, there are also some difficulties for bad debt buyers pointed out in these studies. The most common difficulty is the information related to debts, if these debts are bought and sold by intermediaries, and identifying the original creditor is very difficult and fraudulent acts in forging documents are likely to arise.
1.1.4 Factors affecting the bad debt market
Regarding the bad debt market, up to now, there have been a number of studies that have analyzed and evaluated bad debt trading activities. Starting from 2012, Quach Manh Hao mentioned the bad debt trading market through analyzing the current status of bad debt trading activities of enterprises (DATC) and bad debt trading companies (AMC). The author affirmed that the bad debt trading market in Vietnam does not have many professional bad debt trading organizations. This means that enterprises are almost not actually traded on the market, but bad debts are essentially only transferred from commercial organizations to (AMC) and (DATC) and bad debts have not been thoroughly resolved. Similar to Dao Duy Huan (2013), who also studied the "bad debt trading market in Vietnam". The author pointed out that bad debt is a type of commodity but mainly concentrated in state-owned enterprises and proposed general policies to develop the bad debt market in Vietnam. Along with that in 2014, Hoang Tran Hau has