




































American Research Journal of Economics, Finance and Management 

Volume 10 Issue 3, July-September 2022 

ISSN: 2836-9416 

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ANALYZING THE IMPLICATIONS OF RAISING PAID-UP 
CAPITAL FOR COMMERCIAL BANKS IN DEVELOPING 

ECONOMIES: A NEPALESE PERSPECTIVE" 
 
 

Dr. Rachel U. Kent and Dr. Akhilesh S. Bajaj 
The University of Tulsa, Tulsa, OK, USA 

 
Abstract: Commercial banks hold a pivotal role in driving economic growth within developing 
countries by serving as essential financial intermediaries. In regions where a substantial portion of 
the population depends on low-wage livelihoods, particularly in traditional agriculture, commercial 
banks are instrumental in supplying vital capital for infrastructure development and the 
establishment of new businesses. Term loans serve as a primary mechanism through which these 
banks channel funds to businesses, but efficient asset management and loan collection pose 
continuous challenges for commercial banks in developing nations. 
This paper focuses on the context of Nepal, tracing the evolution of commercial banking from its 
inception with Nepal Bank Ltd. in 1937 to the substantial entry of the private sector in the 1990s. 
Nepal Rastra Bank (NRB) acts as the central bank overseeing monetary policy regulation. As of 2018, 
Nepal hosted 28 commercial banks, categorized into public sector, joint venture, and domestic private 
banks. Historically, public sector banks have dominated loan distribution, but their performance has 
lagged considerably behind joint-venture and domestic private banks, which exhibit similar 
performance metrics. 
This research delves into the dynamics of commercial banking in Nepal, shedding light on the 
disparities in performance and the implications for economic growth and financial stability in the 
region. 
Keywords: Commercial Banks, Economic Growth, Nepal, Asset Management, Loan Collection.  
 
1. Introduction  
Commercial banks play a very important role as financial intermediaries in promoting economic growth 
in developing countries. This is because the majority of the population in these areas lives on low wages, 
and is engaged in traditional agriculture.   
Because of their size, commercial banks provide critical capital needed to develop and maintain 
infrastructure as well as to create new businesses (Beck, Demirgüç-Kunt, & Levine, 2010). Term loans 
are often the instrument used to channel money from the banks to businesses and asset management 
or loan collection is an ongoing issue with commercial banks in developing countries (Dziobek & 
Pazarbasioglu, 1997; Gizaw, Kebede, & Selvaraj, 2015). In the case of Nepal, commercial banking began 
in 1937 with the formation of Nepal Bank Ltd. (Baral, 2005), with the private sector entering the market 
on a large scale in the 1990-s. The Nepal Rastra Bank (NRB) serves as the national or central bank that 
regulates monetary policy. There were 28 commercial banks as of 2018 (Gnawali, 2018), which may be 
broadly divided into public sector banks, joint venture banks and domestic private banks. While the 

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public sector banks have historically enjoyed the largest share of loans, they have also historically 
significantly underperformed compared to joint-venture and domestic private banks, both of which 
were found to be similar in performance (Jha & Hui, 2012).    
Many developing countries, including Nepal, have attempted systemic bank restructuring over the last 
few decades (Pazarbaşioğlu, 1998). Restructuring may include new regulations designed to improve the 
profitability and solvency of banks, and regulations designed to increase the intermediating role of the 
banks in the economy.  With the imposition of successive standards from the Basel Committee on 
Banking Supervision (BCBS), popularly known as Basel I (1988), Basel II 2004 and Basel III (2010), 
the Nepalese commercial banks have seen increasing levels of monitoring and supervision, largely 
strengthening their stability (Uprety, 2013). An earlier examination of Nepalese banks using the 
CAMEL (capital adequacy, asset quality, management quality, earning and liquidity) framework found 
that joint venture banks had a fair capital base and higher liquidity than needed, resulting in lower 
profitability (Baral, 2005).  The paid-up capital requirement (common stock) for commercial banks was 
Rs. 2 billion. However, from 2015, the paid-up capital requirement was increased to Rs. 8 billion, 
thereby increasing the lending capacity, and the credit exposure as well. The primary contribution of 
this work is to analyze if the factors that have driven the operating profitability of commercial banks in 
Nepal have changed as a result of the increased paid-up capital requirement. In order to accomplish 
this, we performed a panel data regression analysis on multiple commercial banks in Nepal over two 
separate time periods: 2007-2014 and 2015-2017.    
The rest of this paper is organized as follows. Section 2 describes the background and the hypotheses 
tested. Section 3 describes the data collection and presents the analysis. Section 4 discusses our findings 
from a theoretical and practical standpoint. We conclude with limitations and suggestions for future 
studies in section 5.    
1. Background and Hypotheses Development  
Until the mid-1970-s, bank safety worldwide was largely the domain of national regulators without 
regard to interdependence among banks (Rost, 2010). The failures of the Herstatt Bank in Germany 
and the Franklin National Bank in New York caused effects across national boundaries, leading to the 
formation of the Basel Committee on Banking Supervision (Basel). The committee consisted of central 
bankers from the G10 countries and Switzerland. The main thrust was to delineate supervisory 
authority between national and transnational bodies. Basel 1 was a framework released in 1988 to 
primarily address the capital adequacy requirement for banks. The main driver here was the Latin 
American debt crisis that occurred in the early 1980-s. A minimum ratio of capital to risk-weighted 
assets of 8% was established starting from 1992 (Jokipii & Milne, 2008).  
Basel 2 was a three pillar framework that expanded on the rules in Basel 1 regarding capital adequacy, 
and additionally recommended supervisory review of institutions‟ capital adequacy and internal 
assessments. A third pillar was also proposed to promote market disclosure, in order to promote sound 
banking practices (Herring, 2002). Basel 3 was begun to be developed in 2007 upon the imminent 
collapse of Lehman Brothers. It includes liquidity requirements and safeguards, such as a counter-
cyclical capital buffer and a minimum liquidity to cover a 30-day period of stress. While Basel 1 and 2 
steered away from defining operational risk, Basel 3 seeks to address this to some degree by enforcing 
liquidity standards and curtailing non-performing assets (Bace, 2016).   

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While the Basel standards have increased the stability of the banking system, the stringent 
requirements that accompany them have impeded the ability of commercial banks to lend in developing 
economies. Basel 2 and 3 have also improved the internal and external operations of commercial banks. 
However, critics of Basel 3 point to the reduced availability of credit and curtailment of economic 
activity if they are to be implemented (Allen, Chan, Milne, & Thomas, 2012).    
Are non-performing loans an issue in developing economies, post Basel 2 and 3? In a recent dissertation 
(Havemann, 2019) points out how capital adequacy requirements instituted pre-2008 prevented bank 
failures during the 2008 crisis. African Bank was an institution that made loans almost exclusively to 
low-income earners on an unsecured basis. Funding came primarily from bond holders as opposed to 
retail deposits. African Bank placed into curatorship in 2014, but central bank intervention led to 
limited loss spillovers and increased losses to the creditors who provided the bail-in. 
Banks in Botswana were studied in (Mathame, 2018) who found that the capital adequacy ratio (CAR) 
was lower based on credit risk and non-performing loans, primarily since the banks were heavily 
dependent on the mining sector. In another survey of 109 European banks in (Bongini, Cucinelli, Di 
Battista, & Nieri, 2018) from 2006-2016, the loss of profitability was found to be influenced by the 
deterioration of the loan portfolios of the banks.Banks that adopted a more conservative lending policy 
went back to profitability more quickly. The lack of an appropriate credit culture in some developing 
countries also leads to increased non-performing loans (Bonga, Chirenje, & Mugayi, 2019). In a study 
of banks in Albania (Duraj, 2015), non-performing loans were found to decrease bank profitability. A 
similar situation was found in a study of banks in Ethopia(Gizaw et al., 2015).  
However, NPLs have not always been found to affect bank performance negatively. As per (Andesfa & 
Masdupi, 2019) some researchers found that non-performing loans did not affect return on assets 
(ROA). A study of Jordanian banks in (Alshatti, 2015) found a positive influence of NPL on ROA. A 
similar finding was reported in (Zou & Li, 2014), where a positive effect was found between NPL ratio 
and ROA as well as return on equity (ROE). Possible explanations for this may include that depositors 
do not take into consideration the credit risk exposure of the bank when deciding to make their deposits 
(Agwu, 2018).  This explanation becomes more plausible if the Basel safeguards are in place in the 
banking system of the country, leading to a macro perception of stability. Macroeconomic factors like 
the money supply and deposit to lending ratios can also drive increased deposits into banks. This 
increases the bank‟s ability to make more loans, and hence improves profitability, even if the 
percentage of nonperforming loans is higher than for smaller banks.  
In the case of Nepal, credit risk (defined as the ratio of non-performing loans to total loans) was found 
to be negatively affected by the capital adequacy ratio in (Poudel, 2013). In a more recent study of 
Nepalese commercial bank ROA performance from 2010-2015, a strong negative relationship between 
non-performing loans and ROA was found, along with a positive influence of costs per loan assets 
(Bhattarai, 2017). Bank size was also found to be positively correlated to bank performance, measured 
by ROA. Based on the review of prior work shown above, we conclude that Basel 2 and 3 requirements 
have imposed some stability in the banking systems of developing economies. However, non-
performing loans or credit risk are still relevant drivers affecting banks‟ financial performance.  
In 2015, the NRB (Nepal Rastriya Bank) mandated banks and financial institutions to raise the 
minimum paid-up capital, or common stock, from Rs. two billion to Rs. eight billion, a four-fold 

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increase, to be implemented over a two year period (Sharma, 2015). A similar move was instituted by 
the bank of Ghana, in 2017 (Young, 2017). The goal behind these moves was to increase the minimum 
size of institutions to improve theoverall stability in the banking systems. While such moves have an 
immediate positive stock market effect, the effect of the increased capitalization requirements on bank 
behavior is not clear. For example, as banks make more loans, will their nonperforming loans have an 
increased affect on profitability? In this work, we investigate the performance of a sample of Nepalese 
commercial banks pre and post mandate, to see how behavior has changed.    
Factors in the Study:   
Operating Profit: 
The dependent variable we look at is the operating profitability of the bank. This is reported in rupees 
every year and is the earnings before interest and tax. A common formula for calculating operating 
profit is: Operating Profit = Operating Revenue – Cost of Goods and Services – Operating Expenses – 
Depreciation  
& Amortization  Non Performing Loans:  
NPL is a ratio defined as:  
NPL = (Non-PerformingLoans / Total Loans) * 100  
Liquidity:  
This is defined as a ratio:  
Current Assets/ Current Liabilities   
Deposits to assets:  
This ratio is defined as:  
(Total Deposits/ Total Assets) * 100   
Credit exposure:   
This variable looks at the overall amount of loans made by the bank, in Rupees.  
Training ratio:  
This ratio is defined as:  
Training = Overall Rupee Amount Spent on Training / Total Number of Staff]  
Based on these variables, the following hypotheses were tested:   
H1: Training ratio affects the Bank‟s operating profit   
H2: Deposits to Assets affect the Bank‟s operating profit   
H3: Credit Exposure affects the Bank‟s operating profit  
H4: NPL affects the Bank‟s Operating profit  
H5: Liquidity affects the Bank‟s Operating profit   
We tested these hypotheses using two separate sets of data: a sample of Nepalese commercial banks 
between 2007-2014, and another sample of the same banks between 2015-2017.   
2. Data Collection and Analysis  
Publicly available financial statements from 2007 – 2017 for six well known joint-venture commercial 
banks in Nepal were used for this study. The data we used are shown in Appendix 1.The names of the 
banks have been masked for anonymity. Panel data regression analysis using the PLM package in the 
R system was used since data is across banks and across time for each bank(Croissant et al., 2017).    

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Table 1 shows the summary statistics for each factor used in our study. The relative standard deviation, 
or the coefficient of variation is (standard deviation / mean) * 100, and gives a dimension free 
illustration of variation in the data (Everitt, 1998). We see that NPL had the most variation while 
deposits to total assets had the least. This is not surprising since NPL reflects the managerial policies of 
the bank regarding lending criteria, while banks are tightly regulated on the latter metric.   
 Table 1. Descriptive Statistics for Factors  
  

  Minimum  Maximum  Mean  Std. Deviation  Coefficient 
of 
Variation 
%  

Training 
ratio  

748  16187  6942  4176.20  60.16  

Operating 
Profit  

78701459.0  5464678241.0  1778578035.25  1127167487.71  63.38  

Credit 
Exposure  

3839128465.0  144429063000.0  46535702441.50  27833065588.74  59.81  

NPL  .004  4.220  1.10  .978  88.58  

Liquidity  3.0200  30.96  13.56  7.42  54.95  

Deposits 
to total 
assets  

67.89  90.27  85.64  3.87  4.51  

  
The model we used is shown below.    
Yit = β0+β1(L)it+β2(NPL)it+β3(CR)it+β4(D)it+β5(TR)it+ µit,     where  
Y – Operating Profit   
NPL – Non-Performing Loan    
CR – Credit exposure  
D - Deposits   
L – Liquidity  
TR – Training Ratio   
β0 - Constant parameter/Intercept  β1-5– Coefficient of independent variables   
µ - Error term  i – Cross Sectional  t – Time Period   
 Table 2 shows the correlation between the factors. 

   Training 
Expense  

Credit  
Exposure  

NPL  Liquidity  Deposits 
 to  
total 
assets  

Training 
Expense  

1          

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Credit 
Exposure  

0.212  1        

NPL  0.08  .160  1      

Liquidity  -0.368  -.228  -.505  1    

Deposits  to 
 total 
assets  

0.261  .177  .349  -.0.49  1  

  
Table 2 Correlation Matrix of Independent variables   
The correlations are low to moderate amongst the factors, with NPL-Liquidity and Liquidity-deposits 
to total assets being the highest in magnitude. Given these correlations, multicollinearity amongst 
factors appears to be low in our sample. Since the levels of correlation are below 0.7, the variance 
inflation values were not calculated for any variable in our analysis.    
3.1 Panel Data Regression Results   
Time Period 2007-2014  

 
Table 3 Analysis of Model in 2007-2014  
Adjusted R Square is 0.66789 i.e. 66.79% variation of dependent variable is explained by the 
independent variables.  
  

 
Independent variables such as liquidity, NPL, Deposits and training ratio are not statistically 
significant.    
Time Period 2015-2017  
 Table 5 Analysis of Model in 2015-2017  

 
Adjusted R Square is 0.92855 i.e. 92.86% variation of dependent variable is explained by the 
independent variables. 

  

Table 4 Coefficients in 2007 - 2014   
  

  
  

From the coefficients, credit exposure is the only independent variable, which is statistically significant. Other  

  

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Table 6 Coefficients in 2015-2017  
  
  

 
Significant. Other independent variables such as liquidity, Deposits and training ratio are not 
statistically significant.  Tables 7 and 8 summarize which hypotheses were supported in the two data 
sets. Note that a null being rejected implies support for the hypothesis.   
 Table 7 Analysis of Hypothesis for 2007-14  
 HYPOTHESIS  VARIABLES   NULL REJECTED?  

H1  Liquidity    

H2  Credit 
Exposure  

Rejected  

H3  NPL    
H4  Deposits     
H5  Training     

  
 Table 8 Analysis of Hypothesis for 2015-17  
 HYPOTHESIS  VARIABLES   NULL REJECTED?  

H1  Liquidity    

H2  Credit 
Exposure  

Rejected  

H3  NPL  Rejected  
H4  Deposits     
H5  Training     

3. Discussion   
Earlier work has shown that non-performing loans impact banks‟ financial performance in developing 
economies. For example, the return on assets of Nigerian banks was found to be affected by the default 
ratio (NPL / total loans) in (Kurawa & Garba, 2014). The return on assets and return on equity of 
Turkish banks was found to be affected by non-performing loans in (Kadioglu, Telceken, & Ocal, 2017). 
However, the impact of non-performing loans on the financial performance of Nepalese banks is 
uncertain. Two unpublished masters theses cited in (Gnawali, 2018) indicate that non-performing 

  
  

From the coefficients, credit exposure and NPL are the only independent variable, which are statistically  

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assets negatively impact commercial banks‟ financial performance in Nepal. In contrast, another study 
found no evidence of non-performing loans impacting financial performance (Subedi & Neupane, 
2013). Our study, using panel data analysis, agreed with the latter finding, and found that non-
performing loans did not impact the operating profit of the commercial banks in our sample during the 
2007-2014 pre-mandate period.  
This is in contrast to studies done on banks in other countries described above. One explanation for this 
finding may be found in the reputation for reliability that is part of the national character of Nepal. 
Nepalese workers, for example, have a well-deserved reputation for reliability and honesty, and are in 
demand around the world(Lokshin, Bontch Osmolovski, & Glinskaya, 2010; Yamanaka, 2000). 
Nepalese men also serve in military and security functions globally(Gould, 2000; Vines, 1999). Another 
reason for the finding in the 2007-2014 periodmay be the Debt Recovery Act passed in 2002 that 
required all Nepal banks to address the large percentage of non-performing loans in their portfolios 
(Shrestha, 2004).   
A third possible explanation is that depositors do not consider the credit risk exposure of the bank 
when making deposits (Agwu, 2018), especially if they have underlying faith in the regulatory 
framework of the banking system. For the 2007-2014 pre-mandate period, we found that credit 
exposure, indicating the overall loans made by the bank, did positively affect the operating profit. The 
paid-up capital (or common stock equity) lower limit till 2014 was Rs. 2 billion. During this period, 
banks that gave out more loans showed greater profitability, as per our findings.This follows directly 
from greater income derived from more loans, especially since non-performing loans were brought 
under control after 2002.    
From 2015 onwards, the paid-up capital requirement minimum was mandated to increase from Rs 2 
billion to Rs. 8 billion for Nepalese banks (Acharya, 2017; Sharma, 2015). Our analysis of the 2015-2017 
post-mandate time period shows that while credit exposure continued to affect operating profit 
positively, non-performing loans now had a significant negative effect. This indicates that credit risk 
had now become an issue. One possible explanation for this finding comes from the fact that in order 
to comply with the paid-up capital requirements, several banks had to merge. The increased paid-up 
capital also increased the amount of loans that banks could provide to borrowers. As confirmation, the 
credit exposure of every bank in our sample increased significantly starting from 2015 onwards (see 
data in Appendix 1). However, apart from size increases, mergers typically lead to rapid change in the 
collective competence and tacit knowledge of the new organization (Kreiner & Lee, 2000) and provide 
a “diminished resource base for organizational learning” (Lei & Hitt, 1995). Thus, a merger may lead to 
a loss of knowledge of local lending practices, and the credit profile of the local business community. 
Localized lending practices have been shown to give greater risk-adjusted yield, for example in (Carter, 
McNulty, & Verbrugge, 2004). The effect of distance between the bank and the borrowers was greater 
in lesser developed economies (Alessandrini, Croci, & Zazzaro, 2009).  Hence, a mandate to 
significantly increase the size of banks in a system may lead to deteriorated lending practices, at least 
in the short term, to the point where the financial performance of the banks can be significantly affected, 
as in our sample.    
The theoretical contribution of this work is an analysis of the effect of increasing minimum capital 
requirements rapidly and significantly on commercial banks in a developing economy. We find that the 

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expected consolidation of banks and increase in number of loans leads to greater credit risk assumed 
by the banks, even if nonperforming assets were not a factor earlier, as was the case in our sample. On 
the methodological side, we use panel data analysis to account for correlation within each bank across 
time.   
From a practical perspective, our work offers many guidelines. First, increases in capital adequacy 
requirements as a result of Basel 2 and 3 must be implemented gradually, so that lending strategies by 
bank management have time to adapt to the larger volume of loans. The situation in Nepal is likely to 
improve since prior to the significant paid-up capital increase, non-performing assets were not an issue 
in determining profitability.However, in other economies where non-performing assets are already 
negatively affecting financial performance, policies regarding an increase in paid-up capital and bank 
consolidation should offer an even more gradual time line than would have been appropriate for Nepal. 
Resources should also be provided to ensure that localized knowledge specific to lending practices is 
not lost in the bank consolidation that follows. In the case of Nepal, it is important for banks to review 
and tighten lending practices and for regulators to increase monitoring, going forward. Any asset 
bubbles created as a result of the increased lending also need to be closely monitored.    
4. Conclusion  
In this work we analyzed the results of a significant increase in paid-up capital or common stock equity 
requirements on the operating profit of a sample of commercial banks in Nepal. The data we used 
offered a unique opportunity to analyze this effect. Prior policies such as the Debt Recovery Act (2002) 
had reduced credit risk to lower levels. The only driver of profit in the 2007-2014 period that we found 
was the total amount of loans (credit exposure) issued by the bank.   
A very significant four-fold increase in paid-up capital led to widespread consolidation among 
banks and a significant increase in the number of loans being issued. A rapid increase in the number of 
loans issued led toa significant negative impact by non-performing loans on operating profit after the 
policy was implemented. Our recommendations include a cautionary approach to implementing similar 
banking requirements in other economies, coupled with adequate training to ensure that specialized 
local lending knowledge is not lost, and the newly formed larger banks do not become more distant 
from their borrowers.     
Our work has some limitations. First, we relied on publicly available data and measures in our model. 
Variables measuring actual lending practices were not available for this study. Second, we used a 
sample of 6 banks over 10 years. A larger sample may have yielded more significant results, though 
statisticians warn of overly large sample sizes where small effects are found to be statistically significant 
(Aguinis & Harden, 2009). 
For future research, we recommend that as Basel 3 is implemented, the performance of banks be 
studied using the increased information that will be available under Basel 3, especially with regard to 
liquidity requirements and management practices. A follow up study on the financial performance of 
Nepalese commercial banks over the next few years is also recommended, to measure if lending 
practices have stabilized and investigate if non-performing assets are still a significant factor.  
References  

Acharya, P. R. (2017, August 22). NRB issues circular on paid-up capital rules. The Himalayan Times.  

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10 | P a g e  

Aguinis, H., & Harden, E. E. (2009). Sample size rules of thumb: Routledge: New York.  

Agwu, E. (2018). Credit risk management: Implications on bank performance and lending growth. 
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Andesfa, D., & Masdupi, E. (2019). Effect of Financial Ratio on Profitability of Comercial Banks: A 
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Bace, E. (2016). Bank profitability: Liquidity, capital and asset quality. Journal of Risk Management 
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Baral, K. J. (2005). Health check-up of commercial banks in the framework of CAMEL: A case study of 
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Bhattarai, Y. R. (2017). Effect of Non-Performing Loan on the Profitability of Commercial Banks in 
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Appendix 1: Data Used in Study   
Table 1. Training Ratio, Operating Profit and Credit Exposure  
  

Year  Banks  Training 
Expense  

Total 
Staff  

Training 
Ratio  

Operating 
Profit  

Credit 
Exposure  

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https://thehimalayantimes.com/business/nrb-raises-bank-capital-requirement-to-rs-8-billion/
https://thehimalayantimes.com/business/nrb-raises-bank-capital-requirement-to-rs-8-billion/
https://thehimalayantimes.com/business/nrb-raises-bank-capital-requirement-to-rs-8-billion/
https://www.ey.com/Publication/vwLUAssets/Bank_of_Ghana_increases_the_minimum_capital_for_banks/$FILE/2017G_05385-171Gbl_Bank%20of%20Ghana%20increases%20the%20minimum%20capital%20for%20banks.pdf
https://www.ey.com/Publication/vwLUAssets/Bank_of_Ghana_increases_the_minimum_capital_for_banks/$FILE/2017G_05385-171Gbl_Bank%20of%20Ghana%20increases%20the%20minimum%20capital%20for%20banks.pdf
https://www.ey.com/Publication/vwLUAssets/Bank_of_Ghana_increases_the_minimum_capital_for_banks/$FILE/2017G_05385-171Gbl_Bank%20of%20Ghana%20increases%20the%20minimum%20capital%20for%20banks.pdf
https://www.ey.com/Publication/vwLUAssets/Bank_of_Ghana_increases_the_minimum_capital_for_banks/$FILE/2017G_05385-171Gbl_Bank%20of%20Ghana%20increases%20the%20minimum%20capital%20for%20banks.pdf
https://www.ey.com/Publication/vwLUAssets/Bank_of_Ghana_increases_the_minimum_capital_for_banks/$FILE/2017G_05385-171Gbl_Bank%20of%20Ghana%20increases%20the%20minimum%20capital%20for%20banks.pdf
https://www.ey.com/Publication/vwLUAssets/Bank_of_Ghana_increases_the_minimum_capital_for_banks/$FILE/2017G_05385-171Gbl_Bank%20of%20Ghana%20increases%20the%20minimum%20capital%20for%20banks.pdf


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2017  Bank1  17,092,478  1,187  14,400  4,729,782,804  144,429,063,000  
2016  Bank1  5,667,870  1,005  5,640  3,699,688,752  111,780,681,000  
2015  Bank1  3,543,367  969  3,657  2,545,848,091  89,584,665,000  
2014  Bank1  4,633,683  942  4,919  2,891,610,284  71,708,512,000  
2013  Bank1  6,400,375  910  7,033  2,145,299,600  60,622,076,000  
2012  Bank1  3,953,243  883  4,477  1,357,096,209  55,874,347,000  
2011  Bank1  4,380,696  877  4,995  1,783,662,202  52,029,461,000  
2010  Bank1  3,162,162  877  3,606  1,928,425,381  50,041,481,000  
2009  Bank1  4,162,374  766  5,434  1,310,854,953  42,975,192,000  
2008  Bank1  4,330,860  622  6,963  1,013,331,907  36,518,503,000  
2017  Bank2  5,194,596  495  10,494  1,985,842,742  50,192,675,000  
2016  Bank2  2,295,460  435  5,277  1,701,248,338  41,402,347,000  
2015  Bank2  2,457,880  433  5,676  1,827,019,810  41,171,574,000  
2014  Bank2  1,553,093  460  3,376  1,978,908,777  39,210,395,000  
2013  Bank2  2,801,446  454  6,171  1,862,481,497  34,321,758,000  
2012  Bank2  3,800,616  424  8,964  1,694,009,908  26,974,977,000  
2011  Bank2  5,695,246  429  13,276  1,707,316,216  23,401,460,000  
2010  Bank2  6,554,738  429  15,279  1,612,467,214  20,701,946,000  
2009  Bank2  4,800,913  392  12,247  1,506,108,858  18,758,432,000  
2008  Bank2  4,714,722  377  12,506  1,248,432,244  17,587,870,443  
2017  Bank3  10,360,820  848  12,218  5,464,678,241  105,621,541,000  
2016  Bank3  5,444,033  792  6,874  4,344,447,596  91,993,791,000  
2015  Bank3  11,428,342  706  16,187  3,235,924,937  78,774,890,000  
2014  Bank3  7,326,161  724  10,119  3,549,363,372  66,294,545,000  
2013  Bank3  8,737,232  742  11,775  3,464,952,933  57,191,503,224  
2012  Bank3  8,934,625  650  13,746  2,640,336,248  50,021,684,138  
2011  Bank3  7,467,211  657  11,366  2,081,190,251  44,468,804,901  
2010  Bank3  8,822,575  557  15,839  1,709,121,201  39,016,206,023  
2009  Bank3  5,681,241  505  11,250  1,570,204,646  32,500,502,288  
2008  Bank3  4,796,328  416  11,530  1,122,713,930  30,256,652,353  
2017  Bank4  7,153,814  601  11,903  1,998,089,550  58,025,513,277  
2016  Bank4  4,395,400  470  9,352  1,478,537,702  45,079,836,617  
2015  Bank4  4,060,981  415  9,785  925,693,203  30,651,616,831  
2014  Bank4  2,111,199  311  6,788  654,893,931  22,680,658,738  
2013  Bank4  678,950  231  2,939  458,938,092  15,989,208,846  
2012  Bank4  396,270  232  1,708  189,934,364  10,212,474,617  
2011  Bank4  238,503  197  1,211  241,935,219  7,200,551,543  
2010  Bank4  111,384  149  748  135,407,713  7,238,558,764  
2009  Bank4  299,978  120  2,500  78,701,459  5,845,136,972  
2008  Bank4  114,864  61  1,883  78,701,459  3,839,128,465  

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2017  Bank5  4,471,852  835  5,356  2,449,761,449  91,557,768,233  
2016  Bank5  5,441,894  857  6,350  2,297,520,673  79,796,981,782  
2015  Bank5  3,538,858  856  4,134  679,560,515  62,815,599,427  
2014  Bank5  3,504,526  835  4,197  982,579,118  55,329,593,123  
2013  Bank5  5,075,617  830  6,115  1,145,973,993  49,526,322,948  
2012  Bank5  3,659,884  793  4,615  1,057,056,360  42,584,895,177  
2011  Bank5  3,184,322  647  4,922  1,015,213,473  39,545,254,061  
2010  Bank5  3,176,851  577  5,506  579,231,460  36,049,314,954  
2009  Bank5  5,538,572  591  9,372  1,029,535,742  32,628,846,005  
2008  Bank5  4582364  584  7,847  954,953,506  26,006,889,740  
2017  Bank6  5,903,090  748  7,892  3,089,925,916  80,133,906,000  
2016  Bank6  2,169,371  739  2,936  2,666,102,674  71,827,799,000  
2015  Bank6  1,112,129  696  1,598  2,252,640,623  56,381,528,000  
2014  Bank6  735,113  696  1,056  2,338,065,548  50,599,467,000  
2013  Bank6  1,488,497  643  2,315  2,302,748,773  44,793,263,000  
2012  Bank6  1,938,143  625  3,101  1,538,338,190  37,792,502,000  
2011  Bank6  1,198,785  586  2,046  1,418,397,900  31,440,377,000  
2010  Bank6  1,824,053  568  3,211  1,272,090,189  27,499,899,000  
2009  Bank6  2,280,943  534  4,271  972,950,326  19,509,798,000  
2008  Bank6  2,495,154  449  5,557  718,833,853  24,131,922,000  

 
Table 2. Non-Performing Loans (NPL), Liquidity  

Yea
r  

Bank
s  

Capital  NPL  Total Assets 
(Size)  

Liquidit
y  

CAR  NumYear
s  

2017  Bank1  20,367,203,000.0
0  

0.83
0  

150,818,033,55
4  

10.50  13.0
2  

32  

2016  Bank1  18,182,544,000.0
0  

0.68
0  

129,782,705,31
4  

7.20  14.92  32  

2015  Bank1  11,754,294,000.0
0  

1.250  104,345,436,41
3  

12.00  11.9  32  

2014  Bank1  8,993,849,000.00  1.770  86,173,927,574  19.20  11.27  32  

2013  Bank1  7,813,057,000.00  1.910  73,152,154,761  16.00  11.49  32  

2012  Bank1  6,963,182,000.00  3.320  65,756,231,954  13.60  11.1  32  

2011  Bank1  6,324,627,000.00  0.94
0  

58,356,827,501  7.70  10.91  32  

2010  Bank1  5,651,045,000.00  0.62
0  

57,305,413,482  7.80  10.55  32  

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200
9  

Bank1  5,095,354,000.00  0.58
0  

53,010,803,126  10.30  11.24  32  

200
8  

Bank1  3,891,236,000.00  1.120  38,873,306,084  10.90  11.28  32  

2017  Bank2  11,975,101,000.00  0.190  77,408,597,693  19.71  21.0
8  

31  

2016  Bank2  7,779,408,000.00  0.32
0  

65,185,732,479  7.98  16.3
8  

31  

2015  Bank2  6,111,788,000.00  0.34
0  

64,926,805,120  24.03  13.1  31  

2014  Bank2  5,333,516,000.00  0.48
0  

53,324,102,172  21.18  12.27  31  

2013  Bank2  4,828,551,000.00  0.770  45,631,100,342  16.43  12.54  31  

2012  Bank2  4,295,167,000.00  0.78
0  

41,677,052,360  22.40  13.93  31  

2011  Bank2  3,835,592,000.00  0.62
0  

43,810,519,664  6.10  14.22  31  

2010  Bank2  3,498,973,000.00  0.610  40,213,319,926  6.74  14.51  31  

200
9  

Bank2  3,190,367,000.00  0.66
0  

40,587,468,00
9  

8.18  14.7  31  

200
8  

Bank2  2,630,900,636.00  0.92
0  

33,335,788,326  5.84  13.15  31  

2017  Bank3  14,752,639,000.0
0  

0.790  140,332,060,18
2  

10.02  12.42  36  

2016  Bank3  12,203,615,000.0
0  

1.140  127,300,195,37
3  

6.77  11.73  36  

2015  Bank3  10,154,456,184.00  1.830  115,985,701,411  14.15  11.57  36  

2014  Bank3  8,259,701,304.00  2.230  87,274,545,920  11.32  11.24  36  

2013  Bank3  7,364,514,686.00  2.130  73,343,593,148  9.32  11.59  36  

2012  Bank3  6,086,741,224.00  2.330  63,250,488,220  8.60  11.01  36  

2011  Bank3  5,173,399,192.00  1.770  58,099,619,842  4.90  10.5
8  

36  

2010  Bank3  4,390,228,607.00  1.480  52,079,725,697  3.02  10.5  36  

200
9  

Bank3  3,727,082,787.00  0.80
0  

43,867,397,504  9.03  10.7  36  

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200
8  

Bank3  2,968,913,131.00  0.740  37,132,759,149  8.37  11.1  36  

2017  Bank4  9,870,186,114  0.010  69,995,901,442  26.08  15.57  14  

2016  Bank4  6,039,446,132  0.019  55,964,557,699  24.24  12.36  14  

2015  Bank4  3,734,498,766  0.07
0  

40,301,197,377  22.32  11.08  14  

2014  Bank4  3,069,210,208  0.017  29,376,985,784  26.68  12.54  14  

2013  Bank4  2,565,034,704  0.027  21,976,539,752  30.96  14.87  14  

2012  Bank4  2,211,515,612  0.479  13,722,466,141  30.24  20.7
4  

14  

2011  Bank4  2,173,184,816  0.00
4  

9,363,380,873  26.57  28.4
1  

14  

2010  Bank4  932,609,659  0.08
0  

7,238,558,764  28.19  16.51  14  

200
9  

Bank4  909,860,064  0.175  5,845,136,972  11.97  19.0
2  

14  

200
8  

Bank4  456,006,865  1.513  3,839,128,465  17.61  17.73  14  

2017  Bank5  12,613,817,027  0.85
0  

107,255,479,96
6  

  12.15  25  

2016  Bank5  9,815,198,969  1.230  99,863,008,08
0  

6.27  10.8
4  

25  

2015  Bank5  8,041,967,083  3.220  82,801,550,614  8.32  11.14  25  

2014  Bank5  7,155,579,476  1.960  73,589,845,698  8.72  11.23  25  

2013  Bank5  6,414,437,452  2.89
0  

61,113,501,223  6.08  11.55  25  

2012  Bank5  5,283,900,074  2.09
0  

54,364,427,882  8.72  11.02  25  

2011  Bank5  4,711,243,495  4.220  46,736,203,884  5.75  10.6
8  

25  

2010  Bank5  4,218,361,500  3.520  42,717,124,613  6.76  10.72  25  

200
9  

Bank5  3,845,211,300  2.160  39,330,131,823  6.76  11.02  25  

200
8  

Bank5  3,253,515,981  2.360  36175531637  5.13  12.42  25  

2017  Bank6  13,063,702,000  0.250  116,510,445,575  16.52  14.69  24  

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2016  Bank6  10,094,804,000  0.38
0  

113,885,046,40
2  

16.61  12.66  24  

2015  Bank6  8,457,023,000  0.66
0  

99,167,293,661  24.27  13.33  24  

2014  Bank6  6,422,257,000  0.970  70,445,082,845  16.91  11.31  24  

2013  Bank6  5,777,682,000  0.62
0  

65,741,150,457  15.91  11.59  24  

2012  Bank6  4,574,753,000  0.84
0  

55,813,129,057  17.22  11.02  24  

2011  Bank6  3,605,841,000  0.34
0  

46,236,212,262  9.55  10.4
3  

24  

2010  Bank6  3,257,142,000  0.160  41,382,760,711  15.53  10.77  24  

200
9  

Bank6  2,348,390,000  0.48
0  

36,916,848,654  14.26  11.34  24  

200
8  

Bank6  2,703,870,000  0.68
0  

27,149,342,884  4.56  11.44  24  

  
  
  

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