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Impact of Credit Risk Management on
Profitability of Nepalese Commercial
Banks
Reema Tuladhar
Master of Research
Western Sydney University
2017
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Dedication
Firstly, I would like to dedicate this thesis to my father, the Late Ashok Ratna
Tuladhar, who always believed in me and supported me throughout his life for all my
decisions.
I also dedicate this thesis to my mother, Laxmi Tara Tuladhar, whose love, affection,
belief, encouragement and prayers made me able to achieve success and honor.
To my sister and her husband, Rinu Tuladhar and Kushal Raj Tuladhar, who have
always supported me in every way as a friend and a mentor.
To my loving husband, Ritesh Tamrakar and his family. His hours of hard work and
the family’s support enabled me to spend time on my research and writing to
complete this thesis.
Lastly, I would like to thank my brother, Anish Ratna Tuladhar, who has been a
constant source of encouragement.
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Acknowledgement
I would like to thank and express my humble appreciation to all those who helped me
with the completion of this thesis. I am grateful to my supervisor, Associate
Professor Maria Estela Varua, whose expertise, understanding, generous guidance
and support made it possible for me to write on a topic which is of great interest to
me. It was an overwhelming experience working with her.
I am highly indebted to Mr. Andy Nguyen and Mr. Md Abdullah Al-Masum for their
kind words and suggestions and in providing me with the materials and links that I
could not possibly have discovered on my own.
Also, I would like to thank my friend, Mr. Mason Prasad for his immense interest in
my topic of research and in helping me resolve some of my confusions.
Finally, I would also like to express my gratitude to all my lecturers who put their
faith in me and motivated me to do better.
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Statement of Authentication
I hereby declare that the work presented in my thesis is original except as acknowledged in the text to the best of my knowledge. The material, either in full or in part, has not been submitted earlier for a degree at this or any other academic institution.
Reema Tuladhar
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Table of Contents Chapter 1 Introduction ................................................................................................. 1
1.0 Background ................................................................................................... 1 1.1 Overview of commercial banks in Nepal ...................................................... 4 1.2 Significance of the study ............................................................................... 6 1.3 Research Gap and Contribution .................................................................... 7 1.4 Objectives ...................................................................................................... 8 1.5 Limitations of the study ................................................................................. 8 1.6 Organization of chapters ............................................................................. 10
Chapter 2 Theoretical Framework ............................................................................. 11 2.0 Risk management and bank performance ................................................... 11 2.1 Review of Empirical studies ....................................................................... 12 2.2 Profitability of Commercial Bank and Credit Risk Management ............... 21
2.2.1 Profitability of commercial bank ...................................................... 21 2.2.2 Bank profitability indicators ............................................................. 22 2.2.3 Bank’s risk management .................................................................. 23
Chapter 3 Method and Data Description .................................................................... 37 3.0 Model Specification……………………………………………………….37 3.1 Method of Analysis………………………………………………………. 39 3.1.1 Pooled Regression Analysis………………………………………...39 3.1.2 Panel Data Analysis………………………………………………...39 3.2 Data and Data Description…………………………………………….......41 3.2.1 Data and sources of data……………………………………………41 3.2.2 Data collection…………...…………………………………………42 3.2.3 Time horizon……………...…………………………………….…. 43 3.3 Descriptive Statistics……………...……………………………………… 43 Chapter 4 Discussion of Results……………………………………...……………..45 4.0 Introduction……………………………………………………………......45 4.1 Stationarity Test………………….….………………………………….…45 4.2 Pooled Regression Analysis…………………….….……………………...46 4.2.1 Determinants of ROA………………………….….…………….….47 4.2.2 Determinants of ROE………………………….….………………...52 4.3 Diagnostic Tests……………….……………………………………….….56 4.4 Model Fit………………………………………….……………………….56 4.5 Results of Panel Data Analysis…………………………………………....57 4.5.1 Panel data analysis on ROA………………………………….….….57 4.5.2 Panel data analysis on ROE………………………………………...60 Chapter 5 Conclusions and Recommendations……………………………………..63 5.0 Summary and Conclusion………………………………………………....63 5.1 Recommendation………………………………………………………….65 5.2 Contributions……………………………………………………………...66 5.3 Areas for Further Research………………………………………………..66 References…………………………………………………………………….….….68 Appendices…………………………………………………………………….…….75 Appendix A: Number of Financial Institutions, per sector and total from 1985-2017………………………………………………………………………………....75 Appendix B: List of Banks and Financial Institutions as of Mid-January, 2017 (Licensed by NRB)……………………………….....................................................75
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List of Tables
Table 1: Summary of Selected Empirical Studies………………………………….12
Table 2: Summary of Expected Relationship with Bank Profitability……………...38
Table 3: Descriptive Statistics………………………………………………………44
Table 4: Results of pooled regression analysis……………………………………..46
Table 5: Post Estimation Diagnostic Tests ………………………………………...56
Table 6: Results of panel data analysis on ROA……………………………………58
Table 7: Results of panel data analysis on ROE……………………………………61
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List of Figures
Figure 1: Number of Financial Institutions, per sector and total from 1985-2017…..6
Figure 2: The risk management process from ISO 31000:2009……………………26
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List of abbreviations
AIC Akaike’s Information Criterion
AQ Asset Quality
BIC Bayesian Information Criterion
BS Bank Size
CAR Capital Adequacy Ratio
CR Coverage Ratio
CRR Cash Reserve Ratio
FBM Female Board Member
GDP Gross Domestic Product
GFC Global Financial Crisis
LER Leverage Ratio
LR Liquidity Ratio
NGO Non-Government Organizations
NPL Non-Performing Loan
NPLR Non-Performing Loan Ratio
NRB Nepal Rastra Bank
P-value Probability Value
Prob. Probability
ROA Return on Asset
ROCE Return on Capital Employed
ROE Return on Equity
STATA Statistical data analysis software
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Abstract
Credit risk management in the banking sector is important not only because of the
Global Financial Crisis (GFC) experienced in recent years but also due to its greater
impact on bank’s financial performance, growth and survival. Credit loans is one of
the key sources of income of commercial banks, therefore managing the risk related
to credit greatly impacts the bank’s profitability.
This study investigates the impact of credit risk management on the profitability of
Nepalese commercial banks. Data from 28 commercial banks for the period 2011 to
2015 have been collected and analyzed using pooled regression analysis and panel
data analysis. In the model specification, return on asset (ROA) and return on equity
(ROE) were used as bank profitability indicators while capital adequacy ratio (CAR),
liquidity ratio (LR), bank size (BS), asset quality (AQ), leverage ratio (LER), non-
performing loan ratio (NPLR), cash reserve ratio (CRR), coverage ratio (CR), and
the number of female board member (FBM) were used as indicators of credit risk
management.
The findings indicate that credit risk management has significant impact on the
profitability of Nepalese commercial banks. Results show that coverage ratio, capital
adequacy ratio, and bank size have a positive impact on bank performance. On the
other hand, leverage ratio, non-performing loan ratio and female board member are
found to have a negative impact on bank performance. However, liquidity ratio, asset
quality, and cash reserve ratio turned out to be not significant variables in
determining bank’s performance. The study thus recommends an effective credit risk
management for commercial banks of Nepal based that maintains an optimum level
of capital adequacy ratio, controls and monitors non-performing loan, enhances
coverage ratio, balances leverage ratio, motivates female board members, and
increases bank size to enhance financial performance.
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Chapter 1 Introduction This chapter provides a general understanding of the research area. It starts with the overview of commercial banks in Nepal followed by the importance of the study and then by the research objetives. The chapter ends with the short description of the remaining chapters.
1.0 Background
The financial sector performs a significant role in a country’s economic development
(Das & Ghosh, 2007). Banks are very important component of a country’s financial
sector as they have many branches and subsidiaries operating domestically and
internationally. Specifically, commercial banks play a vital role in the allocation of
resources in most countries. They have the function of channelling funds from
depositors to investors continuously. However, this is only feasible if they are able to
generate the required income to cover the cost of operations. In short, commercial
banks need to be sustainable in order to perform their intermediation function.
In Nepal, a major proportion of the financial sector’s total assets are held by
commercial banks. Like banks in other countries, the major function of commercial
banks is to extend credit (Timsina, n. d.), and it is with this function that banks are
able to increase their profits. However, it is important to note that banks differ from
each other in various ways such as in their objectives, products, as well as to the
services they provide. Further, in their day to day activities, banks face number of
risks. Bessis (2011) categorized some of the major risks that banks face as: credit
risk, liquidity risk, interest rate risk, mismatch risk, market liquidity risk, market risk,
and foreign exchange risk. These risks will be briefly discussed in the later section.
Amongst these many risks faced by banks, credit risk plays a significant role on its
financial performance as a large chunk of banks income is earned from the loans
provided to their customers in the form of interest income (Kolapo, Ayeni, & Oke,
2012). The Asian financial crisis, which was initiated in Thailand in the middle of
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1997, made the affected countries experience a significant depreciation in their
currencies, depressed equity prices, and several financial and economic dislocations.
Currency markets in emerging Asian economies recorded huge falls ranging from
34% in the Philippines and 49% in Thailand, while the equity markets also declined
abruptly from 29% in Thailand to 50% in South Korea during the second half of
1997 (Shabbir & Rehman, 2016). For example, economic growth in the region,
which stood in the 6% to 8% neighbourhood prior to the crisis, fell into recession a
year after the crisis hit the East Asian.
The East Asia Financial crisis was indeed remarkable in many ways as it hit the most
rapidly growing economies in the world (Radelet & Sachs, 1998). It is the least
anticipated financial crisis in many years yet the sharpest to hit the developing
nations since the 1982 debt crisis (Radelet & Sachs, 1998). When the financial crisis
of the 1980’s and 1990’s became worldwide, new risk management banking
techniques were introduced (Poudel, 2012) and were the focus points during global
financial crisis (GFC) (Bessis, 2011). Janet L. Yellen for example wrote in Global
Economic Viewpoint (2007) that there are several views on why GFC happens. The
first view is that it was a classic “liquidity” crisis much like a banking panic, where
depositors’ fears about insolvency, well-grounded or not, become a self-fulfilling
prophecy as their withdrawals bring the bank to ruin. The second view focuses more
on the vulnerabilities that existed in the affected nations’ economic fundamentals,
which threatened to lead to solvency difficulties. One such vulnerability was the
pursuit of risky lending practices by financial intermediaries. In part, this was due to
problems with the quality of supervision and regulation of the financial sector. But
the problem also lay with the long tradition of so-called “relationship lending.”
Rather than basing lending decisions on sound information about the fundamental
economic value of specific investment projects, banks and other financial
intermediaries based them on personal, business or governmental connections. As a
result, bank loan portfolios became particularly risky. And these risks became grim
realities when economic conditions slowed in these countries in early 1997.
Even after the recent global economic crisis (GFC) credit risk has remained one of
the topical issues of the financial system having caught the attention of both scholars
and industry professionals (Kurawa & Garba, 2014). Credit risk (also known as
default risk, performance risk, or counterparty risk) is defined as the possibility that a
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contractual party will fail to meet its obligations in accordance with the agreed terms
(Brown & Moles, 2012). It is a risk of financial loss whereby money invested by
banks to their customers in the form of loans are not repaid back (Giesecke, 2004).
Credit risk is a significant risk faced by banks by the nature of their activity and the
success of banks in terms of financial performance depends on efficient management
of it than any other type of risk that bank faces (Giesecke, 2004).
Likewise, Burton, Nesiba, and Brown (2015) define credit risk as the probability of
debtor not paying the principal and/or the interest due on an outstanding debt. As
stated earlier, loan interest is one of the major sources of income in commercial
banks but also the primary source of credit risk to the banks (Bhattarai, 2014). When
a bank issues loans to their customers, they expect them to repay the principal and
interest amount on an agreed time. However, if both the principal and interest
payment are received on an agreed time with agreed terms, it is known as performing
loan (Kolapo et al., 2012). If the loan payment is not received on time, it is known as
a non-performing loan (NPL) (Kolapo et al., 2012). NPL is normally classified into
three categories namely: a substandard loan, doubtful loan and loss loan (Kolapo et
al., 2012). If the loan is not repaid more than 90 days from its due date is known as
substandard loan and if it is not repaid more than 180 days from loan due date is
known as a doubtful loan. If the loan is not repaid more than 360 days from its due
date is known as loss loan (International, 2005). When the loss loan category
accumulates to a large amount, it is a huge loss to the bank (Gestel & Baesens,
2009).
Kithinji (2010) identifies major sources of credit risk as limited institutional capacity,
inappropriate credit policies, volatile interest rates, inappropriate laws, low capitals
and liquidity levels, directed lending, massive licencing of banks, poor loan
underwriting, poor management, negligence in credit assessment, poor lending
practices, government interference and inadequate supervision by the central bank. In
order to minimize credit risk arising from these sources, Laker (2007) recommends
the necessity for the financial system to: (i) have well-capitalized banks, (ii) provide
service to a wide range of customers, (iii) share information regarding borrowers,
(iv) have a stabilise interest rate, (v) increase bank deposits and credit to borrowers,
and (vi) reduce non-performance loan.
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In order to mitigate risk and to avoid financial and economic difficulties, risk
management is very important and is essential for long-term success of banks. The
effective management of credit risk not only enhance profitability and viability of
banks but contributes to the systemic stability and efficient allocation of capital in the
economy (Psillaki, Tsolas, & Margaritis, 2010). This is very important to banks as it
is an integral part of the banks’ loan process. Credit risk management can be defined
as identification, measurement, monitoring and control of credit risk arising from the
possibility of default in loan payment (Coyle, 2000; James, 1966). While the banks
do not have a clear signal as to what proportion of the borrowers will likely default,
the uncertainty results to the variation in profitability among banks as well. The main
aim of managing credit risk is to maximize bank’s return adjusted for the risk while
keeping an acceptable level of exposure (Ndoka & Islami, 2016). Generally, senior
management creates and develops policies and procedures for loan administration
and gets the approval from the board of directors and are responsible for
implementing it (Ndoka & Islami, 2016). Ideally, the senior management should
ensure that implementation would involve clear communication of policies and
procedures to all staff related to loan approval process in the hierarchy (Ndoka &
Islami, 2016). Moreover, effective credit risk management is then verified by an
internal risk control and audit that monitors credit discipline, loan policies, approval
policies, portfolio level risk and facility risk exposure (Gestel & Baesens, 2009).
Hence, a sound credit risk management framework is important for efficient
management of credit risks in enhancing profitability and its survival.
To summarize, the financial system of a nation holds critical importance on banks’
credit risk and its management. A strong credit risk management avoids significant
drawbacks and increase banks financial performance. Good financial performance
rewards shareholders for their investments. This will then encourage additional
investment and bring economic growth. In contrast, poor banking performance can
lead to banking failure and crisis which may have a negative consequence on
economic growth.
1.1 Overview of commercial banks in Nepal
The banking sector in Nepal first started with the establishment of Nepal Bank
Limited as a first commercial bank in 1937 (Gajurel & Pradhan, 2012). This was a
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joint-venture between the government sector with 51 percent share and the private
sector with 49 percent share (Acharya, 2003). The establishment of Nepal Rastra
Bank as a central bank of Nepal in 1956 gave new momentum to development and
growth of the Nepalese financial system (Gajurel & Pradhan, 2012). Within a
decade, the number of major banking institutions were established in the public
sector such as Agriculture Development Bank, Nepal Industrial Development
Corporation, Employees Provident Fund Corporation, Rastriya Banijya Bank, Credit
Guarantee Corporation, Nepal Insurance Corporation, and Securities Marketing
Centre (Acharya, 2003). The expansion of the sector enabled the start of major
financial activities in the country such as issuance of shares, provident fund,
insurance etc.
The financial history of Nepal indicate that the 1980s saw a major structural change
in financial sector policies, regulations and institutional developments (Bank, 2015a).
For example, in the beginning of the 1980s, there were only two commercial banks
and two development banks in the country (Bank, 2009) but after the expansion of
the liberalization of the financial sector by the government, many private banks,
joint-venture banks and non-banking financial institutions were established in Nepal
(Gajurel & Pradhan, 2012). Thus, by the end of mid-January 2017, altogether 219
banks and non-bank financial institutions were licenced by Nepal Rastra Bank to
operate in the country. Out of these 219, 28 were “A” class commercial banks, 57
were “B” class development banks, and 36 were “C” class finance companies, 48
were “D” class micro-finance financial institutions, 15 saving and credit
cooperatives, 25 non-government organizations (NGOs) and 10 other institutions.
Appendix A presents the historical data of the development of the financial sector in
Nepal. These data are also graphically presented in Figure 1.
Between the years 1970 and 1989, the central bank of Nepal focused on branch
expansion of commercial banks in rural areas while private commercial banks were
more focused on bank expansion in urban area rather than rural areas to avoid higher
cost of operation (Acharya, 2003). The Nepalese banking industry has changed
significantly as a result of liberalization, globalization, deregulation, and
advancement in information technology. The financial sector liberalization led to
entry of new banks in the Nepalese financial market while globalization generated
competitiveness among individual banks. In addition, the deregulation widened the
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scope of activities and defined the banking activities while the advancement in
information technology resulted in the adoption of advanced ways and tools in
performing the various banking activities (Gajurel & Pradhan, 2012).
Figure 1: Number of Financial Institutions, per sector and total from 1985-2017
1.2 Significance of the study
The degree of possible risk in the banking sector is of major concern to the various
stakeholders including the top management who operates the banking activities,
depositors whose funds are being used and regulatory bodies who are responsible for
the protection of banking system. The commercial banks operating in Nepal have
faced difficulties over the past years for multiple reasons. The major reasons
identified were relaxed credit standards and poor portfolio risk management
(Bhattarai, 2014). Most of the commercial banks in Nepal are evidenced to have
approve loans without proper examinations which may lead to increase in a number
of loan defaults and non-performing loans (Bhattarai, 2014). In addition, it is
contended that the existing credit risk management procedures are inadequate to
handle the existing credit risk challenges in Nepal (Bhattarai, 2014). Nevertheless, in
recent years, the central bank of Nepal has introduced policies to improve bank
0
50
100
150
200
250
300
1985 1990 1995 2000 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017Commercial Banks Development Banks
Finance Companies Micro Finance Financial Institutions
Saving and Credit Co-operatives Non-Government Organizations
Total
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performance and has taken measures to minimize the negative effect of lending and
this is done by increasing capital requirement for banks and facilitating the merger of
financial institutions to build resilient and robust financial system (Bank, 2012-
2016). In a country where the financial sector is dominated by the commercial banks,
any failure in the sector has an immense implication on the economic growth of the
country. This is due to the fact that any bankruptcy that could happen in the sector
has a contagious effect that can lead to bank runs, crisis and bring overall financial
crisis and economic tribulations (Ongore & Kusa, 2013). Thus, there is need for the
Nepalese banking industry to ensure that effective strategies are being implemented
to minimize risk as well as maximize financial and market returns.
1.3 Research Gap and Contribution
As discussed earlier, Nepalese commercial banks have faced difficulties over the past
years mostly due to relaxed credit standard and poor portfolio risk management.
There are policies put in placements to improve bank performance as well as
measures to minimize the negative effect of lending. In order to meet the increased
capital requirement set by the central bank of Nepal, there is a tendency among
commercial banks to go into mergers ("Kumari, NCC, four dev banks to merge,"
2016, January 13), which may gradually minimize the level of competition amongst
banks. It is envisioned to result in the avoidance of inappropriate credit approval
processes blamed to be due to competition among banks.
A thorough review of the literature indicates that only a very few study has been
undertaken on bank risk management and bank performance in the context of
Nepalese commercial banks. One of the study was done by Yuga Raj Bhattarai, a
Ph.D. scholar at Tribhuvan University, Nepal in the year 2014 entitled “Effect of
credit risk on the performance of Nepalese commercial banks” and by Ravi Prakash
Sharma Poudel, a Ph.D. student of business school at University of New England,
Australia with a thesis entitled “The impact of credit risk management on financial
performance of commercial banks in Nepal” in the year 2012. Bhattarai (2014) has
considered only 14 commercial banks of Nepal and considered only three factors. He
recommended that a further study be undertaken taking into account other factors
which he also identified. Similarly, Poudel (2012) has considered very few variables
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and also recommended that further research incorporating more explanatory
variables.
Following the suggestions of Bhattarai (2014) and Poudel (2012) this study will
incorporate more explanatory variables. The identification of additional variables are
the outcomes of a thorough literature review (presented in chapter 2). Moreover, the
study will include a larger sample of commercial banks as compared to previous
studies. In additional, the research includes data after the implementation of policies
regarding the credit standard of commercial banks of Nepal.
In spite of the overall good performance of Nepalese banks, there are still a number
of banks declaring losses. Further, the recent experiences during GFC in the
developed countries and the bailouts that then followed are a motivation to carry out
this study as a precautionary and mitigating measure. It is important to understand
the performance of banks and its determinants for future development of the sector
and the economy.
1.4 Objectives
The overall purpose of this research is to investigate how credit risk management
will impact on the profitability of commercial banks of Nepal. Thus, the general
objective of this study is to assess the role of risk management on financial
performance of commercial bank in Nepal. Specifically;
(1) To identify the indications of financial performance of Nepalese commercial
banks.
(2) To ascertain the relationship between the determinants of risk management
and indications of financial performance.
1.5 Limitations of the study
The scope and limitations of this study include:
a. Limitations in research area
Financial sector of Nepal includes commercial banks, development banks, finance
companies, micro-finance financial institutions, savings and credit cooperatives and
non-government organizations which are all licensed by Nepal Rastra Bank (Central
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bank of Nepal). However, this research is limited only to the study of commercial
banks of Nepal and ignores the other types of financial institutions. The reason as to
why commercial banks are chosen for this study is that they have guidelines to
follow and are monitored regularly by the central bank of Nepal. They also hold the
most part of the assets of the sector.
b. Time period
This research includes data on Nepalese commercial banks for the period 2011 to
2015 which is 5 years’ financial period. The time frame includes the data on banks’
performance after the implementation of policies that is geared towards the
improvement of the standard of Nepalese commercial banks.
c. Data
A few of the commercial banks, specifically NIC Asia Bank Limited, Bank of
Kathmandu Lumbini Limited, Global IME Bank Limited, and Prabhu Bank Limited
have already been merged. Hence, the data for these four banks used in the study as
merged data. Therefore, the total number of commercial banks in Nepal as of 2017
rather than in 2011 is considered due to the unavailability of data as separate banks
for the banks which have merged.
Likewise, few banks namely: Civil Bank Limited, Janata Bank Nepal Limited, and
Mega Bank Nepal Limited were established only in 2010, while Century Bank
Limited was established on 2011. Thus a few data for these banks have zero values
for some of the variables.
d. Motivation
It was straightforward for me to decide my research area of study to be finance. I
have been a student of finance in both my undergraduate and postgraduate studies.
However, the decision on choosing the appropriate topic in this area was not very
easy for me. Nevertheless, six years of my work experience at the loan department in
a commercial bank of Nepal attracted me towards choosing this area. Thus, I decided
to finalize the topic that combines my knowledge as well as my work experience.
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Furthermore, this topic is of importance to the Nepalese banking sector as there are
only a few research conducted in the context of Nepal.
1.6 Organization of chapters
The remainder of the thesis is organized as follows:
Chapter 2: Theoretical Framework
In this chapter, the theoretical framework of risk management and bank performance
are first presented followed by the review of the various empirical studies and
researches in Nepal or other countries. The succeeding section of this chapter then
presents the theoretical literature pertinent to the research to better understand the
factors that may influence banks’ financial performance.
Chapter 3: Method and Data Description
This chapter discuss the models and methods used to ascertain the relationship
between bank management and the accounting performance of commercial banks of
Nepal. The data and data description are also presented and discussed. The chapter
ends with the descriptive statistics of the various variables included in the study.
Chapter 4: Discussion of Results
This chapter presents the empirical findings of the study. The chapter starts with a
short introduction, followed by the stationarity tests results and then the discussion of
the pooled regression analysis and ends with results of the panel data analysis.
Chapter 5: Conclusion and recommendation
This chapter presents the conclusions of the study. It starts with a summary of the
findings and then provides recommendations as well as areas of further research at
the end of the chapter.
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Chapter 2 Theoretical Framework In this chapter, the theoretical framework of risk management and bank performance are first presented followed by the review of the various empirical studies and researches in Nepal or other countries. The succeeding section of this chapter then presents the theoretical literature pertinent to the research to better understand the factors that may influence banks’ financial performance.
2.0 Risk management and bank performance
Banks are established with various objectives. These could either be to influence
banks’ performance, enhancing profitability or increasing shareholders return, and
are often accomplished at the cost of increased risk. Risk-taking is an inherent
component of banking and achieving either of these objectives is a reward for
successfully managing risk. Soyemi, Ogunleye, and Ashogbon (2014) observed that
the greater the risk, the higher the return, hence, the business must strike a trade-off
between the two. In addition, risk management in banking impacts significantly on
economic growth of the nation and business development. Inefficient management of
risk by banks may not only prevent banks from achieving its objectives but can also
lead to bankruptcy. Therefore, banking activities are always involved with various
kinds of risk. Risks are considered warranted when they are understandable,
measurable, controllable and within a banks capacity to willingly resist its adverse
effect (NRB, 2010). Sound risk management enables bank management to take risks
knowingly, reduce risks when appropriate, and prepare for the risk that cannot be
predicted (NRB, 2010). If successfully carried out it benefits the banks by increasing
efficiency and profitability, attracting more customers and staying in line with the
guidelines (Adeusi, Akeke, Adebisi, & Oladunjoye, 2014). Therefore, efficient
management of risk by banks have influence on their accounting performance.
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2.1 Review of Empirical studies
A thorough review of literature has been carried out to examine the impact of risk
management on financial performance in several dimensions. As this study is
focused on credit risk management in banking, the review mainly concentrated on
the studies related to the analyses of the impact of credit risk management on bank’s
performance in the context of various countries. Table 1 presents a summary of the
empirical studies undertaken by authors who have investigated the relationship
between credit risk management and bank performance along with the variables and
methods used by them.
Table 1: Summary of Selected Empirical Studies
S. No.
Author/s (year)
Variables Method Independent variables (x) Dependent variables (y)
1. Abdelrahim, K. E. (2013)
Capital Adequacy Ratio Effectiveness of Credit Risk Management
Regression Analysis and Questionnaire
Assets Quality Management Soundness Earnings of Credit Facility Liquidity Bank Size
2. Adeusi, S.O., Akeke, N. I., Adebisi, O. S. and Oladunjoye, O. (2014)
Cost of Bad and Doubt Loans Return on Asset Panel Data Estimation Technique
Non-Performing Loan Return on Equity Liquidity Return on Capital
Employed (ROCE) Equity-Total Asset Ratio Equity-Loan Ratio Debt-Equity Ratio
3. Aduda, J. and Gitonga, J. (2011)
Non-Performing Loan Ratio Return on Equity Regression Analysis and Questionnaire
4. Afriyie, H. O. and Akotey, J. O. (2012)
Non-Performing Loan Return on Equity Panel Data Analysis Capital Adequacy Ratio
5. Alshatti, A. S. (2015)
Capital Adequacy Ratio Return on Equity Panel Regression Model
Credit Interests/Credit Facilities Return on Asset Provision for Facilities Loss/Net Facilities Leverage Ratio Level of Non-Performing Loans
6. Berrios, M. R. (2013)
Insider Variable for Bank Net Interest Margin Regression Model Less Prudence Variable for
Bank Return on Asset
Compensation Variable for Bank
Return on Equity
Tenure Variable for Bank Cash flow to Assets
13
Loans to Deposits Total Debt to Equity
7.
Bhattarai, Y. R. (2014)
Capital Adequacy Ratio Return on Asset Pooled Data Regression Model
Cash Reserve Ratio Bank Size Non-Performing Loan Ratio Cost per Loan Assets
8. Kaaya, I. and Pastory, D. (2013)
Loan Loss to Gross Loan Return on Asset Regression Model Non-Performing Loan
Loan Loss to Net Loan Impaired Loan to Gross Loan
9. Kithinji, A. M. (2010)
Amount of Credit Average Profit Regression Model Non-Performing Loan
10. Kurawa, J. M. and Garba, S. (2014)
Default Rate Ratio Return on Asset Random-Effect Generalized Least Square Regression
Cost per Loan Asset Ratio Capital Adequacy Ratio
11. Li, F. and Zou, Y. (2014)
Capital Adequacy Ratio Return on Asset Multivariate Regression Analysis
Non-Performing Loan Ratio Return on Equity
12. Nawaz, M., Munir, S., Siddiqui, S. A., Tahseen-Ul- Ahad, Afzal, F., Asif, M. and Ateeq, M. (2012)
Non-Performing Loan/ Loan & Advances
Return on Asset Correlation and Multiple Regression models
Loan & Advances/ Total Deposit
13. Ndoka, S. and Islami, M. (2016)
Non-Performing Loan Ratio Return on Asset Multiple Regression Model
Capital Adequacy Ratio Return on Equity
14. Ogboi, C. and Unuafe, O. K. (2013)
Loan Loss Provision Return on Asset Panel Data Estimation Technique
Loan and Advances Non-Performing Loan Capital Adequacy Ratio Liquidity Ratio
15. Ejoh, N. O., Okpa, I. B. and Egbe, A. A. (2014)
Liquidity Profitability Questionnaire
16. Poudel, R. P. S. (2012)
Default Rate Return on Asset Correlation and Regression
Cost per Loan Assets Capital Adequacy Ratio
17. Zubairi, H. J. and Ahson, S. (2014)
Advances and Investments / Total Assets
Return on Asset Regression Analysis
Number of Branches Return on Equity GDP Growth Rate Interest Rates (T- Bill Rates)
14
Abdelrahim (2013) in an attempt to investigate the determinants, challenges and
drivers of developing the effectiveness of credit risk management of Saudi Banks’
have used descriptive and analytical methods. In the said study, CAMEL
independent variables were specified to be: capital adequacy ratio, assets quality,
management soundness, earnings of credit facility, liquidity, and bank size. The
findings of this study show that liquidity has a significant strong impact on the
effectiveness of credit risk management of Saudi Banks, whereas, bank size has a
negative impact on the effectiveness of credit risk management of Saudi Banks. On
the other hand, the other variables like capital adequacy, assets quality, management
soundness and earning were found to have an insignificant impact on the
effectiveness of credit risk management of Saudi Arabian banks.
Moreover, Abdelrahim (2013) has identified various challenges regarding the
effectiveness of credit risk management that are of vital importance to Saudi banks.
They include: low quality of assets, inadequate training, weak corporate governance,
lack of credit diversification, granting credit ceiling exceeding customer’s repayment
capacity, absence of risk premium on risky loans, priority of loan guarantees at
expense of customer repayment capacity, absence of analysis of customer’s financial
position, corruption of some credit officers and priority of profit at expense of credit
safety. To alleviate these challenges, he recommends for Saudi Arabian banks to
have a comprehensive strategy for managing credit risk, to strengthen the role of
credit risk committee, to implement Basel III accord, and to adopt available
sophisticated technique to mitigate credit risk.
Adeusi et al. (2014) evaluated the association of risk management practices and
banks’ financial performance in Nigeria using secondary data from annual reports
and financial statements of ten Nigerian banks for the period 2006 to 2009. The
authors have adopted the panel data estimation technique as the data collected for
their study is cross-sectional units observed over time. The independent variables
used by the authors included the cost of bad and doubt loans, non-performing loan,
liquidity, equity-total asset ratio, equity-loan ratio and debt-equity ratio. Whereas the
dependent variables used are return on asset (ROA) and return on equity (ROE). The
findings of this study show that there is an inverse relationship between banks’
financial performance and cost of bad and doubtful loans; but a positive and
15
significant relationship between capital assets ratio and banks’ financial
performance. The authors concluded that there is a significant relationship between
bank’s performance and risk management. The authors recommend that the credit
risk indicators identified which included cost of bad and doubt loans, debt-equity
ratio, and managed fund needs to be managed in a better way to achieve better
banks’ financial performance.
Aduda and Gitonga (2011) investigated the relationship between credit risk
management and profitability of thirty commercial banks in Kenya using both
primary and secondary data. Primary data was collected through a questionnaire
while secondary data was obtained from bank’s annual report and financial
statements from 2000 to 2009. The authors used non-performing loan ratio as an
independent variable representing credit risk management and ROE as a dependent
variable as a measure of bank profitability. The method used in this study is
regression analysis. The responses from the questionnaire show that profitability
ratios greatly affect credit risk management. Similarly, the findings from regression
analysis show that NPLR is negatively related and statistically significant to ROE.
The study concludes that credit risk management has an effect on profitability at a
reasonable level in the sample commercial banks of Kenya.
Likewise, Afriyie and Akotey (2012) examined the impact of credit risk management
on the profitability of rural and community banks in Ghana using panel regression
model for the period 2006 to 2010. The authors have taken non-performing loan and
capital adequacy ratio as indicators of credit risk management, and ROA and ROE as
indicators of bank profitability. The findings of the study show the existence of a
significant positive relationship between non-performing loans and bank profitability
meaning that even though there is huge loan default, non-performing loans are
increasing proportionately to profitability. The authors have found the reason for
ineffective credit risk management practice among the rural and community banks of
Ghana and reported that banks shift the cost of loan default to other customers with
higher interest on loans. Due to this practice the community banks remained
profitable. This however reveals that rural and community banks in Ghana do not
have sound and effective credit risk management practice because theoretically, non-
performing loan reduces the bank profitability. The authors strongly recommend for
16
the Bank of Ghana to tighten its control mechanism of the rural banking industry to
stop this practice.
In addition, Alshatti (2015) investigated the effect of credit risk indicators on banks’
financial performance during the period of 2005 to 2013 using thirteen commercial
banks of Jordan. The author used secondary sources to collect data through annual
reports of sample banks and carried out panel regression analysis study. The credit
risk management indicators used in this study are capital adequacy ratio, credit
interest/credit facilities, provision for facilities loss/net facilities, leverage ratio and
level of non-performing loans. The bank financial performance indicators are ROA
and ROE. The findings of this study show that there is a positive effect of non-
performing loans/gross loans on banks’ financial performance and a negative effect
of provision for facilities loan/net facilities ratio on banks’ financial performance.
However, he found that capital adequacy ratio and credit interest/credit facilities ratio
have no effect on banks’ financial performance. Further, the significant variables
found in this study are non-performing loans/gross loans, provision for facilities
loss/net facilities and the leverage ratio. The author recommends that the Jordanian
banks design an effective credit risk management system, operate under a sound
credit granting process, and to maintain an appropriate credit administration with
monitoring, processing and control mechanism. Overall, the study recommends
improving banks’ credit risk management to attain higher profitability.
Berrios (2013) attempted to explore the relationship between the increase in bank
risk and the global financial crisis, conducting the analysis in two phases. The first
phase considered the latest data available, including insider holdings and chief
executive officer compensation and tenure. While the second phase employed
regression modelling using the Mergent Online database as data source. The sample
size for the second phase is 40 banks which have been selected randomly from the
database, for the period 2005 to 2009 totalling to about 200 observations. The
performance variables used for the second phase are net interest margin, return on
assets, return on equity, and cash flow to assets. Whereas, the independent variables
used are insider variable for bank, less prudence variable for bank, compensation
variable for bank, tenure variable for bank, loans to deposits, and total debt to equity.
The findings suggest that insider holdings and chief executive officer of higher
17
tenure have a negative impact on banks’ performance. However, it is emphasized in
the study that more evidence needs to be obtained before generalizing this finding.
The findings from regression result show a negative relationship between loans to
deposits and cash flows, but a positive relationship between lesser prudence in
lending and financial performance.
Bhattarai (2014) examined the effect of credit risk on the performance of Nepalese
commercial banks using pooled data of fourteen commercial banks of Nepal for the
period of 2010 to 2015 totalling to 77 observations. The 77 observations include
capital adequacy ratio, non-performing loan ratio, cost per loan assets, cash reserve
ratio and bank size as an independent variable; and return on assets as a dependent
variable. Regression analysis was used to assess the data. The findings of the study
showed that the commercial banks under consideration has been practicing poor
credit risk management. This was further evidenced by the negative effect of non-
performing loan ratio on bank performance and the positive effect of cost per loan
assets on bank performance. In contrast to other studies, the author found that capital
adequacy ratio and cash reserve have no influence on bank performance. Since there
is a significant relationship between credit risk and bank performance, the author
suggests that the banks establish proper credit risk management strategies by
conducting sound credit evaluation procedure before granting loans to customers.
Similarly, Ejoh, Okpa, and Egbe (2014) evaluated the impact of credit risk and
liquidity risk management on the profitability of deposit money held by banks in
Nigeria from 80 respondents using a questionnaire. The data obtained were analysed
using descriptive statistics and correlation analysis. The findings of this study
showed that there is a significant relationship between bank liquidity and profitability
of deposit money among Nigerian banks. Hence, the authors recommend that deposit
money banks should set up an effective system of internal controls to monitor risk in
order to ensure complete compliance. Moreover, the banks should maintain a balance
between deposit-loan ratios in order to avoid asset liabilities mismatch.
Also, Kaaya and Pastory (2013) analyzed the relationship between credit risk and
bank performance of commercial banks in Tanzania using regression analysis. The
credit risk indicators used by the authors include loan loss to gross loan, non-
18
performing loan, loan loss to net loan, and impaired loan to gross loan. As in
previous studies, the bank performance indicator used is return on asset. The overall
findings of this study show that credit risk indicators used in this study have a
negative correlation with bank performance, meaning that an increase in credit risk
tends to lower bank performance. The authors recommend that banks need to
maintain a substantial amount of capital reserve to absorb credit risk in the event of
failure, as well as to enhance lending criteria, portfolio grading and credit mitigation
techniques to reduce chances of default.
Further, Kithinji (2010) examined the relationship between credit risk management
and profitability of commercial banks in Kenya from the period 2004 to 2008 using
regression analysis. The independent variables specified by the author include the
amount of credit and non-performing loans, whereas the dependent variable used is
return on total assets. In contrast to the finding of other studies, the results of this
study shows that there is no relationship between bank profit and the amount of
credit and level of non-performing loans. This means that the bulk of banks’
profitability is not influenced by the amount of credit and non-performing loan.
Hence, the author suggests for commercial banks aiming to enhance profitability to
focus on factors other than the amount of credit and non-performing loans.
Kurawa and Garwa (2014) devoted effort to assess the effect of credit risk
management on the profitability of Nigerian banks during the period 2002 to 2011
using the generalized least square regression technique as a methodology. The credit
risk management indicators used in this study are default rate, cost per loan asset and
capital adequacy ratio. The profitability ratio indicator like many other studies is
ROA. The findings of this study show that default rate, cost per loan assets and
capital adequacy ratio have a significant positive relationship with ROA. The authors
recommend that it is necessary for Nigerian banks to practice scientific credit risk
control, improve their efficacy in credit analysis and loan management, and minimize
the high incidence of non-performing loans and their negative effect on profitability.
As with previous studies, Li and Zou (2014) investigated the relationship between
credit risk management and profitability of commercial banks in Europe from 2007
to 2012. The authors collected data from the largest 47 commercial banks in Europe
19
and analyzed them using multivariate regression analysis. The study used capital
adequacy ratio and non-performing loan ratio as proxies for credit risk management,
and ROA and ROE as proxies for profitability. The overall findings of this study
show that credit risk management has a positive effect on the profitability of
commercial banks in Europe, meaning that the better the credit risk management, the
higher is the profitability of commercial bank.
Likewise, Nawaz et. al. (2012) evaluated the impact of credit risk on the profitability
of Nigerian banks from 2004 to 2008 using multiple regression analysis. The ratio of
non-performing loan to loan & advances and ratio of loan & advances to total deposit
were used as indicators of credit risk. Return on asset was used as an indicator of
financial performance. The findings of this study show that bank profitability is
inversely influenced by the level of loan and advances, non-performing loan and
deposits thus exposing them to risk of illiquidity and distress. The authors
recommend for the management to be cautious when setting up the credit policy as
not to affect profitability.
In addition, Ndoka and Islami (2016) studied the relationship between credit risk
management and profitability of 16 commercial banks in Albania from 2005 to 2015
using a regression model. The independent variable used are non-performing loan
ratio and capital adequacy ratio. Again the dependent variables used are ROA and
ROE. The overall findings of this study show that there exists a correlation between
credit risk management of commercial banks in Albania and their profitability,
meaning that an efficient credit risk management leads to higher profitability. Based
on these findings, the authors recommend that commercial banks of Albania focus on
managing credit risk especially on the control and monitor of non-performing loans.
Similarly, Ogboi and Unuafe (2013) investigated the impact of credit risk
management strategies and capital adequacy on banks financial performance in
Nigeria from 2004 to 2009 using panel data analysis. The study considered loan loss
provision, loan and advances, non-performing loan, capital adequacy ratio and
liquidity as independent variables; and return on asset as the dependent variable. The
result of panel data regression showed that sound credit risk management and capital
adequacy impacted positively on bank’s financial performance with the exception of
20
loan and advances which was found to have a negative impact on bank profitability.
Based on the result, the authors recommend Nigerian banks to establish appropriate
credit risk management strategies by conducting rigorous credit appraisal before loan
disbursement and drawdown. Additionally, the authors recommend Nigerian banks
to pay adequate attention to enhancing Tier-One capital.
Poudel (2012) attempted to identify the various parameters pertinent to credit risk
management as it affects banks’ financial performance by using data of 31
commercial banks of Nepal from 2001 to 2011 and by applying multiple regression
analysis. The parameters specified in the study were default rate, cost per loan assets
and capital adequacy ratio. The findings revealed that all these factors have an
inverse impact on banks’ financial performance, and that default rate is the most
significant predictor of bank financial performance. From the findings, the author
recommends for Nepalese commercial banks to emphasize more on risk management
as risk management, in general, has a significant contribution to bank performance.
Further, the author recommends that in order to reduce risk on loans and achieve
maximum performance, the banks need to allocate more fund to default rate
management and try to maintain an optimum level of capital adequacy.
Finally, Zubairi and Ahson (2014) examined the strength of linkage between current
risk management practices and profitability of five Islamic banks in Pakistan over a
seven-year period (2007-2013) using primary (survey questionnaire) and secondary
data (annual reports). Like many other studies, the dependent variables are in this
ROA and ROE. The explanatory variables are advances and investments/total assets,
number of branches, GDP per capita, interest rates, competition, and taxation. In this
study, a pooled regression analysis was employed to ascertain the relationship
between risk management practices and bank profitability. The study concludes that
risk management have a significantly negative impact on profitability during the
period 2007-2013.
21
2.2 Profitability of Commercial Bank and Credit Risk Management
2.2.1 Profitability of commercial bank
In the banking industry, profitability means the bank’s ability to generate earnings in
comparison to its expenses and incurred costs during a specific period of time. It
shows the capacity of the bank to handle associated risk while increasing their
capital. It also indicates the effectiveness of management and competitiveness
amongst banks. There are various measures to determine bank profitability such as
return on capital employed, return on asset, return on equity, net profit margin, cost
of income ratio, net interest margin, risk-adjusted return on capital, price-earnings
ratio, total share return, return on invested equity, cash flow to assets etc. However,
Brealey, Myers, Allen, and Mohanty (2012) recommends the important measures of
bank profitability to be as return on asset (ROA), return on equity(ROE) and net
profit margin.
Profitability is a key factor for commercial banks as one of the major goals of
commercial banks is to increase their profitability (Duffie & Singleton, 2012). All
the activities within bank seems to affect their own profitability directly or indirectly.
There are several categories to determine bank’s profitability in the literature.
However, these can broadly be categorized into two groups which are internal
determinants and external determinants (Staikouras & Wood, 2011). Internal
determinants are influenced by decisions of bank management and policy objectives
which is controlled by the management (Staikouras & Wood, 2011). It reflects the
sources and uses of capital in the bank as well as liquidity management and expenses
management (Guru, Staunton, & Balashanmugam, 2002). External determinants
refer to factors outside the bank which is beyond the control of management
(Staikouras & Wood, 2011). This study however will mainly focus on internal
determinants as it aims to examine the impact of credit risk management on bank
profitability. However, some credit-related factor like the amount of non-performing
loan is beyond the control of management. In addition, some management decisions
are influenced by external regulations, hence, some external determinants are also
included in the model specification.
22
2.2.2 Bank profitability indicators
As bank profitability is an important aspect of the research, a thorough discussion of
the appropriate measure of bank profitability is presented. As discussed earlier, there
are various measures of bank profitability and the choice of the specific measure will
depend on the objective of the research and practice of the sample banks. For this
research, the return on asset (ROA) and return on equity (ROE) are the measures that
will be used as indicators of bank profitability. ROA and ROE are not only
traditional measures of performance but also the most important measures of bank
profitability in the literature.
2.2.2a Return on Asset (ROA)
Return on Asset (ROA) is the ratio of net income to total assets which measures net
income earned per dollar of assets. It reflects how well the management is utilizing
the bank’s real investment resources to generate profit (Vong & Chan, 2009). Thus,
it shows how efficient and profitable a bank’s management is, on the basis of its total
asset. Mathematically, ROA is expressed as,
𝑅𝑅𝑅𝑅𝑅𝑅 =𝑁𝑁𝑁𝑁𝑁𝑁 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑁𝑁𝑇𝑇𝐼𝐼𝑁𝑁𝑇𝑇𝑇𝑇 𝑅𝑅𝐴𝐴𝐴𝐴𝑁𝑁𝑁𝑁𝐴𝐴
… … … … … … … … … … … (1)
For banks with similar risk profiles, ROA is a useful static for comparing bank
profitability as it avoids distortions produced by differences in financial leverage
(Bhattarai, 2014). From an accounting perspective, ROA is a comprehensive measure
of overall bank performance (Jr Sinkey & Sinkey Jr, 1992). ROA has been widely
used as a metric of bank profitability while examining the relationship between credit
risk management and bank performance in earlier studies such as that of Alshatti
(2015), Berríos (2013), Bhattarai (2014), Kaaya and Pastory (2013), Kurawa and
Garba (2014), Nawaz et al. (2012), Ndoka and Islami (2016), Ogboi and Unuafe
(2013), Adeusi et al. (2014), Poudel (2012), Zou and Li (2014), Zubairi and Ahson
(2014) etc. thus, providing us an argument for using return on asset (ROA) as an
indicator of bank profitability.
23
2.2.2b Return on Equity (ROE)
Return on Equity (ROE) is the ratio of net income to total equity capital which
measures the return to shareholders on their equity. It measures how well the
management is utilizing the shareholder’s invested money to generate profit
(Athanasoglou, Brissimis, & Delis, 2008). ROE is one of the most important
measures for evaluating efficiency and profitability of bank’s management based on
the equity that shareholders have contributed to the bank.
The equation for ROE is written as,
𝑅𝑅𝑅𝑅𝑅𝑅 =𝑁𝑁𝑁𝑁𝑁𝑁 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑁𝑁𝑇𝑇𝐼𝐼𝑁𝑁𝑇𝑇𝑇𝑇 𝑅𝑅𝐸𝐸𝐸𝐸𝐸𝐸𝑁𝑁𝐸𝐸
… … … … … … … … … … … (2)
Generally, a bank with higher ROE has a tendency to be able to generate more return
to their shareholders. The higher the bank’s ROE compared to its competitors, the
better the bank is. Therefore, the stockholders of the banks always prefer higher ROE
however this could sometimes be a threat to the bank (Saunders & Cornett, 2011)
because an increase ROE implies that net income is increasing faster relative to total
equity. Further, a huge drop in equity capital may result in a violation of minimum
regulatory capital standards which tends to increase the risk of solvency for the banks
(Saunders & Cornett, 2011). A number of previous empirical studies (Aduda and
Gitonga (2011), Afriyie and Akotey (2012), Alshatti (2015), Berríos (2013), Ndoka
and Islami (2016), Adeusi et al. (2014), Zou and Li (2014), Zubairi and Ahson
(2014)) examining the relationship between credit risk management and bank
performance have used ROE as a metric of profitability. Thus, the second measures
of profitability used in the study is ROE.
In this study, the dependent variables use to measure the effectiveness of
management in utilizing assets and shareholder’s equity of commercial banks are the
ROA and ROE respectively.
2.2.3 Bank’s risk management
Another important aspect of this study is credit risk management. In this section, a
brief introduction of the various types of risks associated with banks and overall risk
management process are presented. The succeeding subsection will discuss these in
more detail.
24
2.2.3.1 Bank’s risk
Risk can be defined as the probability of various outcomes occurring. The various
activities performed in a bank are exposed to different types of risk. In the context of
banking and risk management, risk can be categorized as: (i) the risk that can be
managed, (ii) the risk that can be transferred to others, and (iii) the risk that can be
eliminated (Santomero, 1997).
As mentioned before Bessis (2011) has identified and categorized bank risk as credit
risk, liquidity risk or funding risk, interest rate risk, mismatch risk, market liquidity
or market price risk, market risk, and foreign exchange risk. A brief description of
these risks are as follows:
Credit or default risk: Credit risk is the most important type risk amongst the many
types of risks that bank faces which influences bank performance (Boffey & Robson,
1995). Credit risk is the situation where the actual return of an investment differs
from the expected return. It may represent the possibility of losing the principal
amount of investment as well as interest amount accrued on it (Gestel & Baesens,
2009). Whenever, a borrower, counterparty, or debtor does not honour to pay their
debt obligation as their specified contract terms, there arises credit risk to the lender
(Dam, 2010). In banking, credit risk affects the bank’s profitability, liquidity position
and cash flows factors that are identified as principal causes of bank failure and the
greatest threat to the bank performance (Van Greuning & Brajovic-Bratanovic,
2009). Bessis (2011) has further classified credit risk into: default risk, migration
risk, exposure risk, counterparty risk, recovery risk, and correlation and
concentration risk.
Liquidity risk or funding risk: Liquidity risk is the situation whereby the financial
institutions have to make payment but the available assets are long-term and can only
be converted quickly with the capital loss (Burton, Nesiba, & Brown, 2015). This
situation can arise when depositors withdraw their funds unexpectedly and raising
further deposits becomes impossible to do. To avoid such condition, a financial
institution can hold highly liquid assets which can then be converted quickly into the
required amount of fund to reduce their liquidity risk (Burton et al., 2015).
25
Interest rate risk: Interest rate risk for the financial institution is represented by a
decline in net interest income (Bessis, 2011). It is the situation where the interest rate
will change unexpectedly such that the interest cost exceeds interest revenues. Such
condition arises when a financial institution raises deposits through short-term
borrowings such as savings deposit or commercial papers and lend long term such as
mortgages or bonds. If the interest rate goes up, the cost of short-term liabilities rises
quickly than the returns on the long-term assets (Burton et al., 2015).
Mismatch risk: It arises when there is the gap between maturities and interest rate
reset dates of assets and liabilities (Bessis, 2011). Mismatch risk implies that there is
an interest rate risk as well as liquidity risk. Interest rate risk arises from the
difference between short-term deposit rate and long-term lending rate in a given
period of time. Similarly, liquidity risk arises when financial institutions fall short of
the required funds and the reason for this is a mismatch between maturity times
(Bessis, 2011). Banks and financial institutions can avoid such situation by lending
in higher rates and borrowing at lower rates.
Market liquidity or market price risk: It arises only for those assets which are
traded on low volume. For the assets which are highly liquid such as Treasury bills
or bonds, market liquidity risk does not exist at all.
Market risk: It is the risk of possible losses due to adverse movements in market
prices (Bulletin, 1996) such as short-term loss in foreign exchange and long-term
loss for derivatives (Bessis, 2011).
Foreign exchange risk: It is a risk that arises when the financial institution holds
foreign currencies and incurs losses due to an adverse change in exchange rate
between the currencies (Burton et al., 2015).
Of all these risks faced by the banks, credit risk plays a significant role as it
influences the profitability of the bank more directly. When the bank does not
receive its interest on the loan from borrowers, the bank losses its interest income
resulting in a decline in its profitability. The principal amount is more important in
case of default as the invested principal amount is accumulated through a number of
26
depositor’s fund. Banks usually secure its loan amount provided to borrowers with
secured mortgages and various recovery options. The loss in principal amount
creates an additional burden of recovery and most of the times, the bank does not
receive its full amount of default which directly impacts on bank profitability. This
study mainly focuses on credit risk in banks and how it influences the commercial
banks’ profitability.
2.2.3.2 Risk Management
Risk management is focused on eliminating volatility in earning while avoiding
possible losses. Adeusi et al. (2014) state that risk management process is a
procedure whereby banks identify risk, measure risk, monitor risk and control risk, as
well as determine whether they hold adequate capital to mitigate risks. ISO 31000:
2009 have presented the risk management process to be as follows:
Source: Purdy, 2010
Figure 2: The risk management process from ISO 31000:2009
Figure 2 illustrated the risk management process from ISO 31000:2009. It indicates
that risk management process has basically two elements which are communication
and consultation with internal and external stakeholders, and monitor and review of
the internal and external environment. The main backbone of risk management
process includes three steps that of establishing the context, risk assessment, and risk
treatment. The first step starts with defining what the organization aims to achieve;
27
and outlining the internal and external factors which could influence the organization
goals (Purdy, 2010).
Under ISO 31000, the second step of risk management includes risk assessment
which comprises of three vital phases which include the identification of the risk by
understanding what could happen, how, when and why. Followed by the risk analysis
which involve the understanding of each risk and its consequences. The final phase is
risk evaluation which involves making a decision about the level of risk (Purdy,
2010). The third step of risk management includes risk treatment, which is done
either by improving existing controls or by developing and implementing new
controls (Purdy, 2010).
The risk management process that we discussed has been considered after numerous
options by (ISO 31000:2009) which is a general risk management process. In
practice, there is iteration among the steps and between elements of communication
and consultation, and monitor and review. However, we can relate it with bank’s risk
management process as well. The banking activities like accepting deposits, lending
money, foreign exchange transactions, money transfers etc. are significantly evolving
over time resulting in the banks’ having more exposure to the various kinds of risks.
The need for efficient management of risk in the banking sector is in order to avoid
possible losses, avoid bankruptcy, provide benefit for shareholders and depositors,
and enhance profitability (Gestel & Baesens, 2009).
2.2.3.3 Credit Risk Management
Credit risk management is a critical component of a comprehensive approach to risk
management and is essential for long-term success of commercial banks. Managing
credit risk is one of the multidimensional tasks and can be done through various
approaches. Afriyie and Akotey (2012) define credit risk management as a structured
approach to manage uncertainties through risk assessment; mitigate risk using
managerial resources; strategies development such as transferring risk to another
party, avoiding risk, reducing the negative effect of the risk, accepting some or all of
the consequences of particular risk. Similarly, Bielecki and Rutkowski (2013)
explain credit risk management can be done through hedging of default able claims,
integration of risks and portfolio management.
28
Santomero and Babbel (1997) have outlined the basic principles of managing credit
risk as:
(i) Standard setting and financial reporting
(ii) Underwriting authority and loan limits
(iii) Investment guidelines or strategies and
(iv) Incentive schemes
To summarize, a good credit risk management avoids important drawbacks like lack
of credit discipline, credit concentrations, aggressive underwriting and products at
inadequate prices (Gestel & Baesens, 2009).
2.2.3.4 Credit Risk Management Indicators
Various credit risk management indicators are chosen for this study on the basis of
their importance to credit risk management. Some of the indicators are chosen based
on their significance in previous studies. Further two variables are introduced on the
basis of their theoretical relevance to credit risk management.
Capital Adequacy Ratio (CAR)
Capital adequacy ratio, calculated as the ratio of the amount of capital to the risk-
weighted sum of bank’s assets, is a measure of bank’s capital amount expressed as a
percentage of its risk-weighted credit exposure (Poudel, 2012). It is the percentage of
capital that a bank has to hold as specified by regulatory requirement. It is essential
to maintain a specified CAR in order to determine the capacity of banks in meeting
losses and ensure that banks would still bear a reasonable level of losses in worst
scenario (Reddy & Prasad, 2011). In general, banks with high CAR are considered to
have low risk and likely to meet its financial obligations. The higher the ratio, the
more will be the depositors’ protection and stability of the financial system. As banks
with strong capital adequacy are able to absorb possible losses thus preventing them
from failure and insolvency, it could be considered as enhancing profitability.
Capital-based regulation has become a key issue in the banking industry caused by
the sub-prime mortgage problems which led to a financial crisis of 2007 (Hyun &
Rhee, 2011). To maintain the specified capital adequacy ratio, capital constrained
banks either collect outstanding loans or becomes reluctant to issue new lending
29
(Hyun & Rhee, 2011). The bank capital is more likely to be obligatory during
economic downturns, recapitalization would not be easy, and hence, banks meet the
capital ratio by reducing its lending (Hyun & Rhee, 2011).
A number of empirical studies reviewed that used CAR in their analysis have mixed
results. For example, Abdelrahim (2013); Afriyie and Akotey (2012); Bhattarai
(2014); Kurawa and Garba (2014); and Ogboi and Unuafe (2013) found a significant
positive relationship between capital adequacy ratio and bank performance. On the
other hand, Alshatti (2015); Zou and Li (2014); Ndoka and Islami (2016); and
Poudel (2012) found a negative association between capital adequacy ratio and bank
performance. Most studies however indicate that CAR is to be maintained in banks
in order to prevent them from possible losses. Thus a positive relationship between
capital adequacy ratio and profitability is expected.
The equation for capital adequacy ratio (CAR) is given by:
𝐶𝐶𝑅𝑅𝑅𝑅 =𝐶𝐶𝑇𝑇𝐶𝐶𝐸𝐸𝑁𝑁𝑇𝑇𝑇𝑇
𝑅𝑅𝐸𝐸𝐴𝐴𝑅𝑅 𝑊𝑊𝑁𝑁𝐸𝐸𝑊𝑊ℎ𝑁𝑁𝑁𝑁𝑡𝑡 𝑅𝑅𝐴𝐴𝐴𝐴𝑁𝑁𝑁𝑁𝐴𝐴… … … … … … … … … … … … (3)
Liquidity Ratio (LR)
Liquidity in banks refers to a situation where they can manage sufficient funds either
by increasing liability or converting their assets to cash at a reasonable cost in a short
span of time (Abdelrahim, 2013). It is the ability of banks to fund all short-term
obligations when they fall due. These short-term obligations may include lending,
deposit withdrawals, investment commitments, and liability matures (Amengor,
2010). It is measured by the ratio of credit facility to total deposit (Cole, Gunther, &
Cornyn, 1995). The risk of liquidity to a bank is the risk of loss resulting from the
inability to meet its need for funding (Lartey, Antwi, & Boadi, 2013). A high
liquidity ratio means that a bank is holding too much of its liquid assets which could
be utilized in other profitable areas whereas, a low liquidity ratio denotes that a bank
might struggle to fund its short-term obligations, which is a greater concern to
investors.
A number of empirical studies based on the relationship between liquidity ratio and
bank profitability showed mixed results. Abdelrahim (2013) found a strong
significant positive impact of LR on bank performance whereas, Adeusi et. al. (2014)
30
and Ogboi and Unuafe (2013) found a negative effect of liquidity ratio on the
financial performance of banks. Hence, in view of previous studies and theory, a
negative relationship could be expected between change in liquidity ratio and bank’s
financial performance. This is because when the bank holds too much of it in cash, it
losses opportunity to capitalize its fund in more profitable areas.
The equation for liquidity ratio (LR) is mathematically given by:
𝐿𝐿𝑅𝑅 =𝐿𝐿𝐼𝐼𝑇𝑇𝐼𝐼
𝑇𝑇𝐼𝐼𝑁𝑁𝑇𝑇𝑇𝑇 𝑡𝑡𝑁𝑁𝐶𝐶𝐼𝐼𝐴𝐴𝐸𝐸𝑁𝑁… … … … … … … … … … … … (4)
Bank Size (BS)
Bank size accounts for the existing economies and diseconomies of scale in the
banking market (Athanasoglou, Brissimis, & Delis, 2008). Larger banks tend to be
more active in markets, have a greater product and have better possibilities for risk
diversification (Lehar, 2005). Also, larger banks can make efficiency gains as they
do not operate in the too competitive market (Flamini, Schumacher, & McDonald,
2009). However, Demirgüç‐Kunt and Maksimovic (1998) have the view that the
extent to which financial, legal and other factors affect the profitability of bank is
closely related to its size. Bank size, measured by the log of the book value of total
assets calculated in its currency (Lehar, 2005) has been taken as one of the control
variables in this study to analyze bank financial performance.
Only a few authors have used bank size as a control variable to investigate the impact
of credit risk management on bank profitability. Bhattarai (2014) found that bank
size has a positive relationship with bank performance, which implies that as bank
size increases profitability also increases, especially in the case of small and
medium-sized banks. In contrast, Abdelrahim (2013) found a significant strong
negative impact of bank size on the effectiveness of Saudi bank’s credit risk
management. Hence, in view of theory and past studies a positive relationship is
expected between bank size and bank profitability.
The calculation for bank size used in this study is as follows:
𝐵𝐵𝑇𝑇𝐼𝐼𝑅𝑅 𝐴𝐴𝐸𝐸𝑠𝑠𝑁𝑁 = 𝑁𝑁𝑇𝑇𝑁𝑁𝐸𝐸𝑁𝑁𝑇𝑇𝑇𝑇 𝑇𝑇𝐼𝐼𝑊𝑊𝑇𝑇𝑁𝑁𝐸𝐸𝑁𝑁ℎ𝐼𝐼 𝐼𝐼𝑜𝑜 𝑁𝑁𝐼𝐼𝑁𝑁𝑇𝑇𝑇𝑇 𝑇𝑇𝐴𝐴𝐴𝐴𝑁𝑁𝑁𝑁𝐴𝐴… … … … … … (5)
31
Asset Quality (AQ)
Asset quality determines the strength of financial institution against the loss of assets
value (Kwan & Eisenbeis, 1997). It can be measured by calculating the growth of
total loans (Abdelrahim, 2013). Monitoring growth of loan is very important in banks
since they provide earnings to the bank and that decreasing the value of loans often
relates to the risk of solvency to financial institutions (Hassan & Bashir, 2003). The
growth of gross loan also enhances bank profitability unless bank takes on an
unacceptable level of risk (Anbar & Alper, 2011). However, growing value of the
asset in banking is not enough if they are not of food quality, meaning that, the loans
approved and sanctioned by the banks should be good quality loans. Bad quality
loans have high chances of becoming non-performing loan thus creating no return to
the bank.
In this context, Hassan and Bashir (2003) recommends that the quality of asset
depends on the quality of credit assessment, monitoring and collection within the
bank. The quality can be improved by securitizing the loans by collateral, having
adequate provisions for potential losses and avoiding risky lending. Few studies on
the impact of credit risk management on profitability have been undertaken
considering asset quality as control variable. Abdelrahim (2013) found an
insignificant negative impact of asset quality on the effectiveness of credit risk
management. However, taking into account theory, this study hypothesises a positive
relationship between change in asset quality and financial performance of the bank.
The calculation for asset quality (AQ) is given by:
𝑅𝑅𝐴𝐴𝐴𝐴𝑁𝑁𝑁𝑁 𝐸𝐸𝐸𝐸𝑇𝑇𝑇𝑇𝐸𝐸𝑁𝑁𝐸𝐸 = 𝐺𝐺𝑁𝑁𝐼𝐼𝐺𝐺𝑁𝑁ℎ 𝐼𝐼𝑜𝑜 𝑊𝑊𝑁𝑁𝐼𝐼𝐴𝐴𝐴𝐴 𝑇𝑇𝐼𝐼𝑇𝑇𝐼𝐼… … … … … … … … … (6)
Leverage Ratio (LER)
As noted earlier, one of the causes of the global financial crisis is widely believed to
be excessive leverage ratio in the banking system (Board, 2009; Turner, 2009).
Leverage ratio indicates how much debt a bank is using to finance their operations in
relation to the shareholder’s equity value (Reddy & Prasad, 2011). It measures how
much capital in the bank is in the form of debt and assesses the bank’s ability to meet
its financial obligations. Bank relies on a mixture of shareholder’s equity and
liabilities to finance their operations. High leverage ratio shows that a bank is
32
aggressive in financing with debt. Aggressive leveraging practices often indicates a
high level of risks as uncontrollable debt indicates too much liabilities to pay off.
Also, high leverage ratio would potentially result in unstable earnings to banks due to
additional interest expense. In this case, shareholders benefit as long as earnings
increases with the same amount of shareholders and their investment. However, if
the cost of debt exceeds returns, it can even lead to bankruptcy, leaving shareholders
without any return. Further high leverage ratio indicates less protection to depositors
and is always risky to them (Reddy & Prasad, 2011). Maintaining leverage ratio in
banking is extremely important as it limits the bank to build up excessive leverage
which could damage the overall financial system and the economy; and strengthen
risk control measures (Miu, Ozdemir, & Giesinger, 2010). The central bank of Nepal
has directed the commercial banks to maintain minimum leverage ratio at 4% (Bank,
2016).
Very few studies investigating the impact of credit risk management on banks
financial performance have used leverage ratio as an indicator of credit risk
management. Alshatti (2015) found a negative effect of leverage ratio on banks
financial performance. Following previous studies, it is hypothesised that a negative
association exist between leverage ratio and bank performance.
The equation for leverage ratio (LER) is:
𝐿𝐿𝑅𝑅𝑅𝑅 =𝑇𝑇𝐼𝐼𝑁𝑁𝑇𝑇𝑇𝑇 𝑇𝑇𝐸𝐸𝑇𝑇𝑎𝑎𝐸𝐸𝑇𝑇𝐸𝐸𝑁𝑁𝐸𝐸𝑁𝑁𝐴𝐴
𝑇𝑇𝐼𝐼𝑁𝑁𝑇𝑇𝑇𝑇 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑁𝑁𝐸𝐸𝐸𝐸𝐸𝐸𝑁𝑁𝐸𝐸… … … … … … … … … (7)
Non-Performing Loan Ratio (NPLR)
Among various indicators of credit risk and financial stability, non-performing loan
ratio (NPLR) holds critical importance as an increase in NPLR is regarded as the
failure of credit policy in banks, a reduction in bank’s earnings and a major reason
for the financial crisis (Saba, Kouser, & Azeem, 2012). It is also viewed a measure of
how banks manage their credit assessment as NPLR indicates the proportion of non-
performing loan to total loan portfolio (Hosna, Manzura, & Juanjuan, 2009). A non-
performing loan is often characterized as late payment rather than default loan if the
borrower is still undertaking the loan (Choudhry, 2011). However, once a loan is
non-performing, the chances of it being repaid fully is nominal (Saba et al., 2012).
33
The non-performing loan includes all loans overdue on principal or interest payment
or both for more than 90 days (Wahlen, 1994). According to BIS, the standard loan
classification can be defined as (Hou & Dickinson, 2007):
• Passed: Loans which are repaid back.
• Substandard: The loans whose overdue amount are longer than three months.
The banks usually make 10% provision for the overdue portion.
• Doubtful: The loans whose overdue amount appears doubtful and the exact
amount of which cannot be determined. Banks make 50% provision for
doubtful loans.
• Virtual loss and loss (unrecoverable): The outstanding loans provided to
firms which applied for legal resolution and protection under bankruptcy
laws. Banks make 100% provision for unrecoverable loan.
Non-performing loan comprises of substandard, doubtful, and virtual loss and loss,
and are categorized as per their degree of collection difficulty. However, if the
borrower starts making payment again on a non-performing loan, it becomes a re-
performing loan, even though the borrower has not repaid all the unpaid amount.
Due to the importance of non-performing loan in financial institutions, numerous
studies have been conducted on the relationship between non-performing loan and
financial performance and the authors seems to have found mixed results. Among the
studies, Aduda and Gitonga (2011), Li and Zou (2014), Bhattarai (2014), Kaaya and
Pastory (2013), Ndoka and Islami (2016) found an inverse impact of non-performing
loans on the bank profitability while, Afriyie and Akotey (2012), and Alshatti (2015)
found a positive effect of non-performing loans on bank financial performance. On
the other hand, Adeusi et al. (2014), Kithinji (2010), Nawaz (2012), and Ogboi and
Unuafe (2013) could not find a relationship between bank performance and non-
performing loans.
The positive impact of the non-performing loan on financial performance signifies
that even though the borrowers are not paying the loan, profitability is increasing. A
similar result is found by Afriyie and Akotey (2012) who found that the rural banks
in Ghana shift the cost of loan default to other customers by increasing their interest
rate on loans, thus rural banks remained profitable. Despite the mixed results, a
34
negative relationship is hypothesised between non-performing loan and bank
profitability.
The equation for Non-performing loan ratio (NPLR) is expressed as:
𝑁𝑁𝑁𝑁𝐿𝐿𝑅𝑅 =𝐼𝐼𝐼𝐼𝐶𝐶𝑇𝑇𝑁𝑁𝐸𝐸𝑁𝑁𝑁𝑁𝑡𝑡 𝑇𝑇𝐼𝐼𝑇𝑇𝐼𝐼𝐴𝐴 (𝑁𝑁𝑁𝑁𝐿𝐿𝐴𝐴)
𝐺𝐺𝑁𝑁𝐼𝐼𝐴𝐴𝐴𝐴 𝑇𝑇𝐼𝐼𝑇𝑇𝐼𝐼𝐴𝐴… … … … … … … … … (8)
Where,
NPLs=Non-Performing Loans
Cash Reserve Ratio (CRR)
Cash reserve ratio is specified as a percentage of total deposit of customers held with
the central bank. It is one of the monetary policy tool used by the reserve bank to
control money supply in the economy (Abid & Lodhi, 2015). This in turn has a
significant impact on the interest rates, liquidity (Teja, Tejaswi, Madhavi, & Ujwala,
2013) and profitability of the banks (Bhattarai, 2014). When a central bank lowers
CRR, the availability of funds in the bank increases, the interest rate decreases, and
the bank profitability increases with the availability of money for funding generating
more interest earnings. In contrast, when CRR increases, the availability of fund
decreases with the banks, which means that less money to loan out is available
resulting to fewer interest earnings and decline in profitability. The increase and
decrease of CRR will result in non-availability and availability of fund in the banks
denoting liquidity status of the banks. However, CRR itself does not earn any income
for the financial institutions but acts as a hindrance to the profitability of the banks.
The specified regulatory requirement of CRR to be maintained at the central bank of
Nepal has been stated different rate for different types of bank and financial
institutions. As per Nepal Rastra Bank directives 2014/15, the CRR to be maintained
by commercial banks has been fixed at 6% (Bank, 2015b).
Few studies based on the effect of credit risk management on bank performance have
considered CRR as control variable. Bhattarai (2014) has found that cash reserve
ratio has an inverse impact on bank profitability. Therefore, based on the literature
survey a negative relationship between CRR and bank profitability is expected.
The equation for cash reserve ratio (CRR) is given by:
35
𝐶𝐶𝑅𝑅𝑅𝑅 =𝑅𝑅𝑁𝑁𝐴𝐴𝑁𝑁𝑁𝑁𝑅𝑅𝑁𝑁𝐴𝐴 𝑁𝑁𝑁𝑁𝐸𝐸𝐸𝐸𝐸𝐸𝑁𝑁𝑁𝑁𝐼𝐼𝑁𝑁𝐼𝐼𝑁𝑁 𝐺𝐺𝐸𝐸𝑁𝑁ℎ 𝑁𝑁ℎ𝑁𝑁 𝐼𝐼𝑁𝑁𝐼𝐼𝑁𝑁𝑁𝑁𝑇𝑇𝑇𝑇 𝑎𝑎𝑇𝑇𝐼𝐼𝑅𝑅
𝑇𝑇𝐼𝐼𝑁𝑁𝑇𝑇𝑇𝑇 𝑡𝑡𝑁𝑁𝐶𝐶𝐼𝐼𝐴𝐴𝐸𝐸𝑁𝑁𝐴𝐴 𝐼𝐼𝑜𝑜 𝐼𝐼𝐸𝐸𝐴𝐴𝑁𝑁𝐼𝐼𝐼𝐼𝑁𝑁𝑁𝑁𝐴𝐴… … … … … … (9)
Coverage Ratio (CR)
Coverage ratio is an important factor in evaluating the bank’s performance as it is
related to the interest income which is the main source of income of the financial
sector, especially the banking sector. It is influenced by factors that affects interest
rates such as the type of assets and liabilities held by the bank as well as monetary
policy changes (Ho & Saunders, 1981). It can be measured by calculating the ratio of
bank’s interest income to total gross loans. Coverage ratio (CR) though not
previously used by other studies is hypothesized to be positively related to ROA as
knowing that CR increases if interest income is increasing more than the increases in
loans.
The equation for coverage ratio (CR) is given by:
𝐶𝐶𝑅𝑅 =𝐼𝐼𝐼𝐼𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝐴𝐴𝑁𝑁 𝐸𝐸𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑁𝑁 𝐼𝐼𝐼𝐼 𝑇𝑇𝐼𝐼𝑇𝑇𝐼𝐼𝐴𝐴𝑅𝑅𝑅𝑅𝑁𝑁𝑁𝑁𝑇𝑇𝑊𝑊𝑁𝑁 𝑊𝑊𝑁𝑁𝐼𝐼𝐴𝐴𝐴𝐴 𝑇𝑇𝐼𝐼𝑇𝑇𝐼𝐼𝐴𝐴
… … … … … … … … … . (10)
Female Board Member (FBM)
Board members play a vital role in the governance of bank from appointing and
supervising top managers (Wachudi & Mboya, 2012) to manage risk-reward profile,
monitor financials of the bank, establish credit facilities etc. (Charkham, 2003). The
gender of top executive and board members has been one of the focused areas from
last decade. In most countries, the proportion of women reaching the top position in
firms is still very low. Many European countries have already introduced regulations
for a minimum required female percentage on boards for public trading companies
(Smith, Smith, & Verner, 2006), Norway being the first to quote the requirement of
at least 40% females for such companies by 2008 (Hoel, 2008).
Despite the commonly held view that females are more risk averse than male, a
number of studies conducted on women in top management and performance of firm
have shown mixed results. Smith et al. (2006) found that women in top management
have positive effect on firm performance depending on the qualification of women in
36
top position. While, Carter, D'Souza, Simkins, and Simpson (2010) and Wachudi and
Mboya (2012) found an insignificant relationship between gender diversity of the
board and financial performance. García-Meca, García-Sánchez, and Martínez-
Ferrero (2015) found that gender diversity enhances bank performance.
This variable is introduced in this study as none of the previous Nepalese studies
have used female board member as an indicator of credit risk management in
determining the relationship between credit risk management and bank profitability.
On the basis of review of literature, a positive association between female board
member and bank profitability is expected.
The female board member (FBM) variable is defined as:
𝐹𝐹𝐵𝐵𝐹𝐹 = 𝑇𝑇𝐼𝐼𝑁𝑁𝑇𝑇𝑇𝑇 𝐼𝐼𝐸𝐸𝐼𝐼𝑎𝑎𝑁𝑁𝑁𝑁 𝐼𝐼𝑜𝑜 𝑜𝑜𝑁𝑁𝐼𝐼𝑇𝑇𝑇𝑇𝑁𝑁 𝑎𝑎𝐼𝐼𝑇𝑇𝑁𝑁𝑡𝑡 𝐼𝐼𝑁𝑁𝐼𝐼𝑎𝑎𝑁𝑁𝑁𝑁… … … … … … … … … (11)
In summary, this study will include CAR, LR, BS, AQ, LER, NPLR, CRR, CR, and
FBM as variables of credit risk management. These indicators will measure the
effectiveness of managing credit risk by commercial banks. CAR, LR, BS, AQ, LER,
NPLR and CRR have been used in previous empirical studies as important variables
for credit risk management. CR and FBM are the additional variables introduced in
this study.
37
Chapter 3 Method and Data Description
This chapter discuss the models and methods used to ascertain the relationship between bank management and the accounting performance of commercial banks of Nepal. The data and data description are also presented and discussed. The chapter ends with the descriptive statistics of the various variables included in the study.
3.0 Model Specification
Chapter 2 presents various studies on bank risk management and financial
performance in developed and developing countries. Factors that influence the
banks’ performance were identified and as a consequence, this study specifies the
following models:
𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 = 𝛼𝛼0 + 𝛼𝛼1𝐶𝐶𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼2𝐿𝐿𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼3𝐵𝐵𝐵𝐵𝑖𝑖𝑖𝑖 + 𝛼𝛼4𝑅𝑅𝐴𝐴𝑖𝑖𝑖𝑖 + 𝛼𝛼5𝐿𝐿𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼6𝑁𝑁𝑁𝑁𝐿𝐿𝑅𝑅𝑖𝑖𝑖𝑖 +
𝛼𝛼7𝐶𝐶𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼8𝐶𝐶𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼9𝐹𝐹𝐵𝐵𝐹𝐹𝑖𝑖𝑖𝑖 + 𝜇𝜇𝑖𝑖𝑖𝑖 … … … … … … … … … … … … …(12)
And,
𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 = 𝛼𝛼0 + 𝛼𝛼1𝐶𝐶𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼2𝐿𝐿𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼3𝐵𝐵𝐵𝐵𝑖𝑖𝑖𝑖 + 𝛼𝛼4𝑅𝑅𝐴𝐴𝑖𝑖𝑖𝑖 + 𝛼𝛼5𝐿𝐿𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼6𝑁𝑁𝑁𝑁𝐿𝐿𝑅𝑅𝑖𝑖𝑖𝑖 +
𝛼𝛼7𝐶𝐶𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼8𝐶𝐶𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼9𝐹𝐹𝐵𝐵𝐹𝐹𝑖𝑖𝑖𝑖 + 𝜇𝜇𝑖𝑖𝑖𝑖 … … … … … … … … … … … … …(13)
Where
𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 = Return on Assets of bank i in the year t
𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 = Return on Equity of bank i in the year t
𝐶𝐶𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 = Capital Adequacy Ratio of bank i in the year t
𝐿𝐿𝑅𝑅𝑖𝑖𝑖𝑖 = Liquidity Ratio of bank i in the year t
𝐵𝐵𝐵𝐵𝑖𝑖𝑖𝑖 = Bank Size of bank i in the year t
𝑅𝑅𝐴𝐴𝑖𝑖𝑖𝑖 = Asset Quality of bank i in the year t
𝐿𝐿𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 = Leverage Ratio of bank i in the year t
𝑁𝑁𝑁𝑁𝐿𝐿𝑅𝑅𝑖𝑖𝑖𝑖= Non-Performing Loan Ratio of bank i in the year t
𝐶𝐶𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 = Cash Reserve Ratio of bank i in the year t
𝐶𝐶𝑅𝑅𝑖𝑖𝑖𝑖 = Coverage Ratio of bank i in the year t
38
𝐹𝐹𝐵𝐵𝐹𝐹𝑖𝑖𝑖𝑖 = Number of Female Board Member of bank i in the year t
αn = intercept term
𝜇𝜇𝑖𝑖𝑖𝑖 = error term
The variables ROA and ROE represent bank profitability of Nepalese commercial
banks. Similarly, the variables CAR, LR, BS, AQ, LER, NPLR, CRR, CR and FBM
represent credit risk management of Nepalese commercial banks. Like previous
studies, it is hypothesised that the variables representing credit risk management will
have an effect on the bank profitability as shown in Table 2.
Table 2: Summary of Expected Relationship with Bank Profitability
Abbreviation variables
Description Measurement Expected sign
CR Coverage Ratio Interest income on loans/Average gross loans
+
CAR Capital Adequacy Ratio
Capital/Risk-weighted assets
+
LR Liquidity Ratio Loan/Total deposit - BS Bank Size Natural logarithm of total
assets +
AQ Asset Quality Growth of gross loans + LER Leverage Ratio Total liabilities/Total
common equity -
NPLR Non-Performing Loan Ratio
Impaired loans (NPLs)/Gross loans
-
CRR Cash Reserve Ratio
Reserves requirement with the central bank/ Total deposits of customers
-
FBM Female Board Member
Number of female board member
+
Unlike previous studies, this study includes female board member (FBM) and
coverage ratio (CR) as introductory variables. The theoretical literature shows the
importance of decision of women as board members in firms regarding various
countries. In this study, FBM has been included to measure the significance and the
effect of women in board member in Nepalese bank profitability. Likewise, CR has
been included to measure the significance and its effect of changes in CR on
Nepalese bank profitability.
39
3.1 Method of Analysis
The study uses following method of analysis to examine the relationship between
credit risk management and bank profitability of Nepalese commercial banks:
1. Pooled Regression Analysis
2. Panel Data Analysis
2a. Fixed Effect Model
2b. Random Effect Model
3.1.1 Pooled Regression Analysis
Pooled regression analysis is used to establish the functional relationship between
two or more independent variables and a given dependent variable. It calculates the
value of coefficient of correlation interpreted as the fraction of the sample. Most
importantly, pooled regression analysis will reveal which of the factors specified are
important in explaining financial performance. To run pooled regression analysis
however, a stationary balanced series is required. Further, the major short coming
with this model is that it does not distinguish between the banks. In other words, it
denies the heterogeneity or individuality that may exist among the banks. The models
specified in the previous chapter for pooled regression analysis are as follows:
𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 = 𝛼𝛼0 + 𝛼𝛼1𝐶𝐶𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼2𝐿𝐿𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼3𝐵𝐵𝐵𝐵𝑖𝑖𝑖𝑖 + 𝛼𝛼4𝑅𝑅𝐴𝐴𝑖𝑖𝑖𝑖 + 𝛼𝛼5𝐿𝐿𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼6𝑁𝑁𝑁𝑁𝐿𝐿𝑅𝑅𝑖𝑖𝑖𝑖 +
𝛼𝛼7𝐶𝐶𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼8𝐶𝐶𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼9𝐹𝐹𝐵𝐵𝐹𝐹𝑖𝑖𝑖𝑖 + 𝜇𝜇𝑖𝑖𝑖𝑖 … … … … … … … … … … … … (12)
And,
𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 = 𝛼𝛼0 + 𝛼𝛼1𝐶𝐶𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼2𝐿𝐿𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼3𝐵𝐵𝐵𝐵𝑖𝑖𝑖𝑖 + 𝛼𝛼4𝑅𝑅𝐴𝐴𝑖𝑖𝑖𝑖 + 𝛼𝛼5𝐿𝐿𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼6𝑁𝑁𝑁𝑁𝐿𝐿𝑅𝑅𝑖𝑖𝑖𝑖 +
𝛼𝛼7𝐶𝐶𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼8𝐶𝐶𝑅𝑅𝑖𝑖𝑖𝑖 + 𝛼𝛼9𝐹𝐹𝐵𝐵𝐹𝐹𝑖𝑖𝑖𝑖 + 𝜇𝜇𝑖𝑖𝑖𝑖 … … … … … … … … … … … … (13)
3.1.2 Panel Data Analysis
The information for this study were collected from 28 commercial banks of Nepal
between the period 2011 and 2015. Since the data contains information on cross-
sectional units observed across time, this study also adopts a panel data estimation
technique. Hsiao (2014) describes panel data analysis as the statistical method to
analyze a given sample of individuals over time providing multiple observations on
each individual in the sample. It is an analysis of data containing two dimensions that
40
of cross-sectional and longitudinal. Panel data method allows analysis of a number of
important economic factors that cannot be measured or observed such as difference
in banking practices among banks (Torres-Reyna, 2007). It takes care of an
unobserved heterogeneity associated with individual banks thus allowing for
individual specific variables.
Ogboi and Unuafe (2013) describes the benefits of using panel data as (i) provision
of informative data, (ii) more variability, (iii) less collinearity, (iv) more degrees of
freedom, and (v) more efficient estimates. Further, it is argued that the method also
allows the study of individual dynamics and gives information on time ordering
events. Additionally, Hsiao (2014) identifies advantages of panel data analysis over
cross-sectional study and time-series study as its capacity for accurate prediction of
individual outcomes, accurate inference for model parameters, greater capacity for
constructing realistic behavioural hypotheses and its computational simplicity. There
are studies that have used panel data method in their analysis of different banks such
as that of Adeusi et al. (2014), Afriyie and Akotey (2012), Alshatti (2015), and
Ogboi and Unuafe (2013).
The models for panel data analysis can be expressed as,
3.1.2a Fixed Effect Model
Fixed effect model is used to analyze panel data if the objective is to analyze the
impact of variables that vary over time (Torres-Reyna, 2007). In this model
specification, the parameters are fixed or of non-random quantities. It removes the
effect of time-invariant characteristic allowing for the analysis of net effect of the
variables on the outcome (Torres-Reyna, 2007). Allison (2009) pointed out that, “In
fixed effect model, the unobserved variables are allowed to have associations with
the observed variables.”
The fixed effect model allows for heterogeneity or individuality among banks by
allowing each bank to have its own intercept value. In this model, although the
intercept may differ across banks, it does not vary over time that is it is time
invariant.
41
3.1.2b Random Effect Model
The random effect model is also used to analyze panel data and is often used when
the variations across entities are assumed to be random and uncorrelated with the
independent variables (Torres-Reyna, 2007). It is based on the assumption that the
difference between entities has some influence on the dependent variable (Torres-
Reyna, 2007). It also assumes that the entity’s error term is not correlated with the
predictors which allow time-invariant variables as explanatory variables (Torres-
Reyna, 2007). Hence, in this method, individual characteristics needs to be specified
which may or may not influence the predictor variables. An advantage of random
effect model is that it includes time-invariant variable such as gender while in fixed
effect such time-invariant variable is absorbed by the intercept (Torres-Reyna, 2007).
To determine as to which model (fixed effect model and random effect model) best
describes the relationship, a Hausman test was carried out to choose between two
models using STATA (Statistical data analysis software) at five percent level of
significance. The Hausman test evaluates the null hypothesis that the coefficient
estimated by the random effect estimator is same as estimated by fixed effect
estimator (Afriyie & Akotey, 2012). Specifically, the null hypothesis is that the
preferred model is random effects vs the alternative fixed effects (Greene, 2008).
Following from the Hausman test, if the p-value is less than α=0.05 the fixed effect
model is better than the random effect.
3.2 Data and Data Description
3.2.1 Data and sources of data
Data used for this study were obtained from the audited annual reports and Fitch
connect for all sample banks for the year 2011 to 2015. There were only 28
commercial banks included in the study. Some of the banks were not included
because of the lack of vital information needed to do the analysis. The selected 28
commercial banks of Nepal are the total number of commercial banks operating in
the country in 2017 although there were 30 commercial banks in 2015, the number of
banks differs in different years due to acquisition and merger. The commercial banks
42
included in the study is based on the year 2017 data because it was difficult to obtain
information on banks that have already been merged.
The selected 28 sample banks includes: Agriculture Development Bank Limited,
Bank of Kathmandu Lumbini Limited, Century Bank Limited, Citizens Bank
International Limited, Civil Bank Limited, Everest Bank Limited, Global IME Bank
Limited, Himalayan Bank Limited, Janata Bank Nepal Limited, Kumari Bank
Limited, Laxmi Bank Limited, Machhapuchchhre Bank Limited, Mega Bank Nepal
Limited, Nabil Bank Limited, Nepal Bangladesh Bank Limited, Nepal Bank Limited,
Nepal Credit and Commerce Bank Limited, Nepal Investment Bank Limited, Nepal
SBI Bank Limited, NIC Asia Bank Limited, NMB Bank Nepal Limited, Prabhu
Bank Limited, Prime Commercial Bank Limited, Rastriya Banijya Bank Limited,
Sanima Bank Limited, Siddhartha Bank Limited, Standard Chartered Bank Nepal
Limited, and Sunrise Bank Limited. All of these commercial banks are “A” class
commercial banks of Nepal and are listed in the Nepalese Stock Exchange. The
overall observation for all years for all banks adds up to 138.
3.2.2 Data collection
The variables included in the study were based on return on asset, return on equity,
capital adequacy ratio, liquidity ratio, bank size, asset quality, leverage ratio, non-
performing loan ratio, cash reserve ratio, coverage ratio, and female board member.
The description and calculation for each of these variables were presented in Chapter
two theoretical framework. Fitch connects and audited annual report for the financial
year 2011 to 2015 of all sample banks were accessed via Fitch connects and data
stream. Fitch connect is a world-class platform from Fitch solutions which offers
fundamental data, proprietary research, credit ratings, and analytics of the banking
sector. Fitch solutions are part of Fitch group, which is a global leader in financial
information services operating in more than 30 countries. Few variables such as cash
reserve ratio and female board member were sourced through audited annual reports
of all sample banks that are available online.
43
3.2.3 Time horizon
Suanders, Lewis, and Thornhill (2009) have distinguished time horizon in two
categories depending on whether the research is to be done at a particular point of
time known as a cross-sectional study or extended period known as a longitudinal
study. Both the cross-sectional and longitudinal studies are observational studies.
The Cross-sectional study examines different population group at a single point of
time (Health, 2015). It is mostly employed in studies based on interviews conducted
over a short time period using qualitative method (Suanders et al., 2009).
Longitudinal study, on the other hand, examines several observations of the same
population group over a period of time, which may last many years (Health, 2015).
Hence, under longitudinal study, it is necessary to collect data for at least two periods
for the same variable on the same population group.
As stated earlier, Nepalese commercial banks have faced difficulties over the past
years for many reasons including relaxed credit standard, identified by many as the
major reason. Also, most recently some policies have been reformed to improve
credit standard of Nepalese commercial banks. Hence, the aim here is also to detect
changes and development, by including data after implementation of policies that
improved credit standard. Taking all of these into account, the study include data
over the financial period of 5 years that is from 2011 to 2015.
3.3 Descriptive Statistics
Table 3 presents the descriptive statistics of the variables included in the study of the
28 commercial banks on Nepal from 2011 to 2015 (5 years’ period) of 138
observations.
Table 3 shows that the average value for bank’s profitability as measured by ROA is
1.46%, indicating that during the period of 2011-2015, the total assets of commercial
banks generated 1.46% return. Further, the average value for ROE is 14.72%,
indicating that during the period of 2011-2015, the equity of commercial banks
generated 14.72% return. The minimum capital adequacy ratio is -10.15%, which is
very low as the regulatory requirement of Nepal Rastra Bank (NRB) directives 2015
is 10%. However, the average CAR is 12.46%, which is greater than the regulatory
requirement of NRB.
44
Table 3: Descriptive Statistics
Variables N Mean Std. Dev. Min. Max. ROA 138 1.46 0.90 -1.37 4.41 ROE 138 14.72 14.90 -37.88 102.37 CR 138 11.14 2.85 0 16.28 CAR 138 12.46 6.01 -10.15 41.82 LR 138 78.62 12.40 46.08 117.38 BS 138 24.23 0.70 21.7 25.66 AQ 138 28.90 49.89 -18.39 489.2 LER 138 6.73 30.94 -339.65 70.34 NPLR 138 2.23 2.89 0 24.29 CRR 138 15.77 8.14 4 36.65 FBM 138 0.29 0.55 0 2
Where
N = Number of observations
ROA = Return on Assets
ROE = Return on Equity
CR = Coverage Ratio
CAR = Capital Adequacy Ratio
LR = Liquidity Ratio
BS = Bank Size
AQ = Asset Quality
LER = Leverage Ratio
NPLR = Non-Performing Loan Ratio
CRR = Cash Reserve Ratio
FBM = Female Board Member The minimum cash reserve ratio is 4%, which is lower than regulatory requirement
of 6%. This can be taken as non-compliance of maintaining CRR by commercial
banks as regulated by Nepal Rastra Bank’s (NRB’s) Unified 2015. The average non-
performing loan ratio during the period of 2011-2015 is 2.23%. The column of
standard deviation denotes how much the variable deviates from the mean. Here,
ROA, BS, and FBM have least standard deviation, while AQ and LER have high
standard deviation. The higher the standard deviation, the higher the variability of
that factor.
45
Chapter 4 Discussion of Results
This chapter presents the empirical findings of the study. The chapter starts with a short introduction, followed by the stationarity tests results and then the discussion of the pooled regression analysis and ends with results of the panel data analysis.
4.0 Introduction
Following from the discussion of the method and data description presented in
chapter 3, secondary data for all the variables included in the study were collected for
the period 2011 to 2015 for the 28 commercial banks of Nepal. Descriptive statistics
and unit root tests were then calculated first to ascertain the nature of the dataset.
Pooled regression analysis and panel data analysis were also carried out to test the
various hypotheses set for this study. Results of the various tests are discussed in the
succeeding sections.
4.1 Stationarity Test
The descriptive statistics were tabled and discussed in chapter 3 while results of the
stationarity tests are presented are discussed in this section. It is often recommended
that prior to running a time series analysis, a stationarity test be conducted for each
of the series included in the study. This is done in order to avoid spurious regression.
Spurious regression refers to the regression that tends to accept a false relation or
reject a true relation by flawed regression schemes (Chiarella & Gao, 2002).
A series is said to be stationary if the joint probability of the time series does not
change over time, meaning that the variance remains constant over time and that the
series has no trend (Jeffrey, 2009). Likewise, a series is said to be stationary if a shift
in time does not cause a change in shape of its distribution (Andale, 2017). However,
as in the case of most economic and financial series, the presence of trends results to
a series being non-stationary. The trend could be a deterministic trend describe as
shocks having transitory effects or a stochastic trend or unit-root that is shocks
having permanent effects (Jeffrey, 2009). Since the data set is an unbalanced panel
46
data set, the Fisher unit root test was carried out to ascertain whether a given series is
stationary or not. The Fisher type unit root test works well with an unbalanced panel.
The Fisher tests were calculated in STATA (Statistical data analysis software). The
test specifies the null hypothesis that the variable is non-stationarity (presence of
unit-root) and the alternative hypothesis that it is stationary. The Fisher unit root tests
result show that all variables included in the study are stationary. The calculated p-
value for all variables are smaller than 0.01, the null hypothesis was rejected at 1%
level of statistical significance.
4.2 Pooled Regression Analysis
Once the variables were verified to be stationary, equation (12) and (13) were
calculated using pooled regression data analysis. The results are presented in Table 4.
Table 4: Results of pooled regression analysis
Independent Variables Dependent Variables ROA ROE
CRit 0.0878 (0.000)***
-0.3405 (0.407)
CARit 0.0356 (0.018)**
-0.1643 (0.524)
LRit -0.0049 (0.459)
0.1524 (0.190)
BSit 0.7134 (0.000)***
12.6600 (0.000)***
AQit -0.0013 (0.362)
0.0190 (0.441)
LERit -0.0044 (0.039)**
0.0408 (0.270)
NPLRit -0.0575 (0.016)**
-0.4989 (0.225)
CRRit 0.0070 (0.392)
-0.0409 (0.773)
FBMit 0.0191 (0.872)
-4.2119 (0.042)**
Constant -16.7738 (0.000)***
-296.023 (0.000)***
F-statistic 0.000 0.000 R2 0.3691 0.3034 Adjusted R2 0.3247 0.2544
***, ** and * significant at 1%, 5% and 10% respectively.
47
The results will be discussed in turn starting first with ROA and then followed by
ROE.
4.2.1 Determinants of ROA
This section presents the findings of the pooled regression analysis using ROA as the
dependent variable.
Coverage Ratio (CR)
Coverage ratio (CR) measures the bank’s interest income on total gross loans. It is an
important factor in evaluating the bank’s performance as interest income is the main
source of income of the financial sector specially the banking sector. Coverage ratio
in turn influence by factors that affects interest rates such as the type of assets and
liabilities held by the bank as well as monetary policy changes. CR though not
previously used by other studies is hypothesized to be positively related to ROA as
interest income from loans increases, CR increases if interest income is increasing
more than the increases in loans.
Table 4 reveals that coverage ratio has a positive relationship with return on asset.
When all other factors are held constant, an increase in 1 percent of coverage ratio
leads to an increase in ROA by 0.0878 percent. The result further indicate that CR is
statistically significant at 1 percent level of significance, meaning that CR is a very
important variable affecting financial profitability of Nepalese commercial banks.
The positive relationship between coverage ratio and return on asset shows that when
the bank’s interest income increases, the bank’s return on asset also increases. This
implies that Nepalese commercial banks have effective measure to deal with credit
risk management.
Capital Adequacy Ratio (CAR)
Capital adequacy ratio determines the capacity of banks to meet possible losses. It
shows the bank’s financial strength in utilizing its capital and assets. Banks with
strong CAR tends to absorb possible loan losses and thus preventing them from
failing or be insolvent. This study hypothesized that CAR and ROA are positively
related to each other. However, various studies reviewed that used CAR in their
48
analysis have mixed results. The results of this study though show that CAR is
positively related to ROA of Nepalese commercial banks and is found to be
significant at 5% level of significance. Further it shows that 1 percent increase in
CAR holding, all given other factors held constant, will increase ROA by 0.0356
percent. The positive relationship between CAR and bank profitability is consistent
with the findings of Abdelrahim (2013); Afriyie and Akotey (2012); Bhattarai
(2014); Kurawa and Garba (2014); and Ogboi and Unuafe (2013) who also found a
positive relationship between CAR and bank profitability. However, this contradicts
with the findings of Alshatti (2015); Poudel (2012); Zou and Li (2014); and Ndoka
and Islami (2016) who found negative linkage between CAR and bank profitability.
Liquidity Ratio (LR)
Liquidity ratio (LR) refers to the ability of banks to convert assets into cash at a
reasonable cost in the short term to meet its financial obligations. In other words, it
measures the bank’s ability to fund all short-term obligations when they fall due. As
the motive of commercial banks is to enhance profitability, they are supposed to
maintain their liquidity ratio accordingly that is not too high whereby the bank is not
utilizing its fund efficiently nor too low, whereby the bank cannot pay off its short-
term obligations. Previous studies and theory of liquidity ratio indicate that a
negative association between CAR and bank profitability could be expected.
Table 4 reveals that an insignificant negative relationship exist between LR and
ROA. This is in accordance with theory. This result denotes that LR does not
significantly affect ROA of commercial banks in Nepal. This finding is also
consistent with the findings of Adeusi et. al. (2014) and Ogboi and Unuafe (2013)
who also found a negative impact of LR on bank financial performance. It however
contradicts with the result of Abdelrahim (2013) who found a positive impact of LR
on bank performance.
Bank Size (BS)
As indicated earlier, bank size as measured by the log of total assets shows the size
of the financial institution. The current results reveal that BS is positively and
statistically significant at 1 percent level of significance with ROA. The outcome
49
further indicates that as bank size increases, profitability increases. In the banking
sector, larger banks are likely to be active in the market, have a greater product and
have better possibilities in the diversification of risks. As larger banks do not operate
in a very competitive market, they also tend to make efficiency gains.
The findings in this study is similar to the findings of Bhattarai (2014) who studied
the effect of credit risk on bank performance in the context of Nepalese commercial
banks. However, it is contrary to that of Abdelrahim (2013) who found a negative
impact of bank size on the effectiveness of credit risk management.
Asset Quality (AQ)
The result from Table 4 indicates that an insignificant negative relationship exists
between asset quality and return on asset indicating that asset quality does not
significantly affect the return of asset of Nepalese commercial banks. Assets quality
is measured by growth of total loans. It is used to determine the strength of financial
institution against loss of assets value. Asset quality is very important to all the profit
motive banks as it provides earning to the banks. As discussed in chapter 2, the
growth of gross loans may not be adequate for profitability if they are not good
quality loans. Bad quality loans will not generate earnings to the banks and that an
increase in the value of bad quality loans can lead banks to solvency. The negative
sign may imply that Nepalese commercial banks are not issuing good quality loans.
Therefore, when the amount of loan increases, the return on asset decreases.
Leverage Ratio (LR)
The leverage ratio is measured by total liabilities to total common equity. It analyses
the degree of leverage of a bank indicating the ratio of debt a bank uses to finance its
operations to its shareholders’ equity. In the banking system, a very high and a very
low financial leverage is undesirable. High leverage ratio indicates that bank is
aggressive in financing its operations with debt which may generate additional
interest expenses, and in the worst scenario, may also lead to bankruptcy. Very low
financial leverage may also mark a question on bank’s profitability and its reluctance
in accepting deposits. Bank profitability and leverage ratio is assumed to have a
negative relationship based on theory and previous empirical studies.
50
From Table 4, the result indicates a negative and statistically significant relationship
between leverage ratio and ROA of commercial banks of Nepal. The result shows
that 1 percent increase in leverage ratio leads to a decrease in ROA by 0.0044
percent holding all other factors constant. The sign confirms with theoretical
expectations and the finding of Alshatti (2015) who also found a negative effect of
leverage ratio on banks financial performance. Hence, banks are advised not to be
enormously financed by debt as high financial leverage will increase liabilities and
interest expenses affecting their financial performance. Likewise, they need to
manage debt to the level where they can achieve relatively high profit providing
higher returns to owners. The result is also consistent with the findings of Alshatti
(2015) who found a negative effect of leverage ratio on bank performance.
Non-Performing Loan Ratio (NPLR)
NPLR is the most important factor in managing credit risk and financial stability of
banks. It is the measure of non-performing loans to total loans of a bank. The initial
stage of non-performing loan starts with late payment of principal and interest.
However, the loan is only categorized as a non-performing loan if the loan is not paid
in more than 90 days otherwise it is considered as a default loan. In many cases,
there is a small chance that full payment of non-performing loans will be made.
Thus, NPLR is hypothesized to have an inverse relationship with bank profitability.
The result on Table 4 also indicates that NPLR has a negative impact on ROA and is
statistically significant at 5 percent level of significance. This means that NPLR
impacts ROA of commercial banks of Nepal. It also follows that when NPLR
decreases, return on asset of Nepalese commercial banks increases.
The result is similar to those of Aduda and Gitonga (2011), Li and Zou (2014),
Bhattarai (2014), Kaaya and Pastory (2013) as well as Ndoka and Islami (2016) who
all found negative effect of NPLR on bank performance. However, it is contrary to
that of Alshatti (2015) and Afriyie and Akotey (2012) who found positive influence
of NPLR on bank profitability. From the analysis reported here, it shows that when
all other factors are held constant, an increase in 1 percent of NPLR leads to
reduction in ROA by 0.0575 percent. In summary, the study found that higher the
NPLR, the lower the profitability of the commercial banks of Nepal.
51
Cash Reserve Ratio (CRR)
CRR denotes the portion of total deposits held by the central bank as reserves for all
the banks and financial institutions. As mentioned in chapter 2, it is used as a tool to
control money supply in the economy which in turn has significant influence on the
liquidity, change in interest rates and profitability of the banks.
The CRR is hypothesized to have a negative relationship with the financial
performance of the banks based on literature review of previous empirical studies.
From Table 4, the result indicates a positive and statistically insignificant
relationship between cash reserve ratio and ROA of commercial banks of Nepal. The
result further indicates that cash reserve ratio does not affect the ROA of commercial
bank of Nepal which is contrary to most previous studies. Though the result is not
significant, it is similar to the findings of Bhattarai (2014).
Female Board Member (FBM)
The gender of board member and senior executive has remained an important issue
in the organizations. Many developing nations claim to have a few female members
on the top positions. However, many countries in Europe have provisions of
maintaining minimum required female board members (FBM) for public trading
companies. FBM variable was not previously used by other studies particularly in
examining the relationship between credit risk management and bank profitability. It
is hypothesized to have a positive impact on financial profitability of banks. The
result shown in Table 4 indicates a positive but statistically insignificant relationship,
meaning that female board member does not impact ROA of Nepalese commercial
banks. The positive sign however denotes that when the number of female board
member increases, the profitability of bank also increases. A thorough investigation
of the data show that only a small number of banks have female board members.
This might explain the insignificant result.
In summary, the variables CR, CAR, BS, LER, and NPLR are statistically significant
with ROA of Nepalese commercial banks. CR, CAR, and BS all positively affect
ROA of commercial banks of Nepal. The variables LER and NPLR negatively affect
52
ROA of Nepalese commercial banks. While, LR, AQ, CRR, and FBM are found not
to have an influence on Nepalese commercial banks’ profitability.
4.2.2 Determinants of ROE
The results of the pooled regression analysis on ROE will be presented and discussed
in the succeeding section.
Coverage Ratio (CR)
As indicated earlier, CR is a variable introduced in this study. It is hypothesized that
CR is positively related to bank’s profitability. Table 4 indicate that CR will have an
insignificant negative impact on ROE which is contrary to the prior assumption. The
result implies that CR does not significantly affect return on equity of Nepalese
commercial banks.
Capital Adequacy Ratio (CAR)
Capital adequacy ratio has been used in many studies as a control variable and is
expected to have a positive impact on bank profitability. The result on Table 4 shows
an insignificant negative relationship between CAR and ROE which is again contrary
to expectation but is consistent with the findings of Alshatti (2015); Poudel (2012);
Zou and Li (2014); and Ndoka and Islami (2016) also found negative relationship
between CAR and bank performance. This result is however contrary to the findings
of Abdelrahim (2013); Afriyie and Akotey (2012); Bhattarai (2014); Kurawa and
Garba (2014); and Ogboi and Unuafe (2013) who all found a positive relationship
between CAR and bank performance.
The findings indicate that CAR does not significantly influence ROE of Nepalese
commercial banks.
Liquidity Ratio (LR)
From the literature review in chapter 2, liquidity ratio (LR) has been used as an
independent variable assuming a negative impact of LR on bank profitability. The
result on Table 4 indicates that LR is statistically insignificant and have a positive
relationship with ROE. This result is contrary to prior assumption but is similar to the
53
findings of Abdelrahim (2013). However, the result is inconsistent with the findings
of Adeusi et. al. (2014) and Ogboi and Unuafe (2013) who all found a negative effect
of LR on bank financial performance. The outcome however denotes that LR is not a
significant predictor of ROE for Nepalese commercial banks.
Bank Size (BS)
Bank size (BS) is assumed to have a positive relationship with bank profitability.
From Table 4, the result shows that BS has a positive coefficient and statistically
significant at 1 percent level of significance. This implies that BS affects the
performance of commercial banks of Nepal. Keeping all other variables constant, an
increase in 1 percent of BS leads to increase in ROE by 0.1266 percent.
The result is consistent with the findings of Bhattarai (2014) who also found a
positive association between BS and bank performance. But is contrary to the
findings of Abdelrahim (2013) who found a negative impact of BS on the
effectiveness of Saudi bank’s credit risk management.
Further it indicates that larger Nepalese commercial banks have better performance
compared to smaller banks. The reason for such result may be that larger banks tend
to have greater products and have more possibilities for loan diversification resulting
in better performance.
Asset Quality (AQ)
Following the discussion in chapter 2, AQ is used here as a control variable. It is
further hypothesized that AQ have a positive relationship with bank profitability. The
result presented on Table 4 indicates that AQ has an insignificant positive
relationship with ROE. This denotes that AQ does not significantly affect ROE of
Nepalese commercial banks. The positive sign is what is expected but is contrary to
the finding of Abdelrahim (2013) who found a negative impact of AQ on the
effectiveness of Saudi bank’s credit risk management. The positive sign indicates
that Nepalese commercial banks have good credit assessment implying that when
gross loan increases, profitability also rises simultaneously.
54
Leverage Ratio (LER)
LER is hypothesized to have a negative relationship with bank profitability. Contrary
to the hypothesized relationship, the result on Table 4 indicates that LER has an
insignificant positive relationship with ROA of Nepalese commercial banks. This
means that LER does not significantly impact Nepalese commercial bank’s ROE.
This result is in contrast to the finding of Alshatti (2015) who found a negative effect
of leverage ratio on banks financial performance.
Non-Performing Loan Ratio (NPLR)
As the importance of NPLR has already been discussed previously, it is hypothesized
to have a negative relationship with bank profitability. The result on Table 4
indicates that NPLR has a negative but statistically insignificant relationship with
ROE. This means that NPLR does not influence ROE of commercial banks in Nepal.
The negative result is similar to the assumption and the findings of Aduda and
Gitonga (2011), Li and Zou (2014), Bhattarai (2014), Kaaya and Pastory (2013), and
Ndoka and Islami (2016) who also found negative relationship between NPLR and
bank profitability. However, it is contrary to the findings of Alshatti (2015) and
Afriyie and Akotey (2012) who found a positive effect of NPLR on bank
profitability. Further the result implies that increase in bad loans negatively affect the
bank’s financial performance resulting in lower profitability. This evidences that
Nepalese commercial banks have a good measure to deal with credit risk
management.
Cash Reserve Ratio (CRR)
CRR is hypothesized to have a negative relationship with bank financial
performance. The result of pooled data analysis on Table 4 indicates that CRR has a
negative and statistically insignificant relationship with ROE of Nepalese
commercial banks. The result is similar to finding of Bhattarai (2014) who concluded
that CRR has an inverse impact on bank profitability. However, it shows that CRR
does not significantly influence the ROE of commercial banks of Nepal.
55
This shows that, when the regulatory requirement of CRR increases, banks have
lesser money to loan out, hence proportionately lowering the investment amount,
interest income and its profitability.
Female Board Member (FBM)
FBM has been introduced for the first time in this study. A positive relationship with
bank profitability is expected based on studies pertaining to other sectors. However,
the result on Table 4 shows although FBM is statistically significant at 5 percent
level of significance, it has a negative relationship with the ROE of commercial
banks of Nepal. This indicates that FBM significantly influences ROE of Nepalese
commercial banks but contrary to the prior hypothesis. Keeping all other factors
constant, an increase in FBM by 1 person leads to fall in ROE by 4.211 percent. The
possible rationale for the finding is the lack of possible female board members in the
majority of Nepalese commercial banks as can be seen in Table 3 (descriptive
statistics) which shows that the number of female board members ranges from 0-2.
This is extremely very low in proportion to the number of male board members. The
other possible reason could be the inactiveness of female board members in major
decision making. Thus, the negative result may be explained by these factors.
In short, the variables BS and FBM are statistically significant with the ROE of
Nepalese commercial banks. On the other hand, variables CR, CAR, LR, AQ, LER,
NPLR and CRR does not significantly influence ROE of Nepalese commercial
banks.
56
4.3 Diagnostic Tests
Table 5: Post Estimation Diagnostic Tests
ROA ROE Breush-Pagan/ Heteroskedasticity Test
0.7981
0.000
Ramsay RESET Test 0.8018 0.0037 VIF
CR 1.13 1.13 CAR 1.98 1.98 LR 1.70 1.70 BS 1.73 1.73 AQ 1.25 1.25
LER 1.07 1.07 NPLR 1.16 1.16 CRR 1.10 1.10 FBM 1.09 1.09
AIC 319.9299 1106.386 BIC 349.2024 1135.658
***, ** and * significant at 1%, 5% and 10% respectively
4.4 Model Fit
The results of the pooled regression analysis indicate the existence of a relationship
between some of independent variables and the dependent variables of ROE and
ROA included in this study. The result shows that credit risk management influences
the profitability of Nepalese commercial banks. The result of F-statistic on Table 4
signifies that both models are statistically significant at 1% level of significance with
the corresponding probability value 0.0000 for both ROA and ROE.
The R-square shows how well the regression model fits the data. The higher the R-
square, the closer the estimated regression fits the data. From the result on Table 4, it
implies that 37% of the variation in ROA is explained by the nine (9) explanatory
variables while only 30% of the changes in ROE can be explained by them.
The Adjusted R2 are listed in Table 4 indicating a 0.32 for ROA and 0.25 for ROE.
Model with a higher value of adjusted R-squared is considered to be a better model.
Akaike’s Information Criterion (AIC) and Bayesian Information Criterion (BIC) are
two measures for comparing maximum likelihood models. When two models fit on
the same data, the model with the smaller value of the information criterion is
considered to be better model. From Table 5, the values of AIC and BIC for ROA are
57
349.20 and 319.92 respectively. Likewise, the values of AIC and BIC for ROE are
1135.65 and 1106.38 respectively.
Table 5 further shows that both models do not have problems of multi collinearity as
the variance inflation factor (VIF) are all less than 10. Further, the results reveals that
the ROA model does not suffer from heteroscedasticity and have no omitted variable.
On the other hand, the ROE model suffers from heteroscedasticity (the variance of
the error term is not constant) and have problem of omitted variables.
To conclude, the results of pooled regression analysis show that the selected data is
better fitted on the ROA model. The relationship of CR, CAR, BS, LER, and NPLR
are statistically significant with ROA. Although the ROA model fits the data better,
some variables such as BS and FBM are only statistically significant using the ROE
model.
4.5 Results of Panel Data Analysis
Following the discussion of panel data analysis in chapter three, this section presents
the results for ROA and ROE in determining the impact of credit risk management
on the profitability of commercial banks of Nepal for the period 2011 to 2015.
4.5.1 Panel data analysis on ROA
The results of the panel data analysis on ROA is presented in Table 6 and discussed
in the succeeding sections.
As discussed earlier, the Hausman test is carried out to choose between fixed effect
model and random effect model. The details of this test was discussed in chapter
three. Since the calculated p value of 0.0168 is less than 0.05, it implies that a fixed
effect model is a better model compared to the random effect model. The fixed effect
model allows for heterogeneity or individuality among 28 banks by allowing each of
the bank to have their own intercept value. The term fixed effect refers to the fact
that although the intercept may differ across individual banks, it does not vary over
time. That is, it is time invariant.
58
Table 6: Results of panel data analysis on ROA
Variable FE Model RE Model CR 0.0986
(0.000)*** 0.0972
(0.000)*** CAR 0.0347
(0.023)** 0.0348
(0.013)** LR -0.0095
(0.360) -0.0058 (0.438)
BS 0.5216 (0.003)***
0.6533 (0.000)***
AQ -0.0002 (0.823)
-0.0005 (0.648)
LER -0.0018 (0.302)
-0.0025 (0.151)
NPLR -0.1107 (0.000)***
-0.0911 (0.000)***
CRR -0.0117 (0.432)
0.0005 (0.957)
FBM -0.1432 (0.415)
-0.0173 (0.899)
Constant -11.4563 (0.015)**
-15.1909 (0.000)***
F-Statistic 0.0000 R2 0.7058 Adjusted R2 0.6009 Hausman Test Prob>Chi2 = 0.0168
***, ** and * significant at 1%, 5% and 10% respectively.
Coverage Ratio (CR)
As stated in chapter 3, coverage ratio is hypothesized to have a positive relationship
with bank profitability. The result on Table 6 indicates that CR is positive and
statistically significant at 1 percent level of significance which means that CR is a
very important variable affecting the ROA of Nepalese commercial banks. The
positive sign is what is expected and results mean that 1 percent increase in CR leads
to 0.0986 percent increase in ROA.
Capital Adequacy Ratio (CAR)
In this study, CAR is used as an independent variable as is hypothesized to have a
positive relationship with bank profitability. The result on Table 6 shows that CAR is
positive and statistically significant at 5 percent level of significance which is what is
hypothesized. It indicates that CAR is also an important variable affecting ROA of
59
commercial banks of Nepal. Keeping all the other factors constant, 1 percent increase
in CAR leads to an increase in ROA by 0.0347 percent.
Liquidity Ratio (LR)
LR is hypothesized to have a negative relationship with bank profitability in most of
the theoretical literature. The result on Table 6 shows similar result indicating that
LR is negative but insignificant with ROA.
Bank Size (BS)
From the theoretical literature BS is hypothesized to have a positive relationship with
bank profitability. The result on Table 6 shows that BS is positive and statistically
significant at 1 percent level of significance indicating that BS is very important
variable affecting the ROA of commercial banks of Nepal. Specifically, if all the
other factors are held constant, 1 percent increase in BS leads ROA to increase by
0.5216 percent.
Asset Quality (AQ)
Asset quality is also hypothesized to have a positive relationship with bank
profitability. However, the result on Table 6 indicates that AQ is negative and
insignificant with ROA which is contrary to what is expected. Further, the result also
indicates that AQ does not significantly influence ROA of commercial banks of
Nepal.
Leverage Ratio (LER)
In contrast, LER is hypothesized to have a negative relationship with bank
profitability. The result on Table 6 indicates that LER is indeed negative but
insignificant with ROA meaning that LER does not influence ROA of commercial
banks of Nepal for the years 2011-2015.
Non-Performing Loan Ratio (NPLR)
NPLR identified as an important factor in managing credit risk in the banks. It is
hypothesized to have a negative relationship with bank profitability. The result on
60
Table 6 indicates that NPLR is negative and statistically significant at 1 percent level
of significance which confirms with expectations. Further the result denotes that
NPLR indeed influences ROA of Nepalese commercial banks. Keeping all the other
factors constant, 1 percent increase in NPLR leads to decrease in ROA by 0.1107
percent.
Cash Reserve Ratio (CRR)
The result on Table 6 shows that CRR has a negative relationship with ROA of
commercial banks of Nepal which is what is expected. However, the result further
indicates that CRR is insignificant meaning that CRR does not influence ROA of
Nepalese commercial banks during the study period.
Female Board Member (FBM)
The variable FBM is hypothesized to have a positive relationship with bank
profitability. However, the result on Table 6 shows a contradictory result indicating
that FBM is negative and has an insignificant relationship with ROA indicating that
FBM does not influence ROA of Nepalese commercial banks.
In summary, the panel data analysis on ROA reveals that the independent variables
of CR, CAR, BS, and NPLR are statistically significant with the ROA of Nepalese
commercial banks.
However, the other remaining independent variables of LR, AQ, LER, CRR, and
FBM does not significantly influence ROA of commercial banks of Nepal. In
addition, the fixed effect model specification was found to be the better specification
compared to the random effect model specification as indicated by the Hausman test.
4.5.2 Panel data analysis on ROE
The results of the panel data analysis on ROE is presented in Table 7 and discussed
below.
Table 7, summarises the outcome of panel data analysis using ROE as the dependent
variable. The Hausman test for ROE calculated the p value to be 0.0663 which is
greater than the alpha of 0.05, implying that the random effect model is a better
61
model specification than the fixed effect model. Under random effect model, the
variations across entities are assumed to be random and uncorrelated with the
independent variables.
Table 7: Results of panel data analysis on ROE
Variable FE Model RE Model CR -0.5673
(0.287) -0.3491 (0.398)
CAR 0.2091 (0.524)
-0.1364 (0.600)
LR 0.3483 (0.125)
0.1499 (0.207)
BS 8.5428 (0.025)**
12.5645 (0.000)***
AQ 0.0112 (0.680)
0.0182 (0.458)
LER 0.0782 (0.049)**
0.0432 (0.240)
NPLR -1.1057 (0.028)**
-0.5389 (0.192)
CRR 0.3843 (0.236)
-0.0333 (0.821)
FBM -3.8904 (0.307)
-4.1761 (0.049)**
Constant -219.2536 (0.031)**
-293.8033 (0.000)***
F-statistic 0.0037 R2 0.4899 Adjusted R2 0.3081 Hausman Test Prob>Chi2 = 0.0663
***, ** and * significant at 1%, 5% and 10% respectively.
The panel data analysis on ROE shows that the independent variables BS and FBM
are the only variables that are statistically significant with ROE of Nepalese
commercial banks. On the other hand, the other remaining independent variables of
CR, CAR, LR, AQ, LER, NPLR, and CRR are not statistically significant variables
in explaining ROE.
Bank Size (BS)
Using the result preseted in Table 7, it indicates that BS and ROE have a positive and
statistically significant relationship at 1 percent level of significance. This means that
62
BS influences the ROE of Nepalese commercial banks and is a very important
predictor of ROE of commercial banks of Nepal. Keeping all the other factors
constant, 1 percent increase in BS leads to an increase in ROE by 12.5645 percent.
Female Board Member (FBM)
The variable FBM is hypothesized to have a positive relationship with bank
profitbility based on on literature review of similar studies on other sectors. The
result on Table 7 indicates that FBM and ROE has a negative relatioship. It is also
statistically significant at 5 percent level of significance indicating that FBM is a
very important predictor of ROE of Nepalese commercial banks. One possible
explanation of the negative result is due to a lack of female board members in most
of Nepalese commercial banks and may be due to lack of participation in major
decision making process.
63
Chapter 5 Conclusions and Recommendations
This chapter presents the conclusions of the study. It starts with a summary of the findings and then provides recommendations as well as areas of further reseach at the end of this chapter.
5.0 Summary and Conclusion
In chapter 1, the significance of the study and the purpose of the research were
presented and discussed. As specified earlier, the primary aim of the study is to
examine the impact of credit risk management on the profitability of commercial
banks of Nepal. The various studies on the topic reviewed in the context of
developed and developing countries were presented in chapter 2. Based on the
review, appropriate variables were selected to be included in the analysis. Each of the
variables were then defined and the rationale of choosing them were put forward.
The calculation formula and the expected sign were also discussed. However, two
independent variables which have not been used in previous studies were added in
the finale models. As dependent variables indicating profitability of commercial
banks, ROA and ROE were selected as these were the most popular variables in the
literature. Explanatory variables include: CAR, LR, BS, AQ, LER, NPLR, CRR, CR,
and FBM representing credit risk management. For this study, a pooled regression
analysis and a panel data analysis were applied for all 28 commercial banks of Nepal
for the period 2011 to 2015.
The primary research question of “What is the relationship between credit risk
management and profitability of Nepalese commercial banks from 2011 to 2015?” is
addressed using the results from the various statistical analysis. Overall, the results
imply that there exists a relationship between credit risk management and
profitability of commercial banks of Nepal. Specifically, the study identified factors
that influence the financial performance of Nepalese commercial banks.
The analyses revealed that capital adequacy ratio (CAR) has a positive and
statistically significant impact on bank profitability of Nepalese commercial banks.
64
The finding confirms with expectation and the finding of Ogboi and Unuafe (2013)
but contrary to that of Poudel (2012). In addition, from most of other studies
reviewed there were mixed results and were unable to establish a relationship
between CAR and bank profitability. This study in contrast accepts the hypothesis
that Nepalese commercial banks with higher CAR can absorb credit losses
preventing the banks from financial loss.
Bank size (BS) has a positive and a strong significant relationship with the financial
performance of Nepalese commercial banks. This finding is what is expected and to
the finding of Bhattarai (2014) but contradictory to that of Abdelrahim (2013). This
result is consistent with the hypothesis that larger Nepalese commercial banks are
able to make more profit.
Coverage ratio (CR) is found to have a strong significant positive relationship with
the profitability of Nepalese commercial bank. The finding is consistent with theory.
CR is one of the two variables introduced in this study as it has not been used in
other studies before. The result confirms acceptance of the hypothesis that Nepalese
commercial banks with higher coverage ratio have a good quality of loan portfolio
with good interest income resulting to better bank performance.
Further, the analyses revealed that NPLR has a statistically significant negative
impact on financial performance of Nepalese commercial banks. The finding is
similar to that of Aduda and Gitonga (2011), Bhattarai (2014), Kaaya and Pastory
(2013), Zou and Li (2014), and Ndoka and Islami (2016) but contrary to that of
Afriyie and Akotey (2012), and Alshatti (2015). Some other studies have found
mixed results but were unable to establish a relationship between NPL and bank
performance. This serve as an evidence that Nepalese commercial banks have
efficient ways of assessing credit. The findings prove that NPL in Nepalese
commercial banks decreases loan payment, resulting in less income and less
available capital to invest which leads to decrease in bank profitability.
Leverage ratio (LR) is found to have a negative and statistically significant impact on
financial performance of commercial banks of Nepal. The finding is what is expected
and consistent to that of Alshatti (2015). The study is also confirms the notion that
Nepalese banks can avoid bankruptcy and protect depositors fund by maintaining
low financial leverage.
65
Female board members (FBM) have significant negative impact on Nepalese
commercial bank profitability. The result is however contrary to what is expected as
FBM was hypothesized to have a positive relationship with bank profitability. This
variable was a novelty in this study as this has not been used before. Yet, the study
fails to accept the hypothesis that women in a top position in commercial banks of
Nepal enhance bank performance.
On the other hance, the other independent variables of liquidity ratio (LR), asset
quality (AQ), and cash reserve ratio (CRR) could not be regarded as influencing
variable on bank performance as they are found to be insignificant at 5 percent level
of significance.
In summary, the findings of the study indicate that the commercial banks of Nepal
have a good credit risk management practices which are evidenced by the significant
result for CAR, BS, CR, NPLR, LER, and FBM. The overall result showed that
credit risk management is an important predictor of bank financial performance,
hence indicating that the success of bank in terms of profitability depends on risk
management.
5.1 Recommendation
As the findings of the study have revealed, risk management has a significant
contribution to bank performance. It is recommended for banks to emphasize more
on risk management. In general, banks need to maintain an optimum level of CAR
(or as per regulatory requirement) so that they will not have difficulty in meeting
their financial obligations, protect their depositors’ investment and thus promotes the
stability of the financial system. Also, Nepalese banks are to be made aware that
bank performance is also influenced by its size. Larger banks tend to achieve higher
profits as they differentiate their products and are also able to diversify their risk to
operate in less competitive market.
The study further recommends for banks to control and monitor NPL, and keep the
level of NPL as low as possible by emphasizing more on the ability to pay back
before credit approvals are given, a practice that will enable banks to achieve higher
performance. Also, banks need to emphasize on coverage ratio, meaning that banks
66
monitor all the factors related to interest income on loans such as a change in interest
rate, quality of loans, and assets and liabilities as they affect bank performance.
Further, the banks are recommended not to be highly financed by debt as higher
financial leverage will increase liabilities resulting negative effect on financial
performance. It is also recommended to balance the bank’s capital between
shareholder’s equity and debt in financing its operations. The study encourages
female board members in Nepalese banks to be more active in decision making and
for banks to increase the number of female board member.
Although, the study could not find any relationship between LR, AQ, and CRR with
bank performance, it does not mean that these variables are not important. These
variables need to be considered in managing risk in banks.
5.2 Contributions
Upon the completion of this research study, the research gap of examining the
relationship between credit risk management and profitability of Nepalese
commercial banks taking into account all the commercial banks of Nepal and
introducing 2 new variables that are hypothesized to be affecting bank profitability is
fulfilled. It is hoped that the findings presented are useful for academicians and the
banking industry.
Further, the findings and conclusions of this research also contribute as a source of
valuable information to bank management, investors, stakeholders, regulatory
bodies, financial analysts, economists or any other stakeholders that are making any
relevant decisions.
5.3 Areas for Further Research
A suggestion for further research could be performing research on the relationship
between financial risk management and financial performance of Nepalese banks
focusing on other risk management such as liquidity risk, market risk, or operational
risk.
67
Another area of research could be the inclusion of development banks, finance
companies, and cooperatives which are successfully operating in the Nepalese
market.
68
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Appendices
Appendix A: Number of Financial Institutions, per sector and total from 1985-2017
Types of financial institutions/ Year
1985 1990 1995 2000 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Commercial Banks
3 5 10 13 17 18 20 25 26 27 31 32 31 30 30 30 28
Development Banks
2 2 3 7 26 28 38 58 63 79 87 88 86 84 76 73 57
Finance Companies
- - 21 45 60 70 74 78 77 79 79 69 59 53 47 48 36
Micro-Finance Financial Institutions
- - 4 7 11 11 12 12 15 18 21 24 31 37 38 41 48
Saving and Credit Cooperatives
- - 6 19 20 19 17 16 16 15 16 16 15 16 15 15 15
Non-Government Organizations
- - - 7 47 47 47 46 45 45 38 36 31 30 27 27 25
Total 5 7 44 98 181 193 208 235 242 263 272 265 253 250 233 234 209
Appendix B: List of Banks and Financial Institutions as of Mid-January, 2017 (Licenced by NRB)
Class: "A" (Commercial Banks) (Rs. in Crore) S. No. Name Operation
Date (A.D.) Head Office Paid up
Capital Working Area
1 Nepal Bank Ltd. 1937/11/15 Dharmapath,Kathmandu 649.95 National Level 2 Rastriya Banijya Bank Ltd. 1966/01/23 Singhadurbarplaza,Kathmandu 858.90 National Level 3 Agriculture Development Bank
Ltd. 1968/01/21 Ramshahpath, Kathmandu 1037.44 National Level
4 Nabil Bank Ltd. 1984/07/12 Beena Marg, Kathmandu 618.35 National Level 5 Nepal Investment Bank Ltd. 1986/03/09 Durbarmarg, Kathmandu 870.66 National Level 6 Standard Chartered Bank Nepal
Ltd. 1987/02/28 Nayabaneshwor, Kathmandu 374.99 National Level
7 Himalayan Bank Ltd. 1993/01/18 Kamaladi, Kathmandu 449.91 National Level 8 Nepal SBI Bank Ltd. 1993/07/07 Kesharmahal, Kathmandu 388.37 National Level 9 Nepal Bangladesh Bank Ltd. 1994/06/06 Kamaladi, Kathmandu 401.18 National Level 10 Everest Bank Ltd. 1994/10/18 Lazimpat , Kathmandu 274.26 National Level 11 Kumari Bank Ltd. 2001/04/03 Durbarmarg, Kathmandu 269.92 National Level 12 Laxmi Bank Ltd. 2002/04/03 Hattisar, Kathmandu 303.92 National Level 13 Citizens Bank International Ltd. 2007/04/20 Kamaladi, Kathmandu 553.74 National Level 14 Prime Commercial Bank Ltd. 2007/09/24 Newroad, Kathmandu 489.19 National Level 15 Sunrise Bank Ltd. 2007/10/12 Gairidhara, Kathmandu 530.14 National Level 16 Janata Bank Nepal Ltd. 2010/04/05 Naya Baneshwor, Kathmandu 206.00 National Level 17 Mega Bank Nepal Ltd. 2010/07/23 Kantipath, Kathmandu 401.20 National Level 18 Century Commercial Bank Ltd. 2011/03/10 Putalisadak , Kathmandu 368.90 National Level 19 Sanima Bank Ltd.1 2012/02/15 Nagpokhari, Kathmandu 530.59 National Level 20 Machhapuchhre Bank Ltd. 2012/7/9* New Road, Pokhara, Kaski 386.45 National Level 21 NIC Asia Bank Ltd. 2013/6/30* Thapathali, Kathmandu 581.96 National Level 22 Global IME Bank Ltd. 2014/4/9* Panipokhari, Kathmandu 616.43 National Level 23 NMB Bank Ltd. 2015/10/18* Babarmahal, Kathmandu 543.01 National Level
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24 Prabhu Bank Ltd. 2016/2/12* Babarmahal, Kathmandu 588.14 National Level 25 Siddhartha Bank Ltd. 2016/7/21* Hattisar, Kathmandu 302.21 National Level 26 Bank of Kathmandu Lumbini Ltd. 2016/7/14* Kamaladi, Kathmandu 457.69 National Level 27 Civil Bank Ltd.2 2016/10/17* Kamaladi, Kathmandu 458.38 National Level 28 Nepal Credit and Commerce
Bank Ltd.3 2017/01/01* Siddharthanagar, Rupandehi 467.91 National Level
*Joint operation date after merger. 1 Paidup Capital After acquisition of Bagmati Development Bank Ltd. by Sanima Bank Ltd. 2 After merger of Civil Bank Ltd. and International Leasing and Finance Company Ltd. 3 Paid Capital After merger of Nepal Credit and Commerce Bank Ltd., International Development Bank Ltd., Infrastructure Development Bank Ltd., Supreme Development Bank Ltd. and Apex Development Bank Ltd. Class: "B" (Development Banks) (Rs. in Crore) S. No. Name Operation
Date (A.D.) Head Office Paid up
Capital Working Area
1 NIDC Development Bank Ltd. 1959/06/15 Durbar Marg, Kathmandu 41.58 National Level 2 Narayani Development Bank Ltd. 2001/10/17 Ratna Nagar, Chitawan 5.56 1-3 District
Level (Nawalparasi, Chitwan, Makawanpur)
3 Sahayogi Vikas Bank Ltd. 2003/10/23 Janakpurdham, Dhanusha 25.79 1-3 District Level (Dhanusa, Mahottari, Sindhuli)
4 Karnali Bikash Bank Ltd. 2004/02/18 Nepalgunj, Banke 13.96 1-3 District Level (Banke, Bardiya, Dang)
5 Gandaki Development Bank Ltd. 2005/01/25 Pokhara, Kaski 53.86 4-10 District level (Chitwan, Syanja, Kaski, Lamjung, Parbat, Tanahu, Gorkha, Rupandehi, Nawalparasi, Baglung)
6 Excel Development Bank Ltd. 2005/07/21 Birtamod, Jhapa 30.77 1-3 District Level (Ilam, Jhapa, Morang)
7 Western Development Bank Ltd. 2005/09/15 Ghorahi, Dang 15.70 1-3 District Level (Dang, Banke, Kapilbastu)
8 Miteri Development Bank Ltd. 2006/10/13 Dharan, Sunsari 21.13 1-3 District Level (Jhapa, Morang, Sunsari)
9 Tinau Bikas Bank Ltd. 2006/11/01 Butwal, Rupandehi 27.31 1-3 District Level (Rupandehi, Nawalparasi, Chitwan)
10 Muktinath Bikas Bank Ltd. 2007/01/03 Pokhara, Kaski 153.14 National Level 11 Sewa Bikas Bank Ltd. 2007/02/25 Butwal, Rupandehi 42.56 4-10 District
level (Rupandehi, Nawalparasi, Kapilbastu, Palpa, Syangha,Chitawan, Gulmi, Arghakhachi, Dang, Bake )
12 Kankai Bikas Bank Ltd. 2007/05/03 Damak, Jhapa 12.50 1-3 District
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Level (Jhapa, Ilam, Morang)
13 Ace Development Bank Ltd. 2007/08/15 Narayanchaur, Naxal, Kathmandu
120.30 National Level
14 Mahakali Bikas Bank Ltd. 2007/08/18 Mahendranagar, Kanchanpur 14.88 1-3 District Level (Kanchanpur, Kailali, Dadeldhura)
15 Bhargab Bikas Bank Ltd. 2007/08/30 Nepalgunj, Banke 12.00 1-3 District Level (Banke, Dang, Bardiya)
16 Country Development Bank Ltd. 2007/10/4 Banepa, Kavre 35.69 4-10 District level (Kavrepalanchowk, Sindhupalchowk, Sindhuli, Bara, Parsa, Makawanpur, Chitwan, Nawalparasi, Rupandehi, Kapilvastu)
17 Alpine Development Bank Ltd. 2007/10/05 Hetauda, Makawanpur 22.71 1-3 District Level (Makawanpur, Chitwan, Kavrepalanchowk)
18 Corporate Development Bank Ltd.
2007/11/07 Birgunj, Parsa 20.00 1-3 District Level (Parsa, Makawanpur, Kavrepalanchowk)
19 Kabeli Bikas Bank Ltd. 2007/12/16 Dhankutabazaar, Dhankuta 7.02 1 District Level (Dhankuta)
20 Purnima Bikas Bank Ltd. 2008/05/20 Siddharthanagar, Rupandehi 30.26 1-3 District Level (Rupandehi, Nawalparasi, Chitwan)
21 Hamro Bikas Bank Ltd. 2009/04/19 Battar, Nuwakot 12.23 1 District Level (Nuwakot)
22 Kakre Bihar Bikas Bank Ltd. 2009/05/15 Birendranagar, Surkhet 6.09 1 District Level (Surkhet)
23 Pacific Development Bank Ltd. 2009/07/26 Beshishahar, Lamjung 13.30 1 District Level (Lamjung)
24 Kanchan Development Bank Ltd. 2009/09/19 Mahendranagar, Kanchanpur 19.80 1-3 District Level (Kailali, Kanchanpur, Dadeldhura)
25 Innovative Development Bank Ltd.
2009/11/13 Siddharthanagar, Rupandehi 29.05 1-3 District Level (Rupandehi, Nawalparasi, Chitwan)
26 Raptibheri Bikas Bank Ltd. 2010/01/15 Nepalgunj, Banke 14.38 1-3 District Level(Banke, Bardiya, Dang)
27 Tourism Development Bank Ltd.4 2010/03/18 New Baneshwor, Kathmandu 91.98 National Level 28 Mission Development Bank Ltd. 2010/06/15 Butwal, Rupandehi 19.49 1-3 District
Level (Rupandehi, Nawalparasi, Kapilvastu)
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29 Mount Makalu Development Bank Ltd.
2010/07/21 Basantapur, Terathum 2.60 1 District Level (Terathum)
30 Sindhu Bikas Bank Ltd. 2010/09/09 Barhabise, Sindhupalchowk 25.73 1-3 District Level (Sindhupalchowk, Kavre, Dolkha)
31 Sahara Bikas Bank Ltd. 2010/10/27 Malangawa, Sarlahi 2.36 1 District Level (Sarlahi) 32 Nepal Community Development
Bank Ltd. 2010/11/03 Butwal, Rupendehi 20.95 1-3 District
Level (Rupandehi, Nawalparasi, Chitwan)
33 Cosmos Development Bank Ltd. 2010/11/17 Harmaatarichowk, Gorkha 10.06 1 District Level (Gorkha)
34 Manasalu Bikash Bank Ltd. 2010/12/14 Buspark, Gorkha 18.35 1-3 District Level (Gorkha, Tanahu, Chitwan)
35 Kasthamandap Development Bank Ltd.
2012/4/13* New Road, Kathmandu 67.99 National Level
36 Salapa Bikash Bank Ltd. 2012/07/16 Diktel, Khotang 1.40 1 District Level (Khotang)
37 Saptakoshi Development Bank Ltd.
2012/10/02 Tankisunuwari, Morang 10.00 1-3 District Level (Morang, Ilam, Panchthar)
38 Sajha Bikash Bank Ltd. 2013/4/30 Dhangadi, Kailali 10.00 1-3 District Level (Kailali, Kanchanpur, Doti)
39 Professional Diyalo Bikas Bank Ltd.5
2013/6/30* Banepa, Kavre 24.04 4-10 District level (Kavrepalanchowk, Sindhupalchowk, Dolkha, Sindhuli,, Makwanpur, Nawalparasi, Chitwan, Rupandehi, Tanahu, Kaski)
40 Arniko Development Bank Ltd. 2013/7/14* Dhulikhel, Kavre 25.78 4-10 District level (Kavrepalanchwok, Sindhuli, Dhanusa, Dolkha, Mahottari, Udaypur, Sunsari, Makwanpur, Parsa,Morang)
41 Yeti Development Bank Ltd. 2013/7/15* Durbar Marg, Kathmandu 138.62 National Level 42 Green Development Bank Ltd. 2013/8/25 Baglung Bazar, Baglung 10.00 1-3 District
Level (Baglung, Myagdi, Kaski)
43 Reliable Development Bank Ltd. 2014/4/16* Gyaneshwor, Kathmandu 78.86 National Level
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44 Biratlaxmi Bikas Bank Ltd. 2014/5/17* Biratnagar, Morang 42.54 4-10 District level (Morang, Sunsari, Jhapa, Sankhuwasabha, Dhankuta, Terhathum, Bhojpur, Ilam, Taplejung and
Paanchthar) 45 Sangrila Development Bank Ltd. 2014/7/13* Baluwatar, Kathmandu 107.35 National Level 46 Triveni Bikas Bank Ltd. 2015/6/1* Bharatpur, Chitawan 82.00 National Level 47 Deva Development Bank Ltd. 2015/7/10* Laldurbarmarga, Kathmandu 88.11 National Level 48 Fewa Bikas Bank Ltd. 2015/7/13* Pokhara, Kaski 99.50 National Level 49 Om Development Bank Ltd. 2016/4/4* Pokhara, Kaski 105.28 National Level 50 Kailash Bikash Bank Ltd. 2016/4/4* Putalisadak, Kathmandu 158.03 National Level 51 Siddhartha Development Bank
Ltd. 2000/06/26 Tinkune, Kathmandu 141.92 National Level
52 Shine Resunga Development Bank Ltd.
2013/3/17* Butawal, Rupandehi 86.84 4-10 District level (Rupandehi,Nawalparasi,Arghakhachi
,Gulmi,Palpa,Dang,Pyuthan,Kapilvastu,Baglung and Chitwan)
53 Kamana Bikas Bank Ltd. 2016/6/20* Srijanachowk,Pokhara, Kaski 66.16 National Level 54 Jyoti Bikas Bank Ltd. 2016/8/12* Kamaladi, Kathmandu 98.70 National Level 55 Vibor Socitety Development
Bank Ltd. 2016/9/2* Dillibazar, Kathmandu 181.50 National Level
56 Mahalaxmi Bikash Bank Ltd. 2016/9/4* Thapathali, Kathmandu 115.62 National Level 57 Garima Bikas Bank Ltd. 2016/9/20* Pokhara, Kaski 108.05 National Level
*Joint operation date after merger 4 Paidup Capital After Acquisition of Matribhumi Bikas Bank Ltd. and Kalinchok Development Bank Ltd. by Tourism Development Bank Ltd. 5 Laxmi Bank Ltd. has been given approval to acquire Professional Diyalo Development Bank Ltd. effective from 2017/01/15
Class: "C" (Finance Companies) (Rs. in Crore) S. No. Name Operation
Date (A.D.) Head Office Paid up
Capital Working Area
1 Nepal Finance Ltd. 1993/01/06 Kamaladi, Kathmandu 13.58 National Level 2 NIDC Capital Markets Ltd. 1993/03/11 Kamalpokhari, Kathmandu 23.36 National Level 3 Nepal Share Markets and Finance
Ltd. 1993/10/19 Ramshahapath, Kathmandu 23.33 National Level
4 Union Finance Ltd. 1994/12/12 Narayanchaur, Naxal 17.66 National Level 5 Paschhimanchal Finance Co.Ltd. 1995/04/09 Butawal, Rupandehi 34.90 National Level 6 Goodwill Finance Ltd. 1995/5/15 Hattisar, Kathmandu 45.38 National Level 7 Shree Investment & Finance Co.
Ltd. 1995/06/01 Dillibazar, Kathmandu 24.31 National Level
8 Lumbini Finance & Leasing Co. Ltd.
1995/06/26 Thamel, Kathmandu 41.25 National Level
9 Lalitpur Finance Co. Ltd. 1995/12/14 Lagankhel, Lalitpur 18.79 National Level 10 United Finance Co. Ltd. 1996/01/26 Durbarmarg, Kathmandu 64.38 National Level 11 General Finance Ltd. 1996/2/1 Chabahil, Kathmandu 13.22 National Level 12 Progressive Finance Co. Ltd. 1996/02/26 Newroad, Kathmandu 20.02 National Level 13 Janaki Finance Co. Ltd. 1997/03/07 Janakpurdham, Dhanusha 31.08 1-3 District
Level (Dhanusa, Mahottari, Siraha)
14 Pokhara Finance Ltd. 1997/03/16 Pokhara, Kaski 55.74 National Level 15 Central Finance Ltd. 1997/04/14 Kupondole, Lalitpur 24.68 National Level 16 Arun Finance Ltd. 1997/08/17 Dharan, Sunsari 15.00 National Level 17 Multipurpose Finance Co. Ltd 1998/04/15 Rajbiraj, Saptari 3.70 1 District Level
80
(Saptari) 18 Shrijana Finance Ltd. 1999/12/14 Biratnagar, Morang 20.16 1-3 District
Level (Morang, Sunsari, Saptari)
19 World Merchant Banking & Finance Ltd.
2001/08/10 Hetauda, Makawanpur 18.20 National Level
20 Capital Merchant Banking & Finance Co. Ltd.
2002/02/01 Battisputali, Kathmandu 93.51 National Level
21 Crystal Finance Ltd. 2002/3/13 Thapathali, Kathmandu 7.00 National Level 22 Guheshwori Merchant Banking &
Finance Ltd. 2002/06/13 Pulchowk, Lalitpur 29.37 National Level
23 Everest Finance Ltd. 2003/07/02 Siddharthanagar, Rupandehi 15.75 1-3 District Level (Kapilvastu, Rupandehi, Nawalparasi)
24 ICFC Finance Ltd. 2004/07/15 Bhatbhateni, Kathmandu 80.18 National Level 25 Kuber Merchant Finance Ltd. 2006/03/24 Kamalpokhari, Kathmandu 15.00 National Level 26 Seti Finance Ltd. 2006/05/18 Tikapur, Kailali 6.35 1 District Level
(Kailali) 27 Hama Merchant & Finance Ltd. 2006/06/16 Tripureshwor, Kathmandu 22.10 National Level 28 Namaste Bitiya Sanstha Ltd. 2007/07/09 Ghorahi, Dang 3.75 1 District Level
(Dang) 29 Unique Financial Institution Ltd. 2007/10/12 Putalisadak, Kathmandu 27.68 National Level 30 Manjushree Financial Institution
Ltd. 2007/10/17 Nayabaneshwor, Kathmandu 25.07 National Level
31 Jebil's Finance Ltd. 2009/10/28 Newroad, Kathmandu 25.69 National Level 32 Bhaktapur Finance Ltd. 2011/02/08 Chyamasing , Bhaktapur 21.10 National Level 33 Synergy Finance Ltd. 2012/12/6* Butawal, Rupandehi 47.44 National Level 34 Reliance Finance Ltd. 2014/05/08* Pradarsani Marg, Kathmandu 44.57 National Level 35 Sagarmatha Finance Ltd. 2015/7/16* Maanvawan, Lalitpur 36.93 National Level 36 Gorkhas Finance Ltd. 2016/4/10* Dillibazar, Kathmandu 57.87 National Level
** In the process of liquidation. *Joint operation date after merger
9 Acquired by Citizens Bank International with effective from 2016/7/17 Class: "D" (Micro Finance Financial Institutions) (Rs. in Crore) S. No. Name Operation
Date (A.D.) Head Office Paid up
Capital Working Area
1 Nirdhan Utthan Bank Ltd. 1999/07/17 Naxal, Kathmandu 60.00 National Level 2 Rural Microfinance Development
Centre Ltd. 1999/12/06 Putalisadak, Kathmandu 62.92 National Level
3 Deprosc Microfinance Development Bank Ltd.
2001/07/03 Bharatpur, Chitwan 25.79 National Level
4 Chhimek Microfinance Development Banks Ltd.
2001/12/10 Old Baneshwor, Kathmandu 59.57 National Level
5 Shawalamban Laghu Bitta Bikas Banks Ltd.
2002/02/22 Lalcolony Marg, Kathmandu 31.24 National Level
6 Sana Kisan Bikas Bank Ltd. 2002/03/11 Subidhanagar, Kathmandu 40.24 National Level 7 Nerude Laghu Bitta Bikas Bank
Ltd. 2007/06/15 Biratnagar, Morang 18.00 National Level
8 Naya Nepal Laghu Bitta Bikas Bank Ltd.
2009/03/20 Dhulikhel, Kavre 2.00 4-10 District Level (Kavre, Ramechhap, Sindhuli, Mahottari, Dhanusa, Siraha, Saptari, Sunsari, Morang, Jhapa)
9 Mithila Laghu Bitta Bikas Bank Ltd.
2009/04/29 Dhalkebar, Dhanusha 5.09 4-10 District Level (Sindhuli, Mahottari, Dhanusa, Siraha, Sarlahi,
81
Saptari, Rautahat, Udaypur, Bara, Ramechhap)
10 Summit Microfinance Development Bank Ltd.
2009/05/20 Birtamod, Jhapa 5.00 4-10 District Level (Jhapa, Morang, Sunsari, Taplejung, Ilam, Panchthar, Udayapur, Saptari, Siraha, Dhankuta)
11 Sworojagar Laghu Bitta Bikas Bank Ltd
2009/12/16 Banepa, Kavre 7.00 4-10 District Level (Kavre, Chitwan, Makawanpur,Nawalparasi, Rautahat, Bara, Parsa, Tanahu, Gorkha, Lamjung)
12 First Microfinance Development Bank Ltd
2009/12/28 Gyaneshwor, Kathmandu 26.45 National Level
13 Nagbeli Microfinance Development Bank Ltd
2010/02/04 Birtamod, Jhapa 2.50 1-3 District Level (Jhapa, Morang, Ilam)
14 Kalika Microcredit Development Bank Ltd.
2010/07/21 Waling, Syangja 5.00 4-10 District Level (Syanja, Kaski, Parbat, Palpa, Nawalparasi,Rupandehi, Tanahu,Dhading,Gorkha, Makwanpur)
15 Mirmire Microfinance Development Bank Ltd.
2010/09/23@
Banepa, Kavre 2.00 10+5 District (Rasuwa, Nuwakot, Dhading, Dolkha, Gulmi, Kavrepalanchowk, Makawanpur, Chitwan, Nawalparasi, Palpa, Rukum, Rolpa, Salyan, Arghakhachi, Pyuthan)
16 Janautthan Samudayik Microfinance Dev. Bank Ltd.
2010/11/09 Butwal, Rupandehi 2.40 4-10 District Level (Kailali, Kanchanpur, Banke, Bardiya, Dang, Kapilvastu, Rupandehi, Nawalparasi, Chitwan, Parsa)
17 Womi Microfinance Bittiya Sanstha Ltd.
2012/03/08 Khanikhola, Dhading 5.40 10+5 District (Dhading, Makawanpur, Chitwan, Nawalparasi, Tanahu, Lamjung, Kavrepalancho
82
wk, Kaski, Syanja, Palpa, Sindhuli, Okhaldhunga, Udayapur, Dhankuta, Gorkha)
18 Laxmi Microfinance Bittiya Sanstha Ltd.
2012/06/04 Maharajgunj, Kathmandu 21.84 National Level
19 ILFCO Microfinance Bittiya Sanstha Ltd.
2012/07/05 Chuchepati , Kathmandu 10.00 National Level
20 Mahila Sahayatra Microfinance Bittiya Sanstha Ltd.
2012/12/25 Chitlang, Makwanpur 11.00 National Level
21 Kisan Microfinance Bittiya Sanstha Ltd.
2013/01/16 Lamkichuha, Kailali 2.00 10+5 District (Kailali, Achham, Bajura, Bajhang, Baitadi, Darchula, Kalikot, Humla, Mugu, Doti, Dadheldhura, Dailekh, Salyan, Jajarkot, Jumla)
22 Vijaya Laghubitta Bittiya Sanstha Ltd.
2013/03/28 Gaidakot, Nawalparasi 14.00 National Level
23 NMB Microfinance Bittiya Sanstha Ltd.
2013/03/31 Pokhara-Hemja, Kaski 4.60 10+15 District (Mustang, Manang, Myagdi, Kaski, Lamjung, Gorkha, Rasuwa, Sindhupalchwok, Dolakha, Solukhumbu, Sankhuwasabha, Taplejung, Ramechhap, Parbat, Nuwakot,Okhaldhunga,Bhojpur,Khotang,Dhankuta,Terhathum,Ilam,Panchthar, Rukum,Dhading and Tanahu)
24 FORWARD Community Microfinance Bittiya Sanstha Ltd.
2013/05/17 Duhabi Bhaluwa, Sunsari 14.00 National Level
25 Reliable Microfinance Bittiya Sanstha Ltd.
2013/05/19 Besisahar, Lamjung 5.65 4-10 District Level (Lamjung, Manang, Mustang, Dolpa, Ramechhap, Sindupalchowk, Dhading, Nuwakot, Rasuwa, Gorkha)
26 Mahuli Samudyik Laghubitta Bittiya Sanstha Ltd.
2013/06/15 Bakdhuwa, Saptari 2.80 4-10 District Level (Saptari, Siraha, Udayapur, Khotang,
83
Sunsari, Bhojpur, Okhaldhunga, Sindhuli, Dhankuta, Ramechhap)
27 Suryodaya Laghubitta Bitiya Sanstha Ltd.
2013/07/16 Putalibazar, Syanja 4.00 4-10 District Level (Baglung, Myagdi, Parbat, Syanja, Manang, Lamjung, Mustang, Gulmi, Pyuthan, Rolpa)
28 Mero Microfinance Bittiya Sanatha Ltd.
2013/07/18 Battar, Nuwakot 20.00 National Level
29 Samata Microfinance Bittiya Sanatha Ltd.
2013/08/25 Pipra, Simara 2.21 1 District Level (Bara)
30 RSDC Laghubitta Bitiya Sanstha Ltd.
2013/09/11 Butwal, Rupandehi 10.00 National Level
31 Samudayik Laghubitta Bitiya Sanstha Ltd.
2014/04/13 Panchkhal, Kavre 1.40 4-10 District Level (Paanchkhal, Kavrepalanchowk, Dolakha, Ramechhap, Solukhumbu,Okhaldhunga, Nuwakot, Khotang, Bhojpur, Sankhuasabha)
32 National Microfinance Bittiya Sanstha Ltd.
2014/07/02 Nilkantha, Dhading 10.00 National Level
33 Nepal Grameen Bikas Bank Ltd. 2014/8/15* Butawal, Rupandehi 55.75 National Level 34 Nepal Sewa Laghubitta Bittiya
Sanstha Ltd. 2014/10/26 Phataksila, Sindhupalchok 1.05 1-3 District
Level (Sindhupalchok, Rasuwa, Nuwakot)
35 Unnati Microfinance Bittiya Sanstha Ltd.
2014/11/07 Padsari, Rupandehi 3.08 4-10 District Level (Rupandehi, Palpa, Pyuthan, Kapilvastu, Arghakhhachi, Gulmi, Parbat, Baglung, Myagdi, Mustang)
36 Swadeshi Lagubitta Bittiya Sanstha Ltd.
2014/12/31 Itahari, Sunsari 7.00 National Level
37 NADEP Laghubitta Bittiya Sanstha Ltd.
2015/05/15 Gajuri, Dhading 11.20 National Level
38 Support Microfinance Bittiya Sanstha Ltd.
2015/07/12 Hasposa, Itahari 4.20 4-10 Distrit Level (Sunsari, Terathum, Dhankuta, Panchthar, Bhojpur, Udayapur, Khotang, Sindhuli, Ramechhap, Makwanpur)
84
39 Arambha Microfinance Bittiya Sanstha Ltd.
2015/07/23 Melamchi, Sindhupalchowk 1.96 4-10 District Level(Sindhupalchok, Nuwakot, Dolakha, Ramechhap, Sindhuli, Okhaldhunga, Khotang, Bhojpur, Terathum, Dhankuta)
40 Janasewi Laghubitta Bittiya Sanstha Ltd.
2015/09/29 Kushma, Parbat 2.45 4-10 District Level(Parbat, Baglung, Myagdi, Gulmi, Rukum, Rolpa, Kaski, Tanahu,Lamjung,Gorkha)
41 Chautari Laghubitta Bittiya Sanstha Ltd.
2016/01/03 Butawal, Rupandehi 2.10 4-10 District Level(Nawalparasi,Rupandehi,Kapilvastu,Gulmi,Arghakhachi,Palpa,Rolpa,Dang,Salyan)
42 Ghodighoda Laghubitta Bittiya Sanstha Ltd.
2016/06/12 Sripur Belouri, Kanchanpur 1.11 4-10 District Level (Kailali,Kanchanpur,Banke,Bardiya,Dang,Surkhet,Doti,Dadeldhura,Baitadi,Darchula)
43 Asha Lagubitta Bittiya Sastha Ltd.
Madanpur Nuwakot 7.00 National Level
44 Nepal Agro Microfinance Bittiya Sastha Ltd.
Pokhara, Kaski 1.40 4-10 District Level (Kaski, Parbat, Baglung, Gulmi, Pyuthan, Rolpa, Tanahun, Salyan, Palpa, Lamjung)
45 Rama Roshan Microfinance Bittiya Sastha Ltd.
Mangalsen, Acham 1.34 4-10 District Level (Achham, Dadeldhura, Doti, Bajhang, Bajura, Kailali, Jumla, Kalikot, Dailekh, Surkhet)
46 Creative Laghu Bitta Bittiya Sastha Ltd.
Pratapur, Kailali 1.40 4-10 District Level (Kailali, Kanchanpur, Bardiya, Surkhet, Doti, Achham, Kalikot, Bajura, Darchula, Bajhang)
47 Gurans Laghu Bitta Bittiya Sastha Ltd.
Dhankutabazaar, Dhankuta 1.40 4-10 District Level (Taplejung, Panchthar, Ilam, Terhathum, Dhankuta,
85
Sankhuwasabha, Bhojpur,
48 Ganapati Microfinance Bittiya Sastha Ltd.
Shuklagandaki, Tanahu 7.00 National Level
*Joint operation date after merger.
Savings and Credit Co-operatives Limited banking) (Rs. in Crore) S. No. Name Operation
Date (A.D.) Head Office Paid up
Capital 1 Shree Nabajivan Co-operative Ltd. 1993/12/15 Dhangadi, Kailali 11.72 2 Sagun Sahakari Sanstha Ltd. 1994/10/9 Chhetrapati, Kathamandu 1.17 3 Nepal Sahakari Bittiya Sanstha Ltd. 1994/12/30 Newbaneshwor, athamandu 2.98 4 The Sahara Loan Saving Co-operative
Society Ltd. 1995/4/15 Malangwa, Sarlahi 12.65
5 Bindabasini Saving & Credit Sahakari Sanstha Ltd.
1995/6/21 Khopasi, Kavre 16.42
6 Mahila Sahakari Sanstha Ltd. 1995/9/27 Kuleshwor, Kathmandu 2.66 7 Nepal Multipurpose Sahakari Sanstha
Ltd. 1995/12/25 Mechinagar,Jhapa 36.79
8 Sahakari Bittiya Bikash Sanstha Ltd. 1996/6/16 Nepalgunj, Banke 1.96 9 Shree Manakamana Sahakari Sanstha
Ltd. 1997/2/18 Banepa, Kavre 5.28
10 Bheri Sahakari Bittiya Sanstha Ltd. 1997/3/5 Nepalgunj, Banke 1.70 11 Viccu Saving & Credit Sahakari
Sanstha Ltd. 1997/8/11 Gaidakot, Nawalparasi 15.63
12 Kisan Multipurpose Sahakari Sanstha 1997/12/29 Lamki, Kailali 6.30
Ltd. 13 Star Multipurpose Saving & Credit
Sahakari Sanstha Ltd. 1998/4/14 Biratnagar, Morang 3.24
14 Himalaya Sahakari Sanstha Ltd. 1998/4/29 Purano Baneshwor, Kathmandu 5.75 15 Upakar Saving & Credit Sahakari
Sanstha Ltd. 2000/3/21 Walling, Syangja 5.14
Non-Government Organizations (NGOs) S. No. Name Operation
Date (A.D.) Head Office
1 Chartare Yuba Club 2000/06/05 Tityang,Baglung 2 Unique Nepal 2000/06/29 Naya Gaun, Bardiya 3 Samudayik Mahila Bikas Kendra 2000/07/14 Rajbiraj, Saptari 4 Dhaulagiri Community Researh Dev.
Centre 2000/10/21 Baglung
5 Society of Local Volunteers Efforts Nepal (Solve)
2001/07/10 Dhankuta
6 Center for Women's Right and Development
2002/04/30 Kathmandu
7 MANUSHI 2002/05/03 Kathmandu 8 Jeevan Bikash Samaj 2002/06/18 Bariyati,Morang 9 Mahila Adarsha Sewa Kendra 2002/07/02 New Baneshwor, Kathmandu 10 Patan Buisiness and Professional
Women 2002/07/02 Pulchowk, Lalitpur
11 Womens Self -Relient Society 2002/07/14 Bharatpur, Chitwan 12 Creative Women Environment
Development Association. 2002/07/24 Maharajgunj, Kathmandu
13 Shreejana Development Center 2002/08/22 Pokhara, Kaski 14 Cottage & Small Industries
Organization 2002/09/02 Chabahil, Kathmandu
15 Social Upgrade in Progress of Education Region (SUPER)
2002/10/29 Tulsipur, Dang
16 Nepal Women Community Service Center
2002/10/30 Tribhuwan municipality, Dang
17 Gramin Mahila Bikash Sanstha 2003/04/23 Tribhuwan municipality, Dang 18 Gramin Mahila Utthan Kendra 2003/06/18 Tribhuwan municipality, Dang 19 Gramin Sewa Nepal 2003/09/18 Bhajani, Kailali 20 Mahila Upakar Manch 2003/10/29 Kohalpur, Banke
86
21 Gramin Swayam Sewak Samaj 2005/11/20 Hariwon, Sarlahi 22 Srijana Community Development
Center 2012/11/18 Choharwa, Siraha
23 Rastriya Shaichhik Tatha Samajik Bikas Sanstha
2012/11/18 Kusma, Parbat
24 Nepal Grameen Bikas Sanstha 2012/12/13 Hadigaun,Kathmandu 25 Women Enterprises Association of
Nepal 2013/01/04 Putalisadak, Kathmandu
Other Institutions S. No. Name Office Contact Office 1 Rastriya Sahakari Bank Ltd. Kupondole,
Lalitpur Baneshwor, Kathmandu
2 Mashreq Bank PSC Dubai , UAE Thapathali, Kathmandu 3 Hydroelectricity Investment &
Development Company Ltd. Babarmahal Babarmahal ,Kathmandu
4 Omni Pvt.Ltd.$ Adarshnagar, Birgunj
Adarshanagar, Birgunj
5 Hulas Investment Pvt.Ltd.$ Ganabahal, Kathmandu
Ganabahal, Kathmandu
6 Sipradi Hire Purchase Pvt. Ltd.$ Thapathali, Kathmandu
Thapathali, Kathmandu
7 MAW Investment Pvt. Ltd.$ Biratnagar, Morang
Teku, Kathmandu
8 Batas Investment Co. Pvt. Ltd.$ Pokhara,Kaski Gairidhara, Kathmandu 9 Syakar Investment Pvt. Ltd.$ Kantipath,
Kathmandu Kantipath, Kathmandu
10 Jagadamba Credit & Investment Pvt. Ltd.$
Naxal, Kathmandu
Naxal, Kathmandu
$ For the purpose of hire purchase.
