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Effects of Non-Performing Loans on the Financial Performance of
Selected Banks on the Ghana Stock Exchange
Abubakar Sani
University of New Orleans, United States of America (USA)
DOI: https://dx.doi.org/10.51244/IJRSI.2025.1210000157
Received: 06 October 2025; Accepted: 14 October 2025; Published: 12 November 2025
ABSTRACT
The performance of loans plays a significant role in enhancing the financial performance of banks and
maximizing shareholders’ value. Hence, the study explores the effects of Non-Performing Loans (NPLs) on
the Financial Performance of Selected Banks on the Ghana Stock Exchange. To this end, the researcher
employed a quantitative research approach with a descriptive research design where the impact of NPLs on
financial performance was examined through regression analysis and fixed effect, random effect, as well as
Hausman Test with STATA. The panel data were collected from published financial statements of the selected
banks from the period of 2012 to 2023. The study revealed that the findings present a nuanced picture that
requires immediate attention from stakeholders. While there is strong evidence linking high levels of NPLs
with lower efficiency reflected in lower ROA figures, their impact on ROE remains ambiguous based on
current data sets. This implies that NPLs have no significant impact on returns from equity, while the high
levels of non-performing loans affect the profitability and operational effectiveness of the banks. The study
further observed that the interest rate fluctuations do not play any role in the link between NPLs and the returns
on assets and equity. Given the results, it is recommended that banks adopt robust risk assessment frameworks
specifically focused on closely monitoring non-performing loans. It is also suggested that financial institutions
must implement strategies aimed at minimizing defaults through improved credit assessments and borrower
support programs. Instead of simply expanding asset bases without strategic oversight, banks should focus on
improving operational efficiency along with growth.
Keywords: Non-performing loans, financial performance, selected banks, Ghana Stock Exchange
INTRODUCTION
Throughout the world, the problem of non-performing loans (NPL) has been a pressing concern, particularly
following the economic crises that led to greater breaches among borrowers. According to the International
Monetary Fund (IMF), high levels of NPL can hinder the ability of banks to lend, which subsequently
suffocates economic growth (IMF, 2020). For example, during the Eurozone crisis, countries such as Greece
and Italy faced the growing NPL proportions that prevented recovery efforts by restricting credit availability.
The investigation reveals that the presence of high levels of NPL is negatively correlated with bank
profitability indicators, including asset performance (ROA) and capital yield (ROE) (Beck et al., 2013). Banks
loaded with NPLs may need to assign more capital for loan loss provisions instead of making productive
investments.
High levels of NPL may have serious consequences for banks. When borrowers fail to comply with their loan
terms, banks face greater demands for provisions against potential losses. This not only eats your profits, but
also limits your ability to generate income through interest rates. The repercussions extend beyond individual
institutions; They ripple throughout the economy.
A study by Agyemang et al. (2020) highlights a strong negative correlation between the NPL and the return on
assets (ROA) among African banks. As unrealized losses increase, bank profitability suffers. This relationship
underlines a significant challenge that faces financial institutions in these regions. The decrease in profitability
resulting from high levels of NPL may have broader economic implications. On the one hand, it reduces the
loan capacity of banks, meaning fewer resources are available for both companies and consumers. This can
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suffocate economic growth and development as access to credit decreases. Additionally, the decrease in trust in
the banking sector can lead to a vicious cycle. As customers' distrust of placing their money in banks with high
NPL relationships, liquidity problems may arise. Banks may be unable to meet the demands for retirement or
provide new loans due to a lack of funds.
In many African countries, regulatory agencies have been criticized for inappropriate supervision and the
application of loan standards (Mugano & Mlambo, 2019). This lack of strict regulations can exacerbate the
NPL problem by allowing banks to participate in more risky loan practices without sufficient supervision. The
macroeconomic environment has a significant influence on the NPL level within the banks. Economic
recessions often lead to greater infractions as borrowers fight with the instability of income (Khan et al., 2021).
In many African nations where economies depend largely on exports of basic products, fluctuations in global
prices can lead to sudden increases in NPL relationships during periods of economic stress.
Different banking practices in all regions also contribute to variations in NPL levels. For example, some
studies suggest that state banks tend to have higher NPL ratios compared to private banks due to political
interference and less strict credit evaluation processes (Bokpin & Abo, 2018). On the contrary, private
institutions can adopt more rigorous credit evaluation mechanisms that mitigate the risks associated with loan
violations.
Cultural attitudes towards debt reimbursement also play a role in the configuration of the NPL dynamics
within the banking sector of Africa. In some cultures, there may be less stigma associated with loan defaults
compared to others where maintaining credit solvency is essential (Oseiiassibey et al., 2020). This cultural
dimension complicates the efforts of financial institutions to administer their portfolios effectively.
Loans with no performance, often referred to as NPLs, are those that are in default or are otherwise delinquent.
In general, they are defined as loans that have not received payment for 90 days or more (Monokrou &
Gortsos, 2017). The emergence of NPLs is a pressing concern for banks, particularly in Africa's distinctive
economic environment. The existence of NPL creates significant obstacles to financial institutions. When these
loans accumulate, they lead to a reduction in profitability and force banks to strengthen their reserves against
possible losses. This situation finally affects the financial health of the banks involved. Additionally, a high
volume of NPLs can endanger the liquidity and overall stability of a bank.
One of the most significant factors contributing to the rise in non-performing loans (NPLs) is macroeconomic
instability. When economic conditions fluctuate due to inflation, unemployment peaks, or other disruptions,
borrowers often struggle to fulfill their repayment obligations. This instability creates a domino effect that can
lead to higher default rates in several sectors.
Another factor that contributes is the ineffective practices of loan collection. Banks and loan institutions can
struggle with outdated or inefficient methods for collecting debts. This insufficiency can exacerbate the
problem of NPL, since borrowers may feel less pressure to pay their loans when collection efforts are weak or
poorly managed. In addition, inappropriate risk assessment frameworks can lead banks to extend credit without
thoroughly evaluating borrowers' capacity to repay. When financial institutions rely on outdated models or fail
to consider individual borrower circumstances, they inadvertently increase their exposure to potential breaches.
Despite these challenges, recent advances in Fintech offer promising solutions that could significantly mitigate
the impact of loans with poor performance. Improved credit score systems and improved loan monitoring tools
are at the forefront of these innovations (Afolabi & Ajayi, 2021). By leveraging technology effectively, banks
can enhance their ability to assess risk with greater precision. Advanced data analysis enables financial
institutions to analyze borrowers' behavior patterns more comprehensively.
The implications for banks that appear in the GSE are profound. High NPL levels can lead to a decline in
investor confidence and negatively impact share prices. A study by Osei-Asibey et al. (2019) found a strong
inverse relationship between the performance of banking actions and the increase in NPL relations within the
Ghanaian context. Investors often receive high levels of NPL as an indication of underlying management
problems or an unstable economic environment.
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The banking sector in Ghana plays a crucial role in driving economic growth and stability. However, it faces
challenges, particularly in relation to unrendered loans (NPL). The regulatory framework that governs these
banks plays a crucial role in the effective management of NPL risks. One of the key elements of this
framework is the introduction of more strict capital adaptation requirements. These regulations are designed to
ensure that banks maintain enough capital reserves to absorb potential losses of uncollectible loans. In addition
to capital adaptation requirements, better risk management guidelines have been introduced. As the banking
landscape continues to evolve, so must the approaches adopted for managing credit risks associated with loan
activities. The implementation of a solid risk management framework enables institutions not only to identify
potential violations from the outset but also to equip themselves with the necessary tools to take proactive
measures when necessary.
By focusing on strong frameworks, leveraging technology, ensuring compliance, and cultivating a workforce
with the necessary knowledge, banks can navigate complexities more effectively, safeguard their interests from
the outset, and better serve their customers. However, despite these advances, many banks continue to struggle
with the practical implementation. The complexity of risk management practices often leads to gaps in
understanding and execution among personnel members at different levels.
Another critical initiative implies improving the exchange of credit information between banks. The effective
exchange of credit data can significantly improve loan decision-making processes. When banks have access to
integral credit, they can more effectively evaluate the solvency of borrowers and make informed loan
decisions. This collaboration between financial institutions could also reduce the instances of future breaches
of loan standards. As highlighted by Kwan et al. (2017), effective credit risk management practices are crucial
to maintain stability within financial systems.
Research has widely documented the global effects of NPL on banking institutions. A relevant example is the
European debt crisis, during which many banks found severe difficulties due to the growing NPL relationship.
This situation required regulatory interventions aimed at stabilizing the banking sector (Smith et al., 2020;
Johnson & Lee, 2021). The crisis highlighted how high levels of NPL can create systemic risks that extend
beyond individual institutions to affect the entire financial panorama.
An increase in NPL inherently increases the credit risk profile of a bank. As a consequence, banks face more
strict regulatory scrutiny and can be subjected to sanctions (Lawrence et al., 2024). This greater supervision is
not just an inconvenience; It can lead to operational interruptions and additional costs as banks strive to
comply with regulations. Additionally, the ineffective management of NPL can significantly erode shareholder
trust. The reputation of a bank is essential for attracting both investors and customers. When interested parties
perceive poor management or the inability to control bad loans, trust decreases. This loss of trust can erode a
bank's competitive advantage in a constantly evolving financial environment.
To safeguard financial stability and encourage growth within this dynamic industry, banks must implement
effective strategies to manage low-performance loans. The investigation reveals that high levels of NPLs
negatively impact the bank's overall performance by reducing interest income while increasing simultaneous
supply costs (Busaada et al., 2023; Phung et al., 2022).
As we delve into the complexities of the Ghana Stock Exchange (GSE), it is essential to examine both
macroeconomic influences and the specific challenges faced by Ghana's banking sector. In recent years, Ghana
has experienced notable fluctuations in its proportion of non-performing loans (NPLs). These variations have
been significantly attributed to economic recessions, monetary depreciation, and changes in regulatory
frameworks. Recent data from the Ghana Bank highlight that NPL proportions have intensified at worrying
levels, which raises alarms on financial stability within the region. The banking sector in Ghana has not been
immune to the problems associated with NPLs. As of April 2025, the NPL ratio had shown a promising
decrease to 23.6%, which was below the 25.7% recorded in April 2024 (Banco de Ghana, 2025). This
reduction reflects a positive trend, as losses in completely supplied loans decreased from 11.1% to 9.0% during
this period. The improvement can be proven mainly by a significant increase in total loans issued by banks and
the growth of NPL shares. Specifically, total NPL shares increased by 8.7%, reaching GH¢21.7 billion in April
2025 compared to GH¢20.0 billion the previous year.
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A closer examination of these figures reveals an interesting shift in the allocation of public and private sector
loans. The proportion of NPL linked to the private sector increased to 93.4%, compared to 91%. On the
contrary, the Public Sector NPLs significantly decreased to only 6.6%, falling below the 9%directed threshold.
This trend indicates a gradual decrease in the general rates of NPL within the banking sector, a sign that loan
performance and risk management practices are improving over time. These developments have positive
implications for investors and interested parties alike, suggesting that a more stable economic environment is
emerging within Ghana's financial panorama. The research emphasizes that high levels of NPL can negatively
affect critical metrics of banking performance, such as assets and return on equity (Kasmir, 2014: p. 204; Ayu
et al., 2014).
Banks allocate substantial resources to manage uncollectible debts instead of lending to productive borrowers,
which negatively affects their profitability. This decrease can lead to a decline in share prices on the GSE as
investors' trust decreases due to concerns about bank solvency and general market stability.
In the complex world of finance, non-performing loan management (NPL) is a critical challenge that can
significantly impact the overall performance of an institution. It is believed that some institutions have
successfully mitigated their NPL through the implementation of solid strategies for credit management and
innovative restructuring solutions. These approaches not only improve the performance of the individual bank
but also provide valuable information for others in the industry. Institutions that prioritize this aspect often
develop comprehensive frameworks that evaluate the solvency of the borrowers with greater precision.
The successful approaches employed by these institutions contribute to a deeper understanding of how NPLs
impact market performance. For example, high levels of NPL can negatively affect investors and share prices.
Therefore, interested parties must understand the implications of NPL management in a broader market
dynamic. This knowledge becomes particularly relevant in contexts such as Ghana's financial panorama, where
resilient banking practices are vital for economic stability. By sharing ideas about effective NPL mitigation
strategies, we can encourage other banks to adopt similar practices, ultimately strengthening the financial
sector.
Given this context, our study aims to investigate the impact of loans not generated on the performance of the
selected banks that appear on the Ghana Stock Exchange (GSE). Focusing specifically on key metrics such as
the return on assets (ROA) and the return on equity (ROE), we aim to understand how variations in NPL levels
correlate with overall bank performance. The data analysis of the banks quoted in GSE will shed light on
trends and patterns related to the management of NPLs within Ghana's unique economic environment. The
findings from this research can inform policy recommendations aimed at enhancing financial resilience across
the sector.
LITERATURE REVIEW
This section provides an overview of non-performing loans, including their theoretical foundations and an
empirical review.
Theoretical foundations
Several theoretical models have been proposed to analyze the dynamics between NPLs and bank performance.
Credit risk theory
This framework emphasizes the evaluation of the deputy solvency and its correlation with non-compliance
rates. The growing prevalence of loans not made in European banks highlights significant failures in existing
credit risk models, as evidenced by Altman's work (1968) and Kwan & Eisenbeis (1997), which requires a
reevaluation of these frameworks. The theory suggests that unrealized loans have a significant impact on
financial stability in developing economies, mainly due to inadequate regulatory measures, as evidenced by the
findings of Laeven and Valencia (2013) and Beck et al. (2015).
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The correlation between macroeconomic factors and increased non-performing loans suggests that credit risk
evaluation should incorporate broader economic indicators, as supported by Chen et al. (2012) and Boudriga et
al. (2009). The application of automatic learning techniques to predict unrealized loans can enhance credit risk
management practices, providing a more effective alternative to traditional statistical methods, as Huang et al.
(2020) and Bontemps et al. (2019) have argued. Policy reforms aimed at improving transparency and
accountability in loan practices are crucial for mitigating the risks associated with loans without yield,
particularly in financial environments following the crisis, as Ghosh (2015) and Morsy & Dabo (2016) point
out.
The application of credit risk theory, as articulated by Merton (1974), effectively predicts the probability of
non-compliance in corporate bonds due to its dependence on market-driven factors and real-time data analysis.
Credit risk theory must incorporate behavioral financial principles, as suggested by Biais and Weber (2009),
because investors' emotions significantly influence risk assessments and market stability in financial
institutions. The limitations of traditional credit risk theory, as highlighted by Altman (1968), necessitate the
integration of automatic learning techniques to enhance predictive precision and response capacity in
evaluating a borrower's solvency.
Empirical studies suggest that credit risk theory frameworks can be successfully adapted to emerging markets,
as demonstrated by Liu et al. (2018), due to the unique economic indicators that influence credit behavior in
these regions. The evolution of credit risk theory underlines the importance of regulatory frameworks, with
authors such as the Basel Committee (2010) arguing that the stronger capital requirements are essential to
mitigate systemic risks in the banking sector.
Agency Theory
This theory explores conflicts between bank management and interested parties that can lead to bad loan
decisions. The agency's theory inadequately addresses the rise of loans that do not yield performance in
banking institutions, as evidenced by the lack of alignment between management incentives and shareholder
interests (Jensen & Meckling, 1976). The prevalence of loans not made in financial markets can be attributed
to the agency theory's failure to account for risk management practices, which leads to disincentivized
incentives among executives (Fame, 1980). Empirical studies suggest that the application of agency theory to
the governance of financial institutions reveals significant deficiencies in addressing unrealized loans resulting
from inadequate supervision mechanisms (Shleifer & Vishny, 1997).
The relationship between agency theory and unrealized loans highlights critical failures in corporate
governance structures, as evidenced by the highest breach rates after poor management decision-making
(Eisenhardt, 1989). Loans without yield serve as a fire test for the effectiveness of agency theory in financial
contexts, demonstrating that misaligned interests can exacerbate credit risk and undermine institutional
stability (Admati & Pfleiderer, 2009).
Economic theory
Economic recessions, inflation rates, and unemployment levels are critical factors that influence NPL
relationships, as highlighted in several studies. The persistent increase in non-performing loans (NPLs) in the
face of further financial crises highlights a deficiency in existing economic theories, which necessitates a
reevaluation of risk assessment models, as evidenced by authors such as Altman (2010) and Ghosh (2015).
Economic theories that do not adequately incorporate the complexities of borrower behavior contribute
significantly to the growing rates of unrealized loans, as illustrated in the works of Duygan-Bump et al. (2013)
and Fofack (2005). The relationship between economic recessions and the increase in non-general loans
highlights the limitations of traditional economic theories, underscoring the need for innovative approaches, as
supported by the investigations of Kwan and Eisenbeis (1997) and Salas & Saurina (2002).
A comparative analysis of loans not performed in different banking systems reveals that regulatory
frameworks significantly influence NPL levels, challenging conventional economic theories as evidenced by
Beck et al. (2013) and Jiang et al. (2018). The existing literature on unrealized loans suggests that
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macroeconomic indicators alone are insufficient to predict NPL behavior, emphasizing the need for an
integrated theoretical framework that incorporates microeconomic factors, as noted by Panetta et al. (2009) and
Figueira et al. (2020).
The growing prevalence of loans not made in developing economies, as evidenced by the works of Laeven and
Valencia (2013), suggests a direct correlation with macroeconomic instability resulting from inadequate
regulatory frameworks. The investigation reveals that the increase in interest rates has a significant impact on
loans with poor performance in European banks, underscoring the urgent need for political leaders to address
the implications of monetary policies, as discussed by Bofondi and Rinaldi (2017).
The relationship between economic recessions and loans with poor performance is underscored by the findings
of Berglund and Lindqvist (2019), which show that periods of recession exacerbate credit risk among financial
institutions. An analysis of unrealized loans in several countries reveals that effective risk management
strategies are crucial for mitigating the adverse effects of economic fluctuations, supporting the conclusions
drawn by Liu et al. (2020). Literature on unrealized loans, particularly Jarrow and Van de Guchte (2018),
illustrates how the vulnerabilities of the banking sector intensify during economic crises, which require
comprehensive reforms in financial regulation.
Empirical Review
For policy formulators and banking professionals, it is essential to understand the connection between NPL
and ROA. Numerous studies have documented a negative correlation between these two variables. Khemraj
and Pasha (2017) identified that high levels of NPL result in higher supply costs for banks. This increase in
costs decreases net income, which leads to a decrease in ROA.
Additionally, recent studies highlight the role of regulatory frameworks in mediating this relationship. Basel III
regulations introduced stricter capital requirements aimed at enhancing the resilience of banks against financial
shocks arising from high levels of NPLs (Zhang & Chen, 2022). By ensuring that banks maintain adequate
capital reserves, these measures could mitigate the adverse effects on the ROA by promoting best risk
management practices among financial institutions.
However, the relationship between NPL and Roa is not always simple. Some researchers propose alternative
views regarding this dynamic. A 2020 study by García et al. It suggests that although high NPL ratios often
indicate anguish within individual banks, they could also reflect broader economic conditions, rather than
simply indicating poor management practices.
Studies have identified that interest rates tend to correlate with a decrease in ROA in various sectors due to
reduced capital investments (Smith et al., 2020; Johnson & Lee, 2021; Patel & Kumar, 2023; Williams, 2024).
Supporting this point of view, Aghion et al. (2018) argue that high relations with NPL can hinder the loan
capacity of the banks. When banks face substantial amounts of loans without performance, their ability to
extend new credit decreases. This not only limits potential income flows but can also initiate a harmful cycle of
economic stagnation.
This perspective encourages us to consider macroeconomic factors, such as unemployment rates and GDP
growth, when evaluating NPL levels and bank profitability. In times of economic recession or instability, an
increase in unemployment can lead to a greater number of loans that default, thus inflating the number of non-
performing loans within banks.
Regulatory frameworks are crucial for maintaining the integrity of banking systems worldwide. Not only do
they establish standards for capital adaptation, but they also establish guidelines for risk management and
governance practices within banks. In this context, it is clear that intense regulatory supervision can help
mitigate the negative impact of NPLs on the ROA.
However, it is also essential to consider geographical differences when evaluating this impact. Muda et al.
(2023) highlight how emerging markets may experience more pronounced effects due to less robust regulatory
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environments compared to developed economies, where strict supervision is in place. In regions with weaker
regulations, banks may face more significant challenges due to high levels of NPLs, as they lack sufficient
safeguards against potential losses.
Several studies indicate that an increase in NPLs leads to higher supply costs and lower income from assets,
ultimately decreasing the ROE. For example, a study by Akinlo et al. (2020) found that banks with high NPL
relationships tend to experience a profitability reduction due to the highest provisions of loan loss, which
directly affect their profits and, therefore, their ROE.
Similarly, the investigation carried out by Osei-Asibey et al. (2019) emphasized that banks with higher NPL
ratios often face challenges in adapting their capital and operational efficiency, which further limits their
ability to generate yields for shareholders. On the contrary, some scholars argue that the impact of NPLs on the
ROE may not be uniform across all banking institutions or economic contexts. For example, the research
carried out by Ghosh (2021) suggests that well-capitalized banks can handle their risk exposure more
effectively and can absorb high levels of NPL without significantly compromising their ROE. This perspective
emphasizes the importance of effective risk management practices and capital shock absorbers in mitigating
the adverse effects associated with NPL.
The investigation has consistently shown a strong connection between high NPL ratios and reduced capital
yield (ROE). When banks experience an increase in breaches, their profitability takes a hit. Boudriga et al.
(2018) highlight this relationship, illustrating how economic recessions often result in greater breaches of
loans, which then raise the NPL levels within banks. This increase in NPL can negatively impact the bank's
overall financial health.
However, it is essential to recognize that external economic conditions also play an important role in this
dynamic. While high NPL relationships generally correlate with decreased ROE, several macroeconomic
factors can exacerbate or relieve these impacts.
Khemraj and Pasha (2017) further explore the implications of the increase in NPL levels on asset quality and
loan capacity. As NPLs increase, banks often need to allocate more resources to cover potential losses through
increased supply requirements. This allocation of funds directly affects its ability to increase total assets, since
more capital must be reserved for risk management instead of being used for new loan opportunities. This
situation creates a cycle in which a decrease in asset quality leads to reduced loan capacity, which can hinder
broader economic growth.
Despite the generally negative perception of the high levels of NPL, some researchers argue that not all effects
are uniformly harmful. Ghosh (2020) presents an interesting perspective, suggesting that during periods of
economic recovery, banks can effectively manage their NPLs through proactive strategies, such as
restructuring efforts or improved risk evaluation techniques.
According to Bofondi et al. (2021), strict regulations can prompt banks to write off uncollectible loans more
aggressively, which may temporarily reduce total assets but ultimately lead to healthier balances in the long
term.
Additionally, regional differences significantly contribute to the impact of the NPL on bank assets. A study by
Laeven and Valencia (2022) suggests that banks in emerging markets may experience more pronounced effects
of high NPL ratios compared to those in developed economies, due to varying degrees of financial stability and
institutional robustness. In conclusion, although empirical evidence consistently indicates a negative impact of
loans without performance on banks' total assets, it is essential to consider contextual factors such as
management strategies, regulatory environments, and regional economic conditions.
According to Ghosh (2018), when there is an increase in NPL, banks are often forced to increase their loan loss
provisions, which directly reduces their total assets as these provisions reduce the reported profits. This
phenomenon can lead to a waterfall effect, in which the decreased quality of the assets results in a lower loan
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capacity, ultimately affecting the profitability and value of the shareholders (Ghosh, 2018; Fofack &
Ndikumana, 2020).
On the contrary, some scholars argue that not all NPL impacts are negative for banks. For example, according
to García-Herrero et al. (2021), although high levels of NPL may indicate underlying problems within a bank's
portfolio management or broader economic challenges, they can also catalyze banks to strengthen their risk
assessment frameworks and improve credit evaluation processes. Therefore, from this perspective, addressing
NPLs can lead banks to adopt more prudent loan practices and achieve greater operational efficiency over
time. Additionally, regional differences play a significant role in the way NPL affects total assets.
Aisen and Franken (2019) indicate that emerging markets often experience more pronounced effects due to
weaker regulatory environments and less diversified economies compared to developed markets. In these
contexts, high levels of NPL can lead not only to a decrease in total assets but also contribute to systemic risks
within the financial sector. It is also important to consider the macroeconomic factors that influence this
relationship.
As described by Beck et al. (2022), economic recessions generally correlate with an increase in NPL levels due
to the increase in unemployment rates and decreased consumer confidence. This cyclical nature means that
during periods of economic stress, banks may experience an increase in non-yield loans and a decrease in total
assets due to the highest demand requirements. In conclusion, the interaction between unrealized loans and the
total assets of the banks is multifaceted and influenced by several factors, including regulatory environments,
economic conditions, and institutional responses. Future research should continue to explore these dynamics
with emphasis on longitudinal studies in different banking systems worldwide.
METHODOLOGY
Data
The dataset comprises 7 listed banks on the Ghana Stock Exchange, spanning from 2012 to 2023. The study
focused solely on listed banks for several reasons. Many banks are classified as risky positions due to a high
level of NPLs that weakens their ability to expand their credit capacity.
Research approach
The three main research approaches, as described by Bryman and Bell (2015), are deductive, inductive, and
abductive. The deductive approach is based on existing theories. It implies formulating a hypothesis, designing
a research plan, and guiding data collection to test this hypothesis (Saunders, Lewis & Thornhill, 2016).
Typically associated with quantitative studies, the deductive approach is based on numerical data analysis to
validate or refute established hypotheses. For research on the impact of non-performing loans on the financial
performance of selected listed banks on the Ghana Stock Exchange employed a deductive design was
employed. This methodology is aligned with the guidelines provided by Saunders, Lewis, and Thornhill
(2016). While deductive research can accelerate the completion of the study, it is essential to assign sufficient
time to establish a robust framework.
Research strategy
The three main types of research methods are qualitative, quantitative, and mixed methods. Quantitative
research focuses on collecting numerical data that can be generalized into several groups. This method is
considered more objective and scientific than qualitative research; hence, the author employed a quantitative
method. The data were obtained from several secondary sources to guarantee an exhaustive analysis.
Research design
Research design is destined to address key research questions through a structured series of activities, as
described by Saunders et al. (2016). The study employs a quantitative research approach, which is essential for
testing hypotheses and measuring results using statistical data (Hair Jr et al., 2015). Specifically, the study
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employed panel data regression models to analyze the performance of listed banks in relation to non-
performing loans.
Population of the study
In research terminology, a population refers to a definite group of individuals, events, or elements that share
specific characteristics and exhibit similar behaviors (Cooper & Schindler, 2013). For this study, the
population comprises a ten-year panel data set covering the period from 2012 to 2023, focusing on all listed
banks on the GSE.
Data Source and Data Collection
The data used in this study were obtained from reputable secondary databases, including the Ghana Stock
Exchange and published financial statements of the selected banks. This comprehensive dataset spans a
decade, providing valuable insights into several key economic indicators. According to Burns and Grove
(2010), data collection implies systematically collecting relevant information related to specific research
questions using established criteria. The methods may include interviews, participant observations, focus
groups, case studies, and narratives. In this study, data on non-performing loans on the performance of the
selected banks were collected, along with key economic indicators such as exchange rates and inflation rates.
Data Analysis
The analysis of data employed the use of Stata statistical software. To analyze the panel data, there are several
types of models available, such as common effects, fixed, and random effects models. The common effect
model is one of the simplest models that ignores the effect of individual banks and time, with the assumption
that all individual banks are homogeneous and their nature is the same over time (Gujarati & Dawan, 2015).
So, the common effect model has the same constants and coefficients between individual banks and over time
periods.
Y
t
= α + βX
t
+ ε
t
………………………………………………………… (1)
The fixed effects model is used, controlling for unobserved heterogeneity over time and allowing for variation
in behavior between individual banks, so the model allows for different constants for individual banks, but the
coefficients are fixed over time.
Y
ίt
= α + βX
ίt
+ ε
ίt
……………………………………………………… (2)
Gujarati and Dawan (2015) stated that the random effect is estimated, allowing for the temporal variation of
individual banks. Therefore, the random effects model has fixed coefficients, and in the random effects model,
the constant consists of a random component.
Y
ίt
= α + βX
ίt
+ ε
ίt
+ θ
ίt
………………………………………………… (3)
In this study, the author investigates the relationship between non-performing loans and independent variables
such as total assets and interest rate, as illustrated by panel regression.
Since the panel regression combines cross-sections and time series, the residuals are likely to be correlated
over time and with individual banks; therefore, ordinary least squares will be biased.
The bank's specific model is presented as follows:
NPL
ίt
= β
0
+ β
1
ROA
ίt
+ β
2
ROE
ίt
+ β
3
INTR
ίt
+ μ
ίt
……………………………. (4)
Where NPL
it
denotes the default rate of bank i in period t, β
0
denotes the intercept. ROA
it
shows the bank
profitability of individual banks in period t, ROE
ίt
denotes the return on equity of banks in period t, while
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INTR
ίt
represents the interest rate of banks during period t. t represents the time period from 2012 to 2023, and
I represent the banks, while β1, β2, and β3 represent the respective coefficient terms.
Results
Effect of NPLs on Banks' Performance: ROA
Random Effect Results
The analysis reveals a promising R-squared value of 0.2400, indicating that 24% of the variance of the
dependent variable is explained by the independent variables. While not exceptionally high, it suggests there is
room for improvement. Wald Chi-square (21.59). A significant statistic that shows the study model is
statistically sound. The probability is 0.0001. This p-value indicates strong evidence against the null
hypothesis, affirming the relevance of your model.
With a coefficient of -0.0795537 and a p-value of 0.000, it is evident that there is a statistically significant
negative relationship between non-performing loans and ROA. This means that as non-performing loans
increase (indicating that more loans are not generating income), the return on assets decreases substantially. A
decline in ROA can have serious implications for banks, as it suggests they are less efficient at converting their
assets into profits. This relationship highlights the importance of financial institutions managing their loan
portfolios effectively and minimizing defaults to maintain healthy returns. The findings confirmed the results
obtained by Khemraj and Pasha (2017); Zhang and Chen (2022) that such situations affect the banks’ ability to
create more credit.
The study then focuses its attention on total assets, which has a coefficient of 0.0015659 and a p-value of
0.682. The high p-value indicates that this variable does not have a statistically significant effect on ROA.
While one might intuitively expect larger asset bases to correlate with higher returns due to economies of scale
or diversification benefits, this data suggests the opposite in this case. It raises questions about whether simply
increasing asset size contributes positively to profitability or whether other factors are at play. The outcomes of
the study failed to agree with those revealed by García et al. (2020).
Finally, the study examined interest rates and revealed a coefficient of 0.0006241 with a p-value of 0.176,
again indicating no statistical significance with respect to their impact on ROA. This could suggest that
fluctuations in interest rates within the range analyzed do not directly affect the ability of banks to generate
profits from their assets under the current conditions examined here. This result supports earlier outcomes on
the correlation between interest rates and ROA (Smith et al., 2020; Johnson & Lee, 2021; Patel & Kumar,
2023; Williams, 2024).
Table 1: Random Effects
_cons -.0055824 .0875591 -0.06 0.949 -.1771952 .1660303
INTR .0006241 .000461 1.35 0.176 -.0002794 .0015277
LnTA .0015659 .0038228 0.41 0.682 -.0059268 .0090585
NPL -.0795537 .0216431 -3.68 0.000 -.1219735 -.0371339
ROA Coef. Std. Err. z P>|z| [95% Conf. Interval]
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0001
Wald chi2(3) = 21.59
overall = 0.1584 max = 7
between = 0.0836 avg = 7.0
within = 0.2420 min = 7
R-sq: Obs per group:
Group variable: Bank Number of groups = 12
Random-effects GLS regression Number of obs = 84
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Fixed effect
From the study results, it is revealed that the F statistic (F (3, 69) = 8.74) confirms a significant model fit. The
negative correlation (Corr(U_i, Xb) = -0.3414) suggests an inverse relationship worth exploring further. The
data indicate that non-performing loans have a statistically significant negative coefficient of -0.0840086 for
ROA. This means that as the level of non-performing loans increases, the return on assets decreases
significantly. The p-value of 0.001 confirms that this result is not due to chance; rather, it highlights a strong
inverse relationship between non-performing loans and ROA. Johnson and Lee's (2021) findings are affirmed
by these results, as illustrated by Table 2.
Table 2: Fixed Effect Results
Furthermore, evidence gathered shows that the total assets relative to ROA, as displayed by the coefficient of -
0.0061784 and a P value of 0.171. These figures suggest that changes in total assets do not have a statistically
significant impact on ROA within this data set. While it might seem intuitive that larger asset bases could
generate greater returns through economies of scale or greater operational capacity, this analysis suggests the
opposite for the sample considered here. The lack of significance implies that simply increasing asset size does
not guarantee greater efficiency or profitability. This finding may spark debates among financial analysts about
whether focusing solely on asset growth without addressing underlying operating efficiencies could be a
mistake.
The analysis further reveals an almost insignificant effect on interest rates, with a coefficient of 0.0001011 and
a P value of 0.827, indicating no significant correlation with ROA. Despite common beliefs about the
influence of interest rates on loan profitability, as asserted by Robinson et al. (2025), changes in interest rates
are reflected in profits.
Hausman Test
Table 3 presents the coefficients of the fixed (b) and random (B) effects models along with their differences
and standard errors. These indicators are vital to understanding a bank's performance and risk profile. The
Fixed Effect obtained a coefficient of -0.0840086 while the Random Effect revealed a coefficient of -
0.0795537. The negative coefficients suggest that as non-performing loans increase, there is an associated
decline in some measure of performance or stability within banks. The small difference between fixed and
random effects indicates that while both models suggest similar trends, they do not completely align.
Fixed effect coefficient of -0.0061784 and random effect coefficient of 0.0015659. Here we see a contrasting
image; While the fixed effect shows a slight negative impact on performance relative to total assets, the
random effect indicates a positive relationship. This discrepancy points to possible variability in the way these
two models interpret asset size on bank performance.
The interest rate coefficient showed 0.0001011 and the random effect coefficient 0.0006241. Both models
yield positive coefficients for interest rates, suggesting an overall beneficial influence on bank performance
metrics when interest rates rise; however, we again see different magnitudes between the two approaches. The
_cons .1727461 .1015531 1.70 0.093 -.0298467 .375339
INTR .0001011 .0004612 0.22 0.827 -.0008189 .0010212
LnTA -.0061784 .0044699 -1.38 0.171 -.0150957 .0027389
NPL -.0840086 .0231583 -3.63 0.001 -.1302083 -.037809
ROA Coef. Std. Err. t P>|t| [95% Conf. Interval]
corr(u_i, Xb) = -0.3414 Prob > F = 0.0001
F(3,69) = 8.74
overall = 0.0441 max = 7
between = 0.0321 avg = 7.0
within = 0.2754 min = 7
R-sq: Obs per group:
Group variable: Bank Number of groups = 12
Fixed-effects (within) regression Number of obs = 84
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Chi-square statistic (Chi2) reported at 21.06 with a probability value (Prob > Chi2) of 0.0001 indicates strong
evidence against the null hypothesis that both estimators are consistent under certain conditions. This result
implies that there are significant differences between the fixed and random effects estimates in the examined
variables.
The choice between using fixed or random effects models is not merely academic; It has real-world
implications that can shape how banks operate within dynamic financial landscapes. As demonstrated by the
analysis of the influence of total assets and interest rates on bank performance metrics. The slight negative
impact suggested by fixed effects was the approach adopted by the study.
Table 3: Hausman Test for ROA
Effect of NPL on Banks' Performance: ROE
Random Effect Results
With an R-squared value of 0.0129, the variability explained by the model is minimal, indicating that other
factors may be influencing ROE more substantially. The Wald Chi2 statistic of 1.09 suggests limited statistical
significance in the relationship between non-performing loans and ROE. A probability result, such as Chi2 at
0.7793, reinforces the notion that non-performing loans are not a major driver of changes in ROE.
Furthermore, the assumed correlation of 0.0 between the unobserved effects and the independent variables
suggests independence in these factors.
The results indicated that non-performing loans have a coefficient of 1.096482 and a p-value of 0.9052. Again,
total Assets has a coefficient of -0.1697834 and a p-value: 0.9063, while interest rate obtained a coefficient of
0.215126, and a p-value of 0.359. The coefficient for non-performing loans stands at approximately 1.10,
suggesting that as non-performing loans increase, ROE may also rise—though this seems counterintuitive
since high levels of non-performing loans typically indicate financial distress. The results agreed with Khemraj
and Pasha (2017) increase in NPL levels requires the banks to often allocate more resources to cover potential
losses through increased supply requirements.
However, with a p-value of 0.905, which is significantly above the conventional threshold of 0.05 for
statistical significance, the study can conclude that there is no strong evidence to support that non-performing
loans impact ROE meaningfully. This situation would ultimately affect the profitability and value of the
shareholders as obtained by previous researchers (Ghosh, 2018; Fofack & Ndikumana, 2020).
Upon analyzing these three variables—non-performing loans, total assets, and interest rates- the study finds
little statistical significance regarding their influence on return on equity based on the given data set. The high
p-values across all variables indicate weak relationships between these factors and ROE outcomes, non-
performing loans (p = 0.905), total assets (p = 0.906), and interest Rates (p = 0.359).
Prob>chi2 = 0.0001
= 21.06
chi2(3) = (b-B)'[(V_b-V_B)^(-1)](b-B)
Test: Ho: difference in coefficients not systematic
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
b = consistent under Ho and Ha; obtained from xtreg
INTR .0001011 .0006241 -.000523 .0000128
LnTA -.0061784 .0015659 -.0077443 .0023165
NPL -.0840086 -.0795537 -.0044549 .0082391
fixed random Difference S.E.
(b) (B) (b-B) sqrt(diag(V_b-V_B))
Coefficients
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Table 4: Random Effect for ROE
Fixed Effect Results
With an R-squared value of 0.0314, the model indicates that only a small portion of ROE variability is
explained by NPLs. The F-statistic (F(3, 69) = 0.75) and a high p-value of 0.5289 further imply that the
predictors may not be statistically significant in forecasting ROE trends. The coefficient of non-performing
loans is -1.913463 with a P-value of 0.889. This indicates that there is no statistically significant relationship
between non-performing loans and ROE in our sample. A negative coefficient suggests that as non-performing
loans increase, ROE tends to decrease; however, the high P value means that this finding is not statistically
reliable.
Next is the total assets with a coefficient of 2.844628 and a p-value of 0.282. The positive coefficient indicates
that higher total assets can be correlated with higher ROE. However, like the previous variable, it lacks
statistical significance given its p-value. This finding suggests that, while larger asset bases could theoretically
support better returns on equity, in practice, other factors could be at play, influencing this relationship or
diminishing its strength.
Table 5: Fixed Effect for ROE
Finally, the study examined the interest rate variable, which has a coefficient of -0.0430206 and a p-value of
0.874. Like the other two variables analyzed above, this result does not imply any significant impact on ROE
based on changes in interest rates. The negative coefficient could imply an inverse relationship in which rising
interest rates could reduce equity-related returns; however, again, due to the high p-value, it has no statistical
_cons 8.745133 33.51518 0.26 0.794 -56.94341 74.43368
INTR -.215126 .2344931 -0.92 0.359 -.6747241 .2444721
LnTA -.1697834 1.444128 -0.12 0.906 -3.000221 2.660655
NPL 1.096482 9.183027 0.12 0.905 -16.90192 19.09488
ROE Coef. Std. Err. z P>|z| [95% Conf. Interval]
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.7793
Wald chi2(3) = 1.09
overall = 0.0135 max = 7
between = 0.0471 avg = 7.0
within = 0.0129 min = 7
R-sq: Obs per group:
Group variable: Bank Number of groups = 12
Random-effects GLS regression Number of obs = 84
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weight in our analysis. The study’s results contradict findings from Akinlo et al. (2020) that banks with high
NPL relationships tend to reduce profits and, therefore, ROE. From the suggestion by Ghosh (2021), high
levels of NPL do not significantly compromise the ROE, as supported by this current study. As posited by
Boudriga et al. (2018), the high NPLs may be a result of other economic factors not examined by this study.
Hausman Test
From Table 6, it is portrayed that the coefficient of Non-performing Loans in the fixed effects model is -
1.913463, while in the random effects model it is 1.096482. The difference between these coefficients is -
3.009944 with a standard error (S.E.) of 10.03689. This significant discrepancy suggests that non-performing
loans have a markedly different impact on financial performance depending on which model is used.
According to the total assets, there was a contrasting coefficient of 2.844628 for fixed effects versus -
0.1697834 for random effects, giving a difference here is 3.014412 with an S.E. of 2.192998. These findings
indicate that total assets are perceived very differently between models; Although they appear to positively
influence performance under fixed effects conditions, they have a negative relationship under random effects
conditions. The findings of the study could be attributed to Laeven and Valencia (2022)’s suggestion that
banks in emerging markets may experience more pronounced effects of high NPL ratios compared to those in
developed economies, due to varying degrees of financial stability and institutional robustness. This suggests
that asset management strategies may need to adapt depending on whether a fixed or random analysis approach
is adopted.
For the interest rate, the coefficients reveal another interesting story of -0.0430206 for fixed effects and -
0.215126 for random effects, leading to a difference of 0.1721054 with an S.E. of .135678. It was observed a
different impact was observed depending on the analytical framework used. That is, fixed indicates a less
negative influence compared to the random effects model, which suggests that interest rates significantly
reduce performance outcomes when viewed through its lens.
Table 6: Hausman Test for ROE
CONCLUSIONS AND RECOMMENDATIONS
Conclusions
The interplay between non-performing loans and bank performance is complex but critical to understanding
the financial health of banking sectors around the world. The study explores some of the key factors
influencing non-performing loans in the Ghanaian banking sector, focusing on selected banks listed on the
Ghana Stock Exchange. The evidence gathered revealed that the findings present a nuanced picture that
requires immediate attention from stakeholders. While there is strong evidence linking high levels of NPLs
with lower efficiency reflected in lower ROA figures, their impact on ROE remains ambiguous based on
current data sets. As financial landscapes continue to evolve, both policymakers and banking leaders must
seriously consider these insights as they strategize for future stability and growth within their institutions.
Prob>chi2 = 0.5926
= 1.90
chi2(3) = (b-B)'[(V_b-V_B)^(-1)](b-B)
Test: Ho: difference in coefficients not systematic
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
b = consistent under Ho and Ha; obtained from xtreg
INTR -.0430206 -.215126 .1721054 .135678
LnTA 2.844628 -.1697834 3.014412 2.192998
NPL -1.913463 1.096482 -3.009944 10.03689
fixed random Difference S.E.
(b) (B) (b-B) sqrt(diag(V_b-V_B))
Coefficients
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Policy Recommendations
Given the results, it is recommended that banks adopt robust risk assessment frameworks specifically focused
on closely monitoring non-performing loans. Additionally, it suggested that financial institutions must
implement strategies aimed at minimizing defaults through improved credit assessments and borrower support
programs. Instead of simply expanding asset bases without strategic oversight, banks should focus on
improving operational efficiency along with growth. Management in the banking sector should invest in staff
training on effective loan management practices in order to help mitigate the risks associated with the rise in
non-performing loans in the sector.
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