INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025
www.ijltemas.in Page 901
Risk Management Practices and Financial Performance Among
Deposit Money Banks in Nigeria
1
Adedipe Oluwaseyi Ayodele (PhD),
2
Adeyemi Praise Oluwatobiloba
Department of Accounting and Finance, Faculty of Management Sciences, Ajayi Crowther University, Oyo
DOI: https://doi.org/10.51583/IJLTEMAS.2025.140500094
Received: 03 June 2025; Accepted: 07 June 2025; Published: 21 June 2025
Abstract: The banking sector in Nigeria plays an important role in the nation's financial ecosystem and is a major driver of
economic growth and development. However, despite this essential role, Nigerian banks are exposed to a wide range of risks that
can significantly impact their financial stability and overall performance. This study investigated the effect of credit risk
management on the financial performance of deposit money banks in Nigeria, using panel data from five selected banks. Employing
both Random and Fixed Effects models, the research evaluated how credit risk influences financial performance, with Return on
Assets (ROA) serving as the key performance indicator. The findings revealed a positive and significant relationship between credit
risk and ROA, indicating that higher credit risk levels are associated with improved financial outcomes. This underscores the
importance of robust credit risk management and well-structured governance frameworks in driving financial performance. The
study concluded that credit risk can positively impact market-based performance, as investors may perceive higher risk as a signal
of greater potential returns. It emphasized the importance of robust risk management practices and governance mechanisms in
sustaining long-term financial health. Consequently, the study recommended that banks strengthen their credit risk management
frameworks, promote a culture of transparency and ethical behavior, and conduct regular reviews of governance structures to ensure
alignment with evolving regulatory and market conditions.
Keywords: Credit Risk Management, Financial Performance, Capital Adequacy Ratio, Non-Performing Loans, Loan Loss
Provisioning
I. Introduction
Nigeria's banking sector is a cornerstone of the nation's financial system, significantly contributing to economic expansion and
development. Deposit Money Banks (DMBs) act as essential intermediaries, channeling funds from depositors to borrowers, which
in turn stimulates investment, trade, and overall economic activity. The financial stability and performance of Nigerian banks are
continually challenged by a variety of risks, despite their fundamental importance to the economy.
One of the most significant of these is credit risk, which arises when borrowers default on their loan’s obligations. Credit risk is the
opportunity that the borrower will default in reimbursement of the acquired asset. In a volatile and unpredictable economic
environment such as Nigeria’s, the management of credit risk becomes essential. Effective credit risk management enables banks
to evaluate, monitor, and mitigate the potential losses from lending activities. This not only helps in averting substantial financial
setbacks but also enhances the banks’ profitability and resilience to economic shocks (Oyelakun, et al., 2023; Ugwu, 2025).
Over time, Nigeria's banking sector has undergone significant regulatory and governance reforms. Ugwu (2025) highlighted how
excessive bad debts in Deposit Money Banks (DMBs) stemmed from poor corporate governance, inadequate credit administration,
and weak adherence to credit risk management principles. To address these concerns, the Central Bank of Nigeria (CBN) has
implemented a series of prudential regulations and risk management guidelines. These measures aim to enhance transparency,
fortify governance structures, and ensure robust risk management across the banking industry (Animasaun, et al., 2025).
While risk management has long been acknowledged as essential to banking operations, there remains a significant need for
empirical investigation into how these practices influence the financial performance of DMBs in Nigeria. A deeper understanding
of the distinct types of risk and their respective impacts on key financial indicators such as capital adequacy, asset quality, and
profitability is critical. Such insights are invaluable to policymakers, regulators, and banking professionals committed to fostering
stability and robustness within the sector (Malahim, 2023).
The existing literature presents divergence views on the relationship between risk management practices and the financial
performance of Deposit Money Banks (DMBs) in Nigeria and other African nations. Some scholars argued that credit risk
management and liquidity risk have a significant impact on return on assets (ROA), (Ademola & Ismaila, 2022; Animasaun et al.,
2025). Conversely, other researchers contend that credit risk and financial risk have a positive influence on financial performance;
measured by return on equity (ROE) and ROA, but their effect remains statistically insignificant (Jackson & Tamuke, 2022;
Mbanefo, 2024; Tomomewo et al., 2023; Ugwu, 2025).
This conflicting evidence in findings presents a challenge for policymakers, complicating efforts to ascertain the extent to which
risk management practices affect the financial health of DMBs. In light of this uncertainty, the present study investigated the
relationship between risk management practices and the financial performance of Deposit Money Banks (DMBs) in Nigeria. By
doing so, it will enrich the literature on credit risk management and financial performance. Also, the findings are expected to guide
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025
www.ijltemas.in Page 902
strategic decision-making and inform regulatory policies that foster a more resilient and sustainable banking industry in Nigeria
(Jackson & Tamuke, 2022).
II. Literature Review
Risk management
Risk management involves identifying, assessing, and prioritizing potential threats, followed by strategic measures to mitigate,
monitor, and control their impact or likelihood. Risks can originate from numerous sources, including market volatility, political
instability, and project failures at any stage, legal liabilities, credit risks, accidents, natural disasters, deliberate adversarial attacks,
or unpredictable events. Successfully managing these uncertainties helps organizations maintain stability and optimize
performance. Enoch, Digil, and Arabo (2021) conducted a comparative study on how credit risk management affects the
profitability of microfinance banks in Nigeria. Their research highlighted that banks often struggle with inadequate information
when assessing loan applications, making it difficult to evaluate borrowers' commitment and business viability. The study found
that strengthening credit risk control measures significantly improves profitability by reducing payment defaults.
Loan loss provisioning
Loan loss provisioning is a vital aspect of banking operations, serving as an allowance for potential loan losses including non-
performing loans, customer bankruptcy, and renegotiated loans with lower payments. These provisions are reflected in loan loss
reserves on a bank's balance sheet, which can increase through provisions or decrease via net charge-offs. By continually updating
these provisions based on historical default rates and payment statistics, banks aim to present an accurate financial position. This
practice contributes significantly to financial system stability, as it allows banks to recognize estimated losses preemptively, thereby
preserving capital and sustaining credit supply during economic downturns. Research indicates that effective loan loss provisioning
management correlates with increased profitability, highlighting the importance of prudent risk assessment and management in
banking operations (Mulyanto et al., 2021). Thus, the study examined the first hypothesis:
H0
1
: There is no significant relationship between loan loss provisioning and Financial Performance.
Non-Performing Loan Ratio (NPLR)
The non-performing loan ratio, better known as the NPL ratio, is the ratio of the amount of non-performing loans in a bank's loan
portfolio to the total amount of outstanding loans the bank holds. The Non-performing loans (NPLs) ratio measures the
effectiveness of a bank in receiving repayments on its loans. Non-performing loans (NPLs) can be a significant risk for banks
because they tie up capital that could be used for profitable lending. When a borrower is unable to make payments due to financial
distress, bankruptcy, or loss of income, banks classify these loans as non-performing. Adeusi et al. (2013) found a strong link
between effective risk practices and improved bank performance in Nigeria, with a negative correlation between non-performing
loans and profitability. Financial analysts frequently use the NPL ratio to compare the quality of loan portfolios among banks.
They may view lenders with high NPL ratios as engaging in higher-risk lending, which can lead to bank failures. Economists
examine NPL ratios to predict potential instability in financial markets. Investors can view NPL ratios to choose where to invest
their money; they can view banks with low NPL ratios as being lower-risk investments than those with high ratios. (Ilelaboye et
al., 2023).
Non-Performing Loan Ratio (NPLR) represents the percentage of loans that are not generating income due to delayed or defaulted
payments. If a bank has a high Loan to Deposit Ratio (LTDR) or Loan to Asset Ratio (LTAR), it could indicate aggressive
lending practices, which may increase the likelihood of defaults and thus raise the NPLR. Banks with strong risk management
frameworks can maintain a balance, ensuring profitability while minimizing loan defaults. The Loan to Deposit Ratio (LTDR) and
the Loan to Asset Ratio (LTAR) are both crucial indicators of a bank's liquidity and risk exposure, and they can significantly impact
the Non-Performing Loan Ratio (NPLR).
Loan to Deposit Ratio (LTDR) measures the proportion of a bank’s loans to its total deposits. A high LTDR suggests that a bank
is aggressively lending relative to its deposit base, which can lead to a higher risk of non-performing loans if credit assessment is
weak. Conversely, a lower LTDR indicates a more conservative lending approach, potentially reducing the NPLR. (Adenuga et al.,
2021).
Loan to Asset Ratio (LTAR) assesses the proportion of a bank’s total assets that are allocated to loans. A high LTAR means a
large portion of the bank’s assets is tied up in loans, increasing exposure to credit risk. If the bank does not maintain strong
underwriting standards, this can lead to an elevated NPLR, as more loans default. (Tomomewo et al., 2023). At this point, the
second hypothesis in the study is examined to be:
H0
2
: There is no significant relationship between non-performing loan ratio and Financial Performance.
Capital Adequacy Ratio (CAR)
The capital adequacy ratio (CAR), also referred to as the capital-to-risk weighted assets ratio (CRAR), is a crucial metric for
assessing a bank's financial stability. It evaluates how well a bank can fulfill its obligations by comparing its capital to risk-weighted
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025
www.ijltemas.in Page 903
assets. Regulatory authorities closely monitor this ratio to gauge a bank's risk of failure and ensure depositor protection while
maintaining the overall stability of financial systems. Maintaining a minimum CAR is essential, as it provides banks with a buffer
against potential losses, reducing the likelihood of insolvency and the subsequent loss of depositor funds. However, despite the
Central Bank of Nigeria's (CBN) prudential regulations, the rate of non-performing loans rose from 4.84% to 5.3% between
February and April 2022. This exceeded the regulatory cap of 5%, indicating that banks were holding more problematic loans than
allowed (Ubah, 2021; Animasaun et al., 2025).
Capital Adequacy Ratio (CAR) measures a bank's financial strength by comparing its capital reserves to its risk-weighted assets. It
consists of two tiers of capital, which are summed and divided by risk-weighted assets, adjusted according to the risk levels of the
bank's loans. This method ensures that financial institutions maintain sufficient capital to cover potential losses, helping regulators
uphold stability and efficiency within the banking sector. Thus, the third hypothesis examined in the study is:
H0
3
: There is no significant relationship between capital adequacy ratio and Financial Performance.
Financial Performance
In the banking industry, financial performance refers to the effectiveness with which a bank utilizes its available resources to
generate profits. It is typically evaluated through various key financial indicators, including Return on Assets (ROA), which
measures how efficiently a bank’s assets contribute to its earnings, and Return on Equity (ROE), which reflects the profitability
derived from shareholders’ investments. Additionally, profitability ratios such as the Net Interest Margin (NIM) are employed to
assess a bank's capability to balance its interest income against interest expenses. Collectively, these metrics offer a comprehensive
assessment of a bank’s financial health and operational effectiveness.
Assessing financial performance is vital for maintaining the stability of banking institutions, as it helps determine their capacity to
absorb potential losses and sustain operations during economic downturns. Early identification of financial inefficiencies allows
banks to refine their risk management strategies, thereby minimizing the likelihood of severe financial distress (Animasaun, et al.,
2025). Moreover, strong financial performance enhances a bank’s ability to support economic development by providing more
credit to individuals and businesses, thus fostering increased economic activity (Jackson & Tamuke, 2022).
Conversely, weak financial performance can escalate into bank failures, posing serious threats to the stability of the entire financial
system. Consequently, continuous monitoring and proactive enhancement of financial performance are imperative to ensure
institutional resilience. Sustained financial strength not only promotes the longevity of banks but also contributes significantly to
the overall health and stability of the national economy (Ataine & Osuji, (2024).
Theoretical Review
Agency Theory: Jensen & Meckling (1976) initially proposed the agency hypothesis and proposed implications for the allocation
of ownership and management. The agency problem's fundamental principle is that ownership and control should be kept separate.
This causes a conflict of interest between management and its shareholders. Stated differently, managers and investors usually have
different interests (Schroeder et al., 2014). As a result, Managers diminish the company's worth by trying to maximize their personal
gains while disregarding the shareholders' ones. Soon followed orders from the government to decrease and improve the matter of
the agency. Outside investors utilize corporate governance as a collection of safeguards to protect themselves against expropriation
by insiders (Lonkani, 2018).
Subcommittees are responsible for monitoring and are subordinate to the Board of Directors. According to Jo and Harjoto (2011),
the corporate governance monitoring role positively affects the value of the organization. As a result, the firm's value will increase.
Agency theory states that shareholders and managers have different risk appetites (Jensen and Meckling, 1976; Subramaniam et
al., 2009).
Credit risk theory: One of the biggest risks to banks' position in credit intermediation is credit default, which occurs when
borrowers are unable to repay the loans they took out from their banks (Coyle, 2000). The Credit risk theory was proposed by
Merton (1977) default model and compares a firm's credit risk to the equity and debt obligations in its capital structure. There is
no doubt that the capital will be affected if borrowers don't fulfill their bank commitments. Another difficulty for central banks is
making sure that Banks have sufficient policies and procedures in place to protect them against past-due loans by means of the
regular release of recommendations to banks and the establishment and execution of penalties for violating the criteria. The goal of
these central banks' initiatives is to prevent financial systemic instability and ensure that banks and their clients fulfill the terms and
conditions of financial covenants. However, banks are prepared to impose higher interest rates on loans with likely increased
chances of default (Owojori et al. 2011). Banks' financial results need to be in line with how skillfully their exposures to credit risk
are handled. Additionally, it is anticipated that management teams at banks will look for and use suitable techniques to manage
increasing exposures to credit risk, even while staying inside the parameters set by their individual central banks corporate
governance code and prudential principles (Almustafa et al., 2023)
Loan Pricing Theory: This idea was created in 1981 by Stiglitz and Weiss. According to the hypothesis, banks consistently set
high interest rates. Opponents contend that banks ought to take the moral risks and issues with adverse selection in the loan market
when optimizing interest revenue in light of the robust credit market Asymmetry in information. When banks establish high interest
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025
www.ijltemas.in Page 904
rates, it will cause an adverse selection issue in the market. These high rates are acceptable to borrowers. Upon receiving these
advances and loans, the borrower could experience moral hazard behaviors, also known as "borrower moral hazard," because they
are inclined to undertake extremely dangerous ventures or investments.
Financial Intermediation Theory: According to Besley and Bringham (2009), having intermediaries enhances economic well-
being. They also stated that the purpose of financial intermediaries was to fulfill the specific needs of borrowers and savers, as well
as to lower the inefficiencies that would arise in the case that fund users could obtain loans exclusively by taking out direct loans
from savers. Money is needed for various reasons by various individuals and groups and other economic actors. In order to supply
the required funding, there are many types of organizations that offer financial services. Deposit money banks are among such
institutions that render financial services.
Their primary activity is financial intermediation, often known as indirect financing, which entails transferring money from the
economy's surplus to its deficit units; thus transforming bank deposits into loans and credits. There are companies that possess solid
concepts and commercial prospects that they would like to invest money in, but they lack the necessary funds. They would be ready
to take out a loan from net savers with excess money. For this reason, these subsequent categories are known as net borrowers or
the economy's deficit unit. But there are obstacles that make it challenging for the borrowing to happen. And as a result, in order to
eliminate this obstacle, a go-between, an intermediary is required to serve as a bridge between the net borrowers and net savers.
This position is known as financial intermediary. The function of financial intermediation is directing cash from net savers with
unused savings to Investors or net borrowers that require financial assistance
Empirical Review
Animasaun, et al., (2025) examined the impact of credit risk management on the financial performance of listed deposit money
banks in Nigeria between 2013 and 2022. Using an ex-post facto research design, the authors analyzed data from ten listed banks
and applied Panel Ordinary Least Square (OLS) regression to assess the relationship between credit risk management and financial
performance. The findings indicated that credit risk management significantly affects return on assets (Adj R² = 0.301, F-Statistics
= 4.561471, p-value = 0.000011 < 0.05). The study concluded that effective credit risk management enhances financial performance
and recommends that banks strengthen their risk management practices and maintain sufficient liquidity to mitigate negative
impacts on profitability
Ugwu, (2025) examines the impact of credit risk management on the financial performance of selected deposit money banks in
Nigeria. It analyzes data from 2000 to 2023, focusing on key financial indicators such as non-performing loans, provisions for bad
debt, and loan loss provisions. The research employs various econometric techniques, including unit root tests, cointegration
analysis, and the Auto-Regressive Distributed Lag (ARDL) model, to assess both short-term and long-term effects. Findings
indicate that while credit risk management has a positive impact on financial performance, its effect remains statistically
insignificant. The study recommends that monetary authorities implement policies to enhance loan repayment, including the use of
collateral and guarantees to mitigate potential losses.
Mbanefo, (2024), conducted a study analyzing the impact of risk management on the performance of Deposit Money Banks (DMBs)
in Nigeria. The research focused on credit risk, liquidity risk, operational risk, and capital risk, examining their effects on return on
equity. Using econometric techniques such as Augmented Dickey-Fuller Tests for Unit Roots and Ordinary Least Squares (OLS),
the study found that risk management had an insignificant effect on DMB performance within the study scope. The study
recommended that the Central Bank of Nigeria (CBN) strengthen its regulatory framework to enhance risk management practices.
Additionally, DMBs were advised to equip their credit and risk management officers with better skills and competencies. The CBN
was also encouraged to conduct regular risk assessments and promote a risk-awareness culture through supervision.
Nnaomah, et al., (2024) provides a comparative analysis of AI applications in risk management within the U.S. and Nigerian
banking sectors. It examines how AI technologies are adopted and implemented to address various risk types, including credit,
market, operational, and compliance risks. The researchers conducted a literature review, examining existing studies, industry
reports, and regulatory frameworks to understand AI adoption trends in both countries. Additionally, they employed case studies
of selected banks to assess AI implementation in risk management practices. The study utilized survey research design, collecting
data from banking professionals, AI experts, and regulatory bodies. The analysis incorporated statistical techniques such as
regression analysis and descriptive statistics to evaluate AI's impact on risk mitigation strategies. The findings of the study indicated
that U.S. banks have a more mature AI integration, leveraging advanced analytics, machine learning models, and natural language
processing for fraud detection, compliance monitoring, and decision-making. In contrast, Nigerian banks are in the early stages of
AI adoption, facing challenges such as inadequate technological infrastructure, regulatory hurdles, and a shortage of AI-skilled
professionals. Despite these disparities, the study highlights the growing interest in AI within Nigeria’s banking sector, driven by
the need for enhanced competitiveness and regulatory compliance. The paper underscores the importance of supportive policies,
investment in technology, and capacity building to foster AI-driven risk management.
Tomomewo et al., (2023) examined the impact of credit risk management on non-performing loans in Nigerian deposit money
banks. It evaluates key credit risk management indicators such as capital adequacy, loan and advance, loan loss provision, and loan-
to-total asset ratio, while measuring non-performing loans using the non-performing loan-to-total loan ratio. The research spans
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025
www.ijltemas.in Page 905
2013 to 2022, covering 14 listed banks in Nigeria. Using panel regression estimation techniques including OLS estimation, fixed
effect model, and random effect model, the study finds that Capital adequacy ratio (CAA) has a positive and significant effect on
non-performing loans, Loan loss provision (LLP) and loan-to-total asset ratio (LTAR) show positive but insignificant effects and
Loan and advances (LA) exhibit a negative and insignificant effect on non-performing loans. The study concluded that credit
management components influence non-performing loans at varying significance levels. It recommends that banks adopt different
loan loss provisioning approaches for specific loan categories to minimize adverse effects.
Oyelakun, et al., (2023) investigated the article titled "Credit Risk and its Management in the Banks: A Conceptual Review". The
study explores the concept of credit risk in banking and how it can be managed to prevent financial instability. The authors employed
a conceptual review methodology in their study on credit risk management in banks. This approach involved gathering and
analyzing information from original journal papers that explored the relationship between credit risk and commercial banks'
performance. Instead of conducting primary data collection, they relied on existing literature to assess how credit risk impacts
financial institutions and how it can be effectively managed.
Ademola & Ismaila (2022) examined the financial risk and performance of deposit money banks in Nigeria. The focused on
financial risk and performance in Nigerian deposit money banks, emphasizing credit and liquidity risk. Compared to broader
research on banking performance, their study aligns with key themes found in global literature. The study employed a quasi-
experimental ex post facto design, analyzing how pre-existing independent variables influenced financial performance. Using panel
data regression analysis with STATA 17, they assessed ten banks listed on the Nigerian Stock Exchange between 2010 and 2019.
The findings revealed that Credit risk and liquidity risk significantly impact financial performance (measured by return on equity,
ROE). Also, financial risk negatively affects the financial performance of Nigerian deposit money banks. The study highlights the
importance of risk management in maintaining banking stability.
Jackson & Tamuke (2022) conducted an empirical analysis on the relationship between credit risk management and the financial
performance of domiciled banks in Sierra Leone. Their study utilized panel data from the Bank of Sierra Leone covering the period
from 2008Q1 to 2018Q41. The findings of the study indicated that high levels of non-performing loans (NPLs) contributed to the
fragility of the banking system and the low productive base of the domestic economy exacerbates the prevalence of NPLs. The
Bank of Sierra Leone (BSL) had to intervene with stringent measures spanning from 2015-2017 to stabilize two state-owned
commercial banks. The study recommended that return on assets (ROA) and return on equity (ROE) be used as independent
indicators for monitoring banking performance.
Akintola & Adesanya (2021) examined the relationship between deposit money banks (DMBs) and economic growth in Nigeria
from 1994 to 2017. Using secondary data from sources like the Central Bank of Nigeria and the National Bureau of Statistics, they
applied regression analysis and the Ordinary Least Square (OLS) method to assess the impact of money supply, bank credit, and
interest rates on Nigeria’s real GDP. Their findings indicated that DMBs significantly influence economic growth through credit
provision and interest rates on lending. They recommended that the Central Bank of Nigeria should regulate bank charges and
interest rates to ensure affordable credit access for manufacturers and other productive sectors1. Additionally, they suggested that
government supervision and regulation should enhance public confidence in the banking system.
III. Methodology
Research Design
This study employs both ex-post facto and panel research designs, approaches widely utilized in previous research to examine
relationships between variables (Animasaun et al., 2025). Given that relevant data are readily available in firms' audited financial
reports, the ex-post facto method is particularly valuable. Additionally, a longitudinal research approach was adopted, encompassing
five cross-sectional banking units over a 10-year span (2014–2023), yielding a total of 50 firm-year observations (5 banks times 10
years). The study focuses on Nigerian deposit money banks, with data collected and analyzed quantitatively.
Population of the Study
The population of this study comprised all 24 deposit money banks in Nigeria as listed by the Central Bank of Nigeria (CBN) in
2023. These banks were chosen as they play a significant role in Nigeria's financial sector and provide a complete overview of risk
management and financial performance. The non-deposit money bank was exempted because they were not within the scope of the
study.
Sample size and Sampling Techniques
The study's sample size was 5 listed deposit money banks on the Nigerian Stock Exchange (NSE) which has been rebranded as the
Nigerian Exchange Group Plc (NGX Group) basically because of their financial strength market share influence. The study
employed stratified random sampling technique to ensure that all DMBs were selected. This method has been employed in studies
like Aremu, Arogundade, and Azeez (2022), ensuring that a sufficient number of banks were selected, thereby enhancing the
generalizability and reliability of the results.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025
www.ijltemas.in Page 906
Sources of Data
This study utilized secondary data to obtain information on both dependent and independent variables. These variables include loan
loss provisioning, non-performing loan ratio, capital adequacy ratio, and financial performance indicators. The data were collected
from the audited financial statements of the listed Deposit Money Banks (DMBs) in Nigeria. Additionally, the NGX Group database
and the Central Bank of Nigeria (CBN) annual reports were also consulted. The dataset covered a ten-year period (2014–2023),
allowing for a comprehensive analysis of the trends and relationships between risk management variables and financial
performance.
Table 3.1 Listed Deposit Money Banks and Their Respective Year of Listing
S/N
Name of bank
Year of listing
1
Access Bank plc, Access Holding plc
1998, 2022
2
First Bank Nigeria plc, FBN Holding plc
1991,2012
3
GTB Plc, GTCO plc
1996, 2021
4
Stanbic IBTC Holdings plc
2012
5
Zenith Bank plc
2004
Source: Authors' Compilation, 2025
IV. Results and Discussion
Descriptive Statistics
The descriptive statistics for the variables utilized in the analysis are shown in Table 4.1.
Table 4.1: Descriptive Statistics
ROA
LTDR
LTAR
Mean
1.192
0.533
0.343
Median
1.225
0.550
0.290
Maximum
1.990
1.550
0.780
Minimum
0.120
0.000
0.110
Std. Dev.
0.462
0.246
0.149
Skewness
-0.659
0.694
0.881
Kurtosis
3.213
4.805
2.855
Jarque-Bera
11.273
32.843
19.802
Probability
0.004
0.000
0.000
Sum
181.230
81.000
52.170
Sum Sq. Dev.
32.236
9.128
3.338
Observations
152
152
152
Source: Researcher’s computation, 2025
The mean value of Return on Assets (ROA), a metric used to assess financial performance and which is calculated by dividing the
Net Income by Total Assets., in table 4.1 was 1.192 for all of the chosen institutions. This shows that the financial performance of
these banks' assets is, on average. The standard deviation of 0.462 reflects moderate variability in ROA among the banks, indicating
differences in the values their assets relative to their cost. The minimum ROA observed was 0.120, implying that at least one bank's
assets were valued below their replacement cost, potentially signaling poor market performance or undervaluation. Conversely, the
maximum value of 1.990 indicates that another bank's assets were valued nearly twice their replacement cost, reflecting strong
return on asset confidence.
The Capital Adequacy Ratio (CAR) has a mean value of 17.909, indicating that, on average, the banks maintain capital levels that
are 17.909% of their risk-weighted assets. The standard deviation of 4.973 shows some variation in capital adequacy among the
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025
www.ijltemas.in Page 907
banks, with values ranging from a minimum of 8.020% to a maximum of 37.400%. This suggests that while some banks have
relatively low capital buffers, others maintain significantly higher levels of capital.
The Loan to Deposit Ratio (LTDR) has a mean value of 0.533, meaning that, on average, 53.3% of the banks' deposits are converted
into loans. The standard deviation of 0.246 indicates variability in the extent to which banks utilize their deposits for lending, with
LTDR values ranging from 0.000 (indicating no loans) to 1.550 (indicating that loans exceed deposits by 55%).
The Loan to Asset Ratio (LTAR) has a mean value of 0.343, indicating that, on average, 34.3% of the banks' total assets are
composed of loans. The standard deviation of 0.149 suggests some variation among the banks, with LTAR values ranging from a
minimum of 0.110 to a maximum of 0.780, reflecting differences in the proportion of assets allocated to loans.
Correlation
Table 4.2: Correlation Matrix
ROA
CAR
LTDR
LTAR
ROA
Pearson Correlation
1.000
Sig. (2-tailed)
CAR
Pearson Correlation
.403**
1.000
Sig. (2-tailed)
(0.000)
LTDR
Pearson Correlation
0.116
-0.076
1.000
Sig. (2-tailed)
(0.154)
(0.351)
LTAR
Pearson Correlation
.159*
.190*
.395**
1.000
Sig. (2-tailed)
(0.050)
(0.019)
(0.000)
N
152
152
152
152
Source: Researcher’s Computation, 2025
From table 4.2 above, the correlation between financial performance and Credit risk is positive but statistically insignificant, with
a correlation coefficient of (𝑟 = 0.130; 𝜌 > 0.1). This indicates that while return on asset and credit risk generally move in the
same direction, the strength of their relationship is weak. From an accounting perspective, this suggests that credit risk does not
have a notable impact on financial performance, reflecting that variations in the independence of the risk management are not
significantly influencing financial performance perceptions of the firm's value. In contrast, Return on Assets (ROA) shows a positive
and statistically significant correlation with Capital Adequacy Ratio (CAR), with a correlation coefficient of (𝑟 = 0.403 ∗∗; 𝜌 <
0.05). This significant positive relationship suggests that as CAR increases, ROA also tends to increase. This therefore indicated
that, CAR reflects the firm's financial stability and capital strength, which can enhance investor confidence and positively influence
financial performance, as indicated by ROA.
The correlation between Return on Assets (ROA) and Loan to Asset Ratio (LTAR) is also positive and statistically significant, with
a coefficient of (𝑟 = 0.159; 𝜌 < 0.05). This indicates that a higher LTAR is associated with a higher ROA. This result implies that
firms with a greater proportion of loans relative to their assets are perceived more favorably, reflecting positively in ROA.
The Return on Assets (ROA) and Loan to Deposit Ratio (LTDR) showed a strong and favorable association, with a correlation
coefficient of(𝑟 = 0.165; 𝜌 < 0.05). This significant positive relationship suggests that as LTDR increases, ROA tends to rise as
well. In accounting terms, this indicates that a higher loan-to-deposit ratio may be associated with better market performance or
growth expectations, which enhances the firm's performance as captured ROA.
Table 4.3: Pooled Regression
Pooled Effect
Fixed Effect
Random Effect
Coeff.
𝜌
Coeff
𝜌
Coeff
𝜌
CAR
0.347***
0.000
0.018
0.224
0.035***
0.000
LTDR
0.255*
0.099
0.349*
0.095
0.337**
0.048
LTAR
0.088
0.730
0.108
0.821
0.109
0.731
R
2
0.18
0.44
0.61
F-Stat
11.160
3.437
6.246
Prob.
0.000
0.000
0.000
Source: Researcher’s Computation, 2025
Note:***, **and * denotes significance at 1% , 5% and 10% respectively
Table 4.3 above presented the results of a pooled regression analysis using three different models: Pooled Effect, Fixed Effect, and
Random Effect. CAR (Capital Adequacy Ratio) Shows a strong, significant positive effect in the Pooled Effect and Random Effect
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025
www.ijltemas.in Page 908
models (𝜌 = 0.000). The Fixed Effect model does not show statistical significance (𝜌 = 0.224), suggesting that when controlling
for individual entity characteristics, CAR may not be a strong predictor.
Loan-To Deposit Ratio (LTDR) displays a weakly significant effect in the Pooled Effect and Fixed Effect models (𝜌 = 0.099 and
0.095, respectively). This shows moderate significance in the Random Effect model (𝜌 = 0.048), indicating that LTDR has a more
stable relationship when accounting for individual variations.
Loan-To Asset Ratio (LTAR) does not show a strong relationship in any model (𝜌 -values > 0.7), implying that LTAR does not
strongly affect the dependent variable.
Model Fit (R², F-statistic, 𝜌 value): R² values indicate how well the models explain the variation in the dependent variable; Pooled
Effect: 0.18 (weak fit), Fixed Effect: 0.44 (moderate fit) and Random Effect: 0.61 (strongest fit). F-statistics and P values show that
all models are statistically significant (𝜌 = 0.000), meaning the overall regressions are meaningful.
Table 4.4: Hausmann Test
Test Summary
Chi-Sq. Statistic
Chi-Sq. d.f.
Prob.
Cross-section random
1.551
3.000
0.671
Period random
7.342*
3.000
0.062
Cross-section and period random
2.121
3.000
0.548
Source: Researcher’s Computation, 2025
Hypothesis 1 (H0
1
): There is no significant relationship between loan loss provisioning and financial performance. To test this,
panel regression analysis was conducted using both Fixed and Random Effects models. The Hausman test in table 4.4, (χ² = 1.551,
ρ > 0.1) since the p-value is relatively high, we fail to reject the null hypothesis, suggesting that random effects may be appropriate.
Hypothesis 2 (H0
2
): There is no significant relationship between non-performing loan ratio and financial performance. Analysis
shows that the Loan to Deposit Ratio (LTDR) has a positive and statistically significant effect on Return on Assets (ROA). A 1%
rise in LTDR corresponds to a 0.337% increase in ROA as indicated in table 4.3 above suggesting that efficient use of deposits for
lending improves financial performance and market perception. Although the Loan to Asset Ratio (LTAR) also has a positive effect
on ROA, the relationship is statistically insignificant. This indicates that while allocating more assets to loans may support
profitability, LTAR alone is not a strong predictor of financial performance. The model's R² value of 0.6, means that 61% of ROA
variation is explained by Capital Adequacy Ratio (CAR), LTDR, and LTAR, while the remaining 39% is due to other factors. The
F-statistic (6.246) confirms the overall model is statistically significant.
Hypothesis 3 (H0
3
): There is no significant relationship between capital adequacy ratio and financial performance. Results reveal
a significant positive relationship between CAR and ROA in table 4.3 above indicating that a 1% increase in CAR results in a
0.035% in rise in ROA, confirming that well-capitalized banks are perceived as financially stable and less risky, which boosts their
market valuation.
V. Conclusion and Recommendations
The study examined the effect of credit risk management on the financial performance of deposit money banks in Nigeria, using
data from five selected banks. It applied both Random and Fixed Effects models to assess the influence of credit risk on financial
performance, with ROA serving as the key performance indicator. The findings offered a detailed insight into the relationship
between credit risk and financial outcomes in the banking industry. The analysis revealed that credit risk had a positive and
significant impact on ROA, suggesting that higher levels of credit risk were associated with improved financial performance. This
supports the idea that effective credit risk management and sound governance structures are critical drivers of financial success.
The study concluded that credit risk enhances market-based financial performance, possibly because investors associate higher
credit risk with the potential for greater returns. It also emphasized that strong risk management practices and governance
mechanisms play a vital role in sustaining financial performance.
Based on these findings, the study recommended that banks should strengthen their credit risk management frameworks to ensure
a well-balanced strategy that effectively manages risk while optimizing returns. Establishing a corporate culture rooted in
transparency, ethical practices, and risk awareness would bolster the efficiency of credit risk management and oversight committees.
Additionally, continuously reviewing and updating governance frameworks is crucial to keeping them effective and aligned with
evolving market conditions and regulatory requirements.
References
1. Ademola, O. & Ismaila, S. (2022). Financial risk and financial performance of deposit money banks in Nigeria. Journal
of Economics and International Finance, 14(4), 112-119
2. Adenuga, A. O., Mohammed, J. A., Laniyan, C. V., Akintola, A. A., & Asuzu, O. C (2021) Measuring the Impact of
Loan-to-Deposit Ratio (LDR) on Banks' Liquidity in Nigeria. Central Bank of Nigeria: Economic and Financial Review,
59(2), 43-59
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025
www.ijltemas.in Page 909
3. Adeusi et al. (2013). Risk Management and Financial Performance of Banks in Nigeria. European Journal of Business and
Management, 6(31), 336-342.
4. Akintola, A. F. & Adesanya, A. O. (2021) Contribution of Deposit Money Banks to Economic Growth in Nigeria.
International Journal of Scientific and Research Publications, 11(1), 778-786
5. Ali, A., & Yusof, R. M. (2022). Risk management practices and financial performance: Evidence from Nigerian deposit
money banks. Journal of Accounting and Financial Management, 8(2), 1–12.
6. Animasaun, R. O., Omotunwase, O. M., Babayanju, A. G. A., Bamgboye, A. A. (2025). Effect of Credit Risk Management
on Financial Performance of Listed Deposit Money Banks in Nigeria. International Journal of Research in Social Science
and Humanities (IJRSS), 6(1), 0-11
7. Ataine, M. M., & Osuji, C. C. (2024). Credit Risk Management and Financial Performance of Microfinance Banks in
Nigeria. IIARD International Journal of Banking and Finance Research 10(8), 1-20
8. Besley S. &. Brigham E. F, (2009). Principles of Finance, Publisher Australia; Mason, OH: South-Western Cengage
Learning
9. Coyle, B. (2000). Credit risk management: Framework for credit risk management. Fitzroy Dearborn Publishers.
10. Enoch, E. Y., Digil, A. M. & Arabo, U. A. (2021). A comparative evaluation of the effects of credit risk control on the
profitability of micro-finance bank. European Journal of Business and Management Research, 6(6), 67-74.
11. Ilelaboye, C. S., Alade, M. E. & Oyetayo, A. L. (2023). Non-Performing Loans of Financial Institutions in Nigeria: A
Trend Analysis. International Journal of Novel Research and Development (IJNRD), 8(12), 345-351.
12. Ikpefan, O. A., Ochei, A., & Agbontaen, O. O. (2021). Effect of credit risk management on the performance of deposit
money banks in Nigeria. International Journal of Financial Research, 12(1), 48–60.
13. Jackson, E. A., & Tamuke, E. (2022). Credit risk management and the financial performance of domiciled banks in Sierra
Leone: an empirical analysis. Journal of Economic Policy Researches, 9(1), 139-164.
14. Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure.
Journal of Financial Economics, 3(4), 305–360.
15. Jo, H., & Harjoto, M. A. (2011). Corporate governance and firm value: The impact of corporate social responsibility.
Journal of Business Ethics, 103(3), 351–383.
16. Lonkani, R. (2018). Firm value: Theory and empirical evidence (P. S. Hoffmann, Ed.). BoD – Books on Demand.
17. Malahim, S. S. (2023). The relationship between the risk disclosure and risk management committee on banks value:
empirical evidence from Jordan. International Journal of Professional Business Review, 8(3), 1-29.
18. Mbanefo, P. A. (2024). Risk Management Impact on Deposit Money Banks’ (DMBs’) Performance in Nigeria. IIARD
International Journal of Banking and Finance Research, 10 (4), 98-116.
19. Merton, R. C. (1977). An analytic derivation of the cost of deposit insurance and loan guarantees. Journal of Banking and
Finance, 1, 512–520.
20. Nnaomah, U. I., Odejide, O. A., Aderemi, S., Olutimehin, D. O., Abaku, E. A. & Orieno, O. H. (2024). AI in risk
management: An analytical comparison between the U.S. and Nigerian banking sectors. International Journal of Science
and Technology Research Archive, 6(1), 127–146.
21. Nugroho, M., Arif, D., & Halik, A. (2021). The Effect of Loan-Loss Provision, Non-Performing Loans and Third-Party
Fund on Capital Adequacy Ratio. Accounting, 7(4), 943-950
22. Onaolapo, A. R. (2015). Effects of credit risk on bank performance in Nigeria. International Journal of Business and
Management Review, 3(5), 43–52.
23. Owojori, A. A., Akintoye, I. R., & Adidu, F. A. (2011). The challenge of risk management in Nigerian banks in the post-
consolidation era. Journal of Accounting and Taxation, 3, 23–31.
24. Oyelakun, O., Madugba, J., Oladipo, A., Adeola, O. & Adegbile, G. (2023). Credit Risk and its Management in the Banks:
A Conceptual Review. Journal of International Money, Banking and Finance, 4(1), 23-32
25. Schroeder, R. G., Clark, M. W., & Cathey, J. M. (2014). Financial accounting theory and analysis (11th ed.). John Wiley
& Sons.
26. Stiglitz, J. E., & Weiss, A. (1981). Credit rationing in markets with imperfect information. American Economic Review,
71(3), 393–410.
27. Tomomewo A. O., Falayi, I. & Uhuaba (2023). Credit Risk Management as a Determinant of Non-Performance Loan of
Deposit Money Banks in Nigeria. Journal of Finance and Accounting, 11(6), 179-188
28. Umoh, G. J., & Dimgba, O. N. (2021). Risk management practices and performance of deposit money banks in Nigeria.
International Journal of Economics, Commerce and Management, 9(6), 109–121.
29. Ugwu, C. P. (2025). Effect of Credit Risk Management on the Financial Performance of Selected Deposit Money Bank in
Nigeria. Journal of Management Science & Entrepreneurship (JMSE) 7(7), 108-131.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025
www.ijltemas.in Page 910
Appendix
Unit Root Test
Null Hypothesis: Unit root (individual unit root process)
Series: TQ
Date: 08/24/24 Time: 09:12
Sample: 2014 2023
Exogenous variables: Individual effects
User-specified lags: 1
Total (balanced) observations: 50
Cross-sections included: 19
Method
Statistic
Prob.**
Im, Pesaran and Shin W-stat
-3.28673
0.0005
** Probabilities are computed assuming asympotic normality
Intermediate ADF test results
Cross
Max
section
t-Stat
Prob.
E(t)
E(Var)
Lag
Lag
Obs
1
-0.9801
0.6819
-1.544
2.369
1
1
6
2
-1.5492
0.4456
-1.544
2.369
1
1
6
3
-0.5231
0.8167
-1.544
2.369
1
1
6
4
-1.6471
0.4059
-1.544
2.369
1
1
6
5
-1.0883
0.6409
-1.544
2.369
1
1
6
6
-1.8400
0.3323
-1.544
2.369
1
1
6
7
-2.2145
0.2182
-1.544
2.369
1
1
6
8
-0.9331
0.6989
-1.544
2.369
1
1
6
9
-2.2049
0.2208
-1.544
2.369
1
1
6
10
-2.3836
0.1790
-1.544
2.369
1
1
6
11
-6.2287
0.0039
-1.544
2.369
1
1
6
12
-2.3586
0.1846
-1.544
2.369
1
1
6
13
-4.9313
0.0119
-1.544
2.369
1
1
6
14
-2.0633
0.2596
-1.544
2.369
1
1
6
15
-3.4879
0.0517
-1.544
2.369
1
1
6
16
-7.1417
0.0020
-1.544
2.369
1
1
6
17
-1.8694
0.3219
-1.544
2.369
1
1
6
18
-1.6604
0.4007
-1.544
2.369
1
1
6
19
-6.2834
0.0038
-1.544
2.369
1
1
6
Average
-2.7047
-1.544
2.369
Warning: for some series the expected mean and variance for the given lag
and observation are not covered in IPS paper
Null Hypothesis: Unit root (individual unit root process)
Series: CAR
Date: 08/24/24 Time: 09:13
Sample: 2014 2023
Exogenous variables: Individual effects
User-specified lags: 1
Total (balanced) observations: 50
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025
www.ijltemas.in Page 911
Cross-sections included: 19
Method
Statistic
Prob.**
Im, Pesaran and Shin W-stat
-3.30922
0.0005
** Probabilities are computed assuming asympotic normality
Intermediate ADF test results
Cross
Max
section
t-Stat
Prob.
E(t)
E(Var)
Lag
Lag
Obs
1
0.0791
0.9292
-1.544
2.369
1
1
6
2
-2.1192
0.2434
-1.544
2.369
1
1
6
3
-1.4990
0.4669
-1.544
2.369
1
1
6
4
-2.0110
0.2754
-1.544
2.369
1
1
6
5
-1.8342
0.3344
-1.544
2.369
1
1
6
6
-18.098
0.0000
-1.544
2.369
1
1
6
7
-1.7950
0.3493
-1.544
2.369
1
1
6
8
-1.8489
0.3292
-1.544
2.369
1
1
6
9
-1.3096
0.5495
-1.544
2.369
1
1
6
10
-1.6792
0.3932
-1.544
2.369
1
1
6
11
-1.0698
0.6484
-1.544
2.369
1
1
6
12
0.2033
0.9423
-1.544
2.369
1
1
6
13
-8.2492
0.0009
-1.544
2.369
1
1
6
14
-1.6431
0.4076
-1.544
2.369
1
1
6
15
-2.0336
0.2685
-1.544
2.369
1
1
6
16
-0.8727
0.7196
-1.544
2.369
1
1
6
17
-2.7388
0.1204
-1.544
2.369
1
1
6
18
-0.9227
0.7026
-1.544
2.369
1
1
6
19
-2.0980
0.2493
-1.544
2.369
1
1
6
Average
-2.7126
-1.544
2.369
Warning: for some series the expected mean and variance for the given lag
and observation are not covered in IPS paper
Null Hypothesis: Unit root (individual unit root process)
Series: LTDR
Date: 9/1/25 Time: 09:15
Sample: 2014 2023
Exogenous variables: Individual effects
User-specified lags: 1
Total (balanced) observations: 50
Cross-sections included: 19
Method
Statistic
Prob.**
Im, Pesaran and Shin W-stat
-1.34434
0.0894
** Probabilities are computed assuming asympotic normality
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025
www.ijltemas.in Page 912
Intermediate ADF test results
Cross
Max
section
t-Stat
Prob.
E(t)
E(Var)
Lag
Lag
Obs
1
-3.9833
0.0307
-1.544
2.369
1
1
6
2
-0.6262
0.7905
-1.544
2.369
1
1
6
3
-1.3326
0.5395
-1.544
2.369
1
1
6
4
-2.3073
0.1959
-1.544
2.369
1
1
6
5
-2.2470
0.2101
-1.544
2.369
1
1
6
6
-1.5801
0.4331
-1.544
2.369
1
1
6
7
-0.4788
0.8278
-1.544
2.369
1
1
6
8
-1.7086
0.3814
-1.544
2.369
1
1
6
9
-1.9842
0.2840
-1.544
2.369
1
1
6
10
-0.5021
0.8220
-1.544
2.369
1
1
6
11
-2.7623
0.1171
-1.544
2.369
1
1
6
12
-1.0279
0.6642
-1.544
2.369
1
1
6
13
-9.2458
0.0004
-1.544
2.369
1
1
6
14
-0.9500
0.6928
-1.544
2.369
1
1
6
15
0.2107
0.9430
-1.544
2.369
1
1
6
16
-1.5797
0.4332
-1.544
2.369
1
1
6
17
-1.8098
0.3439
-1.544
2.369
1
1
6
18
-2.2742
0.2035
-1.544
2.369
1
1
6
19
-2.1669
0.2307
-1.544
2.369
1
1
6
Average
-2.0187
-1.544
2.369
Warning: for some series the expected mean and variance for the given lag
and observation are not covered in IPS paper
Null Hypothesis: Unit root (individual unit root process)
Series: D(LTDR)
Date: 9/1/25 Time: 09:15
Sample: 2014 2023
Exogenous variables: Individual effects
User-specified lags: 1
Total (balanced) observations: 50
Cross-sections included: 19
Method
Statistic
Prob.**
Im, Pesaran and Shin W-stat
-2.72758
0.0032
** Probabilities are computed assuming asympotic normality
Intermediate ADF test results
Cross
Max
section
t-Stat
Prob.
E(t)
E(Var)
Lag
Lag
Obs
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025
www.ijltemas.in Page 913
1
-1.1587
0.5979
-1.558
2.648
1
1
5
2
-1.9674
0.2873
-1.558
2.648
1
1
5
3
-1.8108
0.3377
-1.558
2.648
1
1
5
4
-2.0001
0.2774
-1.558
2.648
1
1
5
5
-2.2518
0.2118
-1.558
2.648
1
1
5
6
-1.3311
0.5252
-1.558
2.648
1
1
5
7
-2.4535
0.1705
-1.558
2.648
1
1
5
8
-1.8554
0.3224
-1.558
2.648
1
1
5
9
-1.5640
0.4290
-1.558
2.648
1
1
5
10
-1.1625
0.5960
-1.558
2.648
1
1
5
11
-11.544
0.0003
-1.558
2.648
1
1
5
12
-1.7026
0.3756
-1.558
2.648
1
1
5
13
-5.4845
0.0109
-1.558
2.648
1
1
5
14
-4.6846
0.0209
-1.558
2.648
1
1
5
15
0.1703
0.9310
-1.558
2.648
1
1
5
16
-2.2143
0.2208
-1.558
2.648
1
1
5
17
-1.2466
0.5607
-1.558
2.648
1
1
5
18
-1.8692
0.3178
-1.558
2.648
1
1
5
19
-2.8180
0.1187
-1.558
2.648
1
1
5
Average
-2.5763
-1.558
2.648
Warning: for some series the expected mean and variance for the given lag
and observation are not covered in IPS paper
Null Hypothesis: Unit root (individual unit root process)
Series: LTAR
Date: 9/1/25 Time: 09:15
Sample: 2014 2023
Exogenous variables: Individual effects
User-specified lags: 1
Total (balanced) observations: 50
Cross-sections included: 19
Method
Statistic
Prob.**
Im, Pesaran and Shin W-stat
-2.62190
0.0044
** Probabilities are computed assuming asympotic normality
Intermediate ADF test results
Cross
Max
section
t-Stat
Prob.
E(t)
E(Var)
Lag
Lag
Obs
1
-1.1085
0.6328
-1.544
2.369
1
1
6
2
-2.9765
0.0913
-1.544
2.369
1
1
6
3
-2.6702
0.1301
-1.544
2.369
1
1
6
4
-1.3543
0.5300
-1.544
2.369
1
1
6
5
-5.2295
0.0091
-1.544
2.369
1
1
6
6
-0.6584
0.7817
-1.544
2.369
1
1
6
7
-2.6259
0.1366
-1.544
2.369
1
1
6
8
-1.4161
0.5025
-1.544
2.369
1
1
6
9
-1.0375
0.6608
-1.544
2.369
1
1
6
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue V, May 2025
www.ijltemas.in Page 914
10
-1.9687
0.2888
-1.544
2.369
1
1
6
11
-0.8431
0.7288
-1.544
2.369
1
1
6
12
-4.8485
0.0129
-1.544
2.369
1
1
6
13
-3.2178
0.0697
-1.544
2.369
1
1
6
14
-2.6300
0.1360
-1.544
2.369
1
1
6
15
-4.1017
0.0271
-1.544
2.369
1
1
6
16
-2.9399
0.0952
-1.544
2.369
1
1
6
17
-3.5466
0.0486
-1.544
2.369
1
1
6
18
-0.6302
0.7894
-1.544
2.369
1
1
6
19
-3.1244
0.0772
-1.544
2.369
1
1
6
Average
-2.4699
-1.544
2.369
Warning: for some series the expected mean and variance for the given lag
and observation are not covered in IPS paper