Risk Management Practices and Financial Performance: Analysing Credit and Liquidity Risk Management and Disclosures by Nigerian Banks

Abstract

Nigerian banks encounter persistent difficulties in efficiently managing and disclosing credit and liquidity risks, considerably affecting their financial performance and shareholders’ confidence. This study, therefore, examined the effect of risk-management practices and disclosures on the financial performance of Nigerian commercial banks. The population of the study comprised 13 Nigerian commercial banks, of which 12 were purposively chosen, subject to data availability. The data explored in this study originate from World Development Indicators and the annual reports and accounts of the selected Nigerian commercial banks from 2012 to 2023. The data analysis technique used was panel regression analysis, which was further extended to the generalized method of moments in a bid to account for potential endogeneity. The study made use of EViews 12 software to analyse the data. The results reveal that liquidity risk disclosure and firm size had significant and positive effects on financial performance, while credit risk disclosure, credit risk, firm age, and leverage had significant and negative effects. This study concludes that credit risks significantly undermine commercial banks’ financial performance, as an upsurge in non-performing loans results in reduced financial performance. Conversely, effective liquidity risk disclosure characterized by transparent reporting on liquidity position was found to enhance financial performance. This study, therefore, recommends, among others, that banks should strengthen their credit risk assessment framework and enhance transparent risk reporting to improve performance and financial stability.

1. Introduction

Banking is an industry characterised by risk-taking. Banks typically fulfil an intermediation role by receiving deposits from savers and extending loans to borrowers. By doing so, they encounter numerous risks that directly and/or indirectly affect their financial performance (Bavoso, 2022). The global financial system hinges on effective and efficient risk management by financial institutions, especially the banking industry, as they serve as vital intermediaries in ensuring and fostering economic development and growth. Banks are critical to economic growth since the banking sector influences most economies (Mendoza & Rivera, 2017). Rather than avoiding risk, banking is in the business of accepting and managing risk (Hull, 2012). Jasevičienė (2012) affirms that accepting and managing risks is the cornerstone of a bank’s operation. The increasing competitiveness among organisations today, as well as the possibilities and challenges that all organisations confront at various national and international levels, has sparked interest in risk management (Van et al., 2015; Aripin et al., 2023). Uncertainty regarding an event’s future result is what risk is all about. The proprietor of a business is considered to have taken a risk in every attempt to start a firm. Each transaction or service provided by banks carries a certain level of risk. Banks face risks as a result of failures in their operations, processes, and policies, as well as system inadequacy. As a result, in order for financial institutions to continue and flourish, management must consider measures to reduce unacceptable risks (Walker et al., 2002).

Wherever there is risk, risk management is required. Risk management encompasses the identification, assessment, and prioritisation of risks, alongside the utilisation of coordinated and cost-effective resources to mitigate, track, and regulate the probability and consequences of adverse events (Nwude & Okeke, 2018). Adequately managed risk positively influences the stability of banks, while badly managed risk adversely affects their stability, i.e., the bank may go bankrupt. To put it another way, risk is an opportunity that, if well managed, can yield a large reward, hence enhancing financial performance (FP). Risk disclosure is essential for enhancing transparency and accountability, as it provides stakeholders with critical information about a bank’s risk exposures. Effective risk disclosure not only fosters investor and depositor confidence but also supports regulatory compliance and decision-making. Regular assessments and updates to the risk-management framework are recommended to adapt to changing market conditions (Oladele & Akinwumi, 2024).

Nigeria has a growing and developing economy; risk management in the banking sector is indispensable for maintaining and enhancing investors’ confidence. Ghenimi et al. (2017) affirm that liquidity and credit risks are regarded as some of the most significant risks encountered by banks because of their direct influence on performance and operational stability. Regulators, investors, and consumers are more inclined to trust institutions that exhibit strong risk-management strategies (Adeniran et al., 2024). Credit risk is seen as a significant issue that can lead to financial instability and inefficiencies, jeopardising the continuity of an organisation (Omobolade et al., 2020). The proportion of non-performing loans relative to total loans reflects the credit risks faced by financial institutions. Credit risk management is fundamental to banking operations due to its essential role in maintaining profitability and stability in the marketplace (Addy et al., 2024). Incekara and Çetinkaya (2019) state that credit risk is regarded as the probability that a counterparty or debtor to a financial contract may default on meeting their obligations as stipulated in the agreed upon terms. Jorion (2009) refers to credit risks as the possibility of financial loss resulting from the inability of the counterparty to fulfil its contractual commitments.

Liquidity risks emerge from the inability of banks to fulfil their short-term financial obligations when they become due without incurring significant costs or jeopardising their financial stability. Failure to manage liquidity adequately could lead to regulatory penalties, reputational damage, and, in extreme cases, insolvency. Conversely, effective liquidity risk management enhances operational resilience, safeguards depositors’ confidence, and supports the institution’s profitability. A credit risk arises from a borrower’s inability to meet financial obligations, while liquidity risk is a bank’s potential inability to meet its short-term liabilities as they become due without incurring excessive losses. Proper risk management of both the liquidity and credit risks is very essential for sustaining financial performance and ensuring the long-term viability of the banking sector. Furthermore, risk-management practices, liquidity, and credit risk disclosure also play vital roles in enhancing transparency and boosting stakeholder confidence. Such disclosures are crucial for effective risk governance, as they enable investors, regulators, and other key stakeholders to assess the financial resilience and health of a bank. In the Nigerian context, the relevance of credit and liquidity risk-management practices and their disclosure cannot be overstated, considering the volatile economic conditions and regulatory challenges that the banking sectors faces.

The Nigerian banking sector has faced severe crises over the years, including insolvency and liquidation, which is primarily attributed to inadequate risk disclosure and poor risk-management practices. Furthermore, the country also had a banking consolidation crisis in 2005, when the Central Bank of Nigeria raised the capital base of commercial banks to NGN 25 billion from NGN 2 billion. Significant regulatory reforms, such as the Banking Sector Consolidation Reform in 2005 and the Basel II and Basel III Implementations in 2013, among others, have been introduced over time to mitigate these challenges; however, issues related to credit and liquidity risks persist especially due to the resultant effects of the COVID-19 pandemic, which threatens the stability and performance of many banks. Intense competition among financial institutions and the aftermath of the COVID-19 pandemic have compelled banks to exercise greater caution in risk management and income diversification (Rasyid & Bangun, 2023). Considering the key role of banks in economic development and growth, and recurring cases of financial distress in the sector, it becomes necessary and imperative to assess the nexus of credit and liquidity risk practices and disclosure on the commercial banks’ FP.

This study makes significant theoretical contributions by integrating the balanced portfolio theory, financial intermediation theory, and information asymmetry theory to analyse credit and liquidity risk-management practices and disclosures in Nigerian banks. The financial intermediation theory underscores the role of banks as intermediaries that allocate financial resources efficiently, manage risk, and ensure stability in financial markets. Also, the study further enriches the understanding of information asymmetry in banking by highlighting its role in credit risk exposure. Finally, the application of balanced portfolio theory extends the discourse on liquidity risk management, emphasizing diversification as a crucial strategy for mitigating liquidity constraints.

To the best of the authors’ knowledge, little or no study has considered the effect of credit risk and liquidity risk management and disclosures on FP, especially in Nigeria’s banking sector. The study also introduces macroeconomic variables to improve robustness and contextualize findings. Most studies (Nwude & Okeke, 2018; Saleh & Abu Afifa, 2020; Adusei, 2021; Abdelaziz et al., 2022; Oladele & Akinwumi, 2024) consider the effect of credit and/or liquidity risk management on FP without taking into consideration the complementary effect of disclosures on financial performance. Among others, this study helps regulatory authorities in enacting reforms that will mitigate the adverse effects of these risks on the performance and stability of Nigerian banks. In achieving this objective, this study provides the necessary background. Section 2 of this study reviews the relevant literature and theories. Section 3 presents the methodology required to achieve this objective. Section 4 focuses on a discussion of the findings, and the remaining sections address the conclusion, including recommendations for enhancing financial performance and building stakeholder confidence.

2. Literature Review

2.1. Theoretical Review

This study employed information asymmetry theory, financial intermediation theory, and balanced portfolio theory. The financial intermediation theory was proposed by Marty (1961) and later expanded by Diamond (1984) and others. The theory posits that banks act as intermediaries between surplus and deficit economic agents, facilitating the allocation of resources, managing risks, and enhancing market efficiency. The theory of intermediation posits that those financial intermediate institutions, such as banks, function as problem solvers. Bikker (2003) and Safitri et al. (2020) asserted that banks exist to limit the risks associated with the cashflow from surplus parties (i.e., depositors) by acting as intermediaries for those in need of funds. Allen and Santomero (1997) assert that risk management has emerged as a vital sphere of intermediary activity. The theory underscores the relevance of effective liquidity and credit risks in ensuring the stability and performance of banks, which are critical for maintaining their intermediary role.

Joseph Stiglitz (1961), George Akerlof (1970), and Michael Spence (1973) are the three economists who formulated the theory of asymmetric information, which was formalized in 2001 (Matagu, 2018). The theory elucidates scenarios in which one side of a transaction possesses superior information compared to the other, resulting in adverse selection and moral hazard issues. The asymmetric information theory posits that thorough screening is essential for obtaining accurate information from potential borrowers; nevertheless, differentiating between a reliable borrower and an unreliable one may be unattainable (Auronen, 2003; Hunjra et al., 2022). Information asymmetry arises when one party in a transaction possesses superior or more comprehensive information than the other party. This imbalance can lead to adverse selection and moral hazards, increasing the likelihood of credit risk. For instance, in the banking sector, information asymmetry is a significant cause of credit risk, as it can lead to poor lending decisions and higher default rates (Shi & Zhang, 2009; Dashottar & Srivastava, 2021). Balanced portfolio theory emphasizes diversification, which can help manage liquidity risk by spreading investments across assets with varying liquidity profiles. This reduces the impact of liquidity constraints on the overall portfolio (Al Janabi, 2011; Allaj, 2017). Effective portfolio diversification helps manage liquidity risk, while transparent disclosures ensure that stakeholders are well-informed about the liquidity aspects of the portfolio. This holistic approach supports financial stability and aligns with regulatory requirements.

2.2. Empirical Review

Ariffin (2012) carried out a study on liquidity risks and disclosures in Malaysian Islamic banks from 2006 to 2008. The results showed strong relationships between liquidity risks and performance metrics like ROA and ROE, especially throughout the global financial crisis. This research enhanced the literature on Islamic banking, as it relates to risk management by emphasizing its crucial role in sustaining financial stability. Effiong and Ejabu (2020) assessed the effects of liquidity risk management (LRM) on the financial performance (FP) of consumer goods. Exploring multiple regression analyses in evaluating parameters, such as cash defensives, long-term debt, and quick ratios. The study demonstrated that significant relationships exist between LRM and financial performance indicators, including returns on assets, earnings per share, and returns on capital employed. The paper recommended that firms should integrate robust liquidity risk-management strategies into their operational frameworks to improve financial performance. In the Muscat Stock Exchange, Alabdullah et al. (2022) analysed the relationships between risk-management practices and banks’ FP. Their study, utilising secondary data and structural equation modeling (SEM), identified a significant positive relationship between risk management and return on assets, but the relationship with ROA was determined to be insignificant. Their results highlight the vital role of effective and efficient risk-management practices in influencing bank FP and underscore the necessity for such measures to maximise ROA.

Al-Husainy and Jadah (2021) assessed the impacts of liquidity and credit risk on the profitability of Iraq’s commercial banks. A sample of 18 banks was explored and the generalised method of moments (GMM) was employed for panel regression. The research indicated that credit risk adversely affects banks’ FP, whereas liquidity risks exerted a significant positive impact. The paper emphasised the relevance of risk-management practices for improving bank performance and contributed to comprehending the dynamics of risk in banks. Mahmood and Ahmed (2023) analysed the mediating role of risk-management practices in the relationships between risk identification and monitoring and the FP of private banks in Iraq. Through qualitative and quantitative data analyses, they determined that effective risk-management techniques are crucial in aligning risk-related aspects with enhanced financial performance. The findings recommend that banks develop comprehensive risk-management systems to meet regulatory requirements while achieving better financial outcomes.

Scannella and Polizzi (2021) provided evidence that banks differ in disclosure practices for credit risk despite standardised regulatory frameworks. By employing a non-automated content analysis methodology, the study revealed correlations between disclosure practices, bank size, and business models. These insights have regulatory and strategic implications for credit risk reporting. In MENA nations, Abdelaziz et al. (2022) investigated the impact of liquidity and credit risks on banks’ profitability from 2004 to 2015. This paper demonstrated that employing the seemingly unrelated regression (SUR) approach shows that escalations in credit and liquidity risks significantly reduce profitability. The findings highlight institutional quality’s role in mediating these relationships, demonstrating how governance structures influence financial outcomes.

Eyalsalman et al. (2024) examined the impact of credit risk, IFRS9, liquidity risk, and capital on the FP of banks in Jordan from 2012 to 2021, revealing that while IFRS 9 significantly affects performance, liquidity risk has an insignificant effect, while credit risk negatively affects ROA and ROE. Haris et al. (2024) investigated the effect of credit and liquidity risks on the profitability of banks in Pakistan before and during COVID-19 (Q1 2018–Q4 2021), exploring pooled OLS, random and fixed effects models, and quantile regression. The findings revealed that credit risks have a negative effect on all profitability metrics, while liquidity risk positively affects ROA and ROE but not the interest margin. Also, Abbas and Jawad (2023) explored the relationship between liquidity, credit risk, and the profitability of commercial banks in Iraq over ten years, highlighting the significant impacts of credit risk on liquidity and profitability. They concluded that the banks should diversify their investments, increase foreign currency loans, restructure finance, and invest in stable international companies to enhance their financial stability and profitability.

3. Methodology

3.1. Population and Sample

This study primarily concentrates on publicly traded commercial banks in Nigeria. The study’s population consists of 13 commercial banks registered with the Nigerian Exchange Group as of 2023. This paper utilised a purposive sampling technique to select 12 banks that were actively traded on the stock market throughout the sample periods, ensuring the availability of relevant data. The dataset comprises secondary data.

3.2. Research Design, Data, and Sources of Data

This study explored longitudinal and ex post facto research designs. An ex post facto research design is crucial, since the data on relevant factors are already available in the firms’ audited financial reports. The study also utilised a longitudinal research design, as the data obtained are panel (cross- and time-series) in nature, encompassing 12 cross-sectional units from 2012 to 2023. A longitudinal research design is suitable as it allows for extensive data gathering, hence enhancing the accuracy of the resulting estimates. The study employed secondary data, which included return on assets, credit risk, liquidity risk, credit risk disclosure, liquidity risk disclosure, leverage, firm size, and firm age of the Nigerian commercial banks. The data were sourced from their audited annual reports and accounts and World Development Indicators, covering the relevant years of the study from 2012 to 2023 (

Table 1. Measurement of variables.

S/N	Variable	Measurement of Variable	Authors	Expected Sign
Dependent Variable
1	Return on Assets (ROA)	Measured as earnings before interest and taxes (EBIT) in relation to total assets	Evoney and Margaretha (2024), Al-Aaraji (2024), Bhuiya et al. (2023), Nguyen (2023)
Independent Variables
2	Credit Risk (CRR)	Proxied as non-performing loans divided by total loans (%)	Antony and Suresh (2023), Nguyen (2023), Evoney and Margaretha (2024)	−
3	Credit Risk Disclosure (CRD)	A binary variable: 1 if the firm discloses credit risk information, 0 otherwise	Permana (2023)	+
4	Liquidity Risk (LRR)	Measured as the ratio of total customer deposits to liquid assets	Ghenimi et al. (2017), Mennawi (2020), Rasyid and Bangun (2023)	−
5	Liquidity Risk Disclosure (LRD)	A binary variable: 1 if the firm discloses liquidity risk information, 0 otherwise	Roggi and Giannozzi (2015)	+
Control Variables (Firm-Specific)
6	Leverage (LEV)	Computed as total liabilities divided by total equity	Mennawi (2020), Akinadewo et al. (2023), Al-Aaraji (2024)	−
7	Firm Size (FSZ)	Measured as the natural logarithm of total assets	Onyenwe and Glory (2017), Shaik and Sharma (2021), Bhuiya et al. (2023)	+
8	Firm Age (AGE)	Measured as the number of years since the company’s incorporation	Galletta and Mazzù (2019)	+
Control Variables (Macroeconomic)
9	GDP Growth (GDP)	Proxied by the annual percentage growth rate of the GDP	Romus et al. (2020), Abdelmoneim and Yasser (2023), Akinadewo et al. (2023)	+
10	Inflation (INF)	Measured by the Annual Consumer Price Index inflation rate	Tolulope and Oyeyinka (2014), Akinadewo et al. (2023)	+/−
11	Interest Rates (INRs)	Measured by Central Bank’s policy rate	Godspower-Akpomiemie and Ojah (2017)	−

).

3.3. Model Specification and Data Analysis Technique

The study set used to investigate the effect of credit risk and liquidity risk management and disclosures on Nigerian commercial banks’ financial performance. The data analysis techniques explored are panel regression analysis in addition to other descriptive and inferential statistics. This study goes further by exploring the generalized method of moments to account for endogeneity. Studies such as those by Al-Husainy and Jadah (2021) and Hunjra et al. (2022) have also explored GMM to account for endogeneity. EViews 12 software was also used to analyse the data. The dependent variable is return on assets (ROA); the independent variables are credit risk disclosure (CRD), liquidity risk disclosure (LRD), credit risk (CRR) and liquidity risk (LQR); and the control variables are firm size (FSZ), firm age (FAG), and leverage (LEV). The study also made use of macroeconomic variables, such as inflation (INF), GDP growth (GDP), and interest rate (INR). This paper employed a panel data model, which is expressed in Equation (1).

$${\mathrm{R}\mathrm{O}\mathrm{A}}_{\mathrm{i}\mathrm{t}}={{\mathrm{C}\mathrm{R}\mathrm{R}}_{\mathrm{i}\mathrm{t}}+\mathrm{L}\mathrm{Q}\mathrm{R}}_{\mathrm{i}\mathrm{t}}+{\mathrm{C}\mathrm{R}\mathrm{D}}_{\mathrm{i}\mathrm{t}}+{\mathrm{L}\mathrm{R}\mathrm{D}}_{\mathrm{i}\mathrm{t}}+{\mathrm{A}\mathrm{G}\mathrm{E}}_{\mathrm{i}\mathrm{t}}+{\mathrm{L}\mathrm{E}\mathrm{V}}_{\mathrm{i}\mathrm{t}}+{\mathrm{S}\mathrm{I}\mathrm{Z}}_{\mathrm{i}\mathrm{t}}+{\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i}\mathrm{t}}+{\mathrm{I}\mathrm{N}\mathrm{F}}_{\mathrm{i}\mathrm{t}}+{\mathrm{I}\mathrm{N}\mathrm{R}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(1)

4. Results and Discussion

4.1. Descriptive Statistics

Table 2. Descriptive statistics.

Variable	Mean	Maximum	Minimum	Std. Dev.	Skewness	Kurtosis	Jarque–Bera	Probability
ROA	1.762	8.970	−9.530	1.824	−1.330	15.037	905.373	0.000
CRR	4.998	33.580	0.100	5.942	2.832	11.169	559.949	0.000
CRD	0.951	1.000	0.000	0.216	−4.198	18.623	1887.320	0.000
LQR	0.157	0.559	0.000	0.077	1.133	7.268	138.161	0.000
LRD	0.944	1.000	0.000	0.230	−3.881	16.059	1384.609	0.000
GDP	2.883	6.671	−1.794	2.721	−0.275	2.148	6.175	0.046
INR	7.662	9.373	6.172	1.022	0.144	1.810	8.995	0.011
INF	13.931	24.660	8.047	4.668	0.739	2.969	13.105	0.001
LEV	7.233	16.470	−2.980	3.140	−0.243	5.237	31.217	0.000
FSZ	9.313	10.423	8.195	0.455	0.009	2.579	1.049	0.592

Source: Authors’ computation.

presents the descriptive statistics of key variables as they relate to the study. The mean ROA of 1.762 reveals that banks, on average, generate 1.76% returns on their assets. This reflects a modest profitability. The standard deviation of 1.824 indicates a moderate dispersion of firms’ profitability. The skewness of −1.330 signifies a left-skewed distribution, indicating that more banks reported lower or negative returns compared to those reporting high returns. The mean LRD value of 0.944 indicates that, on average, Nigerian banks disclose about 94.4% of their liquidity-risk-related information. This demonstrates a strong emphasis on transparency. A standard deviation of 0.230 depicts moderate consistency among the banks’ disclosure practices. The skewness of −3.881 suggests that the majority of the banks disclose their liquidity risk comprehensively, with very few providing very low or no disclosure. Also, the average LQR value of 0.157 indicates that liquidity risk levels are generally low across the banks, with a median of 0.149, further indicating that half of the firms report risks below 14.9%. A standard deviation of 0.077 indicates limited variability in liquidity risks, pointing to consistent risk-management practices among the firms. A positive skewness of 1.129 indicates that, while most firms report low risks, a few report significantly higher values. The mean credit risk (CRR) value of 5.030 shows that banks face a moderate level of credit risk, with a median of 3.250. This suggests that half of the banks report risks below this value. A standard deviation of 5.942 further supports the notion of widespread credit risk levels among the banks. The finding of a mean credit risk disclosure of 0.951 reflects that, on average, firms disclose about 95.1% of their credit risk information. The standard deviation of 0.216 establishes low variability, showing that most firms are consistent in their disclosure practice. Also, the average leverage (LEV) of 8.499 suggests that banks carry, on average, debt levels approximately 8.5% times their equity indicating heavy reliance on external financing. A standard deviation of 15.621 reflects pronounced variability in leverage levels. The mean inflation (INF) rate of 13.931 with a standard deviation of 4.668 suggests moderate inflationary trends with some variability, while its skewness (0.739) and kurtosis (2.969) indicate a slightly right-skewed distribution. The interest rate (INR) averaged 7.662, showing relative stability (Std. Dev. = 1.022), whereas the GDP growth rate had a mean of 2.883, with a minimum of −1.794, highlighting periods of economic contraction within the observed timeframe.

4.2. Correlation Analysis and Variance Inflation Factor

To determine the degrees of the associations and strengths of the relationships that exist among the variables, correlation analysis was employed as a preliminary test. A summary of the direction and strength of the linear relationship that exists between two variables is given by the Pearson product–moment correlation, also known as the correlation coefficient. In

Table 3. Correlation matrix.

VARIABLE	ROA	CRR	CRD	LQR	LRD	FSZ	FAG	LEV	GDP	INF	INR
ROA	1.000
	-----
CRR	−0.181	1.000
	(0.035)	-----
CRD	0.120	−0.270	1.000
	(0.162)	(0.002)	-----
LQR	0.360	−0.188	0.132	1.000
	(0.000)	(0.028)	(0.124)	-----
LRD	0.329	−0.222	0.709	−0.040	1.000
	(0.000)	(0.010)	(0.000)	(0.647)	-----
FSZ	0.468	−0.036	0.157	0.134	0.233	1.000
	(0.000)	(0.680)	(0.068)	(0.119)	(0.006)	-----
FAG	−0.159	0.084	−0.080	0.041	−0.040	0.134	1.000
	(0.065)	(0.332)	(0.352)	(0.638)	(0.647)	(0.119)	-----
LEV	−0.085	−0.007	0.129	0.356	0.219	0.277	0.137	1.000
	(0.323)	(0.936)	(0.134)	(0.000)	(0.011)	(0.001)	(0.112)	-----
GDP	0.136	−0.019	−0.040	0.024	0.023	−0.174	−0.142	0.013	1.000
	(0.113)	(0.829)	(0.640)	(0.784)	(0.789)	(0.043)	(0.100)	(0.879)	-----
INF	0.041	0.024	0.044	0.027	0.056	−0.341	−0.126	−0.138	0.560	1.000
	(0.635)	(0.780)	(0.610)	(0.756)	(0.519)	(0.000)	(0.142)	(0.108)	(0.000)	-----
INR	0.090	−0.035	0.081	−0.013	0.089	0.221	0.068	0.071	−0.166	−0.355	1.000
	(0.296)	(0.685)	(0.348)	(0.881)	(0.305)	(0.010)	(0.432)	(0.412)	(0.053)	(0.000)	-----

Correlation coefficients are in the upper section; p-values are in parentheses. Source: authors’ own computations.

, the asymmetrical matrix displays the correlation values between variables over 12 years. The values in Table 3 are below the crucial 0.80 threshold (Judge et al., 1988; Bryman & Cramer, 2004). Furthermore,

Table 4. Variance inflation factors.

Variable	Coefficient Variance	Uncentered VIF	Centred VIF
CRR	0.000	1.936	1.130
CRD	2.048	174.678	6.422
LQR	2.978	7.713	1.266
LRD	1.778	150.530	6.641
GDP	0.002	3.107	1.523
INR	0.013	66.151	1.176
INF	0.001	18.396	1.839
LEV	0.001	7.225	1.169
FSZ	0.090	686.576	1.527
C	8.015	709.758
		MEAN VIF	2.522

Source: authors’ own computations.

demonstrates that the total generated VIF is significantly less than the crucial number of 10. A VIF score of less than 10 suggests that multicollinearity is not a significant worry for interpreting the regression’s results (Neter et al., 1983). Also, the mean-centred VIF of 2.522 is safely below 10. A lower mean VIF reflects that the overall multicollinearity across the model is minimal.

4.3. Hausman Test and Lagrange Multiplier Tests

Table 5. Breusch–Pagan and related tests.

Test	Cross-Section	Time	Both
Breusch–Pagan	182.941	1.502	184.443
p-Value	(0.000)	(0.220)	(0.000)
Honda	13.526	−1.226	8.687
p-Value	(0.000)	(0.890)	(0.000)
King–Wu	13.526	−1.226	8.661
p-Value	(0.000)	(0.890)	(0.000)

Source: authors’ own computations.

showed the Breusch–Pagan, King–Wu, and Honda Lagrange multiplier tests provide evidence of significant random effects, particularly in the cross-sectional dimension, with p-values well below 0.05. This indicates the suitability of a random-effects model over pooled OLS. However, the Hausman test as displayed in

Table 6. Correlated random effects—Hausman test.

Test Summary	Chi-Sq. Statistic	Chi-Sq. d.f.	Prob.
Cross-Section Random	30.119	10	(0.0008)

Source: authors’ own computations.

was employed to determine the choice between fixed-effects and random-effects models, which yielded a chi-square statistic of 30.119 (p-value = 0.0008), which is statistically significant. In as much as the p-value is significant, we reject the null hypothesis that the random effects model is appropriate. This implies that the fixed-effects model is the preferred specification, as it accounts for the potential correlation between the regressors and the individual-specific effects.

4.4. Regression Analysis

The R-squared value of 0.819 for the dependent variable signifies the degree to which the independent variables explain the variation in the dependent variable. The model’s independent variables are thought to be responsible for around 81.9% of the variance seen in the dependent variable. The model is statistically significant, which is evidenced by the p-value of 0.000 and F-statistic of 24.49. The output of a fixed-effects model from the regression analysis is the result, as displayed in

Table 7. Regression analysis.

Dependent Variable: ROA
	Random Effects			Fixed Effects			Pooled OLS
Variable	Coef.	t-Stat	Prob.	Coef.	t-Stat	Prob.	Coef.	t-Stat	Prob.
CRR	−0.045	−3.096	0.002	−0.040	−2.449	0.016	−0.050	−2.67	0.009
CRD	−9.927	−9.871	0.000	−9.836	−9.613	0.000	−10.309	−7.28	0.000
LQR	1.524	1.136	0.258	1.026	0.709	0.480	2.553	1.495	0.137
LRD	9.901	10.524	0.000	9.750	10.109	0.000	10.540	8.002	0.000
GDP	0.043	1.259	0.210	0.045	1.241	0.217	0.055	1.144	0.255
INF	0.048	2.143	0.034	0.059	2.162	0.033	0.036	1.192	0.236
INR	0.067	0.844	0.401	0.043	0.468	0.641	0.077	0.689	0.492
LEV	−0.144	−4.132	0.000	−0.142	−3.297	0.001	−0.166	−4.26	0.000
FAG	−0.018	−1.697	0.092	0.014	0.186	0.853	−0.016	−2.04	0.043
FSZ	2.038	5.841	0.000	1.877	2.025	0.045	1.897	6.412	0.000
C	−16.904	−5.290	0.000	−16.127	−2.254	0.026	−15.781	−5.64	0.000
R-squared	0.581			0.819			0.597
Adjusted R-squared	0.548			0.785			0.564
S.E. of regression	0.929			0.860			1.224
F-statistic	17.359			24.490			18.488
Prob. (F-statistic)	0.000			0.000			0.000
Durbin–Watson stat	1.735			2.217			1.020

Source: authors’ own computations.

.

The results displayed in Table 7 also reveal that there is an adverse relationship between CRD and financial performance (FP), as performance tends to decline. The negative relationship with a coefficient of 9.836 (p = 0.000) could suggest that disclosure by banks with higher credit risk exposure tends to highlight vulnerabilities that affect investors’ confidence or signal financial challenges, which ultimately reduces FP. Also, the results obtained from the regression analysis reveal that CRR has a significant and negative effect on banks’ FP, which is proxied by ROA, which has a coefficient of −0.040 (p = 0.016). This indicates that an upsurge in credit risks is associated with a decline in financial performance. This suggests that higher credit risks, likely stemming from default receivables, impose an additional burden on firms, reducing profitability and, subsequently, financial performance. The findings indicate that banks must maintain a low level of credit risk, which is proxied by non-performing loans, to maximize their financial performance (Evoney & Margaretha 2024). Akinadewo et al. (2023) assert that credit risk is expected to adversely affect a bank’s performance. This result is also in agreement with Bhuiya et al. (2023) and Nguyen (2023), who affirm that credit risks have significant and negative effects on FP. The results reveal that LRD has significant and positive effects on FP, with a coefficient of 9.750 (p = 0.000). This implies that firms that disclose more about their liquidity risk tend to achieve better FP. This result is in consonance with a study by Roggi and Giannozzi (2015). The strength of the relationship suggests that disclosing risks related to liquidity facilitates better decision making and enhances investors’ confidence, all of which positively affect financial performance.

The results reveal that leverage (LEV) has a negative and significant relationship with ROA, having a coefficient of −0.142 (p = 0.001). This negative relationship reflects the possible risk of excessive reliance on debt financing, such as increased financial risk and higher interest obligations, which can constrain financial performance. The findings highlight the need for firms to sustain a balanced capital structure and carefully manage debt levels to avoid a decline in FP. The results obtained are in agreement with studies by Akinadewo et al. (2023), Al-Aaraji (2024), and Segun et al. (2024). They also affirm that leverage has a significant and negative relationship with financial performance. The results of the analysis presented in Table 7 demonstrate that firm size (FSZ) has a statistically significant positive effect on ROA in the fixed-effects model (coefficient = 1.877, p = 0.045), which indicates that larger firms tend to have a better financial performance. This result is consistent with Akinadewo et al. (2023). Hoang et al. (2019) demonstrate that Vietnamese-listed firms also show a positive relationship between FSZ and financial performance. Finally, inflation is also revealed to have a positive and statistically significant effect on ROA (coefficient = 0.059, p = 0.033), suggesting that higher inflation is associated with improved FP. Fathi et al. (2022) also state that inflation positively affects earnings per share, liquidity, and operational costs, leading to improved bank performance.

The findings demonstrate the economic consequences of risk-management and disclosure practices on the FP of commercial banks in Nigeria. Credit risk disclosure implies that banks with higher credit exposure may weaken investors’ confidence, possibly due to concerns over asset quality deterioration and loan default rates. Banks need to focus on evaluating the creditworthiness of borrowers and maintaining a healthy loan portfolio (Hunjra et al., 2022; Razermera et al., 2024). Similarly, the significant and negative effect of credit risk on financial performance suggests that an increase in non-performing loans imposes financial burdens that reduce earnings. Additionally, the presence of credit risks is undesirable to investors, potentially leading to a decrease in stock price and profitability loss (Miglionico, 2019). The significant positive effect of liquidity risk disclosure suggests that the transparent disclosure of such risk strengthens market confidence, attracting investors and enhancing the FP of commercial banks. Banks that maintain an adequate liquidity ratio tend to experience better operating performance, greater stability, and lower bankruptcy risk (Chen et al., 2024). Enhanced disclosure practices, particularly regarding liquidity risk, can improve a bank’s stability and performance. Adequate disclosure helps in maintaining a stable image among investors and withstanding regulatory pressures (Sarker & Bhowmik, 2021; König-Kersting et al., 2022). The economic implications underscore the need for Nigerian banks to enhance their credit risk-management framework while ensuring optimal liquidity levels to maintain stability. On the other hand, failure to mitigate credit risks and excessive leverage can weaken investors’ confidence and lead to financial instability, which may hinder wider economic development.

4.5. Robustness Check

This study used robustness checks to account for endogeneity. The value of R-square indicates that approximately 59.7% of the variation in the ROA is explained by the independent variables in the model, suggesting a moderate goodness-of-fit. The authors re-analysed Equation (1) using the generalized method of moments and the results are reported in

Table 8. Generalized method of moments.

Dependent Variable: ROA
Method: Panel Generalized Method of Moments
Instrument specification: ROA, CRD, CRR, GDP, FAG, FSZ, INF, INR, LEV, LQR, LRD, C
Variable	Coefficient	t-Statistic	Prob.
CRD	−10.309	−7.284	0.000
CRR	−0.050	−2.669	0.009
LQR	2.553	1.495	0.137
LRD	10.540	8.002	0.000
GDP	0.055	1.144	0.255
FAG	−0.016	−2.042	0.043
FSZ	1.897	6.412	0.000
INF	0.036	1.192	0.236
INR	0.077	0.689	0.492
LEV	−0.166	−4.258	0.000
C	−15.781	−5.638	0.000
R-squared	0.597
Adjusted R-squared	0.564
S.E. of Regression	1.224
Durbin–Watson Statistic	1.020
Instrument rank	12
J-statistic	125

Source: authors’ own computations.

. The results show significant relationships between several explanatory variables and return on assets (ROA). It shows that credit risk (CRR) and credit risk disclosure (CRD) have negative effects on ROA while liquidity risk disclosure (LRD) has a positive and significant effect on financial performance, which is proxied by ROA. The results also show that leverage and firm age have significant negative effects on ROA, while firm size has a positive effect on financial performance.

5. Conclusions

This paper examined the effects of credit and liquidity risks on the financial performance of listed Nigerian commercial banks, spanning from 2012 to 2023. The findings from the analysis reveal that credit risks significantly undermined the financial performance of the commercial banks, as an upsurge in non-performing loans and poor credit management practices resulted in reduced financial performance. Conversely, effective liquidity risk-management practices, featured by maximising liquidity and ensuring timely fulfilment of financial obligations, were discovered to enhance the financial performance of the selected banks. The results underscore the key role of sound risk-management practices in ensuring Nigerian banks’ performance and financial stability.

6. Recommendations

Based on the findings, Nigerian commercial banks should endeavour to strengthen their credit risk-management frameworks to minimise the adverse effects of non-performing loans on the financial performance of banks. Financial institutions should leverage credit assessment tools and advance predictive analytics to thoroughly assess and evaluate the creditworthiness of borrowers and forecast potential default. Also, a strict credit approval process and regular monitoring of loan performance can further mitigate risks. Banks should adopt robust liquidity management techniques that align with international standards, such as the Basel III guidelines for liquidity risks. Banks should strengthen their credit risk assessment framework and enhance transparent risk reporting to improve performance and financial stability. Policymakers and stakeholders should invest in financial technology solutions that enable real-time monitoring of risks. This proactive approach will enhance Nigerian banks’ financial resilience and ensure their ability to thrive in a rapidly changing financial system.

7. Limitations of the Study

A limitation of this study is its reliance on only secondary data sourced from the annual reports and accounts of the selected commercial banks. Incorporating qualitative data may provide a better understanding of the subject matter. Also, the study purposively selected 12 commercial banks on the basis of the availability of data, and the findings of this study may not be fully generalisable to all Nigerian financial institutions, especially non-commercial banks, such as the mortgage banks, investment banks, and microfinance banks.

8. Suggestion for Further Studies

Future studies could incorporate other financial institutions and non-bank financial institutions to provide a more wide-ranging understanding of risk-management practices across the entire financial sector. Also, extending the study to a cross-country analysis would offer comprehensive insight into a comparative analysis of how regulatory environments affect the relationship between risk management, disclosure, and financial performance. Future studies could also explore other risks that banks are exposed to and their disclosures on financial performance. These risks include operational risk, capital adequacy risk, market risk, reputational risk, cybersecurity risk, and compliance risk, among others. Other financial performance measures can also be explored. Moreover, future research could also explore emerging financial technologies in risk management and financial performance. This could also offer valuable insights into how digital innovation shapes the banking sector’s resilience to risk. Finally, a qualitative approach, which could involve interviews with regulatory authorities and bank executives, among others, and could provide insight into the motivations and various challenges behind risk disclosure decisions.