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Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

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CORPORATE GOVERNANCE AND BANKRUPTCY RISK IN 

COMMERCIAL BANKS IN NIGERIA 
 

1Ofurum Christmas Ifeanyi D. and 2Nwachukwu Rapheal 
1Department of Accountancy, Alex Ekwueme Federal University, Alike Ikwo, Ebonyi State 

2Department of Accountancy, Tansion University Umunya, Anambra State 

Email: Christmas.ofurum@funai.edu.ng/ chikwute@yahoo.com 

DOI: https://doi.org/10.5281/zenodo.14888292 

 

Abstract: This study determined the effect of corporate governance on bankruptcy risk in 

commercial banks in Nigeria, using risk management committee and board of directors’ 

independence. Ex Post Facto research design was adopted for the study. A sample of eight deposit 

money banks was used for the study. Data were obtained from the annual reports and audited 

accounts of the banks under assessment. Altman's original model for public companies was used to 

extract data and the formulated hypotheses were tested with regression analysis with aid of E-View 

9.0.  The analysis and hypotheses tested shows that risk management committee has no significant 

effect on bankruptcy risk commercial banks in Nigeria. However, the study revealed that board of 

directors’ independence has a positive significant effect on bankruptcy risk commercial banks in 

Nigeria. Based on the results, the study recommended among others that risk management 

committee should be encouraged, since the committee can influence the capacity for problem-

solving as the variety of perspectives that identifying the weakness of internal control and risk 

source that can easily use in checkmating bankruptcy. 

Keywords: Corporate governance, Risk management committee, Board of directors’ independence 

and Bankruptcy risk  

 

Introduction  

Bankruptcy has often been discussed and investigated in the recent years. While there has been 

attention related to corporate bankruptcy in the accounting and finance literature, focus has been 

mainly on predicting bankruptcy based on financial data (Altman, 2000). Though, this phenomenon 

having already been observed in recent high-profile bankruptcy events, some studies has been carried 

out on corporate governance on the bankruptcy risk, it is an open empirical question how the relation 

of corporate governance to the likelihood of bankruptcy. 

Strong corporate governance (SCG) practice guarantees transparency and consistency in financial 

statements. Firms can approach external sources at low costs when they have the confidence of 

investors (Tricker & Tricker, 2015). Additionally, the implementation of SCG practice ensures the 

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American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

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usage of the optimal business strategy to maximize firm value and mitigate related risks in the future 

(Husson-Traore, 2009; Manzaneque et al., 2016). The collapse of corporations resulting from the 

financial crisis of 2008 is evidence of the ramifications of weak corporate governance (WCG) 

implementation (Kumar & Singh, 2013; Mehran et al., 2011). SCG policies shield firms from the risk 

of financial distress or insolvency, which are among the biggest causes of bankruptcy. The role of SCG 

adoption in mitigating financial distress has been well recognized in developed countries. Many 

researchers have conducted empirical research on the impact of good corporate governance (CG) 

implementation on the probability of financial distress. These studies have homogenously proven the 

adverse effects of good CG practice on the likelihood of distress risk (Bravo-Urquiza & Moreno-Ureba, 

2021; Miglani et al., 2015).  Although developing countries appreciate the importance of CG, the 

benefits of CG, which have functioned only with good CG adoption, have not been a priority. 

Therefore, CG implementation in transitional economies is lacking (Nurunnabi, 2020).  

Nigerian Banking Sector plays a very crucial role in the socio-economic development of the country 

and significantly contributes to the Gross Domestic Product of the nation (Ighoroje & Egedi, 2015). 

However, the sector has over the years experienced turbulence and in some cases failure. Between 

2009 and 2019 according to NDIC (2020), the Central Bank of Nigeria (CBN) revoked the licenses of 

8 failed banks. The federal high court issued orders for them to be wound up and appointed the 

Nigeria Deposit Insurance Corporation (NDIC) as liquidator of the banks. The Central Bank of Nigeria 

in these periods, withdrew the banking licenses of some of these banks and the NDIC stepped in, 

creating bridge banks (temporary banks) to acquire the assets of the banks so as to continue 

operations on a fresh note (Wurim, 2013).  

An efficient and effective banking sector in the economy is essential not only for the promotion of 

efficient intermediary role but also for the protection of depositors, encouragement of healthy 

competition, maintenance of confidence in, and stability of the system and protection against 

systemic risk and collapse. The gruesome impact of ill health in the banking sector has affected almost 

all facets of the society - the government, regulatory authorities, creditors, equity investors, the 

bankers as well as the general public.  

This study therefore, ascertains the effect of corporate governance on bankruptcy risk in commercial 

banks in Nigeria. Specifically, the study sought to: 

1. Evaluate the effect of risk management committee on bankruptcy risk deposit money banks in 

Nigeria. 

2. Assess the effect of board of directors’ independence on bankruptcy risk deposit money banks 

in Nigeria. 

Literature Review  

Corporate Governance 

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Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

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The set of recommendations and rewards called "CG" are used to direct and modify an employer's 

management (Ehiedu, 2022; Adeusi, Akeke, Aribaba & Adebisi, 2017). Ehiedu and Ogbeta, (2014) 

opined that company governance is an institutional setup that restrains the excesses of commanding 

managers. Ensuring that the agency is operated efficiently and buyers earn a fair go back is the center 

reason of corporate governance (Kajola, 2018). If an employer is run with diligence, openness, 

accountability, and duty with the aim of maximizing shareholders' wealth, that company is taken into 

consideration to have complied with the CG rule (Pandy, 2018).  

Corporate governance is worried with how all parties (stakeholders) worried inside the firm's 

achievement attempt to guarantee that managers and different insiders are constantly taking proper 

movements or imposing approaches that shield the stakeholders' hobbies. Corporate governance tools 

assure shareholders of adequate returns on investments. Corporate governance changed into created 

to guard the hobbies of shareholders however an increasing number of gained significance has for 

other stakeholders and society (Mohammad, Aly, Dixon, & Startling, 2014).  

For Corporate governance systems, another key component for a business enterprise is the presence 

of inner and external auditors. In this feel, literature has shown that the presence of internal and 

outside audit systems could have a huge impact on changes to a agency’s monetary overall 

performance and on its possibility of default (Guo et al., 2016 and Cenciarelli et al., 2018, amongst 

others). Inner and outside auditors can guarantee the best of the information of the economic reviews 

furnished via the organization for buyers (Bratten et al., 2013), and their role has relevant 

consequences for the duration of a economic crisis (Cenciarelli et al., 2018). On this feel, also the 

presence of the audit committee will have a giant high-quality effect in stopping the danger of frauds 

and irregularities (Beasley et al., 2000). For distressed companies especially, statutory auditors and 

outside auditors are obliged to choose the ability of the business enterprise to operate as a going 

concern entity for the following three hundred and sixty-five days.  

Bankruptcy 

Bankruptcy refers to the situation in which the debtor company becomes unable to repay its debts and 

can be considered to be the consequence of a company’s inability to survive market competition, 

reflected in terms of job losses, the destruction of assets, and in a low productivity (Aleksanyan and 

Huiban 2016). The risk of bankruptcy or insolvency risk shows the possibility that a company will be 

unable to meet its debt obligations, respectively the probability of a company to go bankrupt in the 

next few years. Assessing of bankruptcy risk is important especially for investors in making equity or 

bond investment decisions, but also for managers in financial decision making of funding, 

investments and distribution policy. Failure prediction models are important tools also for bankers, 

rating agencies, and even distressed firms themselves (Altman et al. 2017). 

The essential information for executive financial decisions, but also for investors decisions are 

provided by financial statements. Thus, companies’ financial managers should develop the financial 

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American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

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performance analysis and problem-solving skills (Scapens 2006), without limiting their duties in 

verifying accounting data (Diakomihalis 2012) in order to maintain the firm attractive for investors. 

The image of financial performance of companies is affected by the estimation of its position in front 

of investors, creditors, and stakeholders (Ryu & Jang 2004). For this estimation there are used many 

indicators that reflect the company’s position such as: net working capital, net treasury, liquidity, 

solvency, profitability, funding capacity, cash-flow, etc., or a mix between them, such as Z-scores. 

Bankruptcy Prediction 

In addition, a prediction (Latin præ-, "before," and dicere, "to say"), or forecast, is a statement about 

a future event. A prediction is often, but not always, based upon experience or knowledge. There is no 

universal agreement about the exact difference between the two terms; different authors and 

disciplines ascribe different connotations. Although future events are necessarily uncertain, so 

guaranteed accurate information about the future is in many cases impossible, prediction can be 

useful to assist in making plans about possible developments; Howard H. Stevenson writes that 

prediction in business "... is at least two things: Important and hard (Stevenson, 2008). 

n statistics, prediction is a part of statistical inference. One particular approach to such inference is 

known as predictive inference, but the prediction can be undertaken within any of the several 

approaches to statistical inference. Indeed, one possible description of statistics is that it provides a 

means of transferring knowledge about a sample of a population to the whole population, and to other 

related populations, which is not necessarily the same as prediction over time. When information is 

transferred across time, often to specific points in time, the process is known as forecasting (Cox, 

2006). Forecasting usually requires time series methods, while prediction is often performed 

on cross-sectional data. 

Bankruptcy prediction has been one of the most challenging tasks in accounting since the study of 

FitzPatrick in 1930’s and during the last 60 years an impressive body of theoretical and especially 

empirical research concerning this topic has evolved (Altman, 1968).  

The Altman models have been challenged by approaches directly producing probabilities of 

bankruptcy, such as the logit model, as well as by more advanced machine-learning methods. Direct 

application of the Z-Score or its variants has proved problematic in other countries, under other legal 

regimes (accounting principles), and in other time frames. However, indirect applications (e.g., 

models with the same variables estimated for a new data set) are still acceptable. Let us cite here the 

paper by Altman et al. (2017) that shows the validity of the Z-Score approach internationally with 

large data sets, also compared to logit models that performed similarly or better. It is also worth 

referencing the paper by Barboza et al. (2017), which compares several machine-learning methods to 

discriminant analysis and logistic regression in predicting bankruptcy. It turns out that the Altman Z-

Score variables fare relatively well in other setups and models. 

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https://en.wikipedia.org/wiki/Latin
https://en.wikipedia.org/wiki/Forecasting
https://en.wikipedia.org/wiki/Event_(probability_theory)
https://en.wikipedia.org/wiki/Connotation
https://en.wikipedia.org/wiki/Uncertainty
https://en.wikipedia.org/wiki/Planning
https://en.wikipedia.org/wiki/Statistics
https://en.wikipedia.org/wiki/Statistical_inference
https://en.wikipedia.org/wiki/Predictive_inference
https://en.wikipedia.org/wiki/Forecasting
https://en.wikipedia.org/wiki/Time_series
https://en.wikipedia.org/wiki/Cross-sectional_data


American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

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Today a large area of finance is dedicated to forecasting financial distress or bankruptcy, employing 

appropriate methodology. Nonetheless, it seems that the finance profession in academia still does not 

recognize this new methodology as staple content in core corporate finance and accounting courses. 

The notable exceptions are textbooks by Damodaran (Applied Corporate Finance, 5th ed., 

Damodaran, 2015) and Berk and DeMarzo (Corporate Finance, 4th ed., Berk & DeMarzo 2017). 

The methodology of bankruptcy modelling may be attributed to financial micro econometrics and 

more recently, to advanced data analysis. Financial micro econometrics “emerges as a natural 

consequence of applying statistical and econometric methods to corporate finance, accounting, and 

other fields of finance; the applied edge of research in accounting and corporate finance is inevitably 

linked with the use of notions such as statistical sample, population, and the operation on sets of 

microdata” (Gruszczy´ nski 2018). 

Corporate Governance and Bankruptcy Risk 

Bankruptcy is the consequence of financial distress, which happens when the company defaults its 

financial commitments. Companies try to restructure their assets and liabilities to avoid bankruptcy 

and financial distress. Recent studies have found that corporate governance mechanisms can improve 

a firm's ability to predict bankruptcy (Dalia, 2023). For example, Fich and Slezak (2008) studied the 

effect of corporate governance characteristics on a company's ability to predict and avoid bankruptcy 

for a sample of 781 USA companies from 1992 to 2000. The results indicated that a large number of 

directors, the more independent directors, and the large ownership of inside directors have a 

significant negative effect on bankruptcy risk. Companies with good governance are less likely to 

suffer financial distress. In this context, Hui and Jing-Jing (2008) examined the relationship between 

corporate governance mechanisms and financial distress costs for a sample of 193 companies listed on 

the Shanghai Stock Exchange during the period 2000-2006. The results demonstrated a significant 

negative impact of board independence and the proportion of companies' shares owned by the state 

on the costs of financial distress (Dalia, 2023). 

Further, board size and institutional ownership were found to have an insignificant impact on 

bankruptcy risk. Conversely, Darrat et al. (2014) studied the impact of corporate governance on 

bankruptcy risk for a sample of 217 USA bankrupt companies during 1996-2006. The results 

documented that the large number of directors reduced bankruptcy risk. These results also suggested 

that the proportion of inside directors is negatively associated with bankruptcy risk. Shahwan and 

Habib (2020) found an insignificant negative impact of the board of directors' structure, ownership 

structure, shareholders' rights, and investor relations on companies' financial distress. Further, In the 

Sri Lankan context, Uduwalage (2021) investigated the relationship between corporate governance 

mechanisms and a company's financial distress for a sample of 205 non-financial companies listed on 

the Colombo Stock Exchange in 2012. Uduwalage found that board size, board independence, board 

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American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

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106 | P a g e  

ownership, institutional ownership, and non-institutional ownership concentration enhanced a 

company's prediction of financial distress.  

Empirical Review  

Begum, Sarker and Nahar (2023) investigated the relationship between corporate governance and the 

likelihood of financial distress. To evaluate the impact of corporate governance on financial distress, a 

multiple regression model and longitudinal panel data are used. Corporate governance is determined 

by the board of directors, audit committee, and ownership structure, whereas the Altman Z-score is 

used to indicate financial distress. The findings imply that financial distress is influenced by corporate 

governance variables (board independence, auditor independence, auditor opinion, sponsor directors 

ownership, and foreign shareholders), and firm-level variables (sales growth, performance, liquidity, 

firm size). From an academic standpoint, this paper adds to our understanding of the association 

between corporate governance practices and the risk of financial distress in emerging markets like 

Bangladesh. The findings may encourage Bangladeshi listed companies to follow and implement good 

corporate governance practices, increasing investor, regulator, and stakeholder confidence. 

Alberto, · et al (2022) compare the performance of corporate governance variables in predicting 

corporate defaults, using both the Logit and Random Forest models, which previous researchers have 

deemed to be the most efficient machine learning techniques. They results show that the use of 

corporate governance variables – especially with regards to CEO renewal and stability in the 

composition of the board of directors – increases the accuracy of the Random Forest technique and 

influences the success of the turnaround process. This paper also confirms the Random Forest 

technique’s ability to significantly outperform the Logit model in terms of accuracy. 

Okoye and Okoye (2022) investigated the effect of corporate governance on bankruptcy risk in deposit 

money banks in Nigeria, using board of directors’ independence. Ex Post Facto research design was 

adopted for the study. A sample of nine deposit money banks was used for the study. Data were 

obtained from the annual reports and audited accounts of the banks under assessment. Altman's 

original model for public companies was used to extract data and the formulated hypothesis was 

tested with regression analysis with aid of E-View 9.0. The analysis and hypothesis tested show that 

board of directors’ independence has a positive significant effect on bankruptcy risk of deposit money 

banks in Nigeria. Based on the findings, the study recommended that the board of directors' 

independence be strengthened in order for the board to be more effective at preventing and avoiding 

bankruptcy once the company becomes distressed.  

Ayoola and Obokoh (2018) investigated the effect of corporate governance on financial distress in the 

Nigerian banking industry and examines the discriminatory power of corporate governance 

mechanism of the board, audit committee, executive management and auditor in one model for 

financial distress prediction. Secondary data obtained from annual financial statements of twenty 

banks between 2005 and 2015 were used for the study. The data were analyzed using descriptive 

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American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

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statistics and generalized quantile regression model. The empirical evidence from the study suggests 

that financially distressed banks are characterized by large board size with members who may not be 

well versed in banking complexities, chairmen and CEOs with significant shareholding both 

individually and collectively. Furthermore, the evidence also shows that distressed banks suffer major 

decline in customer deposits despite increase in size. The study concludes that financial distress can 

be caused by poor corporate governance mechanism. 

Mwawughanga and Ochiri (2017) examined the financial health of banks listed and also not listed in 

the Nairobi stock Exchange, Kenya using the Altman Z score model of 2005.The CBK have the 

regulatory mandate to keep on check the financial health of banks considering that the Kenyan 

economy largely depends on banks. Following the many bank failures in Kenya, the CBK and the 

Kenya Bankers association have been pushing for improvement including transparency on 

commercial banks. Ever since the 2008 financial crisis, financial health of banks has been a concern 

to corporate managers and other stakeholders. This study therefore applies Altman Z score, a 

multivariant financial analysis model to gauge the financial health of banks in Kenya. The ratios that 

form the model were the independent variables and they included Working capital to total assets and 

Retained earnings to total assets. Studies on applicability of Z score model appear rear/scanty 

especially on financial institutions and mainly focused on validity and effectiveness. The secondary 

data was extracted from audited annual reports and financial statements of banks’ respective websites 

and CBK for a period from 2010 to 2015. The annual financial statements included the statement of 

comprehensive income and statement of financial position. In the analysis Multivariate Discriminant 

Statistical techniques as used by Altman 2005 was applied. Results indicated that during the period 

under study high percentage of Kenyan banks were on grey zone. Conclusions were made that Altman 

model was an average tool which can only be relied alongside other measure. 

Ezejiofor, Nzewi and Okoye (2014) assessed the extent to which we can rely on the Altman Model to 

predict possibility of corporate bankruptcy/ failure in Nigerian banking sector. Data were collected 

from annual reports and accounts of the banks. Altman prediction was applied. Findings show that 

the Model was capable of measuring accurately the failure potential of sound and healthy banks. 

Findings also show that Altman bankruptcy prediction Model could have successfully predicted the 

failure of the banks that actually went under in the Nigerian banking sector. The implication of this 

finding is that the standard rating system of regulatory Authorities for predicting the extent of failure 

in the Nigerian banks is still low, hence, Nigeria has had ample cases of bank failures in the past; it 

would have been prevented if they had applied a model similar to Altman’s z-score. Based on this, 

researcher recommends that effort should be made by the regulatory authorities and agencies in the 

financial sector to domesticate the Altman’s model for a result-oriented monitoring of the health of 

banks. Again, there is the need to bring financial system under control and make them to fit for the 

service and the interest of depositors and shareholders. 

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Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

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Methodology 

Research Design  

Due to the nature of the study, Ex Post Facto research design was adopted. The study analyzed the 

audited accounts of banks. This involves use of financial accounts of the banks under assessment for 

the period, 2012-2023 to generate the financial ratios that discriminated the most in prediction of 

healthy banks using Altman Model.  

Population of the Study 

This population of this study consists of the 8 deposit money banks quoted on the Nigerian Exchange 

Group. The study covered ten years annual reports and accounts of these banks from 2012 to 2023.  

Sample Size of the Study 

As a result, the "purposive sampling technique was applied (Non-random sample). In this method, 

the sample is chosen based on what the researcher thinks is appropriate for the study. The banks 

licence with international authorization was chosen which consist a total of eight (8) out of the 

twenty-two (22) deposit money banks which was inevitably excluded during the data collection 

process due to incomplete data, hence majority of the other banks are those that either emerged or 

acquired during the period the study covered without international authorization (See appendix for 

details).  

Source of Data Collection 

To obtain reliable information that will help the researcher to ensure the effectiveness of the study in 

question, data were collected from only secondary sources. This data were obtained from the annual 

reports and audited accounts of the banks under assessment  

Model Specification 

The data required were those of the dependent variable that include: Altman prediction model 

(working capital, retained earnings, earnings before interest and tax, equity as well as total assets and 

total book debts) and independent variables: risk management and board of directors. This was 

obtaining from the audited reports and accounts of the banks under assessment.  

The study will use Altman Model given as Zeta “Z” 

Z=1.2X1 + 1.4X2+ 3.3X3 + 0.6X4 + 1.0 X5,  

Where: 

          X1         =       Working capital to total assets 

          X2         =       Retained earnings to total assets 

          X3      =       Earnings before interest and taxes to total asset 

          X4         =       Value of equity to total book debt 

          X5         =       Gross earnings to total assets 

The decision rule is that: 

 (i). For Z<1.81 Bankruptcy region 

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American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

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 (ii). For 1.81<Z>2.675 High bankruptcy potential 

 (iii). For 2.675<Z<2.99 Low bankruptcy potential 

 (iv). For Z>2.99 Strong (No sign of bankruptcy at all). 

The Altman Model will be modified thus to incorporate corporate governance: 

ATMNit = a0 + β1RMCit +β2BINDit + it urt ………………….…..…....(i) 

Where; 

ATMN= Altman Prediction Model 

RMC= Risk Management Committee 

BIND = Board independence 

Method of Data Analysis  

Data were analyzed with descriptive statistics, and the hypotheses will be tested with Pearson 

correlation, and multiple regression analysis. Since the focus of the study is to examine the effect of 

asset composition on financial performance, regression analysis becomes appropriate tool for it.  

Descriptive statistics employed to summarily describe the mean, median, standard deviation, kurtosis 

and skewness of the study variables. Inferential statistics will also be utilized with the aid of E-Views 9 

using: 

i. Coefficient of correlation: which is a good measure of relationship between two variables that tell us 

about the strength of relationship and the direction of the relationship as well?  

ii. Regressions analysis: Regression analysis predicts the value the dependent variable based on the 

value of the independent variable and explains the impact or effect of changes in the values of the 

variables. 

Decision Rule 

Accept the alternative hypothesis, if the Probability value (P-value) of the test is less than 0.05 (5%). 

Otherwise reject 

Data Analysis and Results 

Data Analysis 

Table 1: Descriptive Analysis 

 ATMN RMC BIND 
 Mean  2.913370  14.29207  1.826087 
 Median  3.023000  11.30000  2.000000 
 Maximum  6.598000  38.66000  7.000000 
 Minimum  0.399000  0.060000  0.000000 
 Std. Dev.  1.547708  12.27464  1.813313 
 Skewness  0.121996  0.613932  1.183425 
 Kurtosis  2.306899  2.328440  4.323895 
 Jarque-Bera  2.069699  7.508123  28.19293 
 Probability  0.355280  0.023422  0.000001 

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American Research Journal of Economics, Finance and Management 

Volume 13 Issue 1, January-March 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

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 Sum  268.0300  1314.870  168.0000 
 Sum Sq. Dev.  217.9815  13710.67  299.2174 
 Observations  96  96  96 
Table 1 shows the mean (average) for each of the variables, their maximum values, minimum values, 

standard deviation and Jarque-Bera (JB) Statistics (normality test). The results in table 1 provided 

some insight into the nature of the Nigerian banks that were used in this study. 

It was observed that on the average over the twelve (12) years periods (2012-2023), the sampled 

banks in Nigeria were characterized by positive Altman bankruptcy prediction Model (2.770944), 

also, the large difference between the maximum and minimum value of the risk management 

committee (RMC) and board independence (BIND). 

In this table, the Jarque-Bera (JB) which test for normality or the existence of outliers or extreme 

values among the variables shows that most of the variables are normally distributed at 5% level of 

significance. This means that any variable with outlier are not likely to distort our conclusion and are 

therefore reliable for drawing generalization. This also implies that the least square estimate can be 

used to estimate the pooled regression model. 

Correlation Analysis 

In examining the association among the variables, we employed the Pearson correlation coefficient 

(correlation matrix) and the results are presented in table 2: 

Table 2: Correlation Matrix Analysis 

 ATMN RMC BIND 

ATMN 1   

RMC 0.21505 1  

BIND 0.24597 0.30658 1 

The use of correlation matrix in most regression analysis is to check for multi-colinearity and to 

explore the association between each explanatory variable (RMC and BIND and the dependent 

variable (Altman). Finding from the correlation matrix table shows that all our independent variables, 

(RMC=0.215, BIND= 0.246) were observed to be positively associated with Altman bankruptcy 

prediction Model In checking for multi-colinearity, we notice that no two explanatory variables were 

perfectly correlated. This means that there is no problem of multi-colinearity between the explanatory 

variables. Multi-colinearity may result to wrong signs or implausible magnitudes in the estimated 

model coefficients, and the bias of the standard errors of the coefficients. 

Test of Hypotheses 

Hypotheses One 

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Ho1: Risk management committee has no significant effect on bankruptcy risk deposit money banks 

in Nigeria. 

 

 

Table 3: Regression analysis between Altman predicting model and Risk management 

committee 

Dependent Variable: ATMN  

Method: Least Squares   

Date: 12/21/24   Time: 11:56   

Sample: 1 103    

Included observations: 96   

     
     Variable Coefficient Std. Error t-Statistic Prob.   

     
     C 2.520241 0.257072 9.803644 0.0000 

RMC 0.021909 0.013586 1.612562 0.1103 

     
     R-squared 0.027488     Mean dependent var 2.837883 

Adjusted R-squared 0.016917     S.D. dependent var 1.615218 

S.E. of regression 1.601498     Akaike info criterion 3.800803 

Sum squared resid 235.9612     Schwarz criterion 3.854915 

Log likelihood -176.6377     Hannan-Quinn criter. 3.822660 

F-statistic 2.600357     Durbin-Watson stat 0.573375 

Prob(F-statistic) 0.110265    

     
     In table 3, a simple least square regression analysis was conducted to test the significant effect 

between risk management committee (RMC) and Altman bankruptcy predicting model (ATMN).  The 

R-squared is coefficient of determination which tells us the variation in the dependent variable due to 

changes in the independent variable. From the findings in the table 3, the value of R squared was 

0.027, an indication that there was variation of 3% on ATMN due to changes in RMC. This implies 

that only 3% changes in ATMN of the economy could be accounted for by RMC, while 97% was 

explained by unknown variables that were not included in the model. The probability of the slope 

coefficients indicates that; P (0.110 >0.05). The co-efficient value of; β1= -33.92274 implies that RMC 

is positively related to ATMN, and this is not statistically significant at 5%. 

The Durbin-Watson Statistic of 0.573375 which is less than 2 suggests that the model does not 
contain serial correlation. The F-statistic of the ATMN regression is equal to 2.600357 and the 
associated probability F-statistic is equal to 0.008726, so the null hypothesis was rejected and the 
alternative hypothesis was accepted.  

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Decision 

Since the Prob (F-statistic) of 0.110265 is less than the critical value of 5% (0.05), then, it would be 

upheld that risk management committee has significant effect on bankruptcy risk deposit money 

banks in Nigeria, thus, HI is preferred over HO. 

Hypothesis Two 

Ho5: Board of directors’ independence has no significant effect on bankruptcy risk deposit money 

banks in Nigeria. 

Table 4: Regression analysis between Altman predicting model and Board of directors’ 

independence 

Dependent Variable: CAL_Z_VALUE  

Method: Least Squares   

Date: 12/21/24   Time: 11:58   

Sample: 1 103    

Included observations: 96   

     
     Variable Coefficient Std. Error t-Statistic Prob.   

     
     C 2.493799 0.234081 10.65359 0.0000 

BIND 2.166370 0.089006 2.869208 0.0547 

     
     R-squared 0.035838     Mean dependent var 2.809208 

Adjusted R-squared 0.025580     S.D. dependent var 1.610316 

S.E. of regression 1.589586     Akaike info criterion 3.785438 

Sum squared resid 237.5178     Schwarz criterion 3.838862 

Log likelihood -179.7010     Hannan-Quinn criter. 3.807033 

F-statistic 3.493940     Durbin-Watson stat 0.595410 

Prob(F-statistic) 0.054707    

     
     In table 4, a simple least square regression analysis was conducted to test the significant effect 

between Board of directors’ independence (BIND) and Altman bankruptcy predicting model (ATMN). 

The R-squared is coefficient of determination which tells us the variation in the dependent variable 

due to changes in the independent variable. From the findings in the table 4, the value of R squared 

was 0.04, an indication that there was variation of 4% on ATMN due to changes in BIND. This implies 

that only 4% changes in ATMN of the economy could be accounted for by BIND, while 96% was 

explained by unknown variables that were not included in the model. The probability of the slope 

coefficients indicates that; P (0.05<0.05). The co-efficient value of; β1= 2.166370 implies that BIND is 

positively related to ATMN, and this is not statistically significant at 5%. 

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The Durbin-Watson Statistic of 0.595410 which is less than 2 suggests that the model does not 

contain serial correlation. The F-statistic of the ATMN regression is equal to 3.493940 and the 

associated probability F-statistic is equal to 0.054707, so the null hypothesis was rejected and the 

alternative hypothesis was accepted.  

Decision 

Since the Prob (F-statistic) of 0.054707 is equal to critical value of 5% (0.05), then, it would be upheld 

that board of directors’ independence has a significant effect on bankruptcy risk deposit money banks 

in Nigeria, thus, HI is preferred over HO. 

Discussion and Conclusion  

This study determined the effect of corporate governance on bankruptcy risk in commercial banks in 

Nigeria, using risk management committee, and board of directors’ independence. The study used 

Altman's original model for public companies to extract data and the formulated hypotheses were 

tested with regression analysis with aid of E-View 9.0. The data required were those of the dependent 

variable that include: Altman prediction model (working capital, retained earnings, earnings before 

interest and tax, equity as well as total assets and total book debts) and independent variables: board 

size, audit tenure and board independence. This was obtaining from the audited reports and accounts 

of the banks under assessment. From the results, it was revealed that only board of directors’ 

independence has a statistically significant effect on bankruptcy risk commercial banks in Nigeria, 

this result is in agreement with Elshandidy (2013) argued that having a good number of independent 

directors on the board would foster greater financial stability of the company. 

However, risk management committee has no statistically significant effect on bankruptcy risk 

commercial banks in Nigeria. These results are in line with the study of Boo and Sharma (2008) 

observe no association between audit committee independence and audit fees indicating that auditors 

will minimize their effort in the presence of independent audit committee. Jensen and Meckling 

(1976) argued that the relationship between managerial share ownership and corporate debt is 

complex. It is argued that managerial share ownership can reduce managerial incentives to consume 

perquisites, expropriate wealth and to engage in other non-maximizing behavior.  

Based on the results, the study recommended the followings; 

1. Risk management committee should be encouraged, since the committee can influence the 

capacity for problem-solving as the variety of perspectives that identifying the weakness of internal 

control and risk source that can easily use in checkmating bankruptcy. 

2. There is need to strengthen the board of director’s independency, so as to ensure board are 

more effective at preventing and avoiding bankruptcy once the company becomes distressed. 

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