The Extent to which Audit Committee’s Composition Affects Banks Performance in Nigeria

2.5.3 Composition of Audit Committee and the Performance of Banks

Committees are important corporate governance tools to monitor corporate

activities and can play a valuable role in the protection of shareholder value

(Kesner, 1988). Italian Corporate Governance Self Discipline Code and

the“Supervisory Provisions Concerning Banks’ Organization and Corporate

Governance” of Bank of Italy (2008) require as a best practice that Italian

listed companies and banks have control and risk (audit) committee,

remuneration committee and nomination committee; the first one should

consist of non-executive directors, the majority of which should be

independent. Literature (Larcker et al., 2007) suggests that the presence of

independent directors in the audit committee can imply a strong

independence of the board.

Adams and Mehran (2003) find that US bank holding companies boards

have more committees than manufacturing firms. Later, the same Authors

(Adams and Mehran, 2005) show a significant and negative relationship

between performance and the number of committees. Differently, Selvam et

al. (2006) state that the number of board committees is one of the yardsticks

for better functioning of banks; they find that the number of board

committees is statistically significant to performance for banks where

government has considerable stakes.

Among the committees that can be created within the board of directors,

previous researches assign the most relevant role to the audit committee,

charged with the task of giving advice and making proposals on problems

considered relevant to the internal control of the company’s activities. As a

matter of fact, empirical studies show that US firms committing financial

reporting fraud are less likely to have an audit committee (Dechow et al.,

1996; Beasley et al.; 2000; Uzun et al., 2004).

In the light of the higher relevance of the audit committee compared to the

others, we decide to focus our attention on two specific attributes of it: the

size and the membership.

Although the size of audit committee is influenced mainly by the size of the

company and of its board of directors, a larger audit committee may not

necessary cause in more effective functioning, as a larger committee may

lead to unnecessary debates and delay the decisions (Lin et al., 2008).

However, some Authors have highlighted that a larger audit committee is

required to better perform its role, as it requires more discussions and

assures more skills, backgrounds and competences (Garcia-Sanchez et al.,

2012; Alkdai and Hanefak, 2012; Raghunandan and Rama, 2007). Despite

these considerations, a smaller audit committee can enhance directors’ sense

of participation, can make the group of directors more cohesive and able to

reach consensus (Lipton and Lorsch, 1992; Dalton et al., 1999). This

cohesiveness can increase audit committee vigilance over the board

decisions and curtail potential managerial opportunism (Yermack, 1996).

The second important aspect we decide to focus on is audit committee

membership. Prior researches have principally investigated committee

membership in terms of type, gender and occupation of directors (Kesner,

1988; Klein, 1995; Spira and Bender, 2004). In particular these Authors find

that the presence of outside directors in committees facilitates the strategic

and monitoring role of the board, because they can provide their experience,

external associations and knowledge, and can be more objective. According

to Najjar (2011) outside directors in the audit committee can be considered

as a key monitoring tool since these directors improve the monitoring

resources for financial reporting (Beasley, 1996; Dechow et al., 1996;

Sharma et al., 2009; Romano and Guerrini, 2012). Similarly, Klein (2002)

argues that the greater the number of non-executive directors, the higher the

chances of having more audit committee independence, and hypothesizes a

positive relationship between non-executive directors and audit composition.

Deli and Gillan (2000) and Menon and Williams (1994) follow the same

argument. Raghunandan and Rama (2007) argue that non-executive directors

are important in reflecting efficient corporate governance. Also many

Corporate Governance Codes suggest that audit committee should be

composed by non executive directors, for most independent, in order to

ensure the independence of the audit committee. The rationale behind this is

that outside directors are more likely to defend the interests of outside

shareholders (Belkhir, 2009).

The population of this study is made of all banks that are quoted on the

Nigerian Stock Exchange (NSE).

Due to the requirements of the empirical models, filtering procedures have

been adopted to eliminate some of the banks that were considered unsuitable

for the study. A non- probability method in the form of judgmental sampling

technique was employed in selecting banks into the sample. In nutshell, the

sample size is based on the following criteria;

I. Banks with missing values for the variable used were excluded.

II. The bank was not involved in any merger during the study period.

III. For the empirical part of this study, the data is limited to bank that is

in existence throughout the period of the study.

After applying the above criteria, five banks were selected. Below is the list

of the banks.

I. Access Bank Plc.

II. Eco Bank Nigeria Plc.

III. First Bank Nigeria Plc.

IV. Guarantee Trust Bank Plc.

V. Union bank of Nigeria Plc.

3.4 Sources and Methods of Data Collection

This study will utilise only the secondary source of data. This is because the

estimation of the models in the study requires the use of cross sectional/time

series data in the form of financial information which are available through

the financial statements of the sample banks. Also, Aitken and Harrison

(1990) and Feinberg and Majumdar (2001) have defended the use of

secondary data in advanced research work provided the available data are

accurate, relevant and timely. The data were sourced from the annual reports

and accounts of the sampled companies for all the relevant years covered by

the study. The study will cover a period of nine (9) years from 2004-2012.

This source of data has one feature that makes it a very good source for this

study. The data are also available in the Nigerian Stock Exchange fact

books.

3.5 Method of Data Analysis

This study adopts econometric method of data analysis to investigate the

impact of corporate governance on the performance of Nigerian banks. This

method is considered essential as it allows the study to quantify the rate of

bank performance that is due to some explanatory factor identified in the

study. It also enables the study to reveal the individual directional effect of

these variables on bank performance. Specifically, the method involves a

multivariate regression analysis where bank performance is linked to four

explanatory variables (board size, board composition, composition of audit

committee and managerial shareholding). The data will also be analysed

using descriptive statistics. Furthermore, the study will employed correlation

matrix to examine the nature and the degree of relationship among variables

of consideration.

Finally, the behaviour of the data collected in relation to the selected

variables will be investigated using normal distribution and Augmented

Dickey Fuller (ADF). The ADF consists of running regression of the first

difference of the series against the series lagged once, lagged difference

terms and optionally, by employing a constant and time trend. This is

premised on the need to ensure that findings of the study are statistically

reliable.

3.6 Empirical Model Specification

To investigate the impact of corporate governance on the performance of

Nigerian banks, the study will employ the use of Multivariate Regression

models to analyse the relationship that exist between the variables under

consideration.

The model employed is an Ordinary Least Squares (OLS) regression to

examine the separate and combined effect of board size, board composition,

composition of audit committee bank risk and gender diversity on the

performance of banks in Nigeria. The models are in line with previous

empirical work. See for instance, Klapper and Love (2002), Sanda, Mikailu

and Tukur (2004), Musa (2006), Tahir (2008), and Hassan (2011).

The models are stated below.

ROA = β + λBS + δBC+ γAC + ρBR + bGD + ε………….… (i)

ROE = β + λBS + δBC+ γAC + ρMS + bGD + ε ………….… (ii)

Where:

ROA = Return on asset

ROE = Return on equity

BS = Board Size

BC = Board Composition

ACC = Composition of Audit Committee

BR = Bank Risk

GD = Gender Diversity

β = Intercept

ε = Error term

λ, δ, γ, ρ and b are coefficients of board size, board composition,

composition of audit committee, bank risk and gender diversity respectively.

3.7 Measurement of Variables

3.7.1 Dependent Variable

The dependent variable is bank performance. Several variables have been

used by previous studies as proxies for bank performance. For instance,

Chou (2008) uses profitability measured by return on total assets and equity

as proxies for performance of banks. Also, the same proxies were used in the

studies of Romano and Rigolini (2012), Bino and Tomar (2007), Staikouras

et al., (2007) and Dutta and Boss (2006). Because of the popularity of these

variables, the performance of banks will be measure in this study through

return on asset (ROA) and return on equity (ROE).

3.7.2Explanatory Variables

The independent variable is corporate governance. There are several

corporate governance attributes. However, this study will only consider four

of those attributes in line with the objectives of the study. These attributes

are board size, board composition, composition of audit committee and

managerial shareholding. Each of these attributes constitutes an independent

variable.

Table 3.1 Estimation of VariableS/N VARIABLE ESTIMATION FORMULA 1 Return   on 

Asset (ROA)Ratio   of   profit   after tax to total assets

Profit after taxTotal asset

 2 Return   on Equity (ROE)

Ratio   of   profit   after tax to total equity

Profit after tax Total no. of ord. Shares

 3 Board size

This   is   described   as the   number   of directors on the board at the end of financial year.

Total number of directors

4 Board composition

This is referred to the mix   of   inside   to outside   directors   in the board room.

Non-executive directors  Total no. of directors

 5Audit committee composition

The ratio of  directors to shareholders in the Audit Committee.

No. of directors in audit Cttee  Total no. of Audit Cttee

6 Bank risk

7Gender diversity

The   number   of women   in   the   board room

No. of female directors   Total no. of directors

Source: Author’s estimation

4.2 Descriptive Statistics

Table 4.1 shows the minimum, maximum, mean, and standard deviation

values of the variables used in the study.

Table 4.1 Sample Descriptive StatisticsVariables* ROE ROA BS BC ACC BR GD

Minimum -0.31064 -42.3639 8 0.428571 0 0.1942 0Maximum 0.144407 17.47091 23 0.8 1 0.5823 0.277778Mean 0.017356 1.670671 14.55556 0.623598 0.450899 0.2566 0.113923Std. Deviation 0.059969 7.751281 2.927577 0.081182 0.164804 0.0123 0.066452

Observations 45 45 45 45 45 45 45Source: Econometric–Views Output Result*ROE =Return on Equity, ROA= Return on Asset, BS=Board Size, BC=Board Composition, ACC=Audit Committee Composition, BR=Bank Risk and GD=Gender Diversity.

The table indicates that, on average, returns on equity and asset have mean

values of about 1.7%. and 167% respectively which are proxies for bank

performance. Board size, board composition, audit committee composition

have mean values of about 146%, 62%, and 45%, respectively. Gender

composition on the other hand has mean values of about 11%. The range of

the variables were given by the minimum and the maximum values. The

variable with the highest standard deviation among the explanatory variables

is board size with a value of about 2.928 while gender composition has the

least standard deviation of about 7%. This result is consistent with the idea

that gender diversity is a very important variable in the model formulated.

The variable with the least standard deviation among the two measurement

of bank performance employed in the study is return on equity with a value

of about 6%. This suggests that return on equity is a more appropriate

measure of bank performance over return on asset. The study used a total of

45 observations for each metric variable considered.

4.3.1 Augmented Dickey fuller (ADF) Stationarity Test

The Augmented Dickey Fuller (ADF) has been employed to test the unit

roots of the concerned time series metric variables.

Table 4.2 below displays the estimates of the Augmented Dickey fuller

(ADF) test in levels of the data with an intercept only, with an intercept and

trend and with no intercept and trend. The test has been performed using the

McKinnon Critical Values.

Table 4.2 Stationary Testa

Variableb Test with Intercept

Test with Intercept and Trend

Test with no Intercept and Trend

Levels Levels LevelsROE -3.1259*** -3.2019** -2.7221***

ROA -3.1855*** -3.2221* -2.8025***

BS -3.7264** -3.7636** -2.9649***

BC -3.3275*** -3.5921** -0.6191***

ACC -3.4229*** -3.12219** -2.7824***

BR -3.3255*** -3.2321** -0.5191***

GD -3.4759*** -3.5919** -2.7824***

Source: Econometric–Views Output Result*ROE =Return on Equity, ROA= Return on Asset, BS=Board Size, BC=Board Composition, ACC=Audit Committee Composition, BR=Bank Risk and GD=Gender Ddiversity

The ADF test with an intercept implies that all variables are stationary at

levels at 1% level of significance except board size which is stationary at 5%

level. Similarly, the test with intercept and trend also shows that the

variables are stationary within acceptable level of significance in levels. The

variables are also stationary for ADF test with no intercept and trend.

Collectively, all test results imply that all variables are stationary at levels

and hence variables are integrated at levels. The economic implications of

these results indicate that the time series metric variables employed in this

study are suitable for econometric analysis.

4.3.2 Normality Distribution Test

Diagram 4.1 shows the normal distribution of the univariate time series

employed.

Diagram 4.1 Normal Distribution curves

80.0000060.0000040.0000020.00000

50

40

30

20

10

0

Frequency

Mean =42.8809524Std. Dev. =14.

39398196N =210

80.0000060.0000040.0000020.00000

50

40

30

20

10

0

Frequency

Mean =42.8809524Std. Dev. =14.

39398196N =210

80.0000060.0000040.0000020.00000

50

40

30

20

10

0

Frequency

Mean =42.8809524Std. Dev. =14.

39398196N =210

Return on Equity Distribution Return on Asset Distribution Board Size Distribution

80.0000060.0000040.0000020.00000

50

40

30

20

10

0

Frequency

Mean =42.8809524Std. Dev. =14.

39398196N =210

80.0000060.0000040.0000020.00000

50

40

30

20

10

0

Frequency

Mean =42.8809524Std. Dev. =14.

39398196N =210

80.0000060.0000040.0000020.00000

50

40

30

20

10

0

Frequency

Mean =42.8809524Std. Dev. =14.

39398196N =210

Board Composition distribution Audit Committee Bank Composition distribution distribution

80.0000060.0000040.0000020.00000

50

40

30

20

10

0

Frequency

Mean =42.8809524Std. Dev. =14.

39398196N =210

Gender Diversity distribution

The curves of all the diagrams indicate that the metric variables are normally

distributed. The implication of this is that the univariate time series data

employed are suitable for mutivariate regression analysis.

4.4 Correlation Matrix

Table 4.2 below shows the correlation matrix for the time series metric

variables employed in the study. Precisely, the matrix did not only show the

relationship between the variables but also indicates the direction of the

relationship.

Table 4.3 Correlation matrix for the sample observations

Variablea ROE BS BC ACC BR GDROE 1  BS 0.004 1  BC 0.101 0.257 1  ACC 0.026 0.16 0.32 1  BR -0.434 -0.035 -0.322 0.006 1  GD -0.434 -0.025 0.343 0.063 -0.561 1

Source: Econometric–Views Output ResultaROE =Return on Equity, ROA= Return on Asset, BS=Board Size, BC=Board Composition, ACC=Audit Committee Composition, BR=Bank Risk and GD=Gender Diversity

The above table indicates that there is a positive relationship between board

size, board composition and audit committee composition and the dependent

variable. However, the correlation result shows that there is a negative

relationship between bank risk and gender composition and bank

performance. Results shown in Table 4.3 indicates that most cross-

correlation terms for the independent variables are fairly small, thus, giving

little cause for concern about the problem of multicollinearity among the

independent variables.

4.5 Empirical Results

This section presents and interprets the regression results in respect of the

bank performance and corporate governance equations formulated. The

study used two models for the purpose of examining the effects of corporate

governance on the performance of banks in Nigeria. Table 4.4 presents the

regression result in line with the first model using return on asset as

measurement of bank performance while table 4.5 presents the regression

result in line with the second model using return on equity as the

performance measure. The study hypothesized a relationship between board

size, board composition, audit composition, bank risk, and gender diversity

on one hand and bank performance on the other hand.

Table 4.4 Regression Results on Model 1a

Variableb Coefficients T-Statistics

Intercept

BS

BC

ACC

BR

GD

0.967*

-0.101*

0.0271*

0.006*

-0.478*

-0.252*

20.646

-2.663

5.475

0.644

-12.387

-18.733

R-Squared 0.456

Adjusted R-Squared 0.449

F-Statistics 74.297*Source: Econometric–Views Output ResultaT-Statistics are in parentheses. * indicate that values are significant at 1%

b BS=Board Size, BC=Board Composition, ACC=Audit Committee Composition, BR=Bank Risk and GD=Gender Diversity

Table 4.4 shows the regression results on the relationship between board

size, board composition, audit composition, bank risk, and gender diversity

on one hand and bank performance on the other hand. The estimated

regression relationship for the model is

 

The parameters of all the variables under consideration are statistically

significant at 1% level.

Furthermore, the results also show the coefficient of determination for the

model. This coefficient measures the proportion of the total variation in the

performance of banks that is explained by the considered variables.

Precisely, the adjusted R-squared for the model is approximately 45% which

offers an explanation of the variations in ROA explained by variation in the

independent variables. Also, the value of the F-statistics is 74.297 with a p-

value of 0.001, indicates fitness of the model.

Table 4.5 below also shows the regression results on the relationship

between board size, board composition, audit composition, bank risk, and

gender diversity on one hand and bank performance on the other hand using

return on asset as the proxy for bank performance. The estimated regression

relationship for the model is

 

The parameters of all the variables under consideration are statistically

significant at 1% level.

Table 4.5 Regression Results on Model 1a

Variableb Coefficients T-Statistics

Intercept

BS

BC

ACC

BR

GD

0.568

-0.131*

0.059*

0.058*

-0.394*

-0.178*

6.747

-3.977

5.997

4.867

-7.348

-12.907

R-Squared 0.758

Adjusted R-Squared 0.706

F-Statistics 14.681*Source: Econometric–Views Output ResultaT-Statistics are in parentheses. * indicate that values are significant at 1% b BS=Board Size, BC=Board Composition, ACC=Audit Committee Composition, BR=Bank Risk and GD=Gender Diversity

The results also show the coefficient of determination for the model. This

coefficient as mentioned earlier measures the proportion of the total

variation in the performance of banks that is explained by the considered

variables. The adjusted coefficient of determination (R2) of approximately

71% offers a better explanation of the variations in ROE occasioned by

variation in the independent variables. Also, the value of the F-statistics is

74. 297 with a p-value of 0.001, indicates fitness of the model.

The following five sub-sections present the discussion of findings on the

effect of corporate governance characteristics and the performance of banks

in Nigeria.

4.6.3 Effect of Audit Committee Composition on the Performance of

Banks in Nigeria.

The regression results indicate that Audit committee composition has

coefficients of 0.006 and 0.058 for the two models which are both

statistically significant at 1%. These results provide evidence for the

rejection of the third hypothesis which states that there is no significant

relationship between audit committee composition and performance of

banks in Nigeria. The results show that audit committee composition

significantly affects bank performance in Nigeria positively. The implication

of these results is that the greater the number of non-executive directors, the

higher the chances of having more audit committee independence and the

better the performance on banks. These further suggest that presence of

outside directors in committees facilitates the strategic and monitoring role

of the board, because they can provide their experience, external

associations and knowledge, and can be more objective.

This result confirms the findings of Klein (2002), Deli and Gillan (2000) and

Menon and Williams (1994) who hypothesized a positive relationship

between audit committee composition and performance of banks. However,

the results of Adams and Mehran (2003) revealed otherwise. Their empirical

results revealed a significant and negative relationship between bank

performance and the number of committees

V.1 Conclusions

Based on the findings of the research, the study concludes that the five

corporate governance characteristics analysed in this study have the

following effects on the performance of banks in Nigeria:

I. Board size has significant negative impact on the performance of

banks in Nigeria. This signifies that an increase in Board size would

lead to a decrease in ROE and ROA.

II. The composition of board members has a significant positive effect on

the performance of banks in Nigeria. This signifies that an increase in

Board size would lead to a decrease in ROE and ROA.

III. Audit committee composition has a significant positive effect on the

performance of banks in Nigeria.

IV. Bank risk has significant negative impact on the performance of banks

in Nigeria.

V. Gender diversity has significant negative impact on the performance

of banks in Nigeria.

The overall conclusion of the study is that corporate governance has

significant effect on the performance of banks in Nigeria. However, while

some corporate governance characteristics such as board composition and

audit committee composition positively influenced the performance of banks

in Nigeria, other characteristics such as board size, bank risk and gender

diversity negatively affect the performance of banks in Nigeria.

V.2 Recommendations

The recommendations of this study are directed at different

parties that are involved in monitoring the

institutionalization of an effective system of corporate

governance in Nigeria. These parties include, shareholders,

board of directors, government/regulatory bodies and future

researchers.

V.2.1 Shareholders

Shareholders of banks should seek to positively influence the standard of

corporate governance in the bank in which they invest by making sure

there is strict compliance with the code of corporate governance.

It is the responsibility of the shareholders to ensure that the committee is

constituted in the manner stipulated and is able to effectively discharge

its statutory duties and responsibilities.

V.2.2 Board of Directors

The study indicated that corporate governance characteristics affect the

performance of banks in Nigeria. On the basis of this revelation, the

following recommendations are being made to banks’ boards of directors.

Banks should have adequate board size to the scale and complexity of

the company’s operations and be composed in such a way as to ensure

diversity of experience without compromising independence,

compatibility, integrity and availability of members to attend meetings.

The board size should not be too large and must be made up of

qualified professional who are conversant with oversight function

The Board should comprise a mix of executive and non-executive

directors, headed by a Chairman. The majority of Board members

should be non-executive directors whom should be independent

directors.

A bank should have a risk management function (including a chief risk

officer (CRO) or equivalent, a compliance function and an internal

audit function, each with sufficient authority, stature, independence,

resources and access to the board;

An internal controls system which is effective in design and operation

should be in place;

The sophistication of a bank’s risk management, compliance and

internal control infrastructures should keep pace with any changes to its

risk profile (including its growth) and to the external risk landscape;

and

Effective risk management requires frank and timely internal

communication within the bank about risk, both across the organization

and through reporting to the board and senior management.

V.2.3 Government/Regulators

There should be periodic monitoring functions by

government/regulators of the application of the principles of code of

corporate governance so as to detect rule violations and also monitor

systemic problems for early solutions.

Finally, on the issue of enforcement, government/regulators should

analyze why some banks adhere responsibly to regulatory standards—

and why others do not. Such analysis would help enhance government’s

ability to pursue optimal enforcement, instead of under- or over-

enforcement.

The current code of governance should be reviewed after sometimes so

as accommodate frequent changes in the banking industry.